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This is a deterministic problem, but we will solve it using random sampling.\n", - "\n", - "**Problem to solve:** find value of the integral\n", - "\n", - "$$\\int_a^b f(x)dx. $$\n", - "\n", - "Monte Carlo integration estimates this integral by finding the fraction of random points that fall below $f(x)$.\n", - "\n", - "In the **Bayesian inference** context, we are usually interested in estimating expectations (which are themselves point estimates):\n", - "\n", - "$$ E[h(x)] = \\int h(x)f(x)dx,$$\n", - "\n", - "which can be done with\n", - "\n", - "$$ \\bar{h}_n = \\frac{1}{n} \\sum_i^n h(x_i),$$\n", - "where $x_i ∼ f$ is a draw from the density $f$.\n", - "\n", - "**Exercise:** _Do you see how this is **sampling** to figure out a property?_\n", - "\n", - "The convergence of Monte Carlo integration is $\\mathcal{O}(n^{1/2})$ and is independent of the dimensionality. Hence, Monte Carlo integration **generally** beats numerical intergration for moderate- and high-dimensional integration since numerical integration (quadrature) converges as $0(n^d)$!\n", - "\n", - "### Monte Carlo integration - Example\n", - "\n", - "Estimate the integral $\\int_0^1 e^x dx$ using Monte Carlo integration." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "import math\n", - "import numpy as np\n", - "import jax.numpy as jnp\n", - "import scipy.stats as stats\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.patches as patches" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1707255715.317754 1 tfrt_cpu_pjrt_client.cc:349] TfrtCpuClient created.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.7182817\n", - " 10 1.902797\n", - " 100 1.522238\n", - " 1000 1.785911\n", - " 10000 1.735623\n", - " 100000 1.724967\n", - " 1000000 1.719147\n", - " 10000000 1.718384\n", - " 100000000 1.718485\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(jnp.exp(1) - jnp.exp(0))\n", - "\n", - "plt.figure(figsize=(3, 3))\n", - "\n", - "x = jnp.linspace(0, 1, 100)\n", - "plt.plot(x, jnp.exp(x));\n", - "pts = np.random.uniform(0,1,(100, 2))\n", - "pts[:, 1] *= jnp.e\n", - "\n", - "cols = ['steelblue'] * 100\n", - "for i in range(100):\n", - " if pts[i,1] > jnp.exp(pts[i,0]): # acceptance / rejection step\n", - " cols[i] = 'red'\n", - "\n", - "\n", - "plt.scatter(pts[:, 0], pts[:, 1], c = cols)\n", - "plt.xlim([0,1])\n", - "plt.ylim([0, jnp.e]);\n", - "\n", - "# Monte Carlo approximation\n", - "\n", - "for n in 10**np.array([1, 2, 3, 4, 5, 6, 7, 8]):\n", - " pts = np.random.uniform(0, 1, (n, 2))\n", - " pts[:, 1] *= jnp.e\n", - " count = jnp.sum(pts[:, 1] < jnp.exp(pts[:, 0]))\n", - " volume = jnp.e * 1 # volume of region\n", - " sol = (volume * count)/n\n", - " print('%10d %.6f' % (n, sol))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The Monte Carlo method - computing $\\pi$\n", - "\n", - "We can also use Monte Carlo to estimate the value of π!" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "#@title The Monte Carlo method - computing π\n", - "\n", - "def in_circle(x, y, r):\n", - " # is point (x,y) within circle of radius r?\n", - " return jnp.sqrt(x **2 + y**2) <= r**2\n", - "\n", - "def approx_pi(r, n):\n", - "\n", - " xs, ys, cols = [], [], []\n", - "\n", - " count = 0\n", - "\n", - " for i in range(n):\n", - " x = np.random.uniform(0,r,1)\n", - " y = np.random.uniform(0,r,1)\n", - " xs.append(x)\n", - " ys.append(y)\n", - "\n", - " if in_circle(x, y, r):\n", - " count += 1\n", - " cols.append(\"red\")\n", - " else:\n", - " cols.append(\"steelblue\")\n", - "\n", - " pi_appr = round(4 * count/n, 3)\n", - "\n", - " plt.figure(figsize=(2, 2))\n", - " plt.scatter(xs, ys, c = cols, s=2)\n", - " plt.title(\"pi (approximately) = \" + str(pi_appr))\n", - " plt.xticks([])\n", - " plt.yticks([])\n", - " plt.show()\n", - "\n", - " return pi_appr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Group task Β14.**\n", - "\n", - "Using the functions above, iterate $n$ through vaules $5*10^1, 5*10^2, 5*10^3$ and run the function approximating $\\pi$. How does the result change?" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "r = 1\n", - "\n", - "for n in 5*10**jnp.array([1,2,3]):\n", - " approx_pi(r, n)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Buffon's needle problem\n", - "\n", - "Here is another interesting example where random number generation can help us solve an analytical problem.\n", - "\n", - "Buffon's Needle is a classic probability problem that involves randomly dropping a needle of a certain length onto a floor with parallel lines drawn at regular intervals. The goal is to estimate the probability that the needle will intersect one of the lines. The probability can be calculated using the following formula:\n", - "\n", - "$$\n", - "P = \\frac{2L}{\\pi d}\n", - "$$\n", - "\n", - "Where:\n", - "\n", - "- $P$ is the estimated probability of the needle intersecting a line.\n", - "- $L$ is the length of the needle.\n", - "- $d$ is the distance between the lines on the floor" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualise Buffon's needle problem\n", - "num_lines = 10 # Number of parallel lines\n", - "line_spacing = 1.0 # Distance between lines\n", - "needle_length = 0.8 # Length of the needle\n", - "num_needles = 20 # Number of needles to drop\n", - "\n", - "# Create a figure and axis for visualization\n", - "fig, ax = plt.subplots()\n", - "\n", - "# Draw the parallel lines vertically\n", - "for i in range(num_lines):\n", - " line_x = i * line_spacing\n", - " ax.axvline(x=line_x, color='black', linewidth=2)\n", - "\n", - "# Simulate dropping needles and visualize them\n", - "for _ in range(num_needles):\n", - " # Randomly choose a midpoint and an angle for the needle\n", - " mid_point_x = random.uniform(0, num_lines * line_spacing)\n", - " mid_point_y = random.uniform(0, num_lines * line_spacing)\n", - " angle = random.uniform(0, math.pi / 2)\n", - "\n", - " # Calculate the endpoints of the needle\n", - " x0 = mid_point_x - (needle_length / 2) * math.cos(angle)\n", - " x1 = mid_point_x + (needle_length / 2) * math.cos(angle)\n", - " y0 = mid_point_y - (needle_length / 2) * math.sin(angle)\n", - " y1 = mid_point_y + (needle_length / 2) * math.sin(angle)\n", - "\n", - " # Plot the needle as a line segment\n", - " ax.plot([x0, x1], [y0, y1], color='blue')\n", - "\n", - "# Set plot limits and labels\n", - "ax.set_xlim([0, num_lines * line_spacing])\n", - "ax.set_ylim([0, num_lines * line_spacing])\n", - "ax.set_xlabel('x')\n", - "ax.set_ylabel('y')\n", - "ax.set_title(\"Buffon's Needle problem\")\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's write Python code to simulate Buffon's Needle experiment and estimate the probability.\n", - "\n", - "This code simulates the dropping of needles and calculates the estimated probability of the needle intersecting one of the lines. The more needles you drop, the closer the estimated probability will be to the actual value of $\\frac{2L}{\\pi d}$." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def buffon_needle_simulation(num_needles, needle_length, line_spacing):\n", - " intersected = 0\n", - "\n", - " for _ in range(num_needles):\n", - " # Generate a random angle between 0 and 180 degrees (in radians)\n", - " angle = random.uniform(0, math.pi / 2)\n", - "\n", - " # Generate a random position for the midpoint of the needle\n", - " mid_point = random.uniform(0, line_spacing / 2)\n", - "\n", - " # Check if the needle intersects a line\n", - " if mid_point <= (needle_length / 2) * math.sin(angle):\n", - " intersected += 1\n", - "\n", - " # Estimate the probability\n", - " if intersected == 0:\n", - " estimated_probability = 0\n", - " else:\n", - " estimated_probability = intersected / num_needles\n", - "\n", - " return estimated_probability\n", - "\n", - "def compute_true_value(needle_length, line_spacing):\n", - " true_value = (2 * needle_length) / (math.pi * line_spacing)\n", - " return true_value\n", - "\n", - "\n", - "# Input parameters\n", - "needle_length = 1.0 # Length of the needle\n", - "line_spacing = 2.0 # Distance between the lines\n", - "max_num_needles = 1000 # maximum number of needles to drop \n", - "\n", - "estimates = []\n", - "\n", - "for num_needles in range(max_num_needles):\n", - " estimated_probability = buffon_needle_simulation(num_needles, needle_length, line_spacing)\n", - " estimates.append(estimated_probability)\n", - "\n", - "# Compute the true value\n", - "true_value = compute_true_value(needle_length, line_spacing)\n", - "\n", - "\n", - "# Create a plot\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(range(max_num_needles), estimates)\n", - "plt.xlabel('Number of Iterations')\n", - "plt.ylabel('Estimated probability')\n", - "plt.axhline(y=true_value, color='red', linestyle='--', label='True Value')\n", - "plt.title('Dependence of the estimated probability on the number of iterations')\n", - "plt.grid(True)\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Markov Chain Monte Carlo (MCMC) \n", - "\n", - "We want to estimate the posterior distribution, but this is often intractable.\n", - "\n", - "MCMC is a computational technique used to approximate complex probability distributions by generating a **`sequence of (correlated) samples`**, where each sample is obtained by iteratively transitioning through a Markov chain with carefully designed transition probabilities.\n", - "\n", - "### How does MCMC work (very rough overview)?\n", - "\n", - "- Draw samples from a (simple) proposal distribution so that each draw depends only on the state of the previous draw (i.e. the samples form a Markov chain).\n", - "- Under certain conditions, the Markov chain will have a unique stationary distribution.\n", - "\n", - "- We set up an acceptance criteria for each draw based on comparing successive states with respect to a target distribution that enusre that the stationary distribution is the posterior distribution we are searching for.\n", - "\n", - "- There is no need to evaluate the potentially intractable marginal likelihood.\n", - "\n", - "- After sufficient number of iterations, the Markov chain of accepted draws will converge to the staionary distribution, and we can use those samples as (correlated) draws from the posterior distribution, and find functions of the posterior distribution.\n", - "\n", - "The next optional section demonstrates an example of **`Matrolopolis-Hastings`** algorithm - this is an example of MCMC.\n", - "\n", - "## Metropolis-Hastings random walk algorithm\n", - "\n", - "- Start with an initial guess for $\\theta$\n", - "\n", - "- Chose a new proposed value as $\\theta_p = \\theta + \\delta_\\theta, \\delta_\\theta \\sim N(0, \\sigma).$\n", - " \n", - " Here we have chosen the proposal distribution to be $N(0, \\sigma).$\n", - " \n", - "- If $g$ is the posterior probability, calculate the ratio $\\rho = \\frac{g(\\theta_p \\mid X)}{g(\\theta \\mid X)}$\n", - "\n", - "- (adjust for symmetry of the proposal distribution)\n", - "\n", - "\n", - "- If $\\rho \\ge 1,$ accept $\\theta = \\theta_p;$ if $\\rho < 1,$ accept $\\theta = \\theta_p$ with probability $p,$ otherwise keep $\\theta = \\theta.$ (This step is done with the help of the standard Uniform distribution)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Portion of accepted steps = 0.1913\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#@title Metropolis-Hastings\n", - "\n", - "def target(likelihood, prior, n, h, theta):\n", - " \"\"\"\n", - " define target distribution\n", - " \"\"\"\n", - " if theta < 0 or theta > 1:\n", - " return 0\n", - " else:\n", - " return likelihood(n, theta).pmf(h)*prior.pdf(theta)\n", - "\n", - "\n", - "# number of experiments\n", - "n = 100\n", - "\n", - "# number of successes\n", - "h = 61\n", - "\n", - "# hyperparameters for the prior\n", - "a = 10\n", - "b = 10\n", - "likelihood = stats.binom\n", - "prior = stats.beta(a, b)\n", - "sigma = 0.3\n", - "\n", - "# initilisation\n", - "naccept = 0\n", - "theta = 0.1\n", - "\n", - "# set the number of MCMC iterations\n", - "niters = 10000\n", - "\n", - "# run MH\n", - "samples = np.zeros(niters+1)\n", - "samples[0] = theta\n", - "\n", - "for i in range(niters):\n", - " theta_p = theta + stats.norm(0, sigma).rvs()\n", - " rho = min(1, target(likelihood, prior, n, h, theta_p)/target(likelihood, prior, n, h, theta ))\n", - " u = np.random.uniform()\n", - " if u < rho:\n", - " naccept += 1\n", - " theta = theta_p\n", - " samples[i+1] = theta\n", - "\n", - "# analyse MH output\n", - "nmcmc = len(samples)//2\n", - "print(\"Portion of accepted steps = \" + str(naccept/niters))\n", - "\n", - "post = stats.beta(h+a, n-h+b)\n", - "thetas = np.linspace(0, 1, 200)\n", - "\n", - "plt.figure(figsize=(4, 4))\n", - "plt.hist(samples[nmcmc:], 20, histtype='step', linewidth=1, label='Distribution of posterior samples', density =True);\n", - "plt.hist(prior.rvs(nmcmc), 40, histtype='step', linewidth=1, label='Distribution of prior samples', density=True);\n", - "plt.plot(thetas, post.pdf(thetas), c='red', linestyle='--', alpha=0.5, label='True posterior')\n", - "plt.xlim([0,1]);\n", - "plt.grid(0.3)\n", - "plt.legend(loc='best');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We run the chain for $N$ iterations and discard the first $B$ samples. This is called **`burn-in`** (or \"warm-up\").\n", - "\n", - "We can run several parallel versions of the algorithm. Each of them is called a **`chain`**.\n", - "\n", - "Neigbouring samples will contain similar information. We might want to save only every second, or fifth, or tenth. This is called **`thinning`**." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convergence diagnostics" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#@title Convergence diagnostics\n", - "\n", - "def mh_coin(niters, n, h, theta, likelihood, prior, sigma):\n", - " samples = [theta]\n", - " while len(samples) < niters:\n", - " theta_p = theta + stats.norm(0, sigma).rvs()\n", - " rho = min(1, target(likelihood, prior, n, h, theta_p)/target(likelihood, prior, n, h, theta ))\n", - " u = np.random.uniform()\n", - " if u < rho:\n", - " theta = theta_p\n", - " samples.append(theta)\n", - "\n", - " return samples\n", - "\n", - "n = 100\n", - "h = 61\n", - "lik = stats.binom\n", - "prior = stats.beta(a, b)\n", - "sigma = 0.05\n", - "niters = 100\n", - "\n", - "chains = [mh_coin(niters, n, h, theta, likelihood, prior, sigma) for theta in np.arange(0.1, 1, 0.2)]\n", - "\n", - "# compare multiple chains\n", - "\n", - "plt.figure(figsize=(5, 4))\n", - "\n", - "for chain in chains:\n", - " plt.plot(chain, '-o')\n", - "\n", - "plt.xlim([0, niters])\n", - "plt.ylim([0, 1]);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Was it very painful to write a sampler by hand?\n", - "\n", - "If not, bare in mind that we only wrote the simplest one possible! Sampling algorithms can get very complicated. 🧠" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can read more on the analytical solution of the version of this problem over a grid [here](https://mathworld.wolfram.com/Buffon-LaplaceNeedleProblem.html)." - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/100_acknowledgements.md b/100_acknowledgements.md deleted file mode 100644 index 45dc170..0000000 --- a/100_acknowledgements.md +++ /dev/null @@ -1,8 +0,0 @@ -# Acknowledgements and links - -- AIMS and Ulrich for the invitation -- Kira and James for writing together the DLI-23 practical -- 2021 Statistical Rethinking (with Numpyro) reading group at Imperial: Swapnil, Iwona, Tim (Theo? Giovanni?) -- Stan ODE co-authors -- Lorenzo Ciardo for telling me about the Buffon's needle problem -- Richard McEarlth for posting the prior-likelihood conflict example: https://twitter.com/rlmcelreath/status/1701165075493470644 \ No newline at end of file diff --git a/10_logistic_regression.ipynb b/10_logistic_regression.ipynb deleted file mode 100644 index 89fab83..0000000 --- a/10_logistic_regression.ipynb +++ /dev/null @@ -1,674 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Logistic and other regressions" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "import os\n", - "\n", - "import numpy as np\n", - "\n", - "import jax\n", - "import jax.numpy as jnp\n", - "from jax import random\n", - "\n", - "import numpyro\n", - "import numpyro.distributions as dist\n", - "from numpyro.infer import MCMC, NUTS, init_to_median\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import arviz as az\n", - "\n", - "numpyro.set_platform(\"cpu\")\n", - "numpyro.set_host_device_count(4)\n", - "\n", - "rng_key = random.PRNGKey(67)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Logistics regression: one dimensional version\n", - "\n", - "Let us simulate appropriate data and define a logistic regression model using Numpyro. We will need to chose priors for the intercept (alpha) and for the coefficients (beta). We then will use the NUTS sampler to obtain posterior samples for alpha and beta from the Bayesian model. Finally, we will print the posterior means of alpha and beta." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Generate synthetic data\n", - "np.random.seed(42)\n", - "X = np.random.randn(100, 1)\n", - "true_beta = jnp.array([ -2.0])\n", - "true_alpha = 0.5\n", - "logits = jnp.dot(X, true_beta) + true_alpha\n", - "probs = 1.0 / (1.0 + jnp.exp(-logits))\n", - "y = np.random.binomial(1, probs)\n", - "\n", - "plt.scatter(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the logistic regression model\n", - "def logistic_regression_model(X, y=None):\n", - "\n", - " # dimesionality of X, i.e the number of features\n", - " num_features = X.shape[1]\n", - "\n", - " # nummber of data points\n", - " num_data = X.shape[0]\n", - "\n", - " # Priors\n", - " alpha = numpyro.sample('alpha', dist.Normal(0, 1))\n", - " beta = numpyro.sample('beta', dist.Normal(jnp.zeros(num_features), jnp.ones(num_features)))\n", - "\n", - " # precompute logits, i.e. the linear predictor\n", - " logits = alpha + jnp.dot(X, beta)\n", - "\n", - " # likelihood. Remember how to use plates?\n", - " with numpyro.plate('data', num_data):\n", - " numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=y)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "sample: 100%|██████████| 1500/1500 [00:02<00:00, 527.47it/s, 3 steps of size 7.19e-01. acc. prob=0.93] \n" - ] - } - ], - "source": [ - "# Define the number of MCMC samples and the number of warmup steps\n", - "num_samples = 1000\n", - "num_warmup = 500\n", - "\n", - "# Run NUTS sampler\n", - "nuts_kernel = NUTS(logistic_regression_model)\n", - "mcmc = MCMC(nuts_kernel, num_samples=num_samples, num_warmup=num_warmup)\n", - "\n", - "mcmc.run(rng_key, X=X, y=y)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Posterior mean of alpha: 0.73553663\n", - "\n", - " mean std median 5.0% 95.0% n_eff r_hat\n", - " alpha 0.74 0.26 0.73 0.38 1.20 832.20 1.00\n", - " beta[0] -2.10 0.39 -2.08 -2.74 -1.45 542.46 1.00\n", - "\n", - "Number of divergences: 0\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Get posterior samples\n", - "samples = mcmc.get_samples()\n", - "\n", - "# Print posterior statistics\n", - "print(\"Posterior mean of alpha:\", jnp.mean(samples['alpha']))\n", - "#print(\"Posterior mean of beta:\", jnp.mean(samples['beta'], axis=0))\n", - "\n", - "# mean is not enough\n", - "mcmc.print_summary()\n", - "\n", - "# plot posterior distribution and traceplots\n", - "data = az.from_numpyro(mcmc)\n", - "az.plot_trace(data, compact=True);\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Writing a general function for MCMC inference flow\n", - "\n", - "Note that the Numpyro model which we wrote is generic with respect to dimentionality of `X` (well done us!).\n", - "\n", - "However, we have already repeated the same code several times. Let us wrap the inference flow into a funation, and then apply to the case with two features and weights." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "def run_mcmc(rng_key, # random key\n", - " model, # Numpyro model\n", - " args, # Dictionary of arguments\n", - " verbose=True # boolean for verbose MCMC\n", - " ):\n", - " \n", - " init_strategy = init_to_median(num_samples=10)\n", - " kernel = NUTS(model, init_strategy=init_strategy)\n", - " mcmc = MCMC(\n", - " kernel,\n", - " num_warmup=args[\"num_warmup\"],\n", - " num_samples=args[\"num_mcmc_samples\"],\n", - " num_chains=args[\"num_chains\"],\n", - " thinning=args[\"thinning\"],\n", - " progress_bar=False if \"NUMPYRO_SPHINXBUILD\" in os.environ else True,\n", - " )\n", - " start = time.time()\n", - " mcmc.run(rng_key, args)\n", - " t_elapsed = time.time() - start\n", - " if verbose:\n", - " mcmc.print_summary(exclude_deterministic=False)\n", - "\n", - " print(\"\\nMCMC elapsed time:\", round(t_elapsed), \"s\")\n", - " \n", - " # print summary\n", - " mcmc.print_summary()\n", - "\n", - " # plot posterior distribution and traceplots\n", - " data = az.from_numpyro(mcmc);\n", - " az.plot_trace(data, compact=True);\n", - "\n", - " return mcmc, mcmc.get_samples(), t_elapsed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an input, rather than specofocally providing `X` and `y`, we will provide a dictionary `args` with data, as well as other parameters for MCMC." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Logistics regression: two-dimensional version\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the logistic regression model\n", - "def logistic_regression_model(args): # notice the `args`!\n", - "\n", - " X = args[\"X\"]\n", - " y = args[\"y\"]\n", - "\n", - " # dimesionality of X, i.e the number of features\n", - " num_features = X.shape[1]\n", - "\n", - " # nummber of data points\n", - " num_data = X.shape[0]\n", - "\n", - " # Priors\n", - " alpha = numpyro.sample('alpha', dist.Normal(0, 1))\n", - " beta = numpyro.sample('beta', dist.Normal(jnp.zeros(num_features), jnp.ones(num_features)))\n", - "\n", - " # precompute logits, i.e. the linear predictor\n", - " logits = alpha + jnp.dot(X, beta)\n", - "\n", - " # likelihood. Remember how to use plates?\n", - " with numpyro.plate('data', num_data):\n", - " numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=y)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate synthetic data\n", - "np.random.seed(42)\n", - "X = np.random.randn(100, 2)\n", - "true_beta = jnp.array([1.0, -2.0])\n", - "true_alpha = 0.5\n", - "logits = jnp.dot(X, true_beta) + true_alpha\n", - "probs = 1.0 / (1.0 + jnp.exp(-logits))\n", - "y = np.random.binomial(1, probs)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Compiling.. : 0%| | 0/1500 [00:00,\n", - " {'alpha': Array([0.31299403, 0.49014637, 0.8698226 , ..., 0.6729814 , 0.6768546 ,\n", - " 0.8103018 ], dtype=float32),\n", - " 'beta': Array([[ 1.1431079 , -1.5676624 ],\n", - " [ 0.69187284, -1.580464 ],\n", - " [ 1.1180909 , -2.9557333 ],\n", - " ...,\n", - " [ 0.4585066 , -1.9653267 ],\n", - " [ 0.4983528 , -1.9151963 ],\n", - " [ 0.94535226, -2.2248352 ]], dtype=float32)},\n", - " 4.9618821144104)" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "args = {'X': X, \n", - " 'y':y,\n", - " 'num_mcmc_samples': 1000,\n", - " 'num_warmup': 500,\n", - " 'num_chains': 4, \n", - " 'thinning': 1,\n", - "}\n", - "\n", - "run_mcmc(rng_key, logistic_regression_model, args)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Poisson regression" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate synthetic data\n", - "np.random.seed(42)\n", - "X = np.random.randn(1000, 2)\n", - "true_beta = jnp.array([0.5, -1.5])\n", - "true_alpha = 1.0\n", - "\n", - "true_lambda = jnp.exp(true_alpha + jnp.dot(X, true_beta))\n", - "y = np.random.poisson(true_lambda)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the Poisson regression model\n", - "def poisson_regression_model(args):\n", - "\n", - " X = args[\"X\"]\n", - " y = args[\"y\"]\n", - "\n", - " # dimesionality of X, i.e the number of features\n", - " num_features = X.shape[1]\n", - "\n", - " # nummber of data points\n", - " num_data = X.shape[0]\n", - "\n", - " # priors\n", - " alpha = numpyro.sample('alpha', dist.Normal(0, 1))\n", - " beta = numpyro.sample('beta', dist.Normal(jnp.zeros(num_features), jnp.ones(num_features)))\n", - " \n", - " # Poisson regression\n", - " lambda_ = jnp.exp(alpha + jnp.dot(X, beta))\n", - " \n", - " # likelihood\n", - " with numpyro.plate('data', num_data):\n", - " numpyro.sample('obs', dist.Poisson(lambda_), obs=y)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Running chain 0: 100%|██████████| 1500/1500 [00:04<00:00, 306.10it/s]\n", - "Running chain 1: 100%|██████████| 1500/1500 [00:04<00:00, 306.33it/s] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " mean std median 5.0% 95.0% n_eff r_hat\n", - " alpha 0.98 0.02 0.98 0.95 1.02 715.76 1.00\n", - " beta[0] 0.49 0.01 0.49 0.47 0.51 1134.37 1.00\n", - " beta[1] -1.52 0.01 -1.52 -1.54 -1.50 710.98 1.00\n", - "\n", - "Number of divergences: 0\n", - "\n", - "MCMC elapsed time: 5 s\n", - "\n", - " mean std median 5.0% 95.0% n_eff r_hat\n", - " alpha 0.98 0.02 0.98 0.95 1.02 715.76 1.00\n", - " beta[0] 0.49 0.01 0.49 0.47 0.51 1134.37 1.00\n", - " beta[1] -1.52 0.01 -1.52 -1.54 -1.50 710.98 1.00\n", - "\n", - "Number of divergences: 0\n" - ] - }, - { - "data": { - "text/plain": [ - "(,\n", - " {'alpha': Array([0.98814416, 0.96745694, 0.9721584 , ..., 0.9676529 , 0.9833631 ,\n", - " 0.97209245], dtype=float32),\n", - " 'beta': Array([[ 0.4986962 , -1.5032464 ],\n", - " [ 0.5016759 , -1.5363169 ],\n", - " [ 0.5003391 , -1.5242897 ],\n", - " ...,\n", - " [ 0.4900542 , -1.5274057 ],\n", - " [ 0.48092988, -1.5196061 ],\n", - " [ 0.47770953, -1.5192877 ]], dtype=float32)},\n", - " 5.189333915710449)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "args = {'X': X, \n", - " 'y':y,\n", - " 'num_mcmc_samples': 1000,\n", - " 'num_warmup': 500,\n", - " 'num_chains': 2, \n", - " 'thinning': 1,\n", - "}\n", - "\n", - "run_mcmc(rng_key, poisson_regression_model, args)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Binomial regression" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate synthetic data\n", - "np.random.seed(42)\n", - "num_samples = 100\n", - "X = np.random.randn(num_samples, 2)\n", - "true_beta = np.array([1.0, -2.0])\n", - "true_alpha = 1.0\n", - "logits = true_alpha + X.dot(true_beta)\n", - "num_trials = np.random.randint(1, 10, size=num_samples) # Vector of different numbers of trials\n", - "y = np.random.binomial(num_trials, p=1 / (1 + np.exp(-logits)))" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the binomial regression model\n", - "def binomial_regression_model(args):\n", - "\n", - " num_samples, num_features = X.shape\n", - "\n", - " # Priors for the coefficients\n", - " alpha = numpyro.sample(\"alpha\", dist.Normal(0, 1))\n", - " #beta = numpyro.sample(\"beta\", dist.Normal(0, 1).expand([num_features]))\n", - " beta = numpyro.sample('beta', dist.Normal(jnp.zeros(num_features), jnp.ones(num_features)))\n", - "\n", - " # Linear predictor\n", - " eta = alpha + jnp.dot(X, beta)\n", - "\n", - " # Likelihood\n", - " with numpyro.plate('data', num_samples):\n", - " numpyro.sample('obs', dist.Binomial(total_count=num_trials, logits=eta), obs=y)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Running chain 0: 100%|██████████| 1500/1500 [00:04<00:00, 323.78it/s]\n", - "Running chain 1: 100%|██████████| 1500/1500 [00:04<00:00, 324.10it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " mean std median 5.0% 95.0% n_eff r_hat\n", - " alpha 0.77 0.13 0.77 0.55 0.96 1415.82 1.00\n", - " beta[0] 1.07 0.15 1.07 0.83 1.31 1330.85 1.00\n", - " beta[1] -1.74 0.17 -1.73 -2.02 -1.47 1349.51 1.00\n", - "\n", - "Number of divergences: 0\n", - "\n", - "MCMC elapsed time: 5 s\n", - "\n", - " mean std median 5.0% 95.0% n_eff r_hat\n", - " alpha 0.77 0.13 0.77 0.55 0.96 1415.82 1.00\n", - " beta[0] 1.07 0.15 1.07 0.83 1.31 1330.85 1.00\n", - " beta[1] -1.74 0.17 -1.73 -2.02 -1.47 1349.51 1.00\n", - "\n", - "Number of divergences: 0\n" - ] - }, - { - "data": { - "text/plain": [ - "(,\n", - " {'alpha': Array([0.6880498, 0.7357271, 0.7427765, ..., 0.7393519, 0.7209873,\n", - " 0.9435985], dtype=float32),\n", - " 'beta': Array([[ 1.2091104 , -1.7700318 ],\n", - " [ 1.0821681 , -1.8673537 ],\n", - " [ 1.0768006 , -1.9365392 ],\n", - " ...,\n", - " [ 1.2230068 , -1.5024718 ],\n", - " [ 0.99302703, -1.636328 ],\n", - " [ 1.1817127 , -1.7612263 ]], dtype=float32)},\n", - " 4.95363187789917)" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "args = {'X': X, \n", - " 'y':y,\n", - " 'num_trials': num_trials,\n", - " 'num_mcmc_samples': 1000,\n", - " 'num_warmup': 500,\n", - " 'num_chains': 2, \n", - " 'thinning': 1,\n", - "}\n", - "\n", - "run_mcmc(rng_key, binomial_regression_model, args)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/13_GPs.ipynb b/13_GPs.ipynb deleted file mode 100644 index 2598da4..0000000 --- a/13_GPs.ipynb +++ /dev/null @@ -1,549 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Gaussian Processes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From parameteric to nonparamteric models\n", - "\n", - "So far, in this course, all models we have built were regressions, working in the supervised learning setting and using parametric models. We tried to describe functions with unknown parameters using Bayesian formalism." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "Gaussian processes (GPs) are powerful and flexible statistical models used primarily in machine learning and statistics for regression and classification tasks. At its core, a Gaussian process is a collection of random variables, any finite subset of which has a joint Multivariate distribution. In simpler terms, a GP defines a distribution over functions rather than individual points.\n", - "\n", - "Nonparametric models are statistical models where the number of parameters grows with the size of the dataset or is not fixed beforehand. This allows them to flexibly capture complex patterns in the data without making strong assumptions about the underlying distribution.\n", - "\n", - "A Gaussian process is a nonparametric model because it doesn't fix the number of parameters a priori, instead, it defines a distribution over functions, allowing for flexibility and adaptability to the complexity of the data.\n", - "\n", - "A few key points about Gaussian processes:\n", - "\n", - "- **Function Space Representation**: Unlike parametric models, which learn a fixed number of parameters, Gaussian processes define a distribution over functions. This allows them to capture uncertainty about the function being modeled.\n", - "\n", - "```{margin}\n", - "Well, do assume *something* about the functions, e.g. their smoothness in the form of kernel choice. \n", - "```\n", - "- **Flexibility**: GPs can model a wide variety of functions without assuming a specific functional form. This makes them particularly useful when dealing with complex or unknown relationships in data.\n", - "\n", - "- **Bayesian Framework**: Gaussian processes are inherently Bayesian models, meaning they provide a principled way to quantify uncertainty in predictions. This is achieved by representing the posterior distribution over functions given the observed data.\n", - "\n", - "- **Kernel Functions**: The choice of kernel function determines the behavior and characteristics of the GP. Common kernel functions include the radial basis function (RBF), also known as the squared exponential kernel, and the Matérn kernel, among others. These kernels encode assumptions about the smoothness and structure of the underlying function. We will see specific examples of kernels in this lecture.\n", - "\n", - "- **Regression and Classification**: GPs can be used for both regression and classification tasks where GP is commonly used as a latent variable.\n", - "\n", - "- **Computational Considerations**: While GPs offer many advantages, they can be computationally intensive, especially as the size of the dataset grows. Various approximation methods such as sparse GPs and approximate inference techniques are used to scale Gaussian processes to larger datasets.\n", - "\n", - "Now let's build the inredients which we need to understand Gaussian processes step by step." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From Univariate to Multivariate Gaussians\n", - "\n", - "### Univariate Normal distribution\n", - "```{margins}\n", - "In the chapter about distributions we used notation $X$ for a random variable and $x$ for its values. Here we will denote the random variable of interest $Y$. It will become clear soon why we need to do it.\n", - "```\n", - "\n", - "Recall from previous chapters, the Univaritae normal distribution with PDF\n", - "\n", - "$$\n", - "\\mathcal{N}(y \\mid \\mu, \\sigma) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp\\left(-\\frac{(y - \\mu)^2}{2\\sigma^2}\\right).\n", - "$$\n", - "\n", - "To show that variable $y$ is dustributed normally with mean $\\mathbb{E}[y] = \\mu$ and variance $\\text{Var}(y)=\\sigma^2$, we use notation \n", - "$$y \\sim \\mathcal{N}(\\mu, \\sigma^2).$$\n", - "\n", - "\n", - "### Reparametrization\n", - "\n", - "Note, that in order to sample variable $y$ we could fisrt sample from a standard Normal variable $z \\sim \\mathcal{N}(0,1)$, and then perform a transformation of this variable \n", - "$$\n", - "y = \\mu + \\sigma z \\sim \\mathcal{N}(\\mu, \\sigma^2).\n", - "$$\n", - "\n", - "**Excersise**: prove that $\\mu + \\sigma z$ is indeed distributed as $\\mathcal{N}(\\mu, \\sigma^2).$\n", - "\n", - "### Multivariate Normal distribution\n", - "\n", - "In the multivariate case, instead of using scalar mean and variance parameters $\\mu, \\sigma^2 \\in \\mathbb{R}$, we need to specify a vector mean $\\mu \\in \\mathbb{R}^d$ and covariance matrix $\\Sigma \\in \\mathbb{R}^{d \\times d}.$ To write that variable $y$ follows a Multivariate Normal distribution, we use notation \n", - "$$y \\sim \\mathcal{N}(\\mu, \\Sigma).$$\n", - "\n", - "### Bivariate case\n", - "\n", - "'Bivariate' means $d=2$. Hence,\n", - "\n", - "$$\n", - "y = \\begin{pmatrix} y_1 \\\\ y_2 \\end{pmatrix}, \\quad \\mu = \\begin{pmatrix} \\mu_1 \\\\ \\mu_2 \\end{pmatrix}, \\quad \\Sigma = \\begin{pmatrix} \\sigma_1^2 & \\rho \\sigma_1 \\sigma_2 \\\\ \\rho \\sigma_1 \\sigma_2 &\\sigma_2^2 \\end{pmatrix}.\n", - "$$\n", - "\n", - "Here $\\mu_i$ is the mean of component $y_i$, $\\sigma_i^2$ is the variance for the $i$-th dimension, and $\\rho_{ij} = \\rho_{ji}$ is the *correlation* between the $i$-th and $j$-th dimensions:\n", - "$$\n", - "\\mathbb{E}(y_i) = \\mu_i,\\\\\n", - "\\text{var}(y_i) = \\sigma_i^2,\\\\\n", - "\\text{corr}(y_1, y_2) = \\rho_{12}.\n", - "$$\n", - "The covariance matrix tells us how the \"ball\" of random variables is stretched and rotated in space. Let's visualise a few examples.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "import jax.numpy as jnp\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.stats import norm\n", - "import matplotlib.gridspec as gridspec" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_2d_gp(mu1, mu2, rho, sigma1=1, sigma2=1):\n", - " mu = np.array([mu1, mu2]) # mean\n", - " covariance = jnp.array([[sigma1**2, rho*sigma1*sigma2],[rho*sigma1*sigma2, sigma2**2]]) # covariance matrix\n", - "\n", - " # Generate data points from the 2D Gaussian distribution\n", - " num_samples = 1000\n", - " data = np.random.multivariate_normal(mu, covariance, num_samples)\n", - "\n", - " # Calculate marginal distributions\n", - " x_values = np.linspace(-3, 3, 1000)\n", - " marginal_x = norm.pdf(x_values, loc=mu[0], scale=np.sqrt(covariance[0, 0]))\n", - " marginal_y = norm.pdf(x_values, loc=mu[1], scale=np.sqrt(covariance[1, 1]))\n", - "\n", - " # Create figure and gridspec\n", - " fig = plt.figure(figsize=(6, 4))\n", - " gs = fig.add_gridspec(3, 3)\n", - "\n", - " # Main plot (2D Gaussian distribution)\n", - " ax_main = fig.add_subplot(gs[1:3, :2])\n", - " ax_main.scatter(data[:, 0], data[:, 1], alpha=0.5, label='2D Gaussian')\n", - " ax_main.set_xlabel('X')\n", - " ax_main.set_ylabel('Y')\n", - " ax_main.legend()\n", - " ax_main.grid(True)\n", - "\n", - " # Marginal X plot\n", - " ax_marginal_x = fig.add_subplot(gs[0, :2], sharex=ax_main)\n", - " ax_marginal_x.plot(x_values, marginal_x, label='Marginal X', color='r')\n", - " ax_marginal_x.legend()\n", - " ax_marginal_x.grid(True)\n", - "\n", - " # Marginal Y plot\n", - " ax_marginal_y = fig.add_subplot(gs[1:3, 2], sharey=ax_main)\n", - " ax_marginal_y.plot(marginal_y, x_values, label='Marginal Y', color='g')\n", - " ax_marginal_y.legend()\n", - " ax_marginal_y.grid(True)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "# Parameters for the 2D Gaussian distribution\n", - "mu1 = 0\n", - "mu2 = 0\n", - "plot_2d_gp(mu1, mu2, rho)\n", - "# How does the distribution change with different values of rho?\n", - "for rho in np.linspace(0.2,0.8, 2):\n", - " plot_2d_gp(mu1, mu2, rho)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Definitions\n", - "\n", - "**Definition** (kernel)\n", - "\n", - "$k: \\mathbb{X} \\times \\mathbb{X} \\to \\mathbb{R}$ is a positive definite **kernel**, if for any finite collection $x= [x_1, ..., x_N]$ the matrix $k_{xx}$ with $[k_{xx}]_{ij}=k(x_i, x_j)$ is **positive definite**.\n", - "\n", - "**Definition** (positive definite matrix)\n", - "\n", - "A symmetric matrix $A \\in \\mathbb{R}^{N \\times N}$ is called positve (semi-) definite if\n", - "$$v^T A v \\ge 0$$ \n", - "for any $v \\in \\mathbb{R}^N.$\n", - "\n", - "**Definition** (Gaussian processes)\n", - "\n", - "A **Gaussian process** is a probablity distribution over the function $f: \\mathbb{X} \\to \\mathbb{R}$, such that every finite realisation\n", - "\n", - "$$f(x) = [f(x_1), ..., f(x_N)]$$ \n", - "is a Gaussian (multivariate normal) dsitribution\n", - "\n", - "$$f_X \\sim \\mathcal{N}(\\mu_X, k_{XX}).$$ \n", - "\n", - "## Notation\n", - "\n", - "- To describe that $f$ if a GP with mean $\\mu(x)$ and kernel $k(x, x')$ we will be writing\n", - "\n", - "$$f(x) \\sim \\mathcal{GP}(\\mu(x), k(x, x')).$$\n", - "\n", - "- A Gaussian process is completely characterized by its **mean function** $\\mu(x)$ and its **covariance function** $k(x,x'),$ which define\n", - "\n", - "$$\\mathbb{E}[f(x)]=\\mu(x)$$\n", - "and \n", - "$$\\text{cov}[f(x), f(x')]=k(x, x').$$\n", - "\n", - "## Kernels\n", - "\n", - "GPs are uniquely defined by their mean and covariance functions. We will further assume that the GPs we work with have a zero mean, and, hence, we will focus on the kernels.\n", - "\n", - "Kernel functions $k(x, x′)$ encode prior beliefs of data-generating latent functions. These typically include\n", - "- continuity,\n", - "- smomothness (differentialbility),\n", - "- periodicity,\n", - "- stationarity,\n", - "\n", - "and so on.\n", - "\n", - "The covariance functions typically have **hyperparameters** that we aim to learn from data.\n", - "\n", - "Let us explore some typical covariance functions.\n", - "\n", - "## Cholesky decomposition and reparametrization\n", - "\n", - "Cholesky decomposition is a numerical method used to decompose a positive definite matrix into a lower triangular matrix and its conjugate transpose. For a positive definite matrix $A$, the Cholesky decomposition expresses it as:\n", - "$$A = L L^T$$\n", - "where\n", - "- $L$ is a lower triangular matrix,\n", - "- $L^T$ is the transpose of $L$.\n", - "\n", - "Cholesky decomposition is particularly useful because it provides a computationally efficient way to solve linear systems of equations, including inverting matrices and calculating determinants, especially when the matrix is symmetric and positive definite.\n", - "\n", - "In Gaussian processes, Cholesky decomposition is commonly used to generate samples from a multivariate Gaussian distribution. When you want to generate samples from a Gaussian process, you typically start with a covariance matrix $K$, which represents the covariance between different points in the input space. The Cholesky decomposition of this covariance matrix $K$ yields a lower triangular matrix $L$:\n", - "$$K = L L^T.$$\n", - "\n", - "By multiplying this lower triangular matrix with a vector of independent standard normal variables, you can generate samples from the Gaussian process while ensuring that the resulting samples have the desired covariance structure encoded by the covariance matrix $K$. This is done because the Cholesky decomposition allows you to transform independent standard normal variables into correlated Gaussian variables according to the covariance matrix Cholesky $K$. Hence, we can either sample directly \n", - "$$f \\sim \\mathcal{N}(0, \\Sigma)$$\n", - "or use the reparametrization\n", - "$$f = zL \\sim \\mathcal{N}(0, \\Sigma), \\quad z \\sim \\mathcal{N}(0,I).$$\n", - "\n", - "\n", - "### Kernels - examples\n", - "\n", - "### RBF\n", - "\n", - "The squared exponential kernel, also known as the Gaussian kernel or the radial basis function (RBF) kernel is one of the most commonly used kernels in Gaussian process regression. It is defined as\n", - "$$k(x_i, x_j) = \\sigma^2 \\exp \\left( -\\frac{1}{2\\ell^2} \\|x_i - x_j\\|^2 \\right)$$\n", - "where\n", - "\n", - "- $\\sigma^2$ is the variance parameter (also called the amplitude),\n", - "- $l$ is the lengthscale parameter,\n", - "- $\\|x_i - x_j\\|$ is the Euclidean distance between the points $x_i$ and $x_j$.\n", - "\n", - "This kernel assigns high similarity (and hence high covariance) to points that are close to each other in the input space and low similarity (and low covariance) to points that are far apart. The parameters $\\sigma^2$ and $l$ control the overall variance and the rate at which the covariance decreases with distance, respectively.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a Squared Exponential (RBF) kernel\n", - "def rbf_kernel(x1, x2, sigma=1.0, lengthscale=1.0):\n", - " \"\"\"\n", - " Compute the Radial Basis Function (RBF) kernel matrix between two sets of points.\n", - "\n", - " Args:\n", - " - x1 (array): Array of shape (n1, d) representing the first set of points.\n", - " - x2 (array): Array of shape (n2, d) representing the second set of points.\n", - " - sigma (float): Variance parameter.\n", - " - length_scale (float): Length-scale parameter.\n", - "\n", - " Returns:\n", - " - K (array): Kernel matrix of shape (n1, n2).\n", - " \"\"\"\n", - " sq_dist = jnp.sum(x1**2, axis=1).reshape(-1, 1) + jnp.sum(x2**2, axis=1) - 2 * jnp.dot(x1, x2.T)\n", - " return sigma**2 * jnp.exp(-0.5 / lengthscale**2 * sq_dist)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0.9949115 0.9798007 0.9551244 0.9216181]\n", - " [0.9949115 1. 0.9949115 0.9798007 0.9551244]\n", - " [0.9798007 0.9949115 1. 0.9949115 0.9798007]\n", - " [0.9551244 0.9798007 0.9949115 1. 0.9949115]\n", - " [0.9216181 0.9551244 0.9798007 0.9949115 1. ]]\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Generate some 1D data\n", - "x = jnp.linspace(0, 10, 100).reshape(-1, 1)\n", - "\n", - "# Compute the RBF kernel matrix\n", - "K = rbf_kernel(x, x)\n", - "\n", - "# Look at element of the kernel matrix\n", - "print(K[0:5, 0:5])\n", - "\n", - "# What values \n", - "plt.figure(figsize=(5, 5))\n", - "plt.hist(K.flatten(), bins=20)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "# Visualization\n", - "plt.imshow(K, cmap='viridis', extent=[-5, 5, -5, 5])\n", - "plt.colorbar(label='Kernel Value')\n", - "plt.title('RBF Kernel Matrix')\n", - "plt.xlabel('X')\n", - "plt.ylabel('X')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import jax.numpy as jnp\n", - "from jax import random\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def rbf_kernel(x1, x2, sigma=1.0, length_scale=1.0):\n", - " \"\"\"\n", - " Compute the Radial Basis Function (RBF) kernel matrix between two sets of points.\n", - "\n", - " Args:\n", - " - x1 (array): Array of shape (n1, d) representing the first set of points.\n", - " - x2 (array): Array of shape (n2, d) representing the second set of points.\n", - " - sigma (float): Variance parameter.\n", - " - length_scale (float): Length-scale parameter.\n", - "\n", - " Returns:\n", - " - K (array): Kernel matrix of shape (n1, n2).\n", - " \"\"\"\n", - " sq_dist = jnp.sum(x1**2, axis=1).reshape(-1, 1) + jnp.sum(x2**2, axis=1) - 2 * jnp.dot(x1, x2.T)\n", - " return sigma**2 * jnp.exp(-0.5 / length_scale**2 * sq_dist)\n", - "\n", - "def draw_gp_sample(rng_key, X_pred, X_train, kernel_func, sigma=1.0, length_scale=1.0, noise=1e-6):\n", - " \"\"\"\n", - " Draw a sample from a Gaussian process with given kernel.\n", - "\n", - " Args:\n", - " - rng_key: JAX random key.\n", - " - X_pred (array): Array of shape (n_pred, d) representing the prediction points.\n", - " - X_train (array): Array of shape (n_train, d) representing the training points.\n", - " - kernel_func (function): Function to compute the kernel.\n", - " - sigma (float): Variance parameter.\n", - " - length_scale (float): Length-scale parameter.\n", - " - noise (float): Observation noise.\n", - "\n", - " Returns:\n", - " - f_pred (array): Array of shape (n_pred,) representing the sampled function values at prediction points.\n", - " \"\"\"\n", - " # Compute the kernel matrices\n", - " K_xx = kernel_func(X_train, X_train, sigma, length_scale)\n", - " K_xpred_x = kernel_func(X_pred, X_train, sigma, length_scale)\n", - " K_xpred_xpred = kernel_func(X_pred, X_pred, sigma, length_scale)\n", - "\n", - " # Add observation noise\n", - " K_xx += noise * jnp.eye(X_train.shape[0])\n", - "\n", - " # Compute the mean and covariance of the predictive distribution\n", - " L = jnp.linalg.cholesky(K_xx)\n", - " alpha = jnp.linalg.solve(L.T, jnp.linalg.solve(L, jnp.ones(X_train.shape[0])))\n", - " f_mean = jnp.dot(K_xpred_x, alpha)\n", - " v = jnp.linalg.solve(L, K_xpred_x.T)\n", - " f_cov = K_xpred_xpred - jnp.dot(v.T, v)\n", - "\n", - " # Draw a sample from the predictive distribution\n", - " f_pred = random.multivariate_normal(rng_key, f_mean, f_cov)\n", - " return f_pred, f_mean\n", - "\n", - "# Set random seed\n", - "key = random.PRNGKey(0)\n", - "\n", - "# Define the kernel parameters\n", - "sigma = 1.0\n", - "length_scale = 1.0\n", - "\n", - "# Define the training data\n", - "X_train = jnp.array([-4, -3, -2, -1, 1, 2, 3, 4]).reshape(-1, 1)\n", - "Y_train = jnp.sin(X_train)\n", - "\n", - "# Define the prediction points\n", - "X_pred = jnp.linspace(-5, 5, 100).reshape(-1, 1)\n", - "\n", - "# Draw a sample from the GP\n", - "f_sample, f_mean = draw_gp_sample(key, X_pred, X_train, rbf_kernel, sigma, length_scale)\n", - "\n", - "# Plot the mean prediction\n", - "plt.plot(X_pred, f_mean, color='blue', label='Mean Prediction')\n", - "\n", - "# Plot the sample\n", - "plt.plot(X_pred, f_sample, color='green', linestyle='--', label='Sample')\n", - "\n", - "# Plot the training data\n", - "plt.scatter(X_train, Y_train, color='red', label='Training Data')\n", - "\n", - "plt.xlabel('X')\n", - "plt.ylabel('Y')\n", - "plt.title('Sample from Gaussian Process with RBF Kernel')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - 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Probabilistic Thinking, modelling and programming for Epidemiology.

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Probabilistic Thinking, modelling and programming for Epidemiology.#

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Welcome to the course!

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About this course

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About this course#

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This course will be taught during three weeks to a MSc “AI for Science” cohort at the African Institute for Mathematical Studeis (AIMS), South Africa.

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Tentative outline of the course if presented below but might be adjusted during the course:

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  • Week 1 - Probabilistic programming.

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    • Day 1

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      • Introduction to epidemiology.

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      • Probability distributions refresher.

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      • Bayesian inference

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      • Focus on priors

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      • numerical methods to obtain posterior

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      • MCMC by hand

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      • convergence diagnostics

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      • PPLs

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      • Intro to Numpyro: model, inference, check convergence

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      • Bayesian workflow: prior predictive and posterior predictive

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      • logistic regression with Numpyro

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      • Poisson and NegativeBinomial regression with Numpyro

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    • Day 4

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      • hierarchical modelling

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      • bonus topic: Bayesian neural network with a PPL

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      • Practical 1 submission

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  • Week 2 - spatial modelling.

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      • GPs refresher: MVN, mean, kernel, covariance

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      • kernels: RBF. Matern

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      • non-stationary kernels: linear

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      • combining kernels

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      • scalability: Kronecker

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      • approximations: HSGP

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      • areal data modelling

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    • Day 3

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      • geostatistical data modelling

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    • Day 4

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      • point pattern modelling

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      • Practical 2 submission

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  • Week 3 - infectious disease modelling.

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    • Day 1

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      • disease transmission modelling: overview

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      • ODEs

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      • solve ODEs without Numpyro: SIS, SIR, SIRD, SIRS, SEIR, SIRC

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      • (bonus topics: phase portraits, stability)

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      • estimating R_0, R_t

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      • ODEs with Numpyro

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      • SIR with Numpyro, boarding school example

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      • ABMs

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      • ABMs on networks

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      • renewal equation

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      • Practical 3 submission

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      • Bonus topics: VAEs and diffusion model for prior learning

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      • causality

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-By Elizaveta Semenova -

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Introduction to Modelling in epidemiology

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Introduction to Modelling in epidemiology#

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In this course we will consider a range of models used in epidemiology - from spatial statistics to disease transmission modelling. For each of such models we will consider a probabilistic formulation allowing for Bayesian inference. We will use a probabilistic programing language (PPL) to do that. Next, let’s uncover each of the three key term - epidemioligy, probabilistic modelling amd probablistic programming.

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Epidemiology#

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Epidemiology is the study of how diseases and health-related events are distributed within populations and the factors that influence these distributions. It is a branch of public health that focuses on understanding the patterns, causes, and effects of diseases and health conditions on a large scale. Epidemiologists collect and analyze data to investigate the occurrence of health outcomes, their risk factors, and the impact of various interventions or preventive measures.

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Key aspects of epidemiology include:

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  • Disease Surveillance: Epidemiologists monitor the occurrence of diseases and health-related events over time and across different geographic areas. This involves tracking the number of cases, identifying outbreaks, and assessing trends in disease incidence.

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  • Identifying Risk Factors: Epidemiological studies aim to identify the factors that increase the likelihood of developing a particular disease. These risk factors can include genetic predisposition, environmental exposures, lifestyle choices, and social determinants of health.

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  • Causality Assessment: Epidemiologists use various study designs, including cohort studies, case-control studies, and randomized controlled trials, to determine if a specific factor or intervention causes a particular disease. Establishing causality is a fundamental goal of epidemiology.

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  • Disease Prevention and Control: The insights gained from epidemiological research are crucial for designing and implementing public health interventions and policies aimed at preventing and controlling diseases. This may involve vaccination campaigns, health education programs, quarantine measures, and more.

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  • Public Health Planning: Epidemiological data and findings play a vital role in informing public health planning and resource allocation. This includes assessing healthcare needs, identifying at-risk populations, and developing strategies to improve overall health outcomes.

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  • Outbreak Investigation: Epidemiologists are often involved in investigating disease outbreaks, such as foodborne illnesses, infectious disease outbreaks, or clusters of chronic diseases. They work to identify the source of the outbreak and implement measures to contain and prevent further spread.

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  • Epidemiological Models: Mathematical and statistical models are frequently used in epidemiology to simulate disease spread and predict future trends. These models help in making informed decisions and planning interventions.

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Overall, epidemiology is essential for understanding the health of populations, identifying health disparities, and guiding public health efforts to improve the well-being of communities and societies.

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Probabilistic modelling#

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Probabilistic modeling is a mathematical and statistical approach that involves incorporating uncertainty and randomness into models to account for variability in real-world phenomena. In epidemiology, probabilistic modeling plays a crucial role in understanding the spread of diseases and assessing the impact of interventions. Here’s why probabilistic modeling is important for epidemiology:

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  • Capturing Uncertainty: Epidemics are inherently uncertain processes influenced by numerous variables, including human behavior, environmental factors, and the biology of the infectious agent. Probabilistic models allow epidemiologists to account for this uncertainty by representing it explicitly. This is particularly important when making predictions or assessing the effectiveness of control measures.

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  • Realistic Simulations: Probabilistic models simulate disease spread and transmission more realistically by considering the variability in individual interactions, susceptibility, and infectiousness. This realism helps in better understanding the dynamics of epidemics and how they may evolve over time.

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  • Data Integration: Epidemiological data often contain inherent variability and noise. Probabilistic models can incorporate this variability and provide a framework for data assimilation and model calibration. This helps in refining models to fit observed data more accurately.

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  • Quantifying Risk: Probabilistic models can quantify the likelihood of various outcomes, such as the probability of an outbreak occurring, the probability of disease transmission in a specific setting, or the probability of a particular intervention’s success. This information is essential for risk assessment and decision-making.

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  • Scenario Analysis: Epidemiologists can use probabilistic models to explore a wide range of scenarios and assess the potential impact of different interventions and policies. This aids in designing effective strategies for disease control and prevention.

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  • Sensitivity Analysis: Probabilistic modeling allows for sensitivity analysis, which helps identify which parameters or assumptions in the model have the most significant impact on outcomes. This information guides research priorities and resource allocation.

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  • Incorporating Individual-Level Variation: Probabilistic models can capture individual-level variations in disease transmission, susceptibility, and response to interventions. This is crucial for tailoring public health measures to different population segments.

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  • Policy Decision Support: Probabilistic modeling provides decision-makers with valuable insights into the expected outcomes of different policy choices. This aids in prioritizing resource allocation and optimizing public health interventions.

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  • Adaptability: As new data becomes available during an epidemic, probabilistic models can be updated and refined, allowing epidemiologists to continuously monitor and adapt their strategies in response to changing circumstances.

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In summary, probabilistic modeling in epidemiology helps epidemiologists and public health officials make informed decisions by quantifying uncertainty, simulating realistic disease dynamics, and assessing the potential impact of various interventions. It is a powerful tool for improving our understanding of infectious disease outbreaks and guiding effective public health responses.

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Probabilistics programming#

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Probabilistic programming is a specialized approach to building and analyzing probabilistic models that offers several advantages for epidemiology and the study of infectious disease dynamics:

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  • Flexibility: Probabilistic programming languages, such as Stan, Pyro, or Edward, provide a flexible framework for defining and customizing probabilistic models. This flexibility is crucial in epidemiology, where the complexity of disease transmission models can vary widely depending on the specific disease and the population under study.

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  • Uncertainty Quantification: Probabilistic programming allows for the explicit representation and quantification of uncertainty. Epidemiological models often involve uncertain parameters and data, and probabilistic programming makes it easier to incorporate this uncertainty into the modeling process.

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  • Bayesian Inference: Probabilistic programming languages are particularly well-suited for Bayesian inference, which is a powerful statistical approach used in epidemiology. Bayesian methods enable the estimation of model parameters, predictions, and model comparisons while accounting for prior information and uncertainty.

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  • Model Validation: Probabilistic programming facilitates model validation by enabling researchers to compare model predictions with observed data using techniques like posterior predictive checks. This helps ensure that models accurately capture the underlying dynamics of infectious diseases.

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  • Hierarchical Modeling: Many epidemiological models involve hierarchical structures, where data at multiple levels (e.g., individuals, households, communities) are analyzed simultaneously. Probabilistic programming makes it easier to specify and fit hierarchical models, which can provide more accurate representations of complex disease transmission processes.

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  • Data Integration: Probabilistic programming allows for the integration of various types of data sources, including clinical data, epidemiological surveillance data, and genomic data. This integration can improve the accuracy and informativeness of epidemiological models.

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  • Model Selection and Comparison: Epidemiologists often need to compare different model structures or assess the fit of alternative hypotheses. Probabilistic programming facilitates model selection and comparison through techniques like Bayesian model averaging and model evidence calculation.

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  • Real-time Updates: In the context of infectious disease outbreaks, probabilistic programming allows for real-time updates of models as new data becomes available. This adaptability is critical for guiding public health responses during rapidly evolving situations.

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  • Transparent Communication: Probabilistic programming encourages transparency in modeling and analysis. Researchers can clearly specify their assumptions, priors, and likelihood functions, making it easier to communicate and collaborate with other experts and stakeholders.

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  • Extensible Libraries: Probabilistic programming languages often come with extensive libraries and tools for model development, inference, and visualization, reducing the implementation and computation burden for epidemiologists.

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In summary, probabilistic programming is a valuable tool for epidemiologists because it offers a flexible and powerful framework for modeling disease dynamics, quantifying uncertainty, and making data-informed decisions. It enhances the precision and transparency of epidemiological research, ultimately contributing to better public health outcomes.

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Introduction to NumPyro: Probabilistic Programming

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Introduction to NumPyro: Probabilistic Programming#

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Probabilistic programming is a powerful approach to modeling and inference in machine learning and statistics. It allows us to build models that incorporate uncertainty and make probabilistic predictions. NumPyro is a probabilistic programming library that combines the flexibility of NumPy with the probabilistic modeling capabilities of Pyro, making it an excellent choice for researchers and data scientists. In this introductory tutorial, we’ll explore the basics of NumPyro and how to get started with probabilistic programming.

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Prerequisites#

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# uncomment this line on Colab
-# !pip install numpyro
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import matplotlib.pyplot as plt
-import seaborn as sns
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-import numpyro
-import numpyro.distributions as dist
-from numpyro.infer import MCMC, NUTS
-import jax
-import jax.numpy as jnp
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-import arviz as az
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/opt/anaconda3/envs/aims/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
-  from .autonotebook import tqdm as notebook_tqdm
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Getting Started#

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Now that you have the required packages installed, let’s start with a simple example of a probabilistic model in NumPyro.

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In this code:

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  • We define a simple probabilistic model with two parameters: mean and scale.

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  • We specify priors for these parameters.

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  • The likelihood of the data is assumed to be normally distributed with the specified mean and scale.

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  • In this example, the likelihood is specified within the numpyro.sample statement inside the model function. NumPyro automatically evaluates the likelihood for the observed data points (obs=data) when performing MCMC inference.

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  • We use the No-U-Turn Sampler (NUTS) to perform Markov Chain Monte Carlo (MCMC) inference.

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  • Finally, we visualize the posterior distributions of the parameters.

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# Define a simple probabilistic model
-def model(data):
-    # Define priors
-    mean = numpyro.sample("mean", dist.Normal(0, 1))
-    scale = numpyro.sample("scale", dist.Exponential(1))
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-    # Likelihood
-    with numpyro.plate("data_plate", len(data)):
-        numpyro.sample("obs", dist.Normal(mean, scale), obs=data)
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-# Simulated data
-data = jnp.array([2.3, 3.9, 1.7, -0.8, 2.5])
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-# Initialize the NUTS sampler
-nuts_kernel = NUTS(model)
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-# Perform Markov Chain Monte Carlo (MCMC) inference
-mcmc = MCMC(nuts_kernel, num_samples=1000, num_warmup=1000)
-mcmc.run(jax.random.PRNGKey(0), data)
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-# Get the posterior samples
-posterior_samples = mcmc.get_samples()
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-# Print summary statistics of posterior
-mcmc.print_summary()
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-# Plot posterior distributions
-az.plot_trace(mcmc)
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WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
-I0000 00:00:1706348979.773744       1 tfrt_cpu_pjrt_client.cc:349] TfrtCpuClient created.
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sample:  94%|█████████████████████████████▏ | 1880/2000 [00:01<00:00, 2055.74it/s, 3 steps of size 6.52e-01. acc. prob=0.92]
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sample: 100%|███████████████████████████████| 2000/2000 [00:01<00:00, 1005.04it/s, 3 steps of size 6.52e-01. acc. prob=0.92]
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                mean       std    median      5.0%     95.0%     n_eff     r_hat
-      mean      1.17      0.67      1.21      0.03      2.21    465.31      1.00
-     scale      1.85      0.62      1.75      0.87      2.63    490.01      1.00
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-Number of divergences: 0
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array([[<AxesSubplot:title={'center':'mean'}>,
-        <AxesSubplot:title={'center':'mean'}>],
-       [<AxesSubplot:title={'center':'scale'}>,
-        <AxesSubplot:title={'center':'scale'}>]], dtype=object)
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-_images/fc1fa9ac7f06e5380a5ef018e20fb4d4f3789e07f11332aeeabae3f1add9d2d0.png -
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Conclusion#

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NumPyro is a versatile library for probabilistic programming that combines the power of NumPy and Pyro. In this introductory tutorial, we’ve covered the basics of defining a probabilistic model, performing MCMC inference, and visualizing the results. As you delve deeper into probabilistic programming with NumPyro, you’ll be able to build more complex and customized models for your specific applications. Happy modeling!

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Numpyro distributions#

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The statement import numpyro.distributions as dist is used to import the distributions module from the NumPyro library and give it an alias or nickname dist. This alias makes it easier to access and use the various probability distributions provided by NumPyro throughout your code.

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In probabilistic programming, you often need to specify probability distributions for the prior and likelihood in your models. NumPyro provides a variety of probability distributions that you can use, such as normal distributions, exponential distributions, categorical distributions, and many others. These distributions are organized within the distributions module.

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By using the import numpyro.distributions as dist statement, you create a shorthand reference to the distributions module, so instead of typing numpyro.distributions every time you want to use a distribution, you can simply use dist. This simplifies your code and makes it more concise and readable.

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For example, if you want to create a normal distribution in your code, you can now use dist.Normal(...) instead of numpyro.distributions.Normal(...), thanks to the dist alias. It’s a common practice in NumPyro code to use this alias to improve code clarity and reduce typing effort when working with probability distributions.

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These examples below demonstrate how to create various probability distributions using the dist alias and sample from them. You can then use these distributions as components of your probabilistic models when defining priors or likelihoods in a Bayesian context or when generating random data for simulation and analysis.

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Normal distribution#

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# Normal distribution
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-# Create a normal distribution with mean=0 and standard deviation=1
-normal_dist = dist.Normal(0, 1)
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-# Sample from the normal distribution, once
-sample = normal_dist.sample(jax.random.PRNGKey(0))
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-print(sample)
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-# Sample from the normal distribution, many times
-samples = normal_dist.sample(jax.random.PRNGKey(0), (1000,))
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-# Plot a histogram of the samples
-sns.histplot(samples, kde=True)
-plt.title("Samples from Normal Distribution")
-plt.xlabel("Value")
-plt.ylabel("Frequency")
-plt.show()
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Exponential distribution#

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# exponential distribution
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-# Create an exponential distribution with rate parameter 2.0
-exponential_dist = dist.Exponential(2.0)
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-# Sample from the exponential distribution, once
-sample = exponential_dist.sample(jax.random.PRNGKey(0))
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-print(sample)
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-# Sample from the exponential distribution, many
-samples = exponential_dist.sample(jax.random.PRNGKey(0), (1000,))
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-# Plot a histogram of the samples
-sns.histplot(samples, kde=True)
-plt.title("Samples from Exponential Distribution")
-plt.xlabel("Value")
-plt.ylabel("Frequency")
-plt.show()
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Categorical#

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# categorical
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-# Define probabilities for three categories
-probabilities = jnp.array([0.3, 0.4, 0.3])
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-# Create a categorical distribution
-categorical_dist = dist.Categorical(probabilities)
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-# Sample from the categorical distribution, once
-sample = categorical_dist.sample(jax.random.PRNGKey(0))
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-print(sample)
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-# Sample from the categorical distribution, many
-samples = categorical_dist.sample(jax.random.PRNGKey(0), (1000,))
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-# Plot a bar chart of the samples
-plt.hist(samples, bins=[0, 1, 2, 3], align='left', rwidth=0.8)
-plt.xticks([0, 1, 2], labels=["Category 0", "Category 1", "Category 2"])
-plt.title("Samples from Categorical Distribution")
-plt.xlabel("Category")
-plt.ylabel("Frequency")
-plt.show()
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Beta#

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# beta
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-# Create a beta distribution with alpha=2 and beta=3
-beta_dist = dist.Beta(2, 3)
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-# Sample from the beta distribution
-sample = beta_dist.sample(jax.random.PRNGKey(0))
-print(sample)
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-# Sample from the beta distribution, many
-samples = beta_dist.sample(jax.random.PRNGKey(0), (1000,))
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-# Plot a histogram of the samples
-sns.histplot(samples, kde=True)
-plt.title("Samples from Beta Distribution")
-plt.xlabel("Value")
-plt.ylabel("Frequency")
-plt.show()
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Multivariate Normal#

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Mean mu and covariance K.

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# MVN
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-# Create a multivariate normal distribution with mean vector and covariance matrix
-mean = jnp.array([0.0, 1.0])
-cov_matrix = jnp.array([[1.0, 0.5], [0.5, 2.0]])
-multivariate_normal_dist = dist.MultivariateNormal(mean, cov_matrix)
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-# Sample from the multivariate normal distribution
-sample = multivariate_normal_dist.sample(jax.random.PRNGKey(0))
-print(sample)
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-# Sample from the multivariate normal distribution, many
-samples = multivariate_normal_dist.sample(jax.random.PRNGKey(0), (1000,))
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-# Plot a scatter plot of the samples
-sns.scatterplot(x=samples[:, 0], y=samples[:, 1])
-plt.title("Samples from Multivariate Normal Distribution")
-plt.xlabel("X")
-plt.ylabel("Y")
-plt.show()
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[-0.78476596  1.740587  ]
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-_images/bf726da0cc8f07e04fe26aca9de47581ce467f25efb7ba5e38215da430a8532d.png -
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In a NumPyro program, you define a probabilistic model that consists of various elements. Let’s break down the key elements of a typical NumPyro program:

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  1. Importing Libraries:

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At the beginning of your NumPyro program, you import the necessary libraries, including NumPyro and other required dependencies like JAX and Pyro if applicable. For example:

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import numpyro
-import numpyro.distributions as dist
-import jax
-import jax.numpy as jnp
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  1. Defining the Model Function:

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In NumPyro, you define your probabilistic model as a Python function. This function encapsulates the entire model, including both the prior distributions and the likelihood. Typically, the model function takes one or more arguments, such as data or model parameters, and returns a set of latent variables and observations.

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def model(data):
-    # Define prior distributions for model parameters
-    mean = numpyro.sample("mean", dist.Normal(0, 1))
-    scale = numpyro.sample("scale", dist.Exponential(1))
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-    # Define likelihood
-    with numpyro.plate("data_plate", len(data)):
-        numpyro.sample("obs", dist.Normal(mean, scale), obs=data)
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  1. Prior Distributions:

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  • Inside the model function, you define prior distributions for the model parameters. These prior distributions represent your beliefs about the parameters before observing any data. You use the numpyro.sample function to specify these priors. In the example above, mean and scale are defined as random variables sampled from specific prior distributions.

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  1. Likelihood:

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  • After specifying the prior distributions, you define the likelihood of your observed data. The likelihood represents the probability distribution of your observed data given the model parameters. It describes how likely it is to observe the data under different parameter values. In the example, the numpyro.sample function is used to define the likelihood of the data points given the mean and scale parameters.

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  • In Bayesian modeling, you often work with multiple data points that share the same statistical structure. The numpyro.plate context manager allows you to create a plate, which represents a repeated structure for data. It’s used to efficiently handle repeated observations. In the example, numpyro.plate is used to specify that the likelihood applies to multiple data points.

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  1. Inference Algorithm:

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  • After defining your model, you need to choose an inference algorithm to estimate the posterior distribution of model parameters. NumPyro supports various inference algorithms, including NUTS (No-U-Turn Sampler) and SVI (Stochastic Variational Inference). You initialize and configure the chosen inference algorithm according to your requirements.

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nuts_kernel = NUTS(model)
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  1. Performing Inference:

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mcmc = MCMC(nuts_kernel, num_samples=1000, num_warmup=1000)
-mcmc.run(jax.random.PRNGKey(0), data)
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warmup:  39%|████████████▉                    | 781/2000 [00:01<00:01, 767.80it/s, 1 steps of size 9.07e-01. acc. prob=0.79]
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sample:  59%|██████████████████▏            | 1173/2000 [00:01<00:00, 1214.69it/s, 7 steps of size 6.52e-01. acc. prob=0.92]
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sample:  78%|████████████████████████▎      | 1566/2000 [00:01<00:00, 1671.71it/s, 3 steps of size 6.52e-01. acc. prob=0.92]
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sample:  98%|██████████████████████████████▍| 1962/2000 [00:01<00:00, 2114.88it/s, 3 steps of size 6.52e-01. acc. prob=0.92]
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sample: 100%|███████████████████████████████| 2000/2000 [00:01<00:00, 1035.84it/s, 3 steps of size 6.52e-01. acc. prob=0.92]
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  1. Posterior Analysis:

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  • After running the inference, you can retrieve posterior samples of the model parameters. These samples represent the estimated posterior distribution of the parameters given the observed data. You can then analyze these samples to make inferences about your model.

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posterior_samples = mcmc.get_samples()
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  1. Visualization and Inference:

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  • Finally, you can perform various tasks such as visualizing the posterior distributions, computing summary statistics, and making predictions or inferences based on the posterior samples.

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These elements together form a typical NumPyro program for Bayesian probabilistic modeling. The key steps involve defining the model, specifying prior distributions and likelihood, selecting an inference algorithm, running the inference, and analyzing the posterior samples to draw conclusions about the model parameters.

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b/_build/html/_sources/01_intro.md deleted file mode 100644 index ac838ab..0000000 --- a/_build/html/_sources/01_intro.md +++ /dev/null @@ -1,6 +0,0 @@ -# Probabilistic Thinking, modelling and programming for Epidemiology. - -Welcome to the course! - -```{tableofcontents} -``` diff --git a/_build/html/_sources/02_about.md b/_build/html/_sources/02_about.md deleted file mode 100644 index 1bee465..0000000 --- a/_build/html/_sources/02_about.md +++ /dev/null @@ -1,64 +0,0 @@ -# About this course - -This course will be taught during three weeks to a MSc ["AI for Science"](https://ai.aims.ac.za/) cohort at the [African Institute for Mathematical Studeis (AIMS)](https://aims.ac.za/), South Africa. - -Tentative outline of the course if presented below but might be adjusted during the course: - - -* Week 1 - Probabilistic programming. - * Day 1 - * Introduction to epidemiology. - * Probability distributions refresher. - * Bayesian inference - * Focus on priors - * Day 2 - * numerical methods to obtain posterior - * MCMC by hand - * convergence diagnostics - * PPLs - * Intro to Numpyro: model, inference, check convergence - * Bayesian workflow: prior predictive and posterior predictive - * Day 3 - * logistic regression with Numpyro - * Poisson and NegativeBinomial regression with Numpyro - * Day 4 - * hierarchical modelling - * Day 5 - * bonus topic: Bayesian neural network with a PPL - * Practical 1 submission -* Week 2 - spatial modelling. - * Day 1 - * GPs refresher: MVN, mean, kernel, covariance - * kernels: RBF. Matern - * non-stationary kernels: linear - * combining kernels - * scalability: Kronecker - * approximations: HSGP - * Day 2 - * areal data modelling - * Day 3 - * geostatistical data modelling - * Day 4 - * point pattern modelling - * Practical 2 submission -* Week 3 - infectious disease modelling. - * Day 1 - * disease transmission modelling: overview - * ODEs - * solve ODEs without Numpyro: SIS, SIR, SIRD, SIRS, SEIR, SIRC - * (bonus topics: phase portraits, stability) - * estimating R_0, R_t - * Day 2 - * ODEs with Numpyro - * SIR with Numpyro, boarding school example - * Day 3 - * ABMs - * ABMs on networks - * Day 4 - * renewal equation - * Practical 3 submission - * Day 5 - * Bonus topics: VAEs and diffusion model for prior learning - * causality - - diff --git a/_build/html/_sources/03_intro_epi.md b/_build/html/_sources/03_intro_epi.md deleted file mode 100644 index 1a591e7..0000000 --- a/_build/html/_sources/03_intro_epi.md +++ /dev/null @@ -1,79 +0,0 @@ -# Introduction to Modelling in epidemiology - -In this course we will consider a range of models used in epidemiology - from spatial statistics to disease transmission modelling. For each of such models we will consider a probabilistic formulation allowing for Bayesian inference. We will use a probabilistic programing language (PPL) to do that. Next, let's uncover each of the three key term - epidemioligy, probabilistic modelling amd probablistic programming. - - - -(epidemiology)= -## Epidemiology - -Epidemiology is the study of how diseases and health-related events are distributed within populations and the factors that influence these distributions. It is a branch of public health that focuses on understanding the patterns, causes, and effects of diseases and health conditions on a large scale. Epidemiologists collect and analyze data to investigate the occurrence of health outcomes, their risk factors, and the impact of various interventions or preventive measures. - -Key aspects of epidemiology include: - -- **Disease Surveillance:** Epidemiologists monitor the occurrence of diseases and health-related events over time and across different geographic areas. This involves tracking the number of cases, identifying outbreaks, and assessing trends in disease incidence. - -- **Identifying Risk Factors:** Epidemiological studies aim to identify the factors that increase the likelihood of developing a particular disease. These risk factors can include genetic predisposition, environmental exposures, lifestyle choices, and social determinants of health. - -- **Causality Assessment:** Epidemiologists use various study designs, including cohort studies, case-control studies, and randomized controlled trials, to determine if a specific factor or intervention causes a particular disease. Establishing causality is a fundamental goal of epidemiology. - -- **Disease Prevention and Control:** The insights gained from epidemiological research are crucial for designing and implementing public health interventions and policies aimed at preventing and controlling diseases. This may involve vaccination campaigns, health education programs, quarantine measures, and more. - -- **Public Health Planning:** Epidemiological data and findings play a vital role in informing public health planning and resource allocation. This includes assessing healthcare needs, identifying at-risk populations, and developing strategies to improve overall health outcomes. - -- **Outbreak Investigation:** Epidemiologists are often involved in investigating disease outbreaks, such as foodborne illnesses, infectious disease outbreaks, or clusters of chronic diseases. They work to identify the source of the outbreak and implement measures to contain and prevent further spread. - -- **Epidemiological Models:** Mathematical and statistical models are frequently used in epidemiology to simulate disease spread and predict future trends. These models help in making informed decisions and planning interventions. - -Overall, epidemiology is essential for understanding the health of populations, identifying health disparities, and guiding public health efforts to improve the well-being of communities and societies. - -## Probabilistic modelling - -Probabilistic modeling is a mathematical and statistical approach that involves incorporating *uncertainty* and *randomness* into models to account for variability in real-world phenomena. In epidemiology, probabilistic modeling plays a crucial role in understanding the spread of diseases and assessing the impact of interventions. Here's why probabilistic modeling is important for epidemiology: - -- **Capturing Uncertainty:** Epidemics are inherently uncertain processes influenced by numerous variables, including human behavior, environmental factors, and the biology of the infectious agent. Probabilistic models allow epidemiologists to account for this uncertainty by representing it explicitly. This is particularly important when making predictions or assessing the effectiveness of control measures. - -- **Realistic Simulations:** Probabilistic models simulate disease spread and transmission more realistically by considering the variability in individual interactions, susceptibility, and infectiousness. This realism helps in better understanding the dynamics of epidemics and how they may evolve over time. - -- **Data Integration:** Epidemiological data often contain inherent variability and noise. Probabilistic models can incorporate this variability and provide a framework for data assimilation and model calibration. This helps in refining models to fit observed data more accurately. - -- **Quantifying Risk:** Probabilistic models can quantify the likelihood of various outcomes, such as the probability of an outbreak occurring, the probability of disease transmission in a specific setting, or the probability of a particular intervention's success. This information is essential for risk assessment and decision-making. - -- **Scenario Analysis:** Epidemiologists can use probabilistic models to explore a wide range of scenarios and assess the potential impact of different interventions and policies. This aids in designing effective strategies for disease control and prevention. - -- **Sensitivity Analysis:** Probabilistic modeling allows for sensitivity analysis, which helps identify which parameters or assumptions in the model have the most significant impact on outcomes. This information guides research priorities and resource allocation. - -- **Incorporating Individual-Level Variation:** Probabilistic models can capture individual-level variations in disease transmission, susceptibility, and response to interventions. This is crucial for tailoring public health measures to different population segments. - -- **Policy Decision Support:** Probabilistic modeling provides decision-makers with valuable insights into the expected outcomes of different policy choices. This aids in prioritizing resource allocation and optimizing public health interventions. - -- **Adaptability:** As new data becomes available during an epidemic, probabilistic models can be updated and refined, allowing epidemiologists to continuously monitor and adapt their strategies in response to changing circumstances. - -In summary, probabilistic modeling in epidemiology helps epidemiologists and public health officials make informed decisions by quantifying uncertainty, simulating realistic disease dynamics, and assessing the potential impact of various interventions. It is a powerful tool for improving our understanding of infectious disease outbreaks and guiding effective public health responses. - -## Probabilistics programming - - -Probabilistic programming is a specialized approach to building and analyzing probabilistic models that offers several advantages for epidemiology and the study of infectious disease dynamics: - -- **Flexibility:** Probabilistic programming languages, such as Stan, Pyro, or Edward, provide a flexible framework for defining and customizing probabilistic models. This flexibility is crucial in epidemiology, where the complexity of disease transmission models can vary widely depending on the specific disease and the population under study. - -- **Uncertainty Quantification:** Probabilistic programming allows for the explicit representation and quantification of uncertainty. Epidemiological models often involve uncertain parameters and data, and probabilistic programming makes it easier to incorporate this uncertainty into the modeling process. - -- **Bayesian Inference:** Probabilistic programming languages are particularly well-suited for Bayesian inference, which is a powerful statistical approach used in epidemiology. Bayesian methods enable the estimation of model parameters, predictions, and model comparisons while accounting for prior information and uncertainty. - -- **Model Validation:** Probabilistic programming facilitates model validation by enabling researchers to compare model predictions with observed data using techniques like posterior predictive checks. This helps ensure that models accurately capture the underlying dynamics of infectious diseases. - -- **Hierarchical Modeling:** Many epidemiological models involve hierarchical structures, where data at multiple levels (e.g., individuals, households, communities) are analyzed simultaneously. Probabilistic programming makes it easier to specify and fit hierarchical models, which can provide more accurate representations of complex disease transmission processes. - -- **Data Integration:** Probabilistic programming allows for the integration of various types of data sources, including clinical data, epidemiological surveillance data, and genomic data. This integration can improve the accuracy and informativeness of epidemiological models. - -- **Model Selection and Comparison:** Epidemiologists often need to compare different model structures or assess the fit of alternative hypotheses. Probabilistic programming facilitates model selection and comparison through techniques like Bayesian model averaging and model evidence calculation. - -- **Real-time Updates:** In the context of infectious disease outbreaks, probabilistic programming allows for real-time updates of models as new data becomes available. This adaptability is critical for guiding public health responses during rapidly evolving situations. - -- **Transparent Communication:** Probabilistic programming encourages transparency in modeling and analysis. Researchers can clearly specify their assumptions, priors, and likelihood functions, making it easier to communicate and collaborate with other experts and stakeholders. - -- **Extensible Libraries:** Probabilistic programming languages often come with extensive libraries and tools for model development, inference, and visualization, reducing the implementation and computation burden for epidemiologists. - -In summary, probabilistic programming is a valuable tool for epidemiologists because it offers a flexible and powerful framework for modeling disease dynamics, quantifying uncertainty, and making data-informed decisions. It enhances the precision and transparency of epidemiological research, ultimately contributing to better public health outcomes. \ No newline at end of file diff --git a/_build/html/_sources/04_probability_distributions.md b/_build/html/_sources/04_probability_distributions.md deleted file mode 100644 index 1ae499e..0000000 --- a/_build/html/_sources/04_probability_distributions.md +++ /dev/null @@ -1 +0,0 @@ -# Probability distributions diff --git a/_build/html/_sources/05_intro_to_Numpyro.ipynb b/_build/html/_sources/05_intro_to_Numpyro.ipynb deleted file mode 100644 index 208f1fe..0000000 --- a/_build/html/_sources/05_intro_to_Numpyro.ipynb +++ /dev/null @@ -1,711 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "J1LV5w4eFSzA" - }, - "source": [ - "## Introduction to NumPyro: Probabilistic Programming\n", - "\n", - "Probabilistic programming is a powerful approach to modeling and inference in machine learning and statistics. It allows us to build models that incorporate uncertainty and make probabilistic predictions. NumPyro is a probabilistic programming library that combines the flexibility of NumPy with the probabilistic modeling capabilities of Pyro, making it an excellent choice for researchers and data scientists. In this introductory tutorial, we'll explore the basics of NumPyro and how to get started with probabilistic programming.\n", - "\n", - "## Prerequisites" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "hVzA5-UuFU9Z", - "outputId": "17a25d31-0465-4219-d91d-b9987eb593ed" - }, - "outputs": [], - "source": [ - "# uncomment this line on Colab\n", - "# !pip install numpyro" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "mhhWCsnWNO7Q" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "import numpyro\n", - "import numpyro.distributions as dist\n", - "from numpyro.infer import MCMC, NUTS\n", - "import jax\n", - "import jax.numpy as jnp\n", - "\n", - "import arviz as az" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EfFD5kPmHSog" - }, - "source": [ - "## Getting Started\n", - "\n", - "Now that you have the required packages installed, let's start with a simple example of a probabilistic model in NumPyro.\n", - "\n", - "In this code:\n", - "\n", - "- We define a simple probabilistic model with two parameters: mean and scale.\n", - "- We specify priors for these parameters.\n", - "- The likelihood of the data is assumed to be normally distributed with the specified mean and scale.\n", - "- In this example, the likelihood is specified within the `numpyro.sample` statement inside the model function. NumPyro automatically evaluates the likelihood for the observed data points `(obs=data)` when performing MCMC inference.\n", - "- We use the No-U-Turn Sampler (NUTS) to perform Markov Chain Monte Carlo (MCMC) inference.\n", - "- Finally, we visualize the posterior distributions of the parameters.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 635 - }, - "id": "VBBPGHrmGvj6", - "outputId": "8df2a881-62bf-4d30-c3a2-7cba91fd0cf1" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1705865525.515832 1 tfrt_cpu_pjrt_client.cc:349] TfrtCpuClient created.\n", - "sample: 100%|██████████| 2000/2000 [00:02<00:00, 945.66it/s, 3 steps of size 6.52e-01. acc. prob=0.92] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " mean std median 5.0% 95.0% n_eff r_hat\n", - " mean 1.17 0.67 1.21 0.03 2.21 465.31 1.00\n", - " scale 1.85 0.62 1.75 0.87 2.63 490.01 1.00\n", - "\n", - "Number of divergences: 0\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[,\n", - " ],\n", - " [,\n", - " ]], dtype=object)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "\n", - "# Define a simple probabilistic model\n", - "def model(data):\n", - " # Define priors\n", - " mean = numpyro.sample(\"mean\", dist.Normal(0, 1))\n", - " scale = numpyro.sample(\"scale\", dist.Exponential(1))\n", - "\n", - " # Likelihood\n", - " with numpyro.plate(\"data_plate\", len(data)):\n", - " numpyro.sample(\"obs\", dist.Normal(mean, scale), obs=data)\n", - "\n", - "# Simulated data\n", - "data = jnp.array([2.3, 3.9, 1.7, -0.8, 2.5])\n", - "\n", - "# Initialize the NUTS sampler\n", - "nuts_kernel = NUTS(model)\n", - "\n", - "# Perform Markov Chain Monte Carlo (MCMC) inference\n", - "mcmc = MCMC(nuts_kernel, num_samples=1000, num_warmup=1000)\n", - "mcmc.run(jax.random.PRNGKey(0), data)\n", - "\n", - "# Get the posterior samples\n", - "posterior_samples = mcmc.get_samples()\n", - "\n", - "# Print summary statistics of posterior\n", - "mcmc.print_summary()\n", - "\n", - "# Plot posterior distributions\n", - "az.plot_trace(mcmc)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-fNL2gt-HxCF" - }, - "source": [ - "## Conclusion\n", - "\n", - "NumPyro is a versatile library for probabilistic programming that combines the power of NumPy and Pyro. In this introductory tutorial, we've covered the basics of defining a probabilistic model, performing MCMC inference, and visualizing the results. As you delve deeper into probabilistic programming with NumPyro, you'll be able to build more complex and customized models for your specific applications. Happy modeling!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FNUc0rUuLmPB" - }, - "source": [ - "## Numpyro distributions\n", - "\n", - "The statement `import numpyro.distributions` as dist is used to import the `distributions` module from the NumPyro library and give it an alias or nickname `dist`. This alias makes it easier to access and use the various probability distributions provided by NumPyro throughout your code.\n", - "\n", - "\n", - "In probabilistic programming, you often need to specify probability distributions for the prior and likelihood in your models. NumPyro provides a variety of probability distributions that you can use, such as normal distributions, exponential distributions, categorical distributions, and many others. These distributions are organized within the `distributions` module.\n", - "\n", - "\n", - "By using the `import numpyro.distributions as dist` statement, you create a shorthand reference to the `distributions` module, so instead of typing `numpyro.distributions` every time you want to use a distribution, you can simply use `dist`. This simplifies your code and makes it more concise and readable.\n", - "\n", - "\n", - "For example, if you want to create a normal distribution in your code, you can now use `dist.Normal(...)` instead of `numpyro.distributions.Normal(...)`, thanks to the `dist` alias. It's a common practice in NumPyro code to use this alias to improve code clarity and reduce typing effort when working with probability distributions.\n", - "\n", - "\n", - "These examples below demonstrate how to create various probability distributions using the `dist` alias and sample from them. You can then use these distributions as components of your probabilistic models when defining priors or likelihoods in a Bayesian context or when generating random data for simulation and analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M8QkvptfNSlU" - }, - "source": [ - "### Normal distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 489 - }, - "id": "x_bxwE5yLn7F", - "outputId": "a334a158-6273-4fac-938f-479454e549ab" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-0.20584226\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Normal distribution\n", - "\n", - "# Create a normal distribution with mean=0 and standard deviation=1\n", - "normal_dist = dist.Normal(0, 1)\n", - "\n", - "# Sample from the normal distribution, once\n", - "sample = normal_dist.sample(jax.random.PRNGKey(0))\n", - "\n", - "print(sample)\n", - "\n", - "# Sample from the normal distribution, many times\n", - "samples = normal_dist.sample(jax.random.PRNGKey(0), (1000,))\n", - "\n", - "# Plot a histogram of the samples\n", - "sns.histplot(samples, kde=True)\n", - "plt.title(\"Samples from Normal Distribution\")\n", - "plt.xlabel(\"Value\")\n", - "plt.ylabel(\"Frequency\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "h3ayvTT4O5Nj" - }, - "source": [ - "### Exponential distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 489 - }, - "id": "u77Wfb1-MdOZ", - "outputId": "65e64034-f40d-49e2-b276-42997f9abf4f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2710352\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# exponential distribution\n", - "\n", - "# Create an exponential distribution with rate parameter 2.0\n", - "exponential_dist = dist.Exponential(2.0)\n", - "\n", - "# Sample from the exponential distribution, once\n", - "sample = exponential_dist.sample(jax.random.PRNGKey(0))\n", - "\n", - "print(sample)\n", - "\n", - "# Sample from the exponential distribution, many\n", - "samples = exponential_dist.sample(jax.random.PRNGKey(0), (1000,))\n", - "\n", - "# Plot a histogram of the samples\n", - "sns.histplot(samples, kde=True)\n", - "plt.title(\"Samples from Exponential Distribution\")\n", - "plt.xlabel(\"Value\")\n", - "plt.ylabel(\"Frequency\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vZCIb_T_PGam" - }, - "source": [ - "### Categorical" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 489 - }, - "id": "kd76OYdLMm8w", - "outputId": "79c1499d-0241-4543-d5ff-b4c8f3ce5cc2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# categorical\n", - "\n", - "# Define probabilities for three categories\n", - "probabilities = jnp.array([0.3, 0.4, 0.3])\n", - "\n", - "# Create a categorical distribution\n", - "categorical_dist = dist.Categorical(probabilities)\n", - "\n", - "# Sample from the categorical distribution, once\n", - "sample = categorical_dist.sample(jax.random.PRNGKey(0))\n", - "\n", - "print(sample)\n", - "\n", - "# Sample from the categorical distribution, many\n", - "samples = categorical_dist.sample(jax.random.PRNGKey(0), (1000,))\n", - "\n", - "# Plot a bar chart of the samples\n", - "plt.hist(samples, bins=[0, 1, 2, 3], align='left', rwidth=0.8)\n", - "plt.xticks([0, 1, 2], labels=[\"Category 0\", \"Category 1\", \"Category 2\"])\n", - "plt.title(\"Samples from Categorical Distribution\")\n", - "plt.xlabel(\"Category\")\n", - "plt.ylabel(\"Frequency\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6_bngSJYPQte" - }, - "source": [ - "### Beta" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 489 - }, - "id": "mcIkgrXhMrL3", - "outputId": "0da5f725-9292-41b0-8ddb-b8d808e9a8a1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.41446027\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# beta\n", - "\n", - "# Create a beta distribution with alpha=2 and beta=3\n", - "beta_dist = dist.Beta(2, 3)\n", - "\n", - "# Sample from the beta distribution\n", - "sample = beta_dist.sample(jax.random.PRNGKey(0))\n", - "print(sample)\n", - "\n", - "# Sample from the beta distribution, many\n", - "samples = beta_dist.sample(jax.random.PRNGKey(0), (1000,))\n", - "\n", - "# Plot a histogram of the samples\n", - "sns.histplot(samples, kde=True)\n", - "plt.title(\"Samples from Beta Distribution\")\n", - "plt.xlabel(\"Value\")\n", - "plt.ylabel(\"Frequency\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3It0QjQZPbgX" - }, - "source": [ - "### Multivariate Normal\n", - "\n", - "Mean `mu` and covariance `K`.\n", - "\n", - "```{margin}\n", - "We will use this distribution a lot when dealing with spatial data.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 489 - }, - "id": "lkCeMN5lMw7A", - "outputId": "df522f91-a3cf-4ff2-820c-585d02ebbdb7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.78476596 1.740587 ]\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# MVN\n", - "\n", - "# Create a multivariate normal distribution with mean vector and covariance matrix\n", - "mean = jnp.array([0.0, 1.0])\n", - "cov_matrix = jnp.array([[1.0, 0.5], [0.5, 2.0]])\n", - "multivariate_normal_dist = dist.MultivariateNormal(mean, cov_matrix)\n", - "\n", - "# Sample from the multivariate normal distribution\n", - "sample = multivariate_normal_dist.sample(jax.random.PRNGKey(0))\n", - "print(sample)\n", - "\n", - "# Sample from the multivariate normal distribution, many\n", - "samples = multivariate_normal_dist.sample(jax.random.PRNGKey(0), (1000,))\n", - "\n", - "# Plot a scatter plot of the samples\n", - "sns.scatterplot(x=samples[:, 0], y=samples[:, 1])\n", - "plt.title(\"Samples from Multivariate Normal Distribution\")\n", - "plt.xlabel(\"X\")\n", - "plt.ylabel(\"Y\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Vb4ILFuduHfK" - }, - "source": [ - "In a NumPyro program, you define a probabilistic model that consists of various elements. Let's break down the key elements of a typical NumPyro program:\n", - "\n", - "1. Importing Libraries:\n", - "\n", - "At the beginning of your NumPyro program, you import the necessary libraries, including NumPyro and other required dependencies like JAX and Pyro if applicable. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "NZ7-DnBcuM_7" - }, - "outputs": [], - "source": [ - "import numpyro\n", - "import numpyro.distributions as dist\n", - "import jax\n", - "import jax.numpy as jnp\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "25oL3ubXuWZg" - }, - "source": [ - "2. Defining the Model Function:\n", - "\n", - "In NumPyro, you define your probabilistic model as a Python function. This function encapsulates the entire model, including both the prior distributions and the likelihood. Typically, the model function takes one or more arguments, such as data or model parameters, and returns a set of latent variables and observations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "z_AsEI8SuSAu" - }, - "outputs": [], - "source": [ - "def model(data):\n", - " # Define prior distributions for model parameters\n", - " mean = numpyro.sample(\"mean\", dist.Normal(0, 1))\n", - " scale = numpyro.sample(\"scale\", dist.Exponential(1))\n", - "\n", - " # Define likelihood\n", - " with numpyro.plate(\"data_plate\", len(data)):\n", - " numpyro.sample(\"obs\", dist.Normal(mean, scale), obs=data)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-LqThfWVudNN" - }, - "source": [ - "3. Prior Distributions:\n", - "\n", - "- Inside the model function, you define prior distributions for the model parameters. These prior distributions represent your beliefs about the parameters before observing any data. You use the `numpyro.sample` function to specify these priors. In the example above, `mean` and `scale` are defined as random variables sampled from specific prior distributions.\n", - "\n", - "\n", - "4. Likelihood:\n", - "\n", - "- After specifying the prior distributions, you define the likelihood of your observed data. The likelihood represents the probability distribution of your observed data given the model parameters. It describes how likely it is to observe the data under different parameter values. In the example, the numpyro.sample function is used to define the likelihood of the data points given the mean and scale parameters.\n", - "\n", - "5. Plate for Repetition:\n", - "- In Bayesian modeling, you often work with multiple data points that share the same statistical structure. The `numpyro.plate` context manager allows you to create a plate, which represents a repeated structure for data. It's used to efficiently handle repeated observations. In the example, `numpyro.plate` is used to specify that the likelihood applies to multiple data points.\n", - "\n", - "6. Inference Algorithm:\n", - "\n", - "- After defining your model, you need to choose an inference algorithm to estimate the posterior distribution of model parameters. NumPyro supports various inference algorithms, including NUTS (No-U-Turn Sampler) and SVI (Stochastic Variational Inference). You initialize and configure the chosen inference algorithm according to your requirements.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Qz6nkjdGua0c" - }, - "outputs": [], - "source": [ - "nuts_kernel = NUTS(model)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8C2V-_BdvEwc" - }, - "source": [ - "8. Performing Inference:\n", - "\n", - "- You use the configured inference algorithm to perform Bayesian inference. In the example, MCMC (Markov Chain Monte Carlo) inference is performed using the `MCMC` class. The `run` method of the `MCMC` object is called to run the inference process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xAlfnkP6vCV2", - "outputId": "213c669e-9d36-4398-c4a5-c5b389afcfb8" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "sample: 100%|██████████| 2000/2000 [00:04<00:00, 419.40it/s, 3 steps of size 6.52e-01. acc. prob=0.92] \n" - ] - } - ], - "source": [ - "mcmc = MCMC(nuts_kernel, num_samples=1000, num_warmup=1000)\n", - "mcmc.run(jax.random.PRNGKey(0), data)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2DXdboTTvSNu" - }, - "source": [ - "9. Posterior Analysis:\n", - "\n", - "- After running the inference, you can retrieve posterior samples of the model parameters. These samples represent the estimated posterior distribution of the parameters given the observed data. You can then analyze these samples to make inferences about your model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "wL3YlwkovP6C" - }, - "outputs": [], - "source": [ - "posterior_samples = mcmc.get_samples()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-n-z22VnvbRV" - }, - "source": [ - "10. Visualization and Inference:\n", - "\n", - "- Finally, you can perform various tasks such as visualizing the posterior distributions, computing summary statistics, and making predictions or inferences based on the posterior samples." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "igd5fC_9voTw" - }, - "source": [ - "These elements together form a typical NumPyro program for Bayesian probabilistic modeling. The key steps involve defining the model, specifying prior distributions and likelihood, selecting an inference algorithm, running the inference, and analyzing the posterior samples to draw conclusions about the model parameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kFedhUATvXGz" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/_build/html/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_build/html/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css 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Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; - -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - var bbox = node.parentElement.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } - } - } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } - } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; -}; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} diff --git a/_build/html/_static/basic.css b/_build/html/_static/basic.css deleted file mode 100644 index 9e364ed..0000000 --- a/_build/html/_static/basic.css +++ /dev/null @@ -1,930 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - clear: both; -} - -div.section::after { - display: block; - content: ''; - clear: left; -} - -/* -- relbar ---------------------------------------------------------------- */ - -div.related { - width: 100%; - font-size: 90%; -} - -div.related h3 { - display: none; -} - -div.related ul { - margin: 0; - padding: 0 0 0 10px; - list-style: none; -} - -div.related li { - display: inline; -} - -div.related li.right { - float: right; - margin-right: 5px; -} - -/* -- sidebar --------------------------------------------------------------- */ - -div.sphinxsidebarwrapper { - padding: 10px 5px 0 10px; -} - -div.sphinxsidebar { - float: left; - width: 270px; - margin-left: -100%; - font-size: 90%; - word-wrap: break-word; - overflow-wrap : break-word; -} - -div.sphinxsidebar ul { - list-style: none; -} - -div.sphinxsidebar ul ul, -div.sphinxsidebar ul.want-points { - margin-left: 20px; - list-style: square; -} - -div.sphinxsidebar ul ul { - margin-top: 0; - margin-bottom: 0; -} - -div.sphinxsidebar form { - margin-top: 10px; -} - -div.sphinxsidebar input { - border: 1px solid #98dbcc; - font-family: sans-serif; - font-size: 1em; -} - -div.sphinxsidebar #searchbox form.search { - overflow: hidden; -} - -div.sphinxsidebar #searchbox input[type="text"] { - float: left; - width: 80%; - padding: 0.25em; - box-sizing: border-box; -} - -div.sphinxsidebar #searchbox input[type="submit"] { - float: left; - width: 20%; - border-left: none; - padding: 0.25em; - box-sizing: border-box; -} - - -img { - border: 0; - max-width: 100%; -} - -/* -- search page ----------------------------------------------------------- */ - -ul.search { - margin: 10px 0 0 20px; - padding: 0; -} - -ul.search li { - padding: 5px 0 5px 20px; - background-image: url(file.png); - background-repeat: no-repeat; - background-position: 0 7px; -} - -ul.search li a { - font-weight: bold; -} - -ul.search li p.context { - color: #888; - margin: 2px 0 0 30px; - text-align: left; -} - -ul.keywordmatches li.goodmatch a { - font-weight: bold; -} - -/* -- index page ------------------------------------------------------------ */ - -table.contentstable { - width: 90%; - margin-left: auto; - margin-right: auto; -} - -table.contentstable p.biglink { - line-height: 150%; -} - -a.biglink { - font-size: 1.3em; -} - -span.linkdescr { - font-style: italic; - padding-top: 5px; - font-size: 90%; -} - -/* -- general index --------------------------------------------------------- */ - -table.indextable { - width: 100%; -} - -table.indextable td { - text-align: left; - vertical-align: top; -} - -table.indextable ul { - margin-top: 0; - margin-bottom: 0; - list-style-type: none; -} - -table.indextable > tbody > tr > td > ul { - padding-left: 0em; -} - -table.indextable tr.pcap { - height: 10px; -} - -table.indextable tr.cap { - margin-top: 10px; - background-color: #f2f2f2; -} - -img.toggler { - margin-right: 3px; - margin-top: 3px; - cursor: pointer; -} - -div.modindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -div.genindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -/* -- domain module index --------------------------------------------------- */ - -table.modindextable td { - padding: 2px; - border-collapse: collapse; -} - -/* -- general body styles --------------------------------------------------- */ - -div.body { - min-width: 360px; - max-width: 800px; -} - -div.body p, div.body dd, div.body li, div.body blockquote { - -moz-hyphens: auto; - -ms-hyphens: auto; - -webkit-hyphens: auto; - hyphens: auto; -} - -a.headerlink { - visibility: hidden; -} - -h1:hover > a.headerlink, -h2:hover > a.headerlink, -h3:hover > a.headerlink, -h4:hover > a.headerlink, -h5:hover > a.headerlink, -h6:hover > a.headerlink, -dt:hover > a.headerlink, -caption:hover > a.headerlink, -p.caption:hover > a.headerlink, -div.code-block-caption:hover > a.headerlink { - visibility: visible; -} - -div.body p.caption { - text-align: inherit; -} - -div.body td { - text-align: left; -} - -.first { - margin-top: 0 !important; -} - -p.rubric { - margin-top: 30px; - font-weight: bold; -} - -img.align-left, figure.align-left, .figure.align-left, object.align-left { - clear: left; - float: left; - margin-right: 1em; -} - -img.align-right, figure.align-right, .figure.align-right, object.align-right { - clear: right; - float: right; - margin-left: 1em; -} - -img.align-center, figure.align-center, .figure.align-center, object.align-center { - display: block; - margin-left: auto; - margin-right: auto; -} - -img.align-default, figure.align-default, .figure.align-default { - display: block; - margin-left: auto; - margin-right: auto; -} - -.align-left { - text-align: left; -} - -.align-center { - text-align: center; -} - -.align-default { - text-align: center; -} - -.align-right { - text-align: right; -} - -/* -- sidebars -------------------------------------------------------------- */ - -div.sidebar, -aside.sidebar { - margin: 0 0 0.5em 1em; - border: 1px solid #ddb; - padding: 7px; - background-color: #ffe; - width: 40%; - float: right; - clear: right; - overflow-x: auto; -} - -p.sidebar-title { - font-weight: bold; -} -nav.contents, -aside.topic, - -div.admonition, div.topic, blockquote { - clear: left; -} - -/* -- topics ---------------------------------------------------------------- */ -nav.contents, -aside.topic, - -div.topic { - border: 1px solid #ccc; - padding: 7px; - margin: 10px 0 10px 0; -} - -p.topic-title { - font-size: 1.1em; - font-weight: bold; - margin-top: 10px; -} - -/* -- admonitions ----------------------------------------------------------- */ - -div.admonition { - margin-top: 10px; - margin-bottom: 10px; - padding: 7px; -} - -div.admonition dt { - font-weight: bold; -} - -p.admonition-title { - margin: 0px 10px 5px 0px; - font-weight: bold; -} - -div.body p.centered { - text-align: center; - margin-top: 25px; -} - -/* -- content of sidebars/topics/admonitions -------------------------------- */ - -div.sidebar > :last-child, -aside.sidebar > :last-child, -nav.contents > :last-child, -aside.topic > :last-child, - -div.topic > :last-child, -div.admonition > :last-child { - margin-bottom: 0; -} - -div.sidebar::after, -aside.sidebar::after, -nav.contents::after, -aside.topic::after, - -div.topic::after, -div.admonition::after, -blockquote::after { - display: block; - content: ''; - clear: both; -} - -/* -- tables ---------------------------------------------------------------- */ - -table.docutils { - margin-top: 10px; - margin-bottom: 10px; - border: 0; - border-collapse: collapse; -} - -table.align-center { - margin-left: auto; - margin-right: auto; -} - -table.align-default { - margin-left: auto; - margin-right: auto; -} - -table caption span.caption-number { - font-style: italic; -} - -table caption span.caption-text { -} - -table.docutils td, table.docutils th { - padding: 1px 8px 1px 5px; - border-top: 0; - border-left: 0; - border-right: 0; - border-bottom: 1px solid #aaa; -} - -th { - text-align: left; - padding-right: 5px; -} - -table.citation { - border-left: solid 1px gray; - margin-left: 1px; -} - -table.citation td { - border-bottom: none; -} - -th > :first-child, -td > :first-child { - margin-top: 0px; -} - -th > :last-child, -td > :last-child { - margin-bottom: 0px; -} - -/* -- figures --------------------------------------------------------------- */ - -div.figure, figure { - margin: 0.5em; - padding: 0.5em; -} - -div.figure p.caption, figcaption { - padding: 0.3em; -} - -div.figure p.caption span.caption-number, -figcaption span.caption-number { - font-style: italic; -} - -div.figure p.caption span.caption-text, -figcaption span.caption-text { -} - -/* -- field list styles ----------------------------------------------------- */ - -table.field-list td, table.field-list th { - border: 0 !important; -} - -.field-list ul { - margin: 0; - padding-left: 1em; -} - -.field-list p { - margin: 0; -} - -.field-name { - -moz-hyphens: manual; - -ms-hyphens: manual; - -webkit-hyphens: manual; - hyphens: manual; -} - -/* -- hlist styles ---------------------------------------------------------- */ - -table.hlist { - margin: 1em 0; -} - -table.hlist td { - vertical-align: top; -} - -/* -- object description styles --------------------------------------------- */ - -.sig { - font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; -} - -.sig-name, code.descname { - background-color: transparent; - font-weight: bold; -} - -.sig-name { - font-size: 1.1em; -} - -code.descname { - font-size: 1.2em; -} - -.sig-prename, code.descclassname { - background-color: transparent; -} - -.optional { - font-size: 1.3em; -} - -.sig-paren { - font-size: larger; -} - -.sig-param.n { - font-style: italic; -} - -/* C++ specific styling */ - -.sig-inline.c-texpr, -.sig-inline.cpp-texpr { - font-family: unset; -} - -.sig.c .k, .sig.c .kt, -.sig.cpp .k, .sig.cpp .kt { - color: #0033B3; -} - -.sig.c .m, -.sig.cpp .m { - color: #1750EB; -} - -.sig.c .s, .sig.c .sc, -.sig.cpp .s, .sig.cpp .sc { - color: #067D17; -} - - -/* -- other body styles ----------------------------------------------------- */ - -ol.arabic { - list-style: decimal; -} - -ol.loweralpha { - list-style: lower-alpha; -} - -ol.upperalpha { - list-style: upper-alpha; -} - -ol.lowerroman { - list-style: lower-roman; -} - -ol.upperroman { - list-style: upper-roman; -} - -:not(li) > ol > li:first-child > :first-child, -:not(li) > ul > li:first-child > :first-child { - margin-top: 0px; -} - -:not(li) > ol > li:last-child > :last-child, -:not(li) > ul > li:last-child > :last-child { - margin-bottom: 0px; -} - -ol.simple ol p, -ol.simple ul p, -ul.simple ol p, -ul.simple ul p { - margin-top: 0; -} - -ol.simple > li:not(:first-child) > p, -ul.simple > li:not(:first-child) > p { - margin-top: 0; -} - -ol.simple p, -ul.simple p { - margin-bottom: 0; -} - -/* Docutils 0.17 and older (footnotes & citations) */ -dl.footnote > dt, -dl.citation > dt { - float: left; - margin-right: 0.5em; -} - -dl.footnote > dd, -dl.citation > dd { - margin-bottom: 0em; -} - -dl.footnote > dd:after, -dl.citation > dd:after { - content: ""; - clear: both; -} - -/* Docutils 0.18+ (footnotes & citations) */ -aside.footnote > span, -div.citation > span { - float: left; -} -aside.footnote > span:last-of-type, -div.citation > span:last-of-type { - padding-right: 0.5em; -} -aside.footnote > p { - margin-left: 2em; -} -div.citation > p { - margin-left: 4em; -} -aside.footnote > p:last-of-type, -div.citation > p:last-of-type { - margin-bottom: 0em; 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-} - -div[class*="highlight-"] { - margin: 1em 0; -} - -td.linenos pre { - border: 0; - background-color: transparent; - color: #aaa; -} - -table.highlighttable { - display: block; -} - -table.highlighttable tbody { - display: block; -} - -table.highlighttable tr { - display: flex; -} - -table.highlighttable td { - margin: 0; - padding: 0; -} - -table.highlighttable td.linenos { - padding-right: 0.5em; -} - -table.highlighttable td.code { - flex: 1; - overflow: hidden; -} - -.highlight .hll { - display: block; -} - -div.highlight pre, -table.highlighttable pre { - margin: 0; -} - -div.code-block-caption + div { - margin-top: 0; -} - -div.code-block-caption { - margin-top: 1em; - padding: 2px 5px; - font-size: small; -} - -div.code-block-caption code { - background-color: transparent; -} - -table.highlighttable td.linenos, -span.linenos, -div.highlight span.gp { /* gp: Generic.Prompt */ - user-select: none; - -webkit-user-select: text; /* Safari fallback only */ - -webkit-user-select: none; /* Chrome/Safari */ - -moz-user-select: none; /* Firefox */ - -ms-user-select: none; /* IE10+ */ -} - -div.code-block-caption span.caption-number { - padding: 0.1em 0.3em; - font-style: italic; -} - -div.code-block-caption span.caption-text { -} - -div.literal-block-wrapper { - margin: 1em 0; -} - -code.xref, a code { - background-color: transparent; - font-weight: bold; -} - -h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { - background-color: transparent; -} - -.viewcode-link { - float: right; -} - -.viewcode-back { - float: right; - font-family: sans-serif; -} - -div.viewcode-block:target { - margin: -1px -10px; - padding: 0 10px; -} - -/* -- math display ---------------------------------------------------------- */ - -img.math { - vertical-align: middle; -} - -div.body div.math p { - text-align: center; -} - -span.eqno { - float: right; -} - -span.eqno a.headerlink { - position: absolute; - z-index: 1; -} - -div.math:hover a.headerlink { - visibility: visible; -} - -/* -- printout stylesheet --------------------------------------------------- */ - -@media print { - div.document, - div.documentwrapper, - div.bodywrapper { - margin: 0 !important; - width: 100%; - } - - div.sphinxsidebar, - div.related, - div.footer, - #top-link { - display: none; - } -} \ No newline at end of file diff --git a/_build/html/_static/check-solid.svg b/_build/html/_static/check-solid.svg deleted file mode 100644 index 92fad4b..0000000 --- a/_build/html/_static/check-solid.svg +++ /dev/null @@ -1,4 +0,0 @@ - - - - diff --git a/_build/html/_static/clipboard.min.js b/_build/html/_static/clipboard.min.js deleted file mode 100644 index 54b3c46..0000000 --- a/_build/html/_static/clipboard.min.js +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * clipboard.js v2.0.8 - * https://clipboardjs.com/ - * - * Licensed MIT © Zeno Rocha - */ -!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1 - - - - diff --git a/_build/html/_static/copybutton.css b/_build/html/_static/copybutton.css deleted file mode 100644 index f1916ec..0000000 --- a/_build/html/_static/copybutton.css +++ /dev/null @@ -1,94 +0,0 @@ -/* Copy buttons */ -button.copybtn { - position: absolute; - display: flex; - top: .3em; - right: .3em; - width: 1.7em; - height: 1.7em; - opacity: 0; - transition: opacity 0.3s, border .3s, background-color .3s; - user-select: none; - padding: 0; - border: none; - outline: none; - border-radius: 0.4em; - /* The colors that GitHub uses */ - border: #1b1f2426 1px solid; - background-color: #f6f8fa; - color: #57606a; -} - -button.copybtn.success { - border-color: #22863a; - color: #22863a; -} - -button.copybtn svg { - stroke: currentColor; - width: 1.5em; - height: 1.5em; - padding: 0.1em; -} - -div.highlight { - position: relative; -} - -/* Show the copybutton */ -.highlight:hover button.copybtn, button.copybtn.success { - opacity: 1; -} - -.highlight button.copybtn:hover { - background-color: rgb(235, 235, 235); -} - -.highlight button.copybtn:active { - background-color: rgb(187, 187, 187); -} - -/** - * A minimal CSS-only tooltip copied from: - * https://codepen.io/mildrenben/pen/rVBrpK - * - * To use, write HTML like the following: - * - *

Short

- */ - .o-tooltip--left { - position: relative; - } - - .o-tooltip--left:after { - opacity: 0; - visibility: hidden; - position: absolute; - content: attr(data-tooltip); - padding: .2em; - font-size: .8em; - left: -.2em; - background: grey; - color: white; - white-space: nowrap; - z-index: 2; - border-radius: 2px; - transform: translateX(-102%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); -} - -.o-tooltip--left:hover:after { - display: block; - opacity: 1; - visibility: visible; - transform: translateX(-100%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); - transition-delay: .5s; -} - -/* By default the copy button shouldn't show up when printing a page */ -@media print { - button.copybtn { - display: none; - } -} diff --git a/_build/html/_static/copybutton.js b/_build/html/_static/copybutton.js deleted file mode 100644 index 2ea7ff3..0000000 --- a/_build/html/_static/copybutton.js +++ /dev/null @@ -1,248 +0,0 @@ -// Localization support -const messages = { - 'en': { - 'copy': 'Copy', - 'copy_to_clipboard': 'Copy to clipboard', - 'copy_success': 'Copied!', - 'copy_failure': 'Failed to copy', - }, - 'es' : { - 'copy': 'Copiar', - 'copy_to_clipboard': 'Copiar al portapapeles', - 'copy_success': '¡Copiado!', - 'copy_failure': 'Error al copiar', - }, - 'de' : { - 'copy': 'Kopieren', - 'copy_to_clipboard': 'In die Zwischenablage kopieren', - 'copy_success': 'Kopiert!', - 'copy_failure': 'Fehler beim Kopieren', - }, - 'fr' : { - 'copy': 'Copier', - 'copy_to_clipboard': 'Copier dans le presse-papier', - 'copy_success': 'Copié !', - 'copy_failure': 'Échec de la copie', - }, - 'ru': { - 'copy': 'Скопировать', - 'copy_to_clipboard': 'Скопировать в буфер', - 'copy_success': 'Скопировано!', - 'copy_failure': 'Не удалось скопировать', - }, - 'zh-CN': { - 'copy': '复制', - 'copy_to_clipboard': '复制到剪贴板', - 'copy_success': '复制成功!', - 'copy_failure': '复制失败', - }, - 'it' : { - 'copy': 'Copiare', - 'copy_to_clipboard': 'Copiato negli appunti', - 'copy_success': 'Copiato!', - 'copy_failure': 'Errore durante la copia', - } -} - -let locale = 'en' -if( document.documentElement.lang !== undefined - && messages[document.documentElement.lang] !== undefined ) { - locale = document.documentElement.lang -} - -let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; -if (doc_url_root == '#') { - doc_url_root = ''; -} - -/** - * SVG files for our copy buttons - */ -let iconCheck = ` - ${messages[locale]['copy_success']} - - -` - -// If the user specified their own SVG use that, otherwise use the default -let iconCopy = ``; -if (!iconCopy) { - iconCopy = ` - ${messages[locale]['copy_to_clipboard']} - - - -` -} - -/** - * Set up copy/paste for code blocks - */ - -const runWhenDOMLoaded = cb => { - if (document.readyState != 'loading') { - cb() - } else if (document.addEventListener) { - document.addEventListener('DOMContentLoaded', cb) - } else { - document.attachEvent('onreadystatechange', function() { - if (document.readyState == 'complete') cb() - }) - } -} - -const codeCellId = index => `codecell${index}` - -// Clears selected text since ClipboardJS will select the text when copying -const clearSelection = () => { - if (window.getSelection) { - window.getSelection().removeAllRanges() - } else if (document.selection) { - document.selection.empty() - } -} - -// Changes tooltip text for a moment, then changes it back -// We want the timeout of our `success` class to be a bit shorter than the -// tooltip and icon change, so that we can hide the icon before changing back. -var timeoutIcon = 2000; -var timeoutSuccessClass = 1500; - -const temporarilyChangeTooltip = (el, oldText, newText) => { - el.setAttribute('data-tooltip', newText) - el.classList.add('success') - // Remove success a little bit sooner than we change the tooltip - // So that we can use CSS to hide the copybutton first - setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) - setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) -} - -// Changes the copy button icon for two seconds, then changes it back -const temporarilyChangeIcon = (el) => { - el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) -} - -const addCopyButtonToCodeCells = () => { - // If ClipboardJS hasn't loaded, wait a bit and try again. This - // happens because we load ClipboardJS asynchronously. - if (window.ClipboardJS === undefined) { - setTimeout(addCopyButtonToCodeCells, 250) - return - } - - // Add copybuttons to all of our code cells - const COPYBUTTON_SELECTOR = 'div.highlight pre'; - const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) - codeCells.forEach((codeCell, index) => { - const id = codeCellId(index) - codeCell.setAttribute('id', id) - - const clipboardButton = id => - `` - codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) - }) - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} - - -var copyTargetText = (trigger) => { - var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - - // get filtered text - let exclude = '.linenos'; - - let text = filterText(target, exclude); - return formatCopyText(text, '', false, true, true, true, '', '') -} - - // Initialize with a callback so we can modify the text before copy - const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) - - // Update UI with error/success messages - clipboard.on('success', event => { - clearSelection() - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) - temporarilyChangeIcon(event.trigger) - }) - - clipboard.on('error', event => { - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) - }) -} - -runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_build/html/_static/copybutton_funcs.js b/_build/html/_static/copybutton_funcs.js deleted file mode 100644 index dbe1aaa..0000000 --- a/_build/html/_static/copybutton_funcs.js +++ /dev/null @@ -1,73 +0,0 @@ -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -export function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} diff --git a/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css deleted file mode 100644 index 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Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - parent.insertBefore( - span, - parent.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - 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--git a/_build/html/_static/images/logo_jupyterhub.svg b/_build/html/_static/images/logo_jupyterhub.svg deleted file mode 100644 index 60cfe9f..0000000 --- a/_build/html/_static/images/logo_jupyterhub.svg +++ /dev/null @@ -1 +0,0 @@ -logo_jupyterhubHub diff --git a/_build/html/_static/jquery-3.6.0.js b/_build/html/_static/jquery-3.6.0.js deleted file mode 100644 index fc6c299..0000000 --- a/_build/html/_static/jquery-3.6.0.js +++ /dev/null @@ -1,10881 +0,0 @@ -/*! - * jQuery JavaScript Library v3.6.0 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright OpenJS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2021-03-02T17:08Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 - // Plus for old WebKit, typeof returns "function" for HTML collections - // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) - return typeof obj === "function" && typeof obj.nodeType !== "number" && - typeof obj.item !== "function"; - }; - - -var isWindow = function isWindow( obj ) { - return obj != null && obj === obj.window; - }; - - -var document = window.document; - - - - var preservedScriptAttributes = { - type: true, - src: true, - nonce: true, - noModule: true - }; - - function DOMEval( code, node, doc ) { - doc = doc || document; - - var i, val, - script = doc.createElement( "script" ); - - script.text = code; - if ( node ) { - for ( i in preservedScriptAttributes ) { - - // Support: Firefox 64+, Edge 18+ - // Some browsers don't support the "nonce" property on scripts. - // On the other hand, just using `getAttribute` is not enough as - // the `nonce` attribute is reset to an empty string whenever it - // becomes browsing-context connected. - // See https://github.com/whatwg/html/issues/2369 - // See https://html.spec.whatwg.org/#nonce-attributes - // The `node.getAttribute` check was added for the sake of - // `jQuery.globalEval` so that it can fake a nonce-containing node - // via an object. - val = node[ i ] || node.getAttribute && node.getAttribute( i ); - if ( val ) { - script.setAttribute( i, val ); - } - } - } - doc.head.appendChild( script ).parentNode.removeChild( script ); - } - - -function toType( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; -} -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.6.0", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - even: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return ( i + 1 ) % 2; - } ) ); - }, - - odd: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return i % 2; - } ) ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - copy = options[ name ]; - - // Prevent Object.prototype pollution - // Prevent never-ending loop - if ( name === "__proto__" || target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - src = target[ name ]; - - // Ensure proper type for the source value - if ( copyIsArray && !Array.isArray( src ) ) { - clone = []; - } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { - clone = {}; - } else { - clone = src; - } - copyIsArray = false; - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - // Evaluates a script in a provided context; falls back to the global one - // if not specified. - globalEval: function( code, options, doc ) { - DOMEval( code, { nonce: options && options.nonce }, doc ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return flat( ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), - function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); - } ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = toType( obj ); - - if ( isFunction( obj ) || isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.6 - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://js.foundation/ - * - * Date: 2021-02-16 - */ -( function( window ) { -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - nonnativeSelectorCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ( {} ).hasOwnProperty, - arr = [], - pop = arr.pop, - pushNative = arr.push, - push = arr.push, - slice = arr.slice, - - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[ i ] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + - "ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram - identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + - "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - - // "Attribute values must be CSS identifiers [capture 5] - // or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + - whitespace + "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + - whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + - "*" ), - rdescend = new RegExp( whitespace + "|>" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + - whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + - whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + - "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + - "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rhtml = /HTML$/i, - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), - funescape = function( escape, nonHex ) { - var high = "0x" + escape.slice( 1 ) - 0x10000; - - return nonHex ? - - // Strip the backslash prefix from a non-hex escape sequence - nonHex : - - // Replace a hexadecimal escape sequence with the encoded Unicode code point - // Support: IE <=11+ - // For values outside the Basic Multilingual Plane (BMP), manually construct a - // surrogate pair - high < 0 ? - String.fromCharCode( high + 0x10000 ) : - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + - ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - inDisabledFieldset = addCombinator( - function( elem ) { - return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - ( arr = slice.call( preferredDoc.childNodes ) ), - preferredDoc.childNodes - ); - - // Support: Android<4.0 - // Detect silently failing push.apply - // eslint-disable-next-line no-unused-expressions - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - pushNative.apply( target, slice.call( els ) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - - // Can't trust NodeList.length - while ( ( target[ j++ ] = els[ i++ ] ) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - setDocument( context ); - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { - - // ID selector - if ( ( m = match[ 1 ] ) ) { - - // Document context - if ( nodeType === 9 ) { - if ( ( elem = context.getElementById( m ) ) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && ( elem = newContext.getElementById( m ) ) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[ 2 ] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !nonnativeSelectorCache[ selector + " " ] && - ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && - - // Support: IE 8 only - // Exclude object elements - ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { - - newSelector = selector; - newContext = context; - - // qSA considers elements outside a scoping root when evaluating child or - // descendant combinators, which is not what we want. - // In such cases, we work around the behavior by prefixing every selector in the - // list with an ID selector referencing the scope context. - // The technique has to be used as well when a leading combinator is used - // as such selectors are not recognized by querySelectorAll. - // Thanks to Andrew Dupont for this technique. - if ( nodeType === 1 && - ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - - // We can use :scope instead of the ID hack if the browser - // supports it & if we're not changing the context. - if ( newContext !== context || !support.scope ) { - - // Capture the context ID, setting it first if necessary - if ( ( nid = context.getAttribute( "id" ) ) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", ( nid = expando ) ); - } - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + - toSelector( groups[ i ] ); - } - newSelector = groups.join( "," ); - } - - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - nonnativeSelectorCache( selector, true ); - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return ( cache[ key + " " ] = value ); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement( "fieldset" ); - - try { - return !!fn( el ); - } catch ( e ) { - return false; - } finally { - - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split( "|" ), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[ i ] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( ( cur = cur.nextSibling ) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return ( name === "input" || name === "button" ) && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - inDisabledFieldset( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction( function( argument ) { - argument = +argument; - return markFunction( function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ ( j = matchIndexes[ i ] ) ] ) { - seed[ j ] = !( matches[ j ] = seed[ j ] ); - } - } - } ); - } ); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - var namespace = elem && elem.namespaceURI, - docElem = elem && ( elem.ownerDocument || elem ).documentElement; - - // Support: IE <=8 - // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes - // https://bugs.jquery.com/ticket/4833 - return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9 - 11+, Edge 12 - 18+ - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( preferredDoc != document && - ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, - // Safari 4 - 5 only, Opera <=11.6 - 12.x only - // IE/Edge & older browsers don't support the :scope pseudo-class. - // Support: Safari 6.0 only - // Safari 6.0 supports :scope but it's an alias of :root there. - support.scope = assert( function( el ) { - docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); - return typeof el.querySelectorAll !== "undefined" && - !el.querySelectorAll( ":scope fieldset div" ).length; - } ); - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert( function( el ) { - el.className = "i"; - return !el.getAttribute( "className" ); - } ); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert( function( el ) { - el.appendChild( document.createComment( "" ) ); - return !el.getElementsByTagName( "*" ).length; - } ); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert( function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - } ); - - // ID filter and find - if ( support.getById ) { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute( "id" ) === attrId; - }; - }; - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode( "id" ); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( ( elem = elems[ i++ ] ) ) { - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find[ "TAG" ] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { - - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert( function( el ) { - - var input; - - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll( "[selected]" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push( "~=" ); - } - - // Support: IE 11+, Edge 15 - 18+ - // IE 11/Edge don't find elements on a `[name='']` query in some cases. - // Adding a temporary attribute to the document before the selection works - // around the issue. - // Interestingly, IE 10 & older don't seem to have the issue. - input = document.createElement( "input" ); - input.setAttribute( "name", "" ); - el.appendChild( input ); - if ( !el.querySelectorAll( "[name='']" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + - whitespace + "*(?:''|\"\")" ); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll( ":checked" ).length ) { - rbuggyQSA.push( ":checked" ); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push( ".#.+[+~]" ); - } - - // Support: Firefox <=3.6 - 5 only - // Old Firefox doesn't throw on a badly-escaped identifier. - el.querySelectorAll( "\\\f" ); - rbuggyQSA.push( "[\\r\\n\\f]" ); - } ); - - assert( function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement( "input" ); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll( "[name=d]" ).length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: Opera 10 - 11 only - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll( "*,:x" ); - rbuggyQSA.push( ",.*:" ); - } ); - } - - if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector ) ) ) ) { - - assert( function( el ) { - - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - } ); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - ) ); - } : - function( a, b ) { - if ( b ) { - while ( ( b = b.parentNode ) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { - - // Choose the first element that is related to our preferred document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( a == document || a.ownerDocument == preferredDoc && - contains( preferredDoc, a ) ) { - return -1; - } - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( b == document || b.ownerDocument == preferredDoc && - contains( preferredDoc, b ) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - return a == document ? -1 : - b == document ? 1 : - /* eslint-enable eqeqeq */ - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( ( cur = cur.parentNode ) ) { - ap.unshift( cur ); - } - cur = b; - while ( ( cur = cur.parentNode ) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[ i ] === bp[ i ] ) { - i++; - } - - return i ? - - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[ i ], bp[ i ] ) : - - // Otherwise nodes in our document sort first - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - ap[ i ] == preferredDoc ? -1 : - bp[ i ] == preferredDoc ? 1 : - /* eslint-enable eqeqeq */ - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - setDocument( elem ); - - if ( support.matchesSelector && documentIsHTML && - !nonnativeSelectorCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch ( e ) { - nonnativeSelectorCache( expr, true ); - } - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( context.ownerDocument || context ) != document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( elem.ownerDocument || elem ) != document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return ( sel + "" ).replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - - // If no nodeType, this is expected to be an array - while ( ( node = elem[ i++ ] ) ) { - - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[ 1 ] = match[ 1 ].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[ 3 ] = ( match[ 3 ] || match[ 4 ] || - match[ 5 ] || "" ).replace( runescape, funescape ); - - if ( match[ 2 ] === "~=" ) { - match[ 3 ] = " " + match[ 3 ] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[ 1 ] = match[ 1 ].toLowerCase(); - - if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { - - // nth-* requires argument - if ( !match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[ 4 ] = +( match[ 4 ] ? - match[ 5 ] + ( match[ 6 ] || 1 ) : - 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); - match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); - - // other types prohibit arguments - } else if ( match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[ 6 ] && match[ 2 ]; - - if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[ 3 ] ) { - match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - - // Get excess from tokenize (recursively) - ( excess = tokenize( unquoted, true ) ) && - - // advance to the next closing parenthesis - ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { - - // excess is a negative index - match[ 0 ] = match[ 0 ].slice( 0, excess ); - match[ 2 ] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { - return true; - } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - ( pattern = new RegExp( "(^|" + whitespace + - ")" + className + "(" + whitespace + "|$)" ) ) && classCache( - className, function( elem ) { - return pattern.test( - typeof elem.className === "string" && elem.className || - typeof elem.getAttribute !== "undefined" && - elem.getAttribute( "class" ) || - "" - ); - } ); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - /* eslint-disable max-len */ - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - /* eslint-enable max-len */ - - }; - }, - - "CHILD": function( type, what, _argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, _context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( ( node = node[ dir ] ) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( ( node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - - // Use previously-cached element index if available - if ( useCache ) { - - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - - // Use the same loop as above to seek `elem` from the start - while ( ( node = ++nodeIndex && node && node[ dir ] || - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || - ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction( function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[ i ] ); - seed[ idx ] = !( matches[ idx ] = matched[ i ] ); - } - } ) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - - // Potentially complex pseudos - "not": markFunction( function( selector ) { - - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction( function( seed, matches, _context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( ( elem = unmatched[ i ] ) ) { - seed[ i ] = !( matches[ i ] = elem ); - } - } - } ) : - function( elem, _context, xml ) { - input[ 0 ] = elem; - matcher( input, null, xml, results ); - - // Don't keep the element (issue #299) - input[ 0 ] = null; - return !results.pop(); - }; - } ), - - "has": markFunction( function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - } ), - - "contains": markFunction( function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; - }; - } ), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - - // lang value must be a valid identifier - if ( !ridentifier.test( lang || "" ) ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( ( elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); - return false; - }; - } ), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && - ( !document.hasFocus || document.hasFocus() ) && - !!( elem.type || elem.href || ~elem.tabIndex ); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return ( nodeName === "input" && !!elem.checked ) || - ( nodeName === "option" && !!elem.selected ); - }, - - "selected": function( elem ) { - - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - // eslint-disable-next-line no-unused-expressions - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos[ "empty" ]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( ( attr = elem.getAttribute( "type" ) ) == null || - attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo( function() { - return [ 0 ]; - } ), - - "last": createPositionalPseudo( function( _matchIndexes, length ) { - return [ length - 1 ]; - } ), - - "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - } ), - - "even": createPositionalPseudo( function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "odd": createPositionalPseudo( function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? - argument + length : - argument > length ? - length : - argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ) - } -}; - -Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || ( match = rcomma.exec( soFar ) ) ) { - if ( match ) { - - // Don't consume trailing commas as valid - soFar = soFar.slice( match[ 0 ].length ) || soFar; - } - groups.push( ( tokens = [] ) ); - } - - matched = false; - - // Combinators - if ( ( match = rcombinators.exec( soFar ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - - // Cast descendant combinators to space - type: match[ 0 ].replace( rtrim, " " ) - } ); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || - ( match = preFilters[ type ]( match ) ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - type: type, - matches: match - } ); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[ i ].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || ( elem[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || - ( outerCache[ elem.uniqueID ] = {} ); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( ( oldCache = uniqueCache[ key ] ) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return ( newCache[ 2 ] = oldCache[ 2 ] ); - } else { - - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[ i ]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[ 0 ]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[ i ], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( ( elem = unmatched[ i ] ) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction( function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( - selector || "*", - context.nodeType ? [ context ] : context, - [] - ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( ( elem = temp[ i ] ) ) { - matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) ) { - - // Restore matcherIn since elem is not yet a final match - temp.push( ( matcherIn[ i ] = elem ) ); - } - } - postFinder( null, ( matcherOut = [] ), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) && - ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { - - seed[ temp ] = !( results[ temp ] = elem ); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - } ); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[ 0 ].type ], - implicitRelative = leadingRelative || Expr.relative[ " " ], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - ( checkContext = context ).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { - matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; - } else { - matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[ j ].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens - .slice( 0, i - 1 ) - .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), - - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), - len = elems.length; - - if ( outermost ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - outermostContext = context == document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( !context && elem.ownerDocument != document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( ( matcher = elementMatchers[ j++ ] ) ) { - if ( matcher( elem, context || document, xml ) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - - // They will have gone through all possible matchers - if ( ( elem = !matcher && elem ) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( ( matcher = setMatchers[ j++ ] ) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !( unmatched[ i ] || setMatched[ i ] ) ) { - setMatched[ i ] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[ i ] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( - selector, - matcherFromGroupMatchers( elementMatchers, setMatchers ) - ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( ( selector = compiled.selector || selector ) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[ 0 ] = match[ 0 ].slice( 0 ); - if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { - - context = ( Expr.find[ "ID" ]( token.matches[ 0 ] - .replace( runescape, funescape ), context ) || [] )[ 0 ]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[ i ]; - - // Abort if we hit a combinator - if ( Expr.relative[ ( type = token.type ) ] ) { - break; - } - if ( ( find = Expr.find[ type ] ) ) { - - // Search, expanding context for leading sibling combinators - if ( ( seed = find( - token.matches[ 0 ].replace( runescape, funescape ), - rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || - context - ) ) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert( function( el ) { - - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; -} ); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert( function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute( "href" ) === "#"; -} ) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - } ); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert( function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -} ) ) { - addHandle( "value", function( elem, _name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - } ); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert( function( el ) { - return el.getAttribute( "disabled" ) == null; -} ) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; - } - } ); -} - -return Sizzle; - -} )( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -} -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Filtered directly for both simple and complex selectors - return jQuery.filter( qualifier, elements, not ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, _i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, _i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, _i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( elem.contentDocument != null && - - // Support: IE 11+ - // elements with no `data` attribute has an object - // `contentDocument` with a `null` prototype. - getProto( elem.contentDocument ) ) { - - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && toType( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( _i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // rejected_handlers.disable - // fulfilled_handlers.disable - tuples[ 3 - i ][ 3 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock, - - // progress_handlers.lock - tuples[ 0 ][ 3 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the primary Deferred - primary = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - primary.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( primary.state() === "pending" || - isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return primary.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); - } - - return primary.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( toType( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, _key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; - - -// Matches dashed string for camelizing -var rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g; - -// Used by camelCase as callback to replace() -function fcamelCase( _all, letter ) { - return letter.toUpperCase(); -} - -// Convert dashed to camelCase; used by the css and data modules -// Support: IE <=9 - 11, Edge 12 - 15 -// Microsoft forgot to hump their vendor prefix (#9572) -function camelCase( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); -} -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( camelCase ); - } else { - key = camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var documentElement = document.documentElement; - - - - var isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ); - }, - composed = { composed: true }; - - // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only - // Check attachment across shadow DOM boundaries when possible (gh-3504) - // Support: iOS 10.0-10.2 only - // Early iOS 10 versions support `attachShadow` but not `getRootNode`, - // leading to errors. We need to check for `getRootNode`. - if ( documentElement.getRootNode ) { - isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ) || - elem.getRootNode( composed ) === elem.ownerDocument; - }; - } -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - isAttached( elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, scale, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = elem.nodeType && - ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Support: Firefox <=54 - // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) - initial = initial / 2; - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - while ( maxIterations-- ) { - - // Evaluate and update our best guess (doubling guesses that zero out). - // Finish if the scale equals or crosses 1 (making the old*new product non-positive). - jQuery.style( elem, prop, initialInUnit + unit ); - if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { - maxIterations = 0; - } - initialInUnit = initialInUnit / scale; - - } - - initialInUnit = initialInUnit * 2; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); - -var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); - - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; - - // Support: IE <=9 only - // IE <=9 replaces "; - support.option = !!div.lastChild; -} )(); - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - -// Support: IE <=9 only -if ( !support.option ) { - wrapMap.optgroup = wrapMap.option = [ 1, "" ]; -} - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, attached, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( toType( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - attached = isAttached( elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( attached ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 - 11+ -// focus() and blur() are asynchronous, except when they are no-op. -// So expect focus to be synchronous when the element is already active, -// and blur to be synchronous when the element is not already active. -// (focus and blur are always synchronous in other supported browsers, -// this just defines when we can count on it). -function expectSync( elem, type ) { - return ( elem === safeActiveElement() ) === ( type === "focus" ); -} - -// Support: IE <=9 only -// Accessing document.activeElement can throw unexpectedly -// https://bugs.jquery.com/ticket/13393 -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Only attach events to objects that accept data - if ( !acceptData( elem ) ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = Object.create( null ); - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - - // Make a writable jQuery.Event from the native event object - event = jQuery.event.fix( nativeEvent ), - - handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // If the event is namespaced, then each handler is only invoked if it is - // specially universal or its namespaces are a superset of the event's. - if ( !event.rnamespace || handleObj.namespace === false || - event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - click: { - - // Utilize native event to ensure correct state for checkable inputs - setup: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Claim the first handler - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - // dataPriv.set( el, "click", ... ) - leverageNative( el, "click", returnTrue ); - } - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Force setup before triggering a click - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - leverageNative( el, "click" ); - } - - // Return non-false to allow normal event-path propagation - return true; - }, - - // For cross-browser consistency, suppress native .click() on links - // Also prevent it if we're currently inside a leveraged native-event stack - _default: function( event ) { - var target = event.target; - return rcheckableType.test( target.type ) && - target.click && nodeName( target, "input" ) && - dataPriv.get( target, "click" ) || - nodeName( target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -// Ensure the presence of an event listener that handles manually-triggered -// synthetic events by interrupting progress until reinvoked in response to -// *native* events that it fires directly, ensuring that state changes have -// already occurred before other listeners are invoked. -function leverageNative( el, type, expectSync ) { - - // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add - if ( !expectSync ) { - if ( dataPriv.get( el, type ) === undefined ) { - jQuery.event.add( el, type, returnTrue ); - } - return; - } - - // Register the controller as a special universal handler for all event namespaces - dataPriv.set( el, type, false ); - jQuery.event.add( el, type, { - namespace: false, - handler: function( event ) { - var notAsync, result, - saved = dataPriv.get( this, type ); - - if ( ( event.isTrigger & 1 ) && this[ type ] ) { - - // Interrupt processing of the outer synthetic .trigger()ed event - // Saved data should be false in such cases, but might be a leftover capture object - // from an async native handler (gh-4350) - if ( !saved.length ) { - - // Store arguments for use when handling the inner native event - // There will always be at least one argument (an event object), so this array - // will not be confused with a leftover capture object. - saved = slice.call( arguments ); - dataPriv.set( this, type, saved ); - - // Trigger the native event and capture its result - // Support: IE <=9 - 11+ - // focus() and blur() are asynchronous - notAsync = expectSync( this, type ); - this[ type ](); - result = dataPriv.get( this, type ); - if ( saved !== result || notAsync ) { - dataPriv.set( this, type, false ); - } else { - result = {}; - } - if ( saved !== result ) { - - // Cancel the outer synthetic event - event.stopImmediatePropagation(); - event.preventDefault(); - - // Support: Chrome 86+ - // In Chrome, if an element having a focusout handler is blurred by - // clicking outside of it, it invokes the handler synchronously. If - // that handler calls `.remove()` on the element, the data is cleared, - // leaving `result` undefined. We need to guard against this. - return result && result.value; - } - - // If this is an inner synthetic event for an event with a bubbling surrogate - // (focus or blur), assume that the surrogate already propagated from triggering the - // native event and prevent that from happening again here. - // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the - // bubbling surrogate propagates *after* the non-bubbling base), but that seems - // less bad than duplication. - } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { - event.stopPropagation(); - } - - // If this is a native event triggered above, everything is now in order - // Fire an inner synthetic event with the original arguments - } else if ( saved.length ) { - - // ...and capture the result - dataPriv.set( this, type, { - value: jQuery.event.trigger( - - // Support: IE <=9 - 11+ - // Extend with the prototype to reset the above stopImmediatePropagation() - jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), - saved.slice( 1 ), - this - ) - } ); - - // Abort handling of the native event - event.stopImmediatePropagation(); - } - } - } ); -} - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || Date.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - code: true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - which: true -}, jQuery.event.addProp ); - -jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { - jQuery.event.special[ type ] = { - - // Utilize native event if possible so blur/focus sequence is correct - setup: function() { - - // Claim the first handler - // dataPriv.set( this, "focus", ... ) - // dataPriv.set( this, "blur", ... ) - leverageNative( this, type, expectSync ); - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function() { - - // Force setup before trigger - leverageNative( this, type ); - - // Return non-false to allow normal event-path propagation - return true; - }, - - // Suppress native focus or blur as it's already being fired - // in leverageNative. - _default: function() { - return true; - }, - - delegateType: delegateType - }; -} ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - // Support: IE <=10 - 11, Edge 12 - 13 only - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( elem ).children( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { - elem.type = elem.type.slice( 5 ); - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.get( src ); - events = pdataOld.events; - - if ( events ) { - dataPriv.remove( dest, "handle events" ); - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = flat( args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - valueIsFunction = isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( valueIsFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( valueIsFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl && !node.noModule ) { - jQuery._evalUrl( node.src, { - nonce: node.nonce || node.getAttribute( "nonce" ) - }, doc ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && isAttached( node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html; - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = isAttached( elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - -var swap = function( elem, options, callback ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.call( elem ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - -var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - container.style.cssText = "position:absolute;left:-11111px;width:60px;" + - "margin-top:1px;padding:0;border:0"; - div.style.cssText = - "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + - "margin:auto;border:1px;padding:1px;" + - "width:60%;top:1%"; - documentElement.appendChild( container ).appendChild( div ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; - - // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 - // Some styles come back with percentage values, even though they shouldn't - div.style.right = "60%"; - pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; - - // Support: IE 9 - 11 only - // Detect misreporting of content dimensions for box-sizing:border-box elements - boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; - - // Support: IE 9 only - // Detect overflow:scroll screwiness (gh-3699) - // Support: Chrome <=64 - // Don't get tricked when zoom affects offsetWidth (gh-4029) - div.style.position = "absolute"; - scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - function roundPixelMeasures( measure ) { - return Math.round( parseFloat( measure ) ); - } - - var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, - reliableTrDimensionsVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - jQuery.extend( support, { - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelBoxStyles: function() { - computeStyleTests(); - return pixelBoxStylesVal; - }, - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - }, - scrollboxSize: function() { - computeStyleTests(); - return scrollboxSizeVal; - }, - - // Support: IE 9 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Behavior in IE 9 is more subtle than in newer versions & it passes - // some versions of this test; make sure not to make it pass there! - // - // Support: Firefox 70+ - // Only Firefox includes border widths - // in computed dimensions. (gh-4529) - reliableTrDimensions: function() { - var table, tr, trChild, trStyle; - if ( reliableTrDimensionsVal == null ) { - table = document.createElement( "table" ); - tr = document.createElement( "tr" ); - trChild = document.createElement( "div" ); - - table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; - tr.style.cssText = "border:1px solid"; - - // Support: Chrome 86+ - // Height set through cssText does not get applied. - // Computed height then comes back as 0. - tr.style.height = "1px"; - trChild.style.height = "9px"; - - // Support: Android 8 Chrome 86+ - // In our bodyBackground.html iframe, - // display for all div elements is set to "inline", - // which causes a problem only in Android 8 Chrome 86. - // Ensuring the div is display: block - // gets around this issue. - trChild.style.display = "block"; - - documentElement - .appendChild( table ) - .appendChild( tr ) - .appendChild( trChild ); - - trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + - parseInt( trStyle.borderTopWidth, 10 ) + - parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; - - documentElement.removeChild( table ); - } - return reliableTrDimensionsVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !isAttached( elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style, - vendorProps = {}; - -// Return a vendor-prefixed property or undefined -function vendorPropName( name ) { - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a potentially-mapped jQuery.cssProps or vendor prefixed property -function finalPropName( name ) { - var final = jQuery.cssProps[ name ] || vendorProps[ name ]; - - if ( final ) { - return final; - } - if ( name in emptyStyle ) { - return name; - } - return vendorProps[ name ] = vendorPropName( name ) || name; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }; - -function setPositiveNumber( _elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { - var i = dimension === "width" ? 1 : 0, - extra = 0, - delta = 0; - - // Adjustment may not be necessary - if ( box === ( isBorderBox ? "border" : "content" ) ) { - return 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin - if ( box === "margin" ) { - delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); - } - - // If we get here with a content-box, we're seeking "padding" or "border" or "margin" - if ( !isBorderBox ) { - - // Add padding - delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // For "border" or "margin", add border - if ( box !== "padding" ) { - delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - - // But still keep track of it otherwise - } else { - extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - - // If we get here with a border-box (content + padding + border), we're seeking "content" or - // "padding" or "margin" - } else { - - // For "content", subtract padding - if ( box === "content" ) { - delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // For "content" or "padding", subtract border - if ( box !== "margin" ) { - delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - // Account for positive content-box scroll gutter when requested by providing computedVal - if ( !isBorderBox && computedVal >= 0 ) { - - // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border - // Assuming integer scroll gutter, subtract the rest and round down - delta += Math.max( 0, Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - computedVal - - delta - - extra - - 0.5 - - // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter - // Use an explicit zero to avoid NaN (gh-3964) - ) ) || 0; - } - - return delta; -} - -function getWidthOrHeight( elem, dimension, extra ) { - - // Start with computed style - var styles = getStyles( elem ), - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). - // Fake content-box until we know it's needed to know the true value. - boxSizingNeeded = !support.boxSizingReliable() || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - valueIsBorderBox = isBorderBox, - - val = curCSS( elem, dimension, styles ), - offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); - - // Support: Firefox <=54 - // Return a confounding non-pixel value or feign ignorance, as appropriate. - if ( rnumnonpx.test( val ) ) { - if ( !extra ) { - return val; - } - val = "auto"; - } - - - // Support: IE 9 - 11 only - // Use offsetWidth/offsetHeight for when box sizing is unreliable. - // In those cases, the computed value can be trusted to be border-box. - if ( ( !support.boxSizingReliable() && isBorderBox || - - // Support: IE 10 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Interestingly, in some cases IE 9 doesn't suffer from this issue. - !support.reliableTrDimensions() && nodeName( elem, "tr" ) || - - // Fall back to offsetWidth/offsetHeight when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - val === "auto" || - - // Support: Android <=4.1 - 4.3 only - // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) - !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && - - // Make sure the element is visible & connected - elem.getClientRects().length ) { - - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Where available, offsetWidth/offsetHeight approximate border box dimensions. - // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the - // retrieved value as a content box dimension. - valueIsBorderBox = offsetProp in elem; - if ( valueIsBorderBox ) { - val = elem[ offsetProp ]; - } - } - - // Normalize "" and auto - val = parseFloat( val ) || 0; - - // Adjust for the element's box model - return ( val + - boxModelAdjustment( - elem, - dimension, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles, - - // Provide the current computed size to request scroll gutter calculation (gh-3589) - val - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "gridArea": true, - "gridColumn": true, - "gridColumnEnd": true, - "gridColumnStart": true, - "gridRow": true, - "gridRowEnd": true, - "gridRowStart": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: {}, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append - // "px" to a few hardcoded values. - if ( type === "number" && !isCustomProp ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( _i, dimension ) { - jQuery.cssHooks[ dimension ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = getStyles( elem ), - - // Only read styles.position if the test has a chance to fail - // to avoid forcing a reflow. - scrollboxSizeBuggy = !support.scrollboxSize() && - styles.position === "absolute", - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) - boxSizingNeeded = scrollboxSizeBuggy || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - subtract = extra ? - boxModelAdjustment( - elem, - dimension, - extra, - isBorderBox, - styles - ) : - 0; - - // Account for unreliable border-box dimensions by comparing offset* to computed and - // faking a content-box to get border and padding (gh-3699) - if ( isBorderBox && scrollboxSizeBuggy ) { - subtract -= Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - parseFloat( styles[ dimension ] ) - - boxModelAdjustment( elem, dimension, "border", false, styles ) - - 0.5 - ); - } - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ dimension ] = value; - value = jQuery.css( elem, dimension ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( prefix !== "margin" ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || - tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = Date.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 15 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY and Edge just mirrors - // the overflowX value there. - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - result.stop.bind( result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = Date.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -function classesToArray( value ) { - if ( Array.isArray( value ) ) { - return value; - } - if ( typeof value === "string" ) { - return value.match( rnothtmlwhite ) || []; - } - return []; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value, - isValidValue = type === "string" || Array.isArray( value ); - - if ( typeof stateVal === "boolean" && isValidValue ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( isValidValue ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = classesToArray( value ); - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, valueIsFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - valueIsFunction = isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( valueIsFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -support.focusin = "onfocusin" in window; - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, - stopPropagationCallback = function( e ) { - e.stopPropagation(); - }; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = lastElement = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - lastElement = cur; - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - - if ( event.isPropagationStopped() ) { - lastElement.addEventListener( type, stopPropagationCallback ); - } - - elem[ type ](); - - if ( event.isPropagationStopped() ) { - lastElement.removeEventListener( type, stopPropagationCallback ); - } - - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - - // Handle: regular nodes (via `this.ownerDocument`), window - // (via `this.document`) & document (via `this`). - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = { guid: Date.now() }; - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml, parserErrorElem; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) {} - - parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; - if ( !xml || parserErrorElem ) { - jQuery.error( "Invalid XML: " + ( - parserErrorElem ? - jQuery.map( parserErrorElem.childNodes, function( el ) { - return el.textContent; - } ).join( "\n" ) : - data - ) ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && toType( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - if ( a == null ) { - return ""; - } - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ).filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ).map( function( _i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - -originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() + " " ] = - ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) - .concat( match[ 2 ] ); - } - } - match = responseHeaders[ key.toLowerCase() + " " ]; - } - return match == null ? null : match.join( ", " ); - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 15 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available and should be processed, append data to url - if ( s.data && ( s.processData || typeof s.data === "string" ) ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + - uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Use a noop converter for missing script but not if jsonp - if ( !isSuccess && - jQuery.inArray( "script", s.dataTypes ) > -1 && - jQuery.inArray( "json", s.dataTypes ) < 0 ) { - s.converters[ "text script" ] = function() {}; - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( _i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - -jQuery.ajaxPrefilter( function( s ) { - var i; - for ( i in s.headers ) { - if ( i.toLowerCase() === "content-type" ) { - s.contentType = s.headers[ i ] || ""; - } - } -} ); - - -jQuery._evalUrl = function( url, options, doc ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - - // Only evaluate the response if it is successful (gh-4126) - // dataFilter is not invoked for failure responses, so using it instead - // of the default converter is kludgy but it works. - converters: { - "text script": function() {} - }, - dataFilter: function( response ) { - jQuery.globalEval( response, options, doc ); - } - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var htmlIsFunction = isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( htmlIsFunction ? 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NAME(){return"swipe"}dispose(){ue.off(this._element,ke)}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),Qt(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&Qt(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(ue.on(this._element,$e,(t=>this._start(t))),ue.on(this._element,Ie,(t=>this._end(t))),this._element.classList.add("pointer-event")):(ue.on(this._element,Le,(t=>this._start(t))),ue.on(this._element,Se,(t=>this._move(t))),ue.on(this._element,De,(t=>this._end(t))))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static 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t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&ue.on(this._element,Ve,(t=>this._keydown(t))),"hover"===this._config.pause&&(ue.on(this._element,Ye,(()=>this.pause())),ue.on(this._element,Ke,(()=>this._maybeEnableCycle()))),this._config.touch&&Me.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of ye.find(".carousel-item img",this._element))ue.on(t,Qe,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(We)),rightCallback:()=>this._slide(this._directionToOrder(ze)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new Me(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=ii[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=ye.findOne(Ze,this._indicatorsElement);e.classList.remove(Je),e.removeAttribute("aria-current");const i=ye.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(Je),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===He,s=e||Ut(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>ue.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Re).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),Rt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Je),i.classList.remove(Je,c,l),this._isSliding=!1,r(qe)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ye.findOne(ei,this._element)}_getItems(){return ye.find(ti,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Yt()?t===We?Be:He:t===We?He:Be}_orderToDirection(t){return Yt()?t===Be?We:ze:t===Be?ze:We}static jQueryInterface(t){return this.each((function(){const e=oi.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}ue.on(document,Ue,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=ye.getElementFromSelector(this);if(!e||!e.classList.contains(Ge))return;t.preventDefault();const i=oi.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===ge.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),ue.on(window,Xe,(()=>{const t=ye.find('[data-bs-ride="carousel"]');for(const e of t)oi.getOrCreateInstance(e)})),Kt(oi);const ri=".bs.collapse",ai=`show${ri}`,li=`shown${ri}`,ci=`hide${ri}`,hi=`hidden${ri}`,di=`click${ri}.data-api`,ui="show",fi="collapse",pi="collapsing",mi=`:scope .${fi} .${fi}`,gi='[data-bs-toggle="collapse"]',_i={parent:null,toggle:!0},bi={parent:"(null|element)",toggle:"boolean"};class vi extends be{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=ye.find(gi);for(const t of i){const e=ye.getSelectorFromElement(t),i=ye.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return _i}static get DefaultType(){return bi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>vi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(ue.trigger(this._element,ai).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(fi),this._element.classList.add(pi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(pi),this._element.classList.add(fi,ui),this._element.style[e]="",ue.trigger(this._element,li)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(ue.trigger(this._element,ci).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,Rt(this._element),this._element.classList.add(pi),this._element.classList.remove(fi,ui);for(const t of this._triggerArray){const e=ye.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(pi),this._element.classList.add(fi),ue.trigger(this._element,hi)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(ui)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ft(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(gi);for(const e of t){const t=ye.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ye.find(mi,this._config.parent);return ye.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=vi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}ue.on(document,di,gi,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of ye.getMultipleElementsFromSelector(this))vi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Kt(vi);const yi="dropdown",wi=".bs.dropdown",Ei=".data-api",Ai="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ei}`,Si=`keydown${wi}${Ei}`,Di=`keyup${wi}${Ei}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=Yt()?"top-end":"top-start",ji=Yt()?"top-start":"top-end",Fi=Yt()?"bottom-end":"bottom-start",Hi=Yt()?"bottom-start":"bottom-end",Bi=Yt()?"left-start":"right-start",Wi=Yt()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends be{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ye.next(this._element,Pi)[0]||ye.prev(this._element,Pi)[0]||ye.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Bt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!ue.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))ue.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),ue.trigger(this._element,ki,t)}}hide(){if(Bt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!ue.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),ge.removeDataAttribute(this._menu,"popper"),ue.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!jt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:jt(this._config.reference)?t=Ft(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=St(t,this._menu,i)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return Wi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(ge.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Qt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=ye.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Ht(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ye.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ai,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:ye.prev(this,Ii)[0]||ye.next(this,Ii)[0]||ye.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}ue.on(document,Si,Ii,qi.dataApiKeydownHandler),ue.on(document,Si,Pi,qi.dataApiKeydownHandler),ue.on(document,Li,qi.clearMenus),ue.on(document,Di,qi.clearMenus),ue.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),Kt(qi);const Vi="backdrop",Yi="show",Ki=`mousedown.bs.${Vi}`,Qi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Xi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends _e{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Qi}static get DefaultType(){return Xi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void Qt(t);this._append();const e=this._getElement();this._config.isAnimated&&Rt(e),e.classList.add(Yi),this._emulateAnimation((()=>{Qt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Yi),this._emulateAnimation((()=>{this.dispose(),Qt(t)}))):Qt(t)}dispose(){this._isAppended&&(ue.off(this._element,Ki),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ft(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),ue.on(t,Ki,(()=>{Qt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Xt(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends _e{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),ue.off(document,Gi),ue.on(document,Ji,(t=>this._handleFocusin(t))),ue.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,ue.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ye.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&ge.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=ge.getDataAttribute(t,e);null!==i?(ge.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(jt(t))e(t);else for(const i of ye.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",En="show",An="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends be{constructor(t,e){super(t,e),this._dialog=ye.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||ue.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(ue.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){ue.off(window,hn),ue.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ye.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),Rt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,ue.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){ue.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),ue.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),ue.on(this._element,bn,(t=>{ue.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),ue.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(ue.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(An)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(An),this._queueCallback((()=>{this._element.classList.remove(An),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Yt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Yt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}ue.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=ye.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),ue.one(e,pn,(t=>{t.defaultPrevented||ue.one(e,fn,(()=>{Ht(this)&&this.focus()}))}));const i=ye.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),we(On),Kt(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Bn=`click${xn}${kn}`,Wn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends be{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||ue.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),ue.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(ue.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),ue.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():ue.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){ue.on(this._element,Wn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():ue.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}ue.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=ye.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Bt(this))return;ue.one(e,Fn,(()=>{Ht(this)&&this.focus()}));const i=ye.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),ue.on(window,Ln,(()=>{for(const t of ye.find(In))qn.getOrCreateInstance(t).show()})),ue.on(window,Hn,(()=>{for(const t of ye.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),we(qn),Kt(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Yn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Kn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Qn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Yn.has(i)||Boolean(Kn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Xn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends _e{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Xn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=ye.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?jt(e)?this._putElementInTemplate(Ft(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Qn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Qt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:Yt()?"left":"right",BOTTOM:"bottom",LEFT:Yt()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends be{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),ue.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=ue.trigger(this._element,this.constructor.eventName("show")),e=(Wt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),ue.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.on(t,"mouseover",zt);this._queueCallback((()=>{ue.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!ue.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),ue.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=Qt(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return St(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Qt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Qt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)ue.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");ue.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),ue.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},ue.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=ge.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ft(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Kt(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=ye.find(bs,this._config.target);for(const e of t){if(!e.hash||Bt(e))continue;const t=ye.findOne(decodeURI(e.hash),this._element);Ht(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),ue.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))ye.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of ye.parents(t,".nav, .list-group"))for(const t of ye.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=ye.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=As.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}ue.on(window,gs,(()=>{for(const t of ye.find('[data-bs-spy="scroll"]'))As.getOrCreateInstance(t)})),Kt(As);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Bs="show",Ws=".dropdown-toggle",zs=`:not(${Ws})`,Rs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',qs=`.nav-link${zs}, .list-group-item${zs}, [role="tab"]${zs}, ${Rs}`,Vs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Ys extends be{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),ue.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?ue.trigger(e,Cs,{relatedTarget:t}):null;ue.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(ye.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),ue.trigger(t,ks,{relatedTarget:e})):t.classList.add(Bs)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(ye.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),ue.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Bs)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!Bt(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=Ut(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Ys.getOrCreateInstance(i).show())}_getChildren(){return ye.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=ye.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=ye.findOne(t,i);s&&s.classList.toggle(n,e)};n(Ws,Fs),n(".dropdown-menu",Bs),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Fs)}_getInnerElement(t){return t.matches(qs)?t:ye.findOne(qs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Ys.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}ue.on(document,Ls,Rs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Bt(this)||Ys.getOrCreateInstance(this).show()})),ue.on(window,Ds,(()=>{for(const t of ye.find(Vs))Ys.getOrCreateInstance(t)})),Kt(Ys);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Us=`focusin${Ks}`,Gs=`focusout${Ks}`,Js=`hide${Ks}`,Zs=`hidden${Ks}`,to=`show${Ks}`,eo=`shown${Ks}`,io="hide",no="show",so="showing",oo={animation:"boolean",autohide:"boolean",delay:"number"},ro={animation:!0,autohide:!0,delay:5e3};class ao extends be{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return ro}static get DefaultType(){return oo}static get NAME(){return"toast"}show(){ue.trigger(this._element,to).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(io),Rt(this._element),this._element.classList.add(no,so),this._queueCallback((()=>{this._element.classList.remove(so),ue.trigger(this._element,eo),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(ue.trigger(this._element,Js).defaultPrevented||(this._element.classList.add(so),this._queueCallback((()=>{this._element.classList.add(io),this._element.classList.remove(so,no),ue.trigger(this._element,Zs)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(no),super.dispose()}isShown(){return this._element.classList.contains(no)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){ue.on(this._element,Qs,(t=>this._onInteraction(t,!0))),ue.on(this._element,Xs,(t=>this._onInteraction(t,!1))),ue.on(this._element,Us,(t=>this._onInteraction(t,!0))),ue.on(this._element,Gs,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ao.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named 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(element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n });\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref) {\n var name = _ref.name,\n _ref$options = _ref.options,\n options = _ref$options === void 0 ? {} : _ref$options,\n effect = _ref.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n * Bootstrap v5.3.2 (https://getbootstrap.com/)\n * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map();\nconst Data = {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map());\n }\n const instanceMap = elementMap.get(element);\n\n // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n return;\n }\n instanceMap.set(key, instance);\n },\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null;\n }\n return null;\n },\n remove(element, key) {\n if (!elementMap.has(element)) {\n return;\n }\n const instanceMap = elementMap.get(element);\n instanceMap.delete(key);\n\n // free up element references if there are no instances left for an element\n if (instanceMap.size === 0) {\n elementMap.delete(element);\n }\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend';\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n if (selector && window.CSS && window.CSS.escape) {\n // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`);\n }\n return selector;\n};\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`;\n }\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID);\n } while (document.getElementById(prefix));\n return prefix;\n};\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0;\n }\n\n // Get transition-duration of the element\n let {\n transitionDuration,\n transitionDelay\n } = window.getComputedStyle(element);\n const floatTransitionDuration = Number.parseFloat(transitionDuration);\n const floatTransitionDelay = Number.parseFloat(transitionDelay);\n\n // Return 0 if element or transition duration is not found\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0;\n }\n\n // If multiple durations are defined, take the first\n transitionDuration = transitionDuration.split(',')[0];\n transitionDelay = transitionDelay.split(',')[0];\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END));\n};\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false;\n }\n if (typeof object.jquery !== 'undefined') {\n object = object[0];\n }\n return typeof object.nodeType !== 'undefined';\n};\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object;\n }\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(parseSelector(object));\n }\n return null;\n};\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false;\n }\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible';\n // Handle `details` element as its content may falsie appear visible when it is closed\n const closedDetails = element.closest('details:not([open])');\n if (!closedDetails) {\n return elementIsVisible;\n }\n if (closedDetails !== element) {\n const summary = element.closest('summary');\n if (summary && summary.parentNode !== closedDetails) {\n return false;\n }\n if (summary === null) {\n return false;\n }\n }\n return elementIsVisible;\n};\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true;\n }\n if (element.classList.contains('disabled')) {\n return true;\n }\n if (typeof element.disabled !== 'undefined') {\n return element.disabled;\n }\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null;\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode();\n return root instanceof ShadowRoot ? root : null;\n }\n if (element instanceof ShadowRoot) {\n return element;\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null;\n }\n return findShadowRoot(element.parentNode);\n};\nconst noop = () => {};\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery;\n }\n return null;\n};\nconst DOMContentLoadedCallbacks = [];\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback();\n }\n });\n }\n DOMContentLoadedCallbacks.push(callback);\n } else {\n callback();\n }\n};\nconst isRTL = () => document.documentElement.dir === 'rtl';\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery();\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME;\n const JQUERY_NO_CONFLICT = $.fn[name];\n $.fn[name] = plugin.jQueryInterface;\n $.fn[name].Constructor = plugin;\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT;\n return plugin.jQueryInterface;\n };\n }\n });\n};\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue;\n};\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback);\n return;\n }\n const durationPadding = 5;\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n let called = false;\n const handler = ({\n target\n }) => {\n if (target !== transitionElement) {\n return;\n }\n called = true;\n transitionElement.removeEventListener(TRANSITION_END, handler);\n execute(callback);\n };\n transitionElement.addEventListener(TRANSITION_END, handler);\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement);\n }\n }, emulatedDuration);\n};\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length;\n let index = list.indexOf(activeElement);\n\n // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n }\n index += shouldGetNext ? 1 : -1;\n if (isCycleAllowed) {\n index = (index + listLength) % listLength;\n }\n return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\nlet uidEvent = 1;\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\nfunction getElementEvents(element) {\n const uid = makeEventUid(element);\n element.uidEvent = uid;\n eventRegistry[uid] = eventRegistry[uid] || {};\n return eventRegistry[uid];\n}\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, {\n delegateTarget: element\n });\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn);\n }\n return fn.apply(element, [event]);\n };\n}\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector);\n for (let {\n target\n } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue;\n }\n hydrateObj(event, {\n delegateTarget: target\n });\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn);\n }\n return fn.apply(target, [event]);\n }\n }\n };\n}\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string';\n // TODO: tooltip passes `false` instead of selector, so we need to check\n const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n let typeEvent = getTypeEvent(originalTypeEvent);\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent;\n }\n return [isDelegated, callable, typeEvent];\n}\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n return fn.call(this, event);\n }\n };\n };\n callable = wrapFunction(callable);\n }\n const events = getElementEvents(element);\n const handlers = events[typeEvent] || (events[typeEvent] = {});\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff;\n return;\n }\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n fn.delegationSelector = isDelegated ? handler : null;\n fn.callable = callable;\n fn.oneOff = oneOff;\n fn.uidEvent = uid;\n handlers[uid] = fn;\n element.addEventListener(typeEvent, fn, isDelegated);\n}\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector);\n if (!fn) {\n return;\n }\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n delete events[typeEvent][fn.uidEvent];\n}\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {};\n for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n}\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '');\n return customEvents[event] || event;\n}\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false);\n },\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true);\n },\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n const inNamespace = typeEvent !== originalTypeEvent;\n const events = getElementEvents(element);\n const storeElementEvent = events[typeEvent] || {};\n const isNamespace = originalTypeEvent.startsWith('.');\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return;\n }\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n return;\n }\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n }\n }\n for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '');\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n },\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null;\n }\n const $ = getjQuery();\n const typeEvent = getTypeEvent(event);\n const inNamespace = event !== typeEvent;\n let jQueryEvent = null;\n let bubbles = true;\n let nativeDispatch = true;\n let defaultPrevented = false;\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args);\n $(element).trigger(jQueryEvent);\n bubbles = !jQueryEvent.isPropagationStopped();\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n defaultPrevented = jQueryEvent.isDefaultPrevented();\n }\n const evt = hydrateObj(new Event(event, {\n bubbles,\n cancelable: true\n }), args);\n if (defaultPrevented) {\n evt.preventDefault();\n }\n if (nativeDispatch) {\n element.dispatchEvent(evt);\n }\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault();\n }\n return evt;\n }\n};\nfunction hydrateObj(obj, meta = {}) {\n for (const [key, value] of Object.entries(meta)) {\n try {\n obj[key] = value;\n } catch (_unused) {\n Object.defineProperty(obj, key, {\n configurable: true,\n get() {\n return value;\n }\n });\n }\n }\n return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n if (value === 'true') {\n return true;\n }\n if (value === 'false') {\n return false;\n }\n if (value === Number(value).toString()) {\n return Number(value);\n }\n if (value === '' || value === 'null') {\n return null;\n }\n if (typeof value !== 'string') {\n return value;\n }\n try {\n return JSON.parse(decodeURIComponent(value));\n } catch (_unused) {\n return value;\n }\n}\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n },\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n },\n getDataAttributes(element) {\n if (!element) {\n return {};\n }\n const attributes = {};\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '');\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n attributes[pureKey] = normalizeData(element.dataset[key]);\n }\n return attributes;\n },\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {};\n }\n static get DefaultType() {\n return {};\n }\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!');\n }\n _getConfig(config) {\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n _configAfterMerge(config) {\n return config;\n }\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n return {\n ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n };\n }\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const [property, expectedTypes] of Object.entries(configTypes)) {\n const value = config[property];\n const valueType = isElement(value) ? 'element' : toType(value);\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n }\n }\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.2';\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super();\n element = getElement(element);\n if (!element) {\n return;\n }\n this._element = element;\n this._config = this._getConfig(config);\n Data.set(this._element, this.constructor.DATA_KEY, this);\n }\n\n // Public\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY);\n EventHandler.off(this._element, this.constructor.EVENT_KEY);\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null;\n }\n }\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated);\n }\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n\n // Static\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY);\n }\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n }\n static get VERSION() {\n return VERSION;\n }\n static get DATA_KEY() {\n return `bs.${this.NAME}`;\n }\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`;\n }\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target');\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href');\n\n // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n return null;\n }\n\n // Just in case some CMS puts out a full URL with the anchor appended\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n }\n selector = hrefAttribute && hrefAttribute !== '#' ? parseSelector(hrefAttribute.trim()) : null;\n }\n return selector;\n};\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n },\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector);\n },\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector));\n },\n parents(element, selector) {\n const parents = [];\n let ancestor = element.parentNode.closest(selector);\n while (ancestor) {\n parents.push(ancestor);\n ancestor = ancestor.parentNode.closest(selector);\n }\n return parents;\n },\n prev(element, selector) {\n let previous = element.previousElementSibling;\n while (previous) {\n if (previous.matches(selector)) {\n return [previous];\n }\n previous = previous.previousElementSibling;\n }\n return [];\n },\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling;\n while (next) {\n if (next.matches(selector)) {\n return [next];\n }\n next = next.nextElementSibling;\n }\n return [];\n },\n focusableChildren(element) {\n const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n },\n getSelectorFromElement(element) {\n const selector = getSelector(element);\n if (selector) {\n return SelectorEngine.findOne(selector) ? selector : null;\n }\n return null;\n },\n getElementFromSelector(element) {\n const selector = getSelector(element);\n return selector ? SelectorEngine.findOne(selector) : null;\n },\n getMultipleElementsFromSelector(element) {\n const selector = getSelector(element);\n return selector ? SelectorEngine.find(selector) : [];\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n const name = component.NAME;\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n if (isDisabled(this)) {\n return;\n }\n const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`);\n const instance = component.getOrCreateInstance(target);\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]();\n });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$f;\n }\n\n // Public\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n if (closeEvent.defaultPrevented) {\n return;\n }\n this._element.classList.remove(CLASS_NAME_SHOW$8);\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n }\n\n // Private\n _destroyElement() {\n this._element.remove();\n EventHandler.trigger(this._element, EVENT_CLOSED);\n this.dispose();\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this);\n if (typeof config !== 'string') {\n return;\n }\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](this);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close');\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$e;\n }\n\n // Public\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this);\n if (config === 'toggle') {\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n event.preventDefault();\n const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n const data = Button.getOrCreateInstance(button);\n data.toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n};\nconst DefaultType$c = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n};\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super();\n this._element = element;\n if (!element || !Swipe.isSupported()) {\n return;\n }\n this._config = this._getConfig(config);\n this._deltaX = 0;\n this._supportPointerEvents = Boolean(window.PointerEvent);\n this._initEvents();\n }\n\n // Getters\n static get Default() {\n return Default$c;\n }\n static get DefaultType() {\n return DefaultType$c;\n }\n static get NAME() {\n return NAME$d;\n }\n\n // Public\n dispose() {\n EventHandler.off(this._element, EVENT_KEY$9);\n }\n\n // Private\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX;\n return;\n }\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX;\n }\n }\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX;\n }\n this._handleSwipe();\n execute(this._config.endCallback);\n }\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n }\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX);\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return;\n }\n const direction = absDeltaX / this._deltaX;\n this._deltaX = 0;\n if (!direction) {\n return;\n }\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n }\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n }\n }\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n }\n\n // Static\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n};\nconst DefaultType$b = {\n interval: '(number|boolean)',\n // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._interval = null;\n this._activeElement = null;\n this._isSliding = false;\n this.touchTimeout = null;\n this._swipeHelper = null;\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n this._addEventListeners();\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$b;\n }\n static get DefaultType() {\n return DefaultType$b;\n }\n static get NAME() {\n return NAME$c;\n }\n\n // Public\n next() {\n this._slide(ORDER_NEXT);\n }\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next();\n }\n }\n prev() {\n this._slide(ORDER_PREV);\n }\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element);\n }\n this._clearInterval();\n }\n cycle() {\n this._clearInterval();\n this._updateInterval();\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n }\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return;\n }\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n return;\n }\n this.cycle();\n }\n to(index) {\n const items = this._getItems();\n if (index > items.length - 1 || index < 0) {\n return;\n }\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n return;\n }\n const activeIndex = this._getItemIndex(this._getActive());\n if (activeIndex === index) {\n return;\n }\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n this._slide(order, items[index]);\n }\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose();\n }\n super.dispose();\n }\n\n // Private\n _configAfterMerge(config) {\n config.defaultInterval = config.interval;\n return config;\n }\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n }\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n }\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners();\n }\n }\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n }\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return;\n }\n\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause();\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout);\n }\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n };\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n };\n this._swipeHelper = new Swipe(this._element, swipeConfig);\n }\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return;\n }\n const direction = KEY_TO_DIRECTION[event.key];\n if (direction) {\n event.preventDefault();\n this._slide(this._directionToOrder(direction));\n }\n }\n _getItemIndex(element) {\n return this._getItems().indexOf(element);\n }\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return;\n }\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n activeIndicator.removeAttribute('aria-current');\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n newActiveIndicator.setAttribute('aria-current', 'true');\n }\n }\n _updateInterval() {\n const element = this._activeElement || this._getActive();\n if (!element) {\n return;\n }\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n this._config.interval = elementInterval || this._config.defaultInterval;\n }\n _slide(order, element = null) {\n if (this._isSliding) {\n return;\n }\n const activeElement = this._getActive();\n const isNext = order === ORDER_NEXT;\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n if (nextElement === activeElement) {\n return;\n }\n const nextElementIndex = this._getItemIndex(nextElement);\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n });\n };\n const slideEvent = triggerEvent(EVENT_SLIDE);\n if (slideEvent.defaultPrevented) {\n return;\n }\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // TODO: change tests that use empty divs to avoid this check\n return;\n }\n const isCycling = Boolean(this._interval);\n this.pause();\n this._isSliding = true;\n this._setActiveIndicatorElement(nextElementIndex);\n this._activeElement = nextElement;\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n nextElement.classList.add(orderClassName);\n reflow(nextElement);\n activeElement.classList.add(directionalClassName);\n nextElement.classList.add(directionalClassName);\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName);\n nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n this._isSliding = false;\n triggerEvent(EVENT_SLID);\n };\n this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n if (isCycling) {\n this.cycle();\n }\n }\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE);\n }\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n }\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element);\n }\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval);\n this._interval = null;\n }\n }\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n }\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n }\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n }\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config);\n if (typeof config === 'number') {\n data.to(config);\n return;\n }\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return;\n }\n event.preventDefault();\n const carousel = Carousel.getOrCreateInstance(target);\n const slideIndex = this.getAttribute('data-bs-slide-to');\n if (slideIndex) {\n carousel.to(slideIndex);\n carousel._maybeEnableCycle();\n return;\n }\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next();\n carousel._maybeEnableCycle();\n return;\n }\n carousel.prev();\n carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel);\n }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n parent: null,\n toggle: true\n};\nconst DefaultType$a = {\n parent: '(null|element)',\n toggle: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isTransitioning = false;\n this._triggerArray = [];\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n for (const elem of toggleList) {\n const selector = SelectorEngine.getSelectorFromElement(elem);\n const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem);\n }\n }\n this._initializeChildren();\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n }\n if (this._config.toggle) {\n this.toggle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$a;\n }\n static get DefaultType() {\n return DefaultType$a;\n }\n static get NAME() {\n return NAME$b;\n }\n\n // Public\n toggle() {\n if (this._isShown()) {\n this.hide();\n } else {\n this.show();\n }\n }\n show() {\n if (this._isTransitioning || this._isShown()) {\n return;\n }\n let activeChildren = [];\n\n // find active children\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n toggle: false\n }));\n }\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return;\n }\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n if (startEvent.defaultPrevented) {\n return;\n }\n for (const activeInstance of activeChildren) {\n activeInstance.hide();\n }\n const dimension = this._getDimension();\n this._element.classList.remove(CLASS_NAME_COLLAPSE);\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n this._element.style[dimension] = 0;\n this._addAriaAndCollapsedClass(this._triggerArray, true);\n this._isTransitioning = true;\n const complete = () => {\n this._isTransitioning = false;\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n this._element.style[dimension] = '';\n EventHandler.trigger(this._element, EVENT_SHOWN$6);\n };\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n const scrollSize = `scroll${capitalizedDimension}`;\n this._queueCallback(complete, this._element, true);\n this._element.style[dimension] = `${this._element[scrollSize]}px`;\n }\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return;\n }\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n if (startEvent.defaultPrevented) {\n return;\n }\n const dimension = this._getDimension();\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n reflow(this._element);\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n for (const trigger of this._triggerArray) {\n const element = SelectorEngine.getElementFromSelector(trigger);\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false);\n }\n }\n this._isTransitioning = true;\n const complete = () => {\n this._isTransitioning = false;\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n this._element.classList.add(CLASS_NAME_COLLAPSE);\n EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n };\n this._element.style[dimension] = '';\n this._queueCallback(complete, this._element, true);\n }\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW$7);\n }\n\n // Private\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle); // Coerce string values\n config.parent = getElement(config.parent);\n return config;\n }\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n }\n _initializeChildren() {\n if (!this._config.parent) {\n return;\n }\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n for (const element of children) {\n const selected = SelectorEngine.getElementFromSelector(element);\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected));\n }\n }\n }\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent);\n // remove children if greater depth\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n }\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return;\n }\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n element.setAttribute('aria-expanded', isOpen);\n }\n }\n\n // Static\n static jQueryInterface(config) {\n const _config = {};\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false;\n }\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config);\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n event.preventDefault();\n }\n for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n Collapse.getOrCreateInstance(element, {\n toggle: false\n }).toggle();\n }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n};\nconst DefaultType$9 = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n};\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._popper = null;\n this._parent = this._element.parentNode; // dropdown wrapper\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n this._inNavbar = this._detectNavbar();\n }\n\n // Getters\n static get Default() {\n return Default$9;\n }\n static get DefaultType() {\n return DefaultType$9;\n }\n static get NAME() {\n return NAME$a;\n }\n\n // Public\n toggle() {\n return this._isShown() ? this.hide() : this.show();\n }\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return;\n }\n const relatedTarget = {\n relatedTarget: this._element\n };\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n if (showEvent.defaultPrevented) {\n return;\n }\n this._createPopper();\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n this._element.focus();\n this._element.setAttribute('aria-expanded', true);\n this._menu.classList.add(CLASS_NAME_SHOW$6);\n this._element.classList.add(CLASS_NAME_SHOW$6);\n EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n }\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return;\n }\n const relatedTarget = {\n relatedTarget: this._element\n };\n this._completeHide(relatedTarget);\n }\n dispose() {\n if (this._popper) {\n this._popper.destroy();\n }\n super.dispose();\n }\n update() {\n this._inNavbar = this._detectNavbar();\n if (this._popper) {\n this._popper.update();\n }\n }\n\n // Private\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n if (this._popper) {\n this._popper.destroy();\n }\n this._menu.classList.remove(CLASS_NAME_SHOW$6);\n this._element.classList.remove(CLASS_NAME_SHOW$6);\n this._element.setAttribute('aria-expanded', 'false');\n Manipulator.removeDataAttribute(this._menu, 'popper');\n EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n }\n _getConfig(config) {\n config = super._getConfig(config);\n if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n }\n return config;\n }\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n }\n let referenceElement = this._element;\n if (this._config.reference === 'parent') {\n referenceElement = this._parent;\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference);\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference;\n }\n const popperConfig = this._getPopperConfig();\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n }\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n }\n _getPlacement() {\n const parentDropdown = this._parent;\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER;\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n }\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n }\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null;\n }\n _getOffset() {\n const {\n offset\n } = this._config;\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n return offset;\n }\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n };\n\n // Disable Popper if we have a static display or Dropdown is in Navbar\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // TODO: v6 remove\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }];\n }\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n };\n }\n _selectMenuItem({\n key,\n target\n }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n if (!items.length) {\n return;\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n return;\n }\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle);\n if (!context || context._config.autoClose === false) {\n continue;\n }\n const composedPath = event.composedPath();\n const isMenuTarget = composedPath.includes(context._menu);\n if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n continue;\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue;\n }\n const relatedTarget = {\n relatedTarget: context._element\n };\n if (event.type === 'click') {\n relatedTarget.clickEvent = event;\n }\n context._completeHide(relatedTarget);\n }\n }\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n const isInput = /input|textarea/i.test(event.target.tagName);\n const isEscapeEvent = event.key === ESCAPE_KEY$2;\n const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return;\n }\n if (isInput && !isEscapeEvent) {\n return;\n }\n event.preventDefault();\n\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n const instance = Dropdown.getOrCreateInstance(getToggleButton);\n if (isUpOrDownEvent) {\n event.stopPropagation();\n instance.show();\n instance._selectMenuItem(event);\n return;\n }\n if (instance._isShown()) {\n // else is escape and we check if it is shown\n event.stopPropagation();\n instance.hide();\n getToggleButton.focus();\n }\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n event.preventDefault();\n Dropdown.getOrCreateInstance(this).toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true,\n // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n};\n\nconst DefaultType$8 = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n};\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isAppended = false;\n this._element = null;\n }\n\n // Getters\n static get Default() {\n return Default$8;\n }\n static get DefaultType() {\n return DefaultType$8;\n }\n static get NAME() {\n return NAME$9;\n }\n\n // Public\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n this._append();\n const element = this._getElement();\n if (this._config.isAnimated) {\n reflow(element);\n }\n element.classList.add(CLASS_NAME_SHOW$5);\n this._emulateAnimation(() => {\n execute(callback);\n });\n }\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n this._emulateAnimation(() => {\n this.dispose();\n execute(callback);\n });\n }\n dispose() {\n if (!this._isAppended) {\n return;\n }\n EventHandler.off(this._element, EVENT_MOUSEDOWN);\n this._element.remove();\n this._isAppended = false;\n }\n\n // Private\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div');\n backdrop.className = this._config.className;\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE$4);\n }\n this._element = backdrop;\n }\n return this._element;\n }\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement);\n return config;\n }\n _append() {\n if (this._isAppended) {\n return;\n }\n const element = this._getElement();\n this._config.rootElement.append(element);\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback);\n });\n this._isAppended = true;\n }\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n};\n\nconst DefaultType$7 = {\n autofocus: 'boolean',\n trapElement: 'element'\n};\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isActive = false;\n this._lastTabNavDirection = null;\n }\n\n // Getters\n static get Default() {\n return Default$7;\n }\n static get DefaultType() {\n return DefaultType$7;\n }\n static get NAME() {\n return NAME$8;\n }\n\n // Public\n activate() {\n if (this._isActive) {\n return;\n }\n if (this._config.autofocus) {\n this._config.trapElement.focus();\n }\n EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n this._isActive = true;\n }\n deactivate() {\n if (!this._isActive) {\n return;\n }\n this._isActive = false;\n EventHandler.off(document, EVENT_KEY$5);\n }\n\n // Private\n _handleFocusin(event) {\n const {\n trapElement\n } = this._config;\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return;\n }\n const elements = SelectorEngine.focusableChildren(trapElement);\n if (elements.length === 0) {\n trapElement.focus();\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus();\n } else {\n elements[0].focus();\n }\n }\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return;\n }\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body;\n }\n\n // Public\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth;\n return Math.abs(window.innerWidth - documentWidth);\n }\n hide() {\n const width = this.getWidth();\n this._disableOverFlow();\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n }\n reset() {\n this._resetElementAttributes(this._element, 'overflow');\n this._resetElementAttributes(this._element, PROPERTY_PADDING);\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n }\n isOverflowing() {\n return this.getWidth() > 0;\n }\n\n // Private\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow');\n this._element.style.overflow = 'hidden';\n }\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth();\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return;\n }\n this._saveInitialAttribute(element, styleProperty);\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n };\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty);\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue);\n }\n }\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty);\n // We only want to remove the property if the value is `null`; the value can also be zero\n if (value === null) {\n element.style.removeProperty(styleProperty);\n return;\n }\n Manipulator.removeDataAttribute(element, styleProperty);\n element.style.setProperty(styleProperty, value);\n };\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector);\n return;\n }\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel);\n }\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n backdrop: true,\n focus: true,\n keyboard: true\n};\nconst DefaultType$6 = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._isShown = false;\n this._isTransitioning = false;\n this._scrollBar = new ScrollBarHelper();\n this._addEventListeners();\n }\n\n // Getters\n static get Default() {\n return Default$6;\n }\n static get DefaultType() {\n return DefaultType$6;\n }\n static get NAME() {\n return NAME$7;\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n relatedTarget\n });\n if (showEvent.defaultPrevented) {\n return;\n }\n this._isShown = true;\n this._isTransitioning = true;\n this._scrollBar.hide();\n document.body.classList.add(CLASS_NAME_OPEN);\n this._adjustDialog();\n this._backdrop.show(() => this._showElement(relatedTarget));\n }\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n if (hideEvent.defaultPrevented) {\n return;\n }\n this._isShown = false;\n this._isTransitioning = true;\n this._focustrap.deactivate();\n this._element.classList.remove(CLASS_NAME_SHOW$4);\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n }\n dispose() {\n EventHandler.off(window, EVENT_KEY$4);\n EventHandler.off(this._dialog, EVENT_KEY$4);\n this._backdrop.dispose();\n this._focustrap.deactivate();\n super.dispose();\n }\n handleUpdate() {\n this._adjustDialog();\n }\n\n // Private\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop),\n // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n });\n }\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element);\n }\n this._element.style.display = 'block';\n this._element.removeAttribute('aria-hidden');\n this._element.setAttribute('aria-modal', true);\n this._element.setAttribute('role', 'dialog');\n this._element.scrollTop = 0;\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n if (modalBody) {\n modalBody.scrollTop = 0;\n }\n reflow(this._element);\n this._element.classList.add(CLASS_NAME_SHOW$4);\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate();\n }\n this._isTransitioning = false;\n EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n relatedTarget\n });\n };\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n }\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n if (event.key !== ESCAPE_KEY$1) {\n return;\n }\n if (this._config.keyboard) {\n this.hide();\n return;\n }\n this._triggerBackdropTransition();\n });\n EventHandler.on(window, EVENT_RESIZE$1, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog();\n }\n });\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return;\n }\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition();\n return;\n }\n if (this._config.backdrop) {\n this.hide();\n }\n });\n });\n }\n _hideModal() {\n this._element.style.display = 'none';\n this._element.setAttribute('aria-hidden', true);\n this._element.removeAttribute('aria-modal');\n this._element.removeAttribute('role');\n this._isTransitioning = false;\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN);\n this._resetAdjustments();\n this._scrollBar.reset();\n EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n });\n }\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE$3);\n }\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n if (hideEvent.defaultPrevented) {\n return;\n }\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const initialOverflowY = this._element.style.overflowY;\n // return if the following background transition hasn't yet completed\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return;\n }\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden';\n }\n this._element.classList.add(CLASS_NAME_STATIC);\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC);\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY;\n }, this._dialog);\n }, this._dialog);\n this._element.focus();\n }\n\n /**\n * The following methods are used to handle overflowing modals\n */\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const scrollbarWidth = this._scrollBar.getWidth();\n const isBodyOverflowing = scrollbarWidth > 0;\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n }\n _resetAdjustments() {\n this._element.style.paddingLeft = '';\n this._element.style.paddingRight = '';\n }\n\n // Static\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](relatedTarget);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return;\n }\n EventHandler.one(target, EVENT_HIDDEN$4, () => {\n if (isVisible(this)) {\n this.focus();\n }\n });\n });\n\n // avoid conflict when clicking modal toggler while another one is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide();\n }\n const data = Modal.getOrCreateInstance(target);\n data.toggle(this);\n});\nenableDismissTrigger(Modal);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n backdrop: true,\n keyboard: true,\n scroll: false\n};\nconst DefaultType$5 = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isShown = false;\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._addEventListeners();\n }\n\n // Getters\n static get Default() {\n return Default$5;\n }\n static get DefaultType() {\n return DefaultType$5;\n }\n static get NAME() {\n return NAME$6;\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n show(relatedTarget) {\n if (this._isShown) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n relatedTarget\n });\n if (showEvent.defaultPrevented) {\n return;\n }\n this._isShown = true;\n this._backdrop.show();\n if (!this._config.scroll) {\n new ScrollBarHelper().hide();\n }\n this._element.setAttribute('aria-modal', true);\n this._element.setAttribute('role', 'dialog');\n this._element.classList.add(CLASS_NAME_SHOWING$1);\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate();\n }\n this._element.classList.add(CLASS_NAME_SHOW$3);\n this._element.classList.remove(CLASS_NAME_SHOWING$1);\n EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n relatedTarget\n });\n };\n this._queueCallback(completeCallBack, this._element, true);\n }\n hide() {\n if (!this._isShown) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n if (hideEvent.defaultPrevented) {\n return;\n }\n this._focustrap.deactivate();\n this._element.blur();\n this._isShown = false;\n this._element.classList.add(CLASS_NAME_HIDING);\n this._backdrop.hide();\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n this._element.removeAttribute('aria-modal');\n this._element.removeAttribute('role');\n if (!this._config.scroll) {\n new ScrollBarHelper().reset();\n }\n EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n };\n this._queueCallback(completeCallback, this._element, true);\n }\n dispose() {\n this._backdrop.dispose();\n this._focustrap.deactivate();\n super.dispose();\n }\n\n // Private\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n this.hide();\n };\n\n // 'static' option will be translated to true, and booleans will keep their value\n const isVisible = Boolean(this._config.backdrop);\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n });\n }\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return;\n }\n if (this._config.keyboard) {\n this.hide();\n return;\n }\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n });\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](this);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n if (isDisabled(this)) {\n return;\n }\n EventHandler.one(target, EVENT_HIDDEN$3, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus();\n }\n });\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide();\n }\n const data = Offcanvas.getOrCreateInstance(target);\n data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show();\n }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide();\n }\n }\n});\nenableDismissTrigger(Offcanvas);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\nconst DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n};\n// js-docs-end allow-list\n\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i;\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase();\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue));\n }\n return true;\n }\n\n // Check if a regular expression validates the attribute.\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml;\n }\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml);\n }\n const domParser = new window.DOMParser();\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase();\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove();\n continue;\n }\n const attributeList = [].concat(...element.attributes);\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName);\n }\n }\n }\n return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n allowList: DefaultAllowlist,\n content: {},\n // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n};\nconst DefaultType$4 = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n};\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n};\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n }\n\n // Getters\n static get Default() {\n return Default$4;\n }\n static get DefaultType() {\n return DefaultType$4;\n }\n static get NAME() {\n return NAME$5;\n }\n\n // Public\n getContent() {\n return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n }\n hasContent() {\n return this.getContent().length > 0;\n }\n changeContent(content) {\n this._checkContent(content);\n this._config.content = {\n ...this._config.content,\n ...content\n };\n return this;\n }\n toHtml() {\n const templateWrapper = document.createElement('div');\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector);\n }\n const template = templateWrapper.children[0];\n const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n if (extraClass) {\n template.classList.add(...extraClass.split(' '));\n }\n return template;\n }\n\n // Private\n _typeCheckConfig(config) {\n super._typeCheckConfig(config);\n this._checkContent(config.content);\n }\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({\n selector,\n entry: content\n }, DefaultContentType);\n }\n }\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template);\n if (!templateElement) {\n return;\n }\n content = this._resolvePossibleFunction(content);\n if (!content) {\n templateElement.remove();\n return;\n }\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement);\n return;\n }\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content);\n return;\n }\n templateElement.textContent = content;\n }\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n }\n _resolvePossibleFunction(arg) {\n return execute(arg, [this]);\n }\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = '';\n templateElement.append(element);\n return;\n }\n templateElement.textContent = element.textContent;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 6],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' + '
' + '
' + '
',\n title: '',\n trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n};\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n }\n super(element, config);\n\n // Private\n this._isEnabled = true;\n this._timeout = 0;\n this._isHovered = null;\n this._activeTrigger = {};\n this._popper = null;\n this._templateFactory = null;\n this._newContent = null;\n\n // Protected\n this.tip = null;\n this._setListeners();\n if (!this._config.selector) {\n this._fixTitle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$3;\n }\n static get DefaultType() {\n return DefaultType$3;\n }\n static get NAME() {\n return NAME$4;\n }\n\n // Public\n enable() {\n this._isEnabled = true;\n }\n disable() {\n this._isEnabled = false;\n }\n toggleEnabled() {\n this._isEnabled = !this._isEnabled;\n }\n toggle() {\n if (!this._isEnabled) {\n return;\n }\n this._activeTrigger.click = !this._activeTrigger.click;\n if (this._isShown()) {\n this._leave();\n return;\n }\n this._enter();\n }\n dispose() {\n clearTimeout(this._timeout);\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n }\n this._disposePopper();\n super.dispose();\n }\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements');\n }\n if (!(this._isWithContent() && this._isEnabled)) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n const shadowRoot = findShadowRoot(this._element);\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n if (showEvent.defaultPrevented || !isInTheDom) {\n return;\n }\n\n // TODO: v6 remove this or make it optional\n this._disposePopper();\n const tip = this._getTipElement();\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n const {\n container\n } = this._config;\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip);\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n }\n this._popper = this._createPopper(tip);\n tip.classList.add(CLASS_NAME_SHOW$2);\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n if (this._isHovered === false) {\n this._leave();\n }\n this._isHovered = false;\n };\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n hide() {\n if (!this._isShown()) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n if (hideEvent.defaultPrevented) {\n return;\n }\n const tip = this._getTipElement();\n tip.classList.remove(CLASS_NAME_SHOW$2);\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n this._activeTrigger[TRIGGER_CLICK] = false;\n this._activeTrigger[TRIGGER_FOCUS] = false;\n this._activeTrigger[TRIGGER_HOVER] = false;\n this._isHovered = null; // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return;\n }\n if (!this._isHovered) {\n this._disposePopper();\n }\n this._element.removeAttribute('aria-describedby');\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n };\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n update() {\n if (this._popper) {\n this._popper.update();\n }\n }\n\n // Protected\n _isWithContent() {\n return Boolean(this._getTitle());\n }\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n }\n return this.tip;\n }\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml();\n\n // TODO: remove this check in v6\n if (!tip) {\n return null;\n }\n tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2);\n // TODO: v6 the following can be achieved with CSS only\n tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n const tipId = getUID(this.constructor.NAME).toString();\n tip.setAttribute('id', tipId);\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE$2);\n }\n return tip;\n }\n setContent(content) {\n this._newContent = content;\n if (this._isShown()) {\n this._disposePopper();\n this.show();\n }\n }\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content);\n } else {\n this._templateFactory = new TemplateFactory({\n ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n });\n }\n return this._templateFactory;\n }\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n };\n }\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n }\n\n // Private\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n }\n _isAnimated() {\n return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n }\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n }\n _createPopper(tip) {\n const placement = execute(this._config.placement, [this, tip, this._element]);\n const attachment = AttachmentMap[placement.toUpperCase()];\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n }\n _getOffset() {\n const {\n offset\n } = this._config;\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n return offset;\n }\n _resolvePossibleFunction(arg) {\n return execute(arg, [this._element]);\n }\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [{\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }, {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n }, {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n }\n }]\n };\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n };\n }\n _setListeners() {\n const triggers = this._config.trigger.split(' ');\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context.toggle();\n });\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n context._enter();\n });\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n context._leave();\n });\n }\n }\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide();\n }\n };\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n }\n _fixTitle() {\n const title = this._element.getAttribute('title');\n if (!title) {\n return;\n }\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title);\n }\n this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n this._element.removeAttribute('title');\n }\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true;\n return;\n }\n this._isHovered = true;\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show();\n }\n }, this._config.delay.show);\n }\n _leave() {\n if (this._isWithActiveTrigger()) {\n return;\n }\n this._isHovered = false;\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide();\n }\n }, this._config.delay.hide);\n }\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout);\n this._timeout = setTimeout(handler, timeout);\n }\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true);\n }\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element);\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute];\n }\n }\n config = {\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n };\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container);\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n };\n }\n if (typeof config.title === 'number') {\n config.title = config.title.toString();\n }\n if (typeof config.content === 'number') {\n config.content = config.content.toString();\n }\n return config;\n }\n _getDelegateConfig() {\n const config = {};\n for (const [key, value] of Object.entries(this._config)) {\n if (this.constructor.Default[key] !== value) {\n config[key] = value;\n }\n }\n config.selector = false;\n config.trigger = 'manual';\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config;\n }\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy();\n this._popper = null;\n }\n if (this.tip) {\n this.tip.remove();\n this.tip = null;\n }\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = {\n ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
' + '
' + '

' + '
' + '
',\n trigger: 'click'\n};\nconst DefaultType$2 = {\n ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n};\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default$2;\n }\n static get DefaultType() {\n return DefaultType$2;\n }\n static get NAME() {\n return NAME$3;\n }\n\n // Overrides\n _isWithContent() {\n return this._getTitle() || this._getContent();\n }\n\n // Private\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n };\n }\n _getContent() {\n return this._resolvePossibleFunction(this._config.content);\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n offset: null,\n // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n offset: '(number|null)',\n // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n};\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n\n // this._element is the observablesContainer and config.target the menu links wrapper\n this._targetLinks = new Map();\n this._observableSections = new Map();\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n this._activeTarget = null;\n this._observer = null;\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n };\n this.refresh(); // initialize\n }\n\n // Getters\n static get Default() {\n return Default$1;\n }\n static get DefaultType() {\n return DefaultType$1;\n }\n static get NAME() {\n return NAME$2;\n }\n\n // Public\n refresh() {\n this._initializeTargetsAndObservables();\n this._maybeEnableSmoothScroll();\n if (this._observer) {\n this._observer.disconnect();\n } else {\n this._observer = this._getNewObserver();\n }\n for (const section of this._observableSections.values()) {\n this._observer.observe(section);\n }\n }\n dispose() {\n this._observer.disconnect();\n super.dispose();\n }\n\n // Private\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body;\n\n // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n }\n return config;\n }\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return;\n }\n\n // unregister any previous listeners\n EventHandler.off(this._config.target, EVENT_CLICK);\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash);\n if (observableSection) {\n event.preventDefault();\n const root = this._rootElement || window;\n const height = observableSection.offsetTop - this._element.offsetTop;\n if (root.scrollTo) {\n root.scrollTo({\n top: height,\n behavior: 'smooth'\n });\n return;\n }\n\n // Chrome 60 doesn't support `scrollTo`\n root.scrollTop = height;\n }\n });\n }\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n };\n return new IntersectionObserver(entries => this._observerCallback(entries), options);\n }\n\n // The logic of selection\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n this._process(targetElement(entry));\n };\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n this._previousScrollData.parentScrollTop = parentScrollTop;\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null;\n this._clearActiveClass(targetElement(entry));\n continue;\n }\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop;\n // if we are scrolling down, pick the bigger offsetTop\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry);\n // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n if (!parentScrollTop) {\n return;\n }\n continue;\n }\n\n // if we are scrolling up, pick the smallest offsetTop\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry);\n }\n }\n }\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map();\n this._observableSections = new Map();\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue;\n }\n const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element);\n\n // ensure that the observableSection exists & is visible\n if (isVisible(observableSection)) {\n this._targetLinks.set(decodeURI(anchor.hash), anchor);\n this._observableSections.set(anchor.hash, observableSection);\n }\n }\n }\n _process(target) {\n if (this._activeTarget === target) {\n return;\n }\n this._clearActiveClass(this._config.target);\n this._activeTarget = target;\n target.classList.add(CLASS_NAME_ACTIVE$1);\n this._activateParents(target);\n EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n relatedTarget: target\n });\n }\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n return;\n }\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both