diff --git a/.github/workflows/static-check.yml b/.github/workflows/static-check.yml
index 8bde0789..e03084d4 100644
--- a/.github/workflows/static-check.yml
+++ b/.github/workflows/static-check.yml
@@ -31,7 +31,6 @@ jobs:
run: |
python -m pip install -U pip
pip install -e .[dev]
- bash ./benchmark/install_submodule.sh all
- name: black
id: tool-black
if: steps.python-env-setup.outcome == 'success'
diff --git a/.gitmodules b/.gitmodules
index 191ec35f..e69de29b 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,9 +0,0 @@
-[submodule "benchmark/pyjuice/pyjuice-april"]
- path = benchmark/pyjuice/pyjuice-april
- url = https://github.com/april-tools/pyjuice.git
-[submodule "benchmark/juice.jl/ProbabilisticCircuits.jl"]
- path = benchmark/juice.jl/ProbabilisticCircuits.jl
- url = https://github.com/Juice-jl/ProbabilisticCircuits.jl
-[submodule "3rd-party-stubs"]
- path = 3rd-party-stubs
- url = https://github.com/april-tools/python-type-stubs.git
diff --git a/3rd-party-stubs b/3rd-party-stubs
deleted file mode 160000
index 2c115ee0..00000000
--- a/3rd-party-stubs
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 2c115ee078993fa354e3ee48278cd3c03ee4ba07
diff --git a/benchmark/__init__.py b/benchmark/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/benchmark/cirkit/README.md b/benchmark/cirkit/README.md
deleted file mode 100644
index 1f2fc221..00000000
--- a/benchmark/cirkit/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# `cirkit` Benchmark
-
-## Setup
-
-Just install `cirkit` and you should be able to run benchmarks.
diff --git a/benchmark/cirkit/__init__.py b/benchmark/cirkit/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/benchmark/cirkit/run_cirkit.py b/benchmark/cirkit/run_cirkit.py
deleted file mode 100644
index 1a44ff33..00000000
--- a/benchmark/cirkit/run_cirkit.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import argparse
-import enum
-import functools
-from dataclasses import dataclass
-from typing import List, Tuple
-
-import numpy as np
-import torch
-from torch import Tensor, optim
-from torch.utils.data import DataLoader, TensorDataset
-
-from benchmark.utils import benchmarker
-from cirkit.old.layers.input.exp_family import CategoricalLayer
-from cirkit.old.layers.sum_product.cp import UncollapsedCPLayer
-from cirkit.old.models import TensorizedPC
-from cirkit.old.region_graph import RegionGraph
-from cirkit.old.utils import RandomCtx, set_determinism
-
-device = torch.device("cuda")
-
-
-class _Modes(str, enum.Enum): # TODO: StrEnum introduced in 3.11
- """Execution modes."""
-
- TRAIN = "train"
- EVAL = "eval"
-
-
-@dataclass
-class _ArgsNamespace(argparse.Namespace):
- mode: _Modes = _Modes.TRAIN
- seed: int = 42
- num_batches: int = 20
- batch_size: int = 128
- region_graph: str = ""
- num_latents: int = 32 # TODO: rename this
- first_pass_only: bool = False
-
-
-def process_args() -> _ArgsNamespace:
- """Process command line arguments.
-
- Returns:
- ArgsNamespace: Parsed args.
- """
- parser = argparse.ArgumentParser()
- parser.add_argument("--mode", type=_Modes, choices=_Modes.__members__.values(), help="mode")
- parser.add_argument("--seed", type=int, help="seed, 0 for disable")
- parser.add_argument("--num_batches", type=int, help="num_batches")
- parser.add_argument("--batch_size", type=int, help="batch_size")
- parser.add_argument("--region_graph", type=str, help="region_graph filename")
- parser.add_argument("--num_latents", type=int, help="num_latents")
- parser.add_argument("--first_pass_only", action="store_true", help="first_pass_only")
- return parser.parse_args(namespace=_ArgsNamespace())
-
-
-@torch.no_grad()
-def evaluate(
- pc: TensorizedPC, data_loader: DataLoader[Tuple[Tensor, ...]]
-) -> Tuple[Tuple[List[float], List[float]], float]:
- """Evaluate circuit on given data.
-
- Args:
- pc (TensorizedPC): The PC to evaluate.
- data_loader (DataLoader[Tuple[Tensor, ...]]): The evaluation data.
-
- Returns:
- Tuple[Tuple[List[float], List[float]], float]:
- A tuple consisting of time and memory measurements, and the average LL.
- """
-
- def _iter(x: Tensor) -> Tensor:
- return pc(x)
-
- ll_total = 0.0
- ts, ms = [], []
- batch: Tuple[Tensor]
- for batch in data_loader:
- x = batch[0].to(device)
- ll, (t, m) = benchmarker(functools.partial(_iter, x))
- ts.append(t)
- ms.append(m)
- ll_total += ll.mean().item()
- del x, ll
- return (ts, ms), ll_total / len(data_loader)
-
-
-def train(
- pc: TensorizedPC, optimizer: optim.Optimizer, data_loader: DataLoader[Tuple[Tensor, ...]]
-) -> Tuple[Tuple[List[float], List[float]], float]:
- """Train circuit on given data.
-
- Args:
- pc (TensorizedPC): The PC to optimize.
- optimizer (optim.Optimizer): The optimizer for circuit.
- data_loader (DataLoader[Tuple[Tensor, ...]]): The training data.
-
- Returns:
- Tuple[Tuple[List[float], List[float]], float]:
- A tuple consisting of time and memory measurements, and the average LL.
- """
-
- def _iter(x: Tensor) -> Tensor:
- optimizer.zero_grad()
- ll = pc(x)
- ll = ll.mean()
- (-ll).backward() # type: ignore[no-untyped-call] # we optimize NLL
- optimizer.step()
- return ll.detach()
-
- ll_total = 0.0
- ts, ms = [], []
- batch: Tuple[Tensor]
- for batch in data_loader:
- x = batch[0].to(device)
- ll, (t, m) = benchmarker(functools.partial(_iter, x))
- ts.append(t)
- ms.append(m)
- ll_total += ll.item()
- del x, ll # TODO: is everything released properly
- return (ts, ms), ll_total / len(data_loader)
-
-
-def main() -> None:
- """Execute the main procedure."""
- args = process_args()
- assert args.region_graph, "Must provide a RG filename"
- print(args)
-
- if args.seed:
- # TODO: find a way to set w/o with
- RandomCtx(args.seed).__enter__() # pylint: disable=unnecessary-dunder-call
- set_determinism(check_hash_seed=True)
-
- num_vars = 28 * 28
- data_size = args.batch_size if args.first_pass_only else args.num_batches * args.batch_size
- rand_data = torch.randint(256, (data_size, num_vars), dtype=torch.uint8)
- data_loader = DataLoader(
- dataset=TensorDataset(rand_data),
- batch_size=args.batch_size,
- shuffle=True,
- pin_memory=True,
- drop_last=True,
- )
-
- pc = TensorizedPC.from_region_graph(
- RegionGraph.load(args.region_graph),
- layer_cls=UncollapsedCPLayer,
- efamily_cls=CategoricalLayer,
- layer_kwargs={"rank": 1}, # type: ignore[misc]
- efamily_kwargs={"num_categories": 256}, # type: ignore[misc]
- num_inner_units=args.num_latents,
- num_input_units=args.num_latents,
- )
- pc.to(device)
- print(pc)
- print(f"Number of parameters: {sum(p.numel() for p in pc.parameters())}")
-
- if args.mode == _Modes.TRAIN:
- optimizer = optim.Adam(pc.parameters()) # just keep everything default
- (ts, ms), ll_train = train(pc, optimizer, data_loader)
- print("Train LL:", ll_train)
- elif args.mode == _Modes.EVAL:
- (ts, ms), ll_eval = evaluate(pc, data_loader)
- print("Evaluation LL:", ll_eval)
- else:
- assert False, "Something is wrong here"
- if not args.first_pass_only and args.num_batches > 1:
- # Skip warmup step
- ts, ms = ts[1:], ms[1:]
- mu_t, sigma_t = np.mean(ts).item(), np.std(ts).item() # type: ignore[misc]
- mu_m, sigma_m = np.mean(ms).item(), np.std(ms).item() # type: ignore[misc]
- print(f"Time (ms): {mu_t:.3f}+-{sigma_t:.3f}")
- print(f"Memory (MiB): {mu_m:.3f}+-{sigma_m:.3f}")
-
-
-if __name__ == "__main__":
- main()
diff --git a/benchmark/install_submodule.sh b/benchmark/install_submodule.sh
deleted file mode 100755
index 15658ffa..00000000
--- a/benchmark/install_submodule.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/bash
-
-if [[ ! "$*" =~ (all|pyjuice|einet) ]]; then
- if [[ "$@" != *"--help"* ]]; then
- echo "Wrong usage!"
- echo
- fi
- echo "Usage: $0 [--help] [all] [pyjuice] [einet]"
- echo " --help Print this help message"
- echo " all Install all the following"
- echo " pyjuice Install submodule for pyjuice"
- echo " einet Install submodule for einet"
- exit 0
-fi
-
-# juice.jl does not include pip dependency
-
-if [[ "$@" == *"pyjuice"* || "$@" == *"all"* ]]; then
- echo "Installing submodule for pyjuice: "$(dirname $0)/pyjuice/pyjuice-april""
- pip install -e "$(dirname $0)/pyjuice/pyjuice-april"
-fi
-
-if [[ "$@" == *"einet"* || "$@" == *"all"* ]]; then
- echo "NOT IMPLEMENTED!"
- # TODO: add commands for einet
-fi
diff --git a/benchmark/juice.jl/ProbabilisticCircuits.jl b/benchmark/juice.jl/ProbabilisticCircuits.jl
deleted file mode 160000
index b749816f..00000000
--- a/benchmark/juice.jl/ProbabilisticCircuits.jl
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit b749816f0973312896bfd029afaf57b07d6d6217
diff --git a/benchmark/juice.jl/README.md b/benchmark/juice.jl/README.md
deleted file mode 100644
index c33cc520..00000000
--- a/benchmark/juice.jl/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# Juice.jl Benchmark
-
-To be able to run the benchmark script ```run_benchmark.jl``` install the dependencies first.
-Launch ```julia```, open the package mode with ```]``` and run the following commands.
-
-```
-dev ProbabilisticCircuits.jl/
-add CUDA Images MLDatasets BenchmarkTools JSON StatsBase
-```
-
-Note that ```ProbabilisticCircuits.jl/``` is the local directory of the [ProbabilisticCircuits.jl](https://github.com/Juice-jl/ProbabilisticCircuits.jl) git submodule (tag v0.4.0).
-
-Launch the script ```run_benchmark.jl``` by specifying the CUDA device id, the model to benchmark (either RAT or HCLT) and the batch size, e.g.,
-```
-julia run_benchmark.jl 1 RAT 500
-```
-
-The script will measure times and memory usage of performing EVI, MAR and approximate MAP inference on the training CIFAR10 split.
-Then it will save the results in a JSON file.
diff --git a/benchmark/juice.jl/run_benchmark.jl b/benchmark/juice.jl/run_benchmark.jl
deleted file mode 100644
index df53f31d..00000000
--- a/benchmark/juice.jl/run_benchmark.jl
+++ /dev/null
@@ -1,273 +0,0 @@
-using JSON
-using Random
-using StatsBase
-using Statistics
-using BenchmarkTools
-using ProbabilisticCircuits
-using ProbabilisticCircuits: BitsProbCircuit, CuBitsProbCircuit, loglikelihoods
-using MLDatasets
-using CUDA
-using Images
-
-# From https://github.com/JuliaGPU/CUDA.jl/blob/2ae53a46658f10b0be32ae61fb4e145782ed9208/test/setup.jl
-macro grab_output(ex)
- quote
- mktemp() do fname, fout
- ret = nothing
- open(fname, "w") do fout
- redirect_stdout(fout) do
- ret = $(esc(ex))
-
- # NOTE: CUDA requires a 'proper' sync to flush its printf buffer
- synchronize(context())
- end
- end
- ret, read(fname, String)
- end
- end
-end
-
-
-function mnist_dataset()
- # Load the MNIST dataset and preprocess it
- train_int = transpose(reshape(MNIST(UInt8, split=:train).features, 28 * 28, :));
- test_int = transpose(reshape(MNIST(UInt8, split=:test).features, 28 * 28, :));
- train_data = UInt32.(train_int)
- test_data = UInt32.(test_int)
- train_data, test_data
-end
-
-function truncate_data(data::Matrix; bits = 3)
- data .÷ 2^bits
-end
-
-function convert_to_bytes(amount, fmt)
- if fmt == "KiB"
- to_bytes = 1e3
- elseif fmt == "MiB"
- to_bytes = 1e6
- elseif fmt == "GiB"
- to_bytes = 1e9
- else
- throw(AssertionError("Invalid memory format: " * fmt))
- end
- trunc(Int64, amount * to_bytes)
-end
-
-function retrieve_stats(pc::ProbCircuit)
- # Count the number of nodes (for each type)
- # Note that the internal implementation introduce additional sum units to _balance_ the computational graph
- # So the number of computational units showed here may differ if using other implementations
- num_inputs = length(inputnodes(pc))
- num_prods = length(mulnodes(pc))
- num_sums = length(sumnodes(pc))
- #println("Total number of nodes: $(num_inputs + num_prods + num_sums)")
- #println("Number of input nodes: $(num_inputs)")
- #println("Number of product nodes: $(num_prods)")
- #println("Number of sum nodes: $(num_sums)")
-
- # Count _all_ the learnable parameters, so independent is set to false
- num_params = num_parameters(pc, false)
- #println("Number of learnable parameters: $(num_params)")
-
- Dict(
- "num_inputs" => num_inputs,
- "num_prods" => num_prods,
- "num_sums" => num_sums,
- "num_params" => num_params
- )
-end
-
-function run_benchmark(
- pc::CuBitsProbCircuit, train_data, mis_train_data; batch_size = 128, samples = 100, burnin = 5,
- benchmark_mar = false, benchmark_map = false)
- # Select a batch of data only
- indices = StatsBase.sample(1:size(train_data, 1), batch_size, replace = false)
- train_data = train_data[indices, :]
- if benchmark_mar || benchmark_map
- mis_train_data = mis_train_data[indices, :]
- end
-
- # Benchmark feed-forward pass time (on CPU)
- #trial = @benchmark loglikelihoods($pc, $train_data; batch_size=$batch_size) samples=samples
- # Benchmark feed-forward pass time (on GPU)
- total_samples = burnin + samples
- evi_trial = @benchmark (CUDA.@sync loglikelihoods($pc, $train_data; batch_size=$batch_size)) samples=total_samples
- #dump(trial)
- #evi_trial = median(evi_trial)
- #dump(median(trial))
- #@time ll = loglikelihoods(pc, train_data; batch_size)
- #println("Test LL: $(mean(ll))")
- # Benchmark feed-forward GPU memory
- cuda_trial = @grab_output (CUDA.@time loglikelihoods(pc, train_data; batch_size=batch_size))
- cuda_alloc_memory = parse(Float64, split(split(cuda_trial[2], "GPU allocations:")[2])[1])
- cuda_alloc_memory_fmt = chop(split(split(cuda_trial[2], "GPU allocations:")[2])[2])
- evi_cuda_alloc_memory = convert_to_bytes(cuda_alloc_memory, cuda_alloc_memory_fmt)
- res = Dict(
- "evi" => Dict(
- "times" => last(evi_trial.times, length(evi_trial.times) - burnin),
- "gctimes" => last(evi_trial.gctimes, length(evi_trial.gctimes) - burnin),
- "cpu_alloc_memory" => evi_trial.memory,
- "cuda_alloc_memory" => evi_cuda_alloc_memory
- ))
-
- if benchmark_mar
- # Benchmark MAR inference (on GPU)
- mar_trial = @benchmark (CUDA.@sync loglikelihoods($pc, $mis_train_data; batch_size=$batch_size)) samples=total_samples
- #mar_trial = median(mar_trial)
- cuda_trial = @grab_output (CUDA.@time loglikelihoods(pc, mis_train_data; batch_size=batch_size))
- cuda_alloc_memory = parse(Float64, split(split(cuda_trial[2], "GPU allocations:")[2])[1])
- cuda_alloc_memory_fmt = chop(split(split(cuda_trial[2], "GPU allocations:")[2])[2])
- mar_cuda_alloc_memory = convert_to_bytes(cuda_alloc_memory, cuda_alloc_memory_fmt)
- res["mar"] = Dict(
- "times" => last(mar_trial.times, length(mar_trial.times) - burnin),
- "gctimes" => last(mar_trial.gctimes, length(mar_trial.gctimes) - burnin),
- "cpu_alloc_memory" => mar_trial.memory,
- "cuda_alloc_memory" => mar_cuda_alloc_memory
- )
- end
-
- if benchmark_map
- # Benchmark MAP inference (on GPU)
- map_trial = @benchmark (CUDA.@sync MAP($pc, $mis_train_data; batch_size=$batch_size)) samples=total_samples
- #map_trial = median(map_trial)
- cuda_trial = @grab_output (CUDA.@time MAP(pc, mis_train_data; batch_size=batch_size))
- cuda_alloc_memory = parse(Float64, split(split(cuda_trial[2], "GPU allocations:")[2])[1])
- cuda_alloc_memory_fmt = chop(split(split(cuda_trial[2], "GPU allocations:")[2])[2])
- map_cuda_alloc_memory = convert_to_bytes(cuda_alloc_memory, cuda_alloc_memory_fmt)
- res["map"] = Dict(
- "times" => last(map_trial.times, length(map_trial.times) - burnin),
- "gctimes" => last(map_trial.gctimes, length(map_trial.gctimes) - burnin),
- "cpu_alloc_memory" => map_trial.memory,
- "cuda_alloc_memory" => map_cuda_alloc_memory
- )
- end
-
- res
-end
-
-function run_benchmark_rat(
- train_data, mis_train_data;
- batch_size = 128, num_nodes_region = 2, num_nodes_leaf = 2, rg_depth = 1, rg_replicas = 1)
- num_features = size(train_data, 2)
-
- # Only categorical and binomial distributions are available
- #input_func = RAT_InputFunc(Binomial, 256)
- input_func = RAT_InputFunc(Categorical, 256)
-
- # Instantiate a RAT-SPN with the given parameters
- Random.seed!(42)
- pc = RAT(num_features; input_func, num_nodes_region, num_nodes_leaf, rg_depth, rg_replicas, balance_childs_parents=false)
- init_parameters(pc; perturbation = 0.5)
- stats = retrieve_stats(pc)
-
- # Layerize the circuit, move to GPU and run benchmarks
- pc = CuBitsProbCircuit(BitsProbCircuit(pc))
- results = run_benchmark(pc, train_data, mis_train_data; batch_size=batch_size)
-
- Dict(
- "hparams" => Dict(
- "num_nodes_region" => num_nodes_region,
- "num_nodes_leaf" => num_nodes_leaf,
- "rg_depth" => rg_depth,
- "rg_replicas" => rg_replicas
- ),
- "stats" => stats,
- "results" => results
- )
-end
-
-function run_benchmark_hclt(train_data, mis_train_data, truncated_data; batch_size = 100, latents = 8)
- # Instantiate a HCLT with the given parameters
- Random.seed!(42)
- pc = hclt(truncated_data, latents, num_cats = 256, pseudocount = 0.1, input_type = Categorical)
- init_parameters(pc; perturbation = 0.5)
- stats = retrieve_stats(pc)
-
- # Layerize the circuit, move to GPU and run benchmarks
- pc = CuBitsProbCircuit(BitsProbCircuit(pc));
- results = run_benchmark(pc, train_data, mis_train_data; batch_size=batch_size)
-
- Dict(
- "hparams" => Dict(
- "latents" => latents
- ),
- "stats" => stats,
- "results" => results
- )
-end
-
-# Install the following Julia dependencies
-# add ProbabilisticCircuits.jl CUDA Images MLDatasets BenchmarkTools JSON StatsBase
-#
-# Executes a benchmark on HCLTs on cuda:1 using batch size 128
-# julia run_benchmark.jl 1 HCLT 128
-#
-function main()
- # Get arguments
- if length(ARGS) < 3
- println("Specify CUDA device id (e.g., 1), model name (either RAT or HCLT), and batch size (e.g., 128)")
- exit(1)
- end
- device_id = parse(UInt64, ARGS[1])
- model_name = ARGS[2]
- if !(model_name in ["RAT", "HCLT"])
- println("Specify either RAT or HCLT as model name")
- exit(1)
- end
- batch_size = parse(Int64, ARGS[3])
- device!(device_id)
- BenchmarkTools.DEFAULT_PARAMETERS.seconds = 7200
-
- # Load the dataset
- train_data_cpu, _ = mnist_dataset()
- train_data = cu(train_data_cpu)
- mis_train_data = Array{Union{Missing, UInt8}}(train_data)
- Random.seed!(42)
- # Construct data set with 50% missing features
- cols_to_marginalise = StatsBase.sample(1:size(train_data, 2), trunc(Int64, size(train_data, 2) / 2), replace = false)
- mis_train_data[:, cols_to_marginalise] .= missing
- mis_train_data = cu(mis_train_data)
-
- if model_name == "RAT"
- default_rg_depth = 9
- default_rg_replicas = 1
- hp_num_sum = [16, 32, 64, 128, 256, 512]
- results = Dict()
-
- # Benchmark by varying the number of sum and leaf units per region
- results["num_sum_region"] = []
- for k in hp_num_sum
- println("Benchmarking RAT with K = " * string(k))
- res = run_benchmark_rat(train_data, mis_train_data;
- batch_size = batch_size, num_nodes_region = k, num_nodes_leaf = k,
- rg_depth = default_rg_depth, rg_replicas = default_rg_replicas
- )
- push!(results["num_sum_region"], res)
- end
- else # model_name == "HCLT"
- # Sample and preprocess a subset of the data for the Chow-Liu Tree algorithm
- filtered_data = collect(transpose(reshape(train_data_cpu, 28 * 28, :)))
- truncated_data = truncate_data(filtered_data; bits = 3)
- truncated_data = cu(truncated_data)
-
- hp_latents = [16, 32, 64, 128, 256, 512]
- results = Dict()
-
- # Benchmark by varying the support size for each latent variable
- results["latents"] = []
- for l in hp_latents
- println("Benchmarking HCLT with L = " * string(l))
- res = run_benchmark_hclt(train_data, mis_train_data, truncated_data;
- batch_size = batch_size, latents = l
- )
- push!(results["latents"], res)
- end
- end
-
- open("pcs-jl-" * model_name * "-trials.json", "w") do f
- JSON.print(f, results, 4)
- end
-end
-
-main()
diff --git a/benchmark/pyjuice/README.md b/benchmark/pyjuice/README.md
deleted file mode 100644
index 0d47819b..00000000
--- a/benchmark/pyjuice/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
-# `pyjuice` Benchmark
-
-## Setup
-First, remember to initiate the submodule if you haven't. We use submodule for a managed `pyjuice` version dedicated for this repo.
-
-```shell
-git submodule update --init ./pyjuice-april
-```
-
-Then, install the submodule with dependencies. We use a script to manage all benchmarks collectively.
-
-```shell
-../install_submodule.sh pyjuice
-```
-
-## Run benchmark
-
-The `run_pyjuice.py` runs one pass and generates raw benchmark data with given configs (including several batches). It also includes a sanity check (directly execute `python run_pyjuice.py` without args).
-
-The `run_benchmark.py` runs the whole benchmark suite by calling `run_pyjuice.py` in a child process. In order to correctly evaluate the initialization (compilation) time, it must be put into another process and restarted every time. (For other tests, just increase the number of batches.)
diff --git a/benchmark/pyjuice/__init__.py b/benchmark/pyjuice/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/benchmark/pyjuice/load_rg.py b/benchmark/pyjuice/load_rg.py
deleted file mode 100644
index 615de034..00000000
--- a/benchmark/pyjuice/load_rg.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import json
-from typing import Dict, List, TypedDict, Union
-
-import torch
-from pyjuice.graph.region_graph import InnerRegionNode, InputRegionNode, PartitionNode, RegionGraph
-from pyjuice.layer.input_layers.categorical_layer import CategoricalLayer
-
-
-class _RGJson(TypedDict):
- """The structure of region graph json file."""
-
- regions: Dict[str, List[int]]
- graph: List[Dict[str, int]]
-
-
-def _dfs_build(
- key: int,
- nodes_cache: Dict[int, Union[InnerRegionNode, InputRegionNode]],
- graph_json: _RGJson,
- num_latents: int,
- is_root: bool,
-) -> Union[InnerRegionNode, InputRegionNode]:
- """Build the region graph recursively.
-
- Args:
- key (int): The current region to build.
- nodes_cache (Dict[int, Union[InnerRegionNode, InputRegionNode]]): The cache for built nodes.
- graph_json (_RGJson): The graph structure loaded from json.
- num_latents (int): Num latents for region graph nodes.
- is_root (bool): Whether `key` is the global root.
-
- Returns:
- Union[InnerRegionNode, InputRegionNode]: Region graph rooted at `key`.
- """
- if key in nodes_cache:
- return nodes_cache[key]
-
- if len(scope := graph_json["regions"][str(key)]) == 1: # we only accept uni-var input layer
- nodes_cache[key] = InputRegionNode(scope, num_latents, CategoricalLayer, num_cats=256)
- return nodes_cache[key]
-
- partitions: List[PartitionNode] = []
- for partition in graph_json["graph"]:
- if partition["p"] == key:
- left = _dfs_build(partition["l"], nodes_cache, graph_json, num_latents, False)
- right = _dfs_build(partition["r"], nodes_cache, graph_json, num_latents, False)
-
- edge_ids = torch.arange(num_latents).view(-1, 1).repeat(1, 2)
- partitions.append(PartitionNode([left, right], num_latents, edge_ids))
- assert len(partitions) == 1 # we don't know what to do with more than 1
-
- num_out = 1 if is_root else num_latents
- parent_ids, child_ids = torch.meshgrid(
- torch.arange(num_out), torch.arange(num_latents), indexing="ij"
- )
- nodes_cache[key] = InnerRegionNode(
- partitions, num_out, torch.stack((parent_ids, child_ids), dim=0).view(2, -1)
- )
- return nodes_cache[key]
-
-
-def load_region_graph(filename: str, num_latents: int) -> RegionGraph:
- """Load region graph from json file.
-
- Args:
- filename (str): The filename for region graph.
- num_latents (int): Num latents to build the graph.
-
- Returns:
- RegionGraph: The root node of loaded region graph.
- """
- with open(filename, "r", encoding="utf-8") as f:
- graph_json: _RGJson = json.load(f)
-
- all_children = set(partition["l"] for partition in graph_json["graph"]).union(
- partition["r"] for partition in graph_json["graph"]
- )
- all_regions = set(int(key) for key in graph_json["regions"].keys())
- assert all_children.issubset(all_regions)
- root_region = all_regions.difference(all_children)
- assert len(root_region) == 1
-
- return _dfs_build(root_region.pop(), {}, graph_json, num_latents, True)
diff --git a/benchmark/pyjuice/pyjuice-april b/benchmark/pyjuice/pyjuice-april
deleted file mode 160000
index f7fcadc1..00000000
--- a/benchmark/pyjuice/pyjuice-april
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit f7fcadc14ab4b38506209a0b3356abb0856329d7
diff --git a/benchmark/pyjuice/run_benchmark.py b/benchmark/pyjuice/run_benchmark.py
deleted file mode 100644
index 2716e759..00000000
--- a/benchmark/pyjuice/run_benchmark.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import os
-import subprocess
-from typing import List, Tuple
-
-COMPILE_FLAG = 0b0011_1111_1111_1111
-BASE_SEED = 42
-NUM_BATCHES = 100
-BATCH_SIZE = 128
-K_SETTINGS = (16, 32, 64, 128, 256, 512)
-NUM_FIRST_PASSES = 10
-
-HERE = os.path.split(__file__)[0]
-RESULT_DIR = os.path.join(HERE, "benchmark_results")
-if not os.path.exists(RESULT_DIR):
- os.makedirs(RESULT_DIR)
-
-command = [
- "python",
- "run_pyjuice.py",
- "--mode",
- "eval",
- "--seed",
- str(BASE_SEED),
- "--num_batches",
- str(NUM_BATCHES),
- "--batch_size",
- str(BATCH_SIZE),
- "--region_graph",
- "../quad_tree_28x28.json", # cwd is HERE
- "--num_latents",
- "32",
- "--first_pass_only",
-]
-position = {"mode": 3, "seed": 5, "num_latents": 13, "first_pass_only": 14}
-env = os.environ.copy()
-bench_env = {"PYTHONHASHSEED": str(BASE_SEED), "JUICE_COMPILE_FLAG": "0"}
-env.update(bench_env)
-
-
-def parse_run() -> List[Tuple[float, float]]:
- """Run benchmark once and parse the results.
-
- Require `command` and `env` gloablly set inplace.
-
- Returns:
- List[Tuple[float, float]]: The benchmark results in (time,mem) form.
- """
- result = subprocess.run(
- command,
- capture_output=True,
- cwd=HERE,
- check=False,
- text=True,
- env=env,
- )
- print("STDERR:")
- print(result.stderr) # have an extra newline
-
- print("STDOUT:")
- time_mem: List[Tuple[float, float]] = []
- for ln in result.stdout.splitlines():
- if ln[:5] == "t/m: ":
- t_m = ln[5:].split()
- time_mem.append((float(t_m[0]), float(t_m[1])))
- else:
- print(ln)
- print("\n") # add an extra newline
-
- if result.returncode:
- return [(-1, -1)] # meaning this run is broken
-
- return time_mem
-
-
-for compiled in (False, True):
- bench_env["JUICE_COMPILE_FLAG"] = str(compiled * COMPILE_FLAG)
- for mode in ("batch_em", "full_em", "eval"):
- command[position["mode"]] = mode
- for num_latents in K_SETTINGS:
- command[position["num_latents"]] = str(num_latents)
- for first_pass_only in (False, True):
- command = command[: position["first_pass_only"]] + (
- ["--first_pass_only"] if first_pass_only else []
- )
- # add a dummy entry so that the first is always ignored
- time_memory: List[Tuple[float, float]] = [(-1, -1)] if first_pass_only else []
- for seed in range(1, NUM_FIRST_PASSES) if first_pass_only else (0,):
- bench_env["PYTHONHASHSEED"] = command[position["seed"]] = str(BASE_SEED + seed)
- env.update(bench_env)
- print(" ".join([f"{k}={v}" for k, v in bench_env.items()] + command))
- time_memory.extend(parse_run())
-
- with open(
- os.path.join(
- RESULT_DIR,
- f"{compiled=}-{mode=}-batch_size={BATCH_SIZE}"
- f"-{num_latents=}-{first_pass_only=}.csv",
- ),
- "w",
- encoding="utf-8",
- ) as f:
- for t, m in time_memory[1:]:
- print(f"{t},{m}", file=f)
diff --git a/benchmark/pyjuice/run_pyjuice.py b/benchmark/pyjuice/run_pyjuice.py
deleted file mode 100644
index 80a67d4b..00000000
--- a/benchmark/pyjuice/run_pyjuice.py
+++ /dev/null
@@ -1,270 +0,0 @@
-import argparse
-import enum
-import functools
-import logging
-import os
-import random
-from dataclasses import dataclass
-from typing import Callable, Tuple, TypeVar
-
-import numpy as np
-import torch
-from torch import Tensor
-from torch.utils.data import DataLoader, TensorDataset
-
-from .load_rg import load_region_graph
-
-# cat prod ---sum---
-# 0011_1111_1111_1111
-if "JUICE_COMPILE_FLAG" not in os.environ:
- os.environ["JUICE_COMPILE_FLAG"] = str(0b0011_1111_1111_1111)
-print(os.environ["JUICE_COMPILE_FLAG"])
-import pyjuice as juice # pylint: disable=wrong-import-order,wrong-import-position
-from pyjuice import ProbCircuit # pylint: disable=wrong-import-order,wrong-import-position
-
-# disable only the following warnings (there shouldn't be other warnings by torch._inductor.utils)
-# torch._inductor.utils: [WARNING] skipping cudagraphs due to input mutation
-logging.getLogger("torch._inductor.utils").setLevel(logging.ERROR)
-
-device = torch.device("cuda")
-
-
-def seed_all(seed: int) -> None:
- """Seed all random generators and enforce deterministic algorithms to \
- guarantee reproducible results (may limit performance).
-
- Args:
- seed (int): The seed shared by all RNGs.
- """
- seed %= 2**32 # some only accept 32bit seed
- assert os.environ.get("PYTHONHASHSEED", "") == str(
- seed
- ), "Must set PYTHONHASHSEED to the same seed before starting python."
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.use_deterministic_algorithms(True, warn_only=True)
- torch.backends.cudnn.benchmark = False
- os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
-
-
-class _Modes(str, enum.Enum):
- """Execution modes."""
-
- SANITY = "sanity"
- BATCH_EM = "batch_em"
- FULL_EM = "full_em"
- EVAL = "eval"
-
-
-@dataclass
-class _ArgsNamespace(argparse.Namespace):
- mode: _Modes = _Modes.SANITY
- seed: int = 0xC699345C # default is CRC32 of 'april-tools' = 3331929180
- num_batches: int = 20
- batch_size: int = 512
- region_graph: str = "from_data"
- num_latents: int = 32
- first_pass_only: bool = False
-
-
-def process_args() -> _ArgsNamespace:
- """Process command line arguments.
-
- Returns:
- ArgsNamespace: Parsed args.
- """
- parser = argparse.ArgumentParser()
- parser.add_argument("--mode", type=_Modes, choices=_Modes.__members__.values(), help="mode")
- parser.add_argument("--seed", type=int, help="seed")
- parser.add_argument("--num_batches", type=int, help="num_batches")
- parser.add_argument("--batch_size", type=int, help="batch_size")
- parser.add_argument("--region_graph", type=str, help="region_graph filename")
- parser.add_argument("--num_latents", type=int, help="num_latents")
- parser.add_argument("--first_pass_only", action="store_true", help="first_pass_only")
- return parser.parse_args(namespace=_ArgsNamespace())
-
-
-T = TypeVar("T")
-
-
-def benchmarker(fn: Callable[[], T]) -> Tuple[T, Tuple[float, float]]:
- """Benchmark a given function and record time and GPU memory cost.
-
- Args:
- fn (Callable[[], T]): The function to benchmark.
-
- Returns:
- Tuple[T, Tuple[float, float]]: The original return value, followed by \
- time in milliseconds and peak memory in megabytes (1024 scale).
- """
- torch.cuda.synchronize() # finish all prev ops and reset mem counter
- torch.cuda.reset_peak_memory_stats()
- # TODO: repeated with benchmark/utils/gpu_benchmark.py
- start_event = torch.cuda.Event(enable_timing=True) # type: ignore[no-untyped-call]
- end_event = torch.cuda.Event(enable_timing=True) # type: ignore[no-untyped-call]
- start_event.record(torch.cuda.current_stream()) # type: ignore[no-untyped-call]
-
- ret = fn()
-
- end_event.record(torch.cuda.current_stream()) # type: ignore[no-untyped-call]
- torch.cuda.synchronize() # wait for event finish
- elapsed_time: float = start_event.elapsed_time(end_event) # type: ignore[no-untyped-call] # ms
- peak_memory = torch.cuda.max_memory_allocated() / 2**20 # MB
- return ret, (elapsed_time, peak_memory)
-
-
-@torch.no_grad()
-def evaluate(pc: juice.ProbCircuit, data_loader: DataLoader[Tuple[Tensor, ...]]) -> float:
- """Evaluate circuit on given data.
-
- Args:
- pc (juice.ProbCircuit): The PC to evaluate.
- data_loader (DataLoader[Tuple[Tensor, ...]]): The evaluation data.
-
- Returns:
- float: The average LL.
- """
-
- def _iter(x: Tensor) -> Tensor:
- return pc(x) # type: ignore[no-any-return,misc]
-
- ll_total = 0.0
- batch: Tuple[Tensor]
- for batch in data_loader:
- x = batch[0].to(device)
- ll, (t, m) = benchmarker(functools.partial(_iter, x))
- print("t/m:", t, m)
- ll_total += ll.mean().item()
- del x, ll
- return ll_total / len(data_loader)
-
-
-def batch_em_epoch(
- pc: juice.ProbCircuit,
- optimizer: juice.optim.CircuitOptimizer,
- data_loader: DataLoader[Tuple[Tensor, ...]],
-) -> float:
- """Perform EM optimization on mini batches for one epoch.
-
- Args:
- pc (juice.ProbCircuit): The PC to optimize.
- optimizer (juice.optim.CircuitOptimizer): The optimizer for circuit.
- data_loader (DataLoader[Tuple[Tensor, ...]]): The training data.
-
- Returns:
- float: The average LL.
- """
-
- def _iter(x: Tensor) -> Tensor:
- optimizer.zero_grad()
- ll: Tensor = pc(x)
- ll = ll.mean()
- ll.backward() # type: ignore[no-untyped-call]
- optimizer.step()
- return ll.detach()
-
- ll_total = 0.0
- batch: Tuple[Tensor]
- for batch in data_loader:
- x = batch[0].to(device)
- ll, (t, m) = benchmarker(functools.partial(_iter, x))
- print("t/m:", t, m)
- ll_total += ll.item()
- del x, ll
- return ll_total / len(data_loader)
-
-
-@torch.no_grad()
-def full_em_epoch(pc: juice.ProbCircuit, data_loader: DataLoader[Tuple[Tensor, ...]]) -> float:
- """Perform EM optimization on full dataset for one epoch.
-
- Args:
- pc (juice.ProbCircuit): The PC to optimize.
- data_loader (DataLoader[Tuple[Tensor, ...]]): The training data.
-
- Returns:
- float: The average LL.
- """
-
- def _iter(x: Tensor) -> Tensor:
- ll: Tensor = pc(x)
- pc.backward(x, flows_memory=1.0)
- return ll
-
- ll_total = 0.0
- batch: Tuple[Tensor]
- for batch in data_loader:
- x = batch[0].to(device)
- ll, (t, m) = benchmarker(functools.partial(_iter, x))
- print("t/m:", t, m)
- ll_total += ll.mean().item()
- del x, ll
-
- # TODO: this is mypy bug
- _, (t, m) = benchmarker( # type: ignore[misc]
- functools.partial(pc.mini_batch_em, step_size=1.0, pseudocount=0.1)
- )
- print("t/m:", t, m)
- return ll_total / len(data_loader)
-
-
-def main() -> None:
- """Execute the main procedure."""
- args = process_args()
- print(args)
- assert not args.mode == _Modes.SANITY or (
- args.seed == _ArgsNamespace.seed
- and args.num_batches == _ArgsNamespace.num_batches
- and args.batch_size == _ArgsNamespace.batch_size
- and args.region_graph == _ArgsNamespace.region_graph
- and args.num_latents == _ArgsNamespace.num_latents
- and args.first_pass_only == _ArgsNamespace.first_pass_only
- ), "Must use default hyper-params for sanity check."
-
- seed_all(args.seed)
-
- num_features = 28 * 28
- data_size = args.batch_size if args.first_pass_only else args.num_batches * args.batch_size
- rand_data = torch.randint(256, (data_size, num_features), dtype=torch.uint8)
- data_loader = DataLoader(
- dataset=TensorDataset(rand_data),
- batch_size=args.batch_size,
- shuffle=True,
- pin_memory=True,
- drop_last=True,
- )
-
- if args.region_graph == _ArgsNamespace.region_graph:
- num_bins = 32
- sigma = 0.5 / 32
- chunk_size = 32
- pc = juice.structures.HCLT(
- rand_data.to(device),
- num_bins=num_bins,
- sigma=sigma,
- chunk_size=chunk_size,
- num_latents=args.num_latents,
- )
- else:
- rg = load_region_graph(args.region_graph, args.num_latents)
- pc = ProbCircuit(rg)
- pc.to(device)
-
- if args.mode in (_Modes.BATCH_EM, _Modes.SANITY):
- optimizer = juice.optim.CircuitOptimizer(pc) # just keep it default
- ll_batch = batch_em_epoch(pc, optimizer, data_loader)
- print("batch_em LL:", ll_batch)
- if args.mode in (_Modes.FULL_EM, _Modes.SANITY):
- ll_full = full_em_epoch(pc, data_loader)
- print("full_em LL:", ll_full)
- if args.mode in (_Modes.EVAL, _Modes.SANITY):
- ll_eval = evaluate(pc, data_loader)
- print("eval LL:", ll_eval)
- if args.mode == _Modes.SANITY:
- # -4285.7583... is an exact float32 number, 2^-10 is 2eps
- assert abs(ll_eval - -4285.75830078125) < 2**-10, "eval LL out of allowed error range"
-
-
-if __name__ == "__main__":
- main()
diff --git a/benchmark/rgraphs/pd_28x28_d5.json b/benchmark/rgraphs/pd_28x28_d5.json
deleted file mode 100644
index 095c6e7b..00000000
--- a/benchmark/rgraphs/pd_28x28_d5.json
+++ /dev/null
@@ -1 +0,0 @@
-{"regions": {"0": [771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755], "1": [768, 769, 770, 771, 772, 773, 774, 775, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767], "2": [776, 777, 778, 779, 780, 781, 782, 783, 720, 721, 722, 723, 724, 725, 726, 727, 748, 749, 750, 751, 752, 753, 754, 755], "3": [768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767], "4": [781, 782, 783, 753, 754, 755, 725, 726, 727], "5": [512, 513, 5, 6, 7, 8, 9, 537, 538, 539, 540, 541, 33, 34, 35, 36, 37, 565, 566, 567, 568, 569, 61, 62, 63, 64, 65, 593, 594, 595, 596, 597, 89, 90, 91, 92, 93, 621, 622, 623, 624, 625, 117, 118, 119, 120, 121, 649, 650, 651, 652, 653, 145, 146, 147, 148, 149, 677, 678, 679, 680, 681, 173, 174, 175, 176, 177, 705, 706, 707, 708, 709, 201, 202, 203, 204, 205, 733, 734, 735, 736, 737, 229, 230, 231, 232, 233, 761, 762, 763, 764, 765, 257, 258, 259, 260, 261, 285, 286, 287, 288, 289, 313, 314, 315, 316, 317, 341, 342, 343, 344, 345, 369, 370, 371, 372, 373, 397, 398, 399, 400, 401, 425, 426, 427, 428, 429, 453, 454, 455, 456, 457, 481, 482, 483, 484, 485, 509, 510, 511], "6": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 145, 146, 147, 148, 149, 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\ No newline at end of file
diff --git a/benchmark/rgraphs/pd_28x28_d7.json b/benchmark/rgraphs/pd_28x28_d7.json
deleted file mode 100644
index 4ed94652..00000000
--- a/benchmark/rgraphs/pd_28x28_d7.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/benchmark/rgraphs/qt_28x28.json b/benchmark/rgraphs/qt_28x28.json
deleted file mode 100644
index 27b28398..00000000
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diff --git a/benchmark/rgraphs/qt_28x28_sd.json b/benchmark/rgraphs/qt_28x28_sd.json
deleted file mode 100644
index 29c94316..00000000
--- a/benchmark/rgraphs/qt_28x28_sd.json
+++ /dev/null
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diff --git a/benchmark/rgraphs/rbt_784_d4_r16.json b/benchmark/rgraphs/rbt_784_d4_r16.json
deleted file mode 100644
index 9d401dd8..00000000
--- a/benchmark/rgraphs/rbt_784_d4_r16.json
+++ /dev/null
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\ No newline at end of file
diff --git a/benchmark/submodule-usage.md b/benchmark/submodule-usage.md
deleted file mode 100644
index 9b97c425..00000000
--- a/benchmark/submodule-usage.md
+++ /dev/null
@@ -1,38 +0,0 @@
-**The following takes pyjuice as an example.**
-
-## Add a new submodule
-```bash
-git submodule add https://github.com/Juice-jl/pyjuice.git
-```
-This will update the `/.gitmodule` file and clone the submodule repo into the *current pwd*. The HEAD commit of submodule main(master) *at the time of adding* is tracked unless updated later.
-
-
-About submodule file
-
-`.gitmodule` in repo root contains config for all submodules. The command before generates:
-```git
-[submodule "benchmark/pyjuice"]
- path = benchmark/pyjuice
- url = https://github.com/Juice-jl/pyjuice.git
-```
-(this is the minimum profile and more can be configured)
-
-`pyjuice` will be committed as a mode 160000 file with content:
-```
-Subproject commit f00f1743fe237091c78e7f786396d0cfb7f68ecf
-```
-(`f00f17` is the latest commit at the time of writing)
-
-
-
-Any repo (public/private) can be added as a submodule of any repo (public/private), as long as you have proper access to the repos.
-
-## Clone a repo with submodule(s)
-```shell
-git clone --recurse-submodules https://github.com/april-tools/cirkit.git
-```
-
-If you forgot to clone with submodules (or switched branch, etc.), run to fetch:
-```shell
-git submodule update --init --recursive
-```
diff --git a/benchmark/utils/__init__.py b/benchmark/utils/__init__.py
deleted file mode 100644
index c044fa03..00000000
--- a/benchmark/utils/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .gpu_benchmark import benchmarker as benchmarker
-from .gpu_benchmark import timer as timer
diff --git a/benchmark/utils/gpu_benchmark.py b/benchmark/utils/gpu_benchmark.py
deleted file mode 100644
index c3bf0561..00000000
--- a/benchmark/utils/gpu_benchmark.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from typing import Callable, Tuple, TypeVar
-
-import torch
-
-T = TypeVar("T")
-
-
-def timer(fn: Callable[[], T]) -> Tuple[T, float]:
- """Time a given function for GPU time cost.
-
- Args:
- fn (Callable[[], T]): The function to time.
-
- Returns:
- Tuple[T, float]: The original return value, and time in ms.
- """
- # TODO: torch typing issue
- start_event = torch.cuda.Event(enable_timing=True) # type: ignore[no-untyped-call]
- end_event = torch.cuda.Event(enable_timing=True) # type: ignore[no-untyped-call]
- start_event.record(torch.cuda.current_stream()) # type: ignore[no-untyped-call]
-
- ret = fn()
-
- end_event.record(torch.cuda.current_stream()) # type: ignore[no-untyped-call]
- torch.cuda.synchronize() # wait for event to be recorded
- # TODO: Event.elapsed_time is not typed
- elapsed_time: float = start_event.elapsed_time(end_event) # type: ignore[no-untyped-call]
- return ret, elapsed_time
-
-
-def benchmarker(fn: Callable[[], T]) -> Tuple[T, Tuple[float, float]]:
- """Benchmark a given function for GPU time and (peak) memory cost.
-
- Args:
- fn (Callable[[], T]): The function to benchmark.
-
- Returns:
- Tuple[T, Tuple[float, float]]: The original return value, and time in ms and memory in MiB.
- """
- torch.cuda.synchronize() # finish all previous work before resetting mem stats
- torch.cuda.reset_peak_memory_stats()
-
- ret, elapsed_time = timer(fn)
-
- peak_memory = torch.cuda.max_memory_allocated() / 2**20
- return ret, (elapsed_time, peak_memory)
diff --git a/cirkit/backend/torch/layers/__init__.py b/cirkit/backend/torch/layers/__init__.py
index 63f61e17..4c2ff292 100644
--- a/cirkit/backend/torch/layers/__init__.py
+++ b/cirkit/backend/torch/layers/__init__.py
@@ -11,5 +11,6 @@
from .input import TorchGaussianLayer as TorchGaussianLayer
from .input import TorchInputLayer as TorchInputLayer
from .input import TorchLogPartitionLayer as TorchLogPartitionLayer
+from .input import TorchPolynomialLayer as TorchPolynomialLayer
from .optimized import TorchCPTLayer as TorchCPTLayer
from .optimized import TorchTuckerLayer as TorchTuckerLayer
diff --git a/cirkit/backend/torch/layers/input.py b/cirkit/backend/torch/layers/input.py
index f17299e8..72c17866 100644
--- a/cirkit/backend/torch/layers/input.py
+++ b/cirkit/backend/torch/layers/input.py
@@ -7,7 +7,7 @@
from cirkit.backend.torch.layers import TorchLayer
from cirkit.backend.torch.parameters.parameter import TorchParameter
-from cirkit.backend.torch.semiring import LSESumSemiring, Semiring
+from cirkit.backend.torch.semiring import LSESumSemiring, Semiring, SumProductSemiring
class TorchInputLayer(TorchLayer, ABC):
@@ -288,7 +288,7 @@ def log_partition_function(self) -> Tensor:
class TorchGaussianLayer(TorchExpFamilyLayer):
"""The Normal distribution layer.
- This is fully factorized down to univariate Categorical distributions.
+ This is fully factorized down to univariate Gaussian distributions.
"""
def __init__(
@@ -416,6 +416,9 @@ def forward(self, x: Tensor) -> Tensor:
value = value.expand(value.shape[0], x.shape[2], value.shape[2])
return self.semiring.map_from(value, LSESumSemiring)
+ def integrate(self) -> Tensor:
+ raise TypeError("Cannot integrate a layer computing a log-partition function")
+
class TorchEvidenceLayer(TorchConstantLayer):
def __init__(
@@ -471,3 +474,106 @@ def forward(self, x: Tensor) -> Tensor:
obs = obs.unsqueeze(dim=2) # (F, C, 1, D)
obs = obs.expand(obs.shape[0], obs.shape[1], x.shape[2], obs.shape[3]) # (F, C, B, D)
return self.layer(obs) # (F, B, K)
+
+
+# TODO: could be in backends/torch/utils, can be reused by PolyGaussian
+def polyval(coeff: Tensor, x: Tensor) -> Tensor:
+ """Evaluate polynomial given coefficients and point, with the shape for PolynomialLayer.
+
+ Args:
+ coeff (Tensor): The coefficients of the polynomial, shape (F, Ko, deg+1).
+ x (Tensor): The point of the variable, shape (F, H, B, Ki), where H=Ki=1.
+
+ Returns:
+ Tensor: The value of the polymonial, shape (F, B, Ko).
+ """
+ x = x.squeeze(dim=1) # shape (F, H=1, B, Ki=1) -> (F, B, 1).
+ y = x.new_zeros(*x.shape[:-1], coeff.shape[-2]) # shape (F, B, Ko).
+
+ # TODO: iterating over dim=2 is inefficient
+ for a_n in reversed(coeff.unbind(dim=2)): # Reverse iterator of the degree axis, shape (F, Ko).
+ # a_n shape (F, Ko) -> (F, 1, Ko).
+ y = torch.addcmul(a_n.unsqueeze(dim=1), x, y) # y = a_n + x * y, by Horner's method.
+ return y # shape (F, B, Ko).
+
+
+class TorchPolynomialLayer(TorchInputLayer):
+ """The Polynomial layer.
+
+ This is fully factorized down to univariate polynomials w.r.t. channels.
+ """
+
+ def __init__(
+ self,
+ scope_idx: Tensor,
+ num_output_units: int,
+ *,
+ num_channels: int = 1,
+ degree: int,
+ coeff: TorchParameter,
+ semiring: Optional[Semiring] = None,
+ ) -> None:
+ """Init class.
+
+ Args:
+ scope_idx: A tensor of shape (F, D), where F is the number of folds, and
+ D is the number of variables on which the input layers in each fold are defined on.
+ Alternatively, a tensor of shape (D,) can be specified, which will be interpreted
+ as a tensor of shape (1, D), i.e., with F = 1.
+ num_output_units (int): The number of output units.
+ num_channels (int): The number of channels. Defaults to 1.
+ degree (int): The degree of polynomial.
+ coeff (AbstractTorchParameter): The reparameterization for layer parameters.
+ """
+ num_variables = scope_idx.shape[-1]
+ if num_variables != 1:
+ raise ValueError("The Polynomial layer encodes a univariate distribution")
+ if num_channels != 1:
+ raise ValueError("The Polynomial layer encodes a univariate distribution")
+ super().__init__(
+ scope_idx,
+ num_output_units,
+ num_channels=num_channels,
+ semiring=semiring,
+ )
+ self.degree = degree
+ if not self._valid_parameters_shape(coeff):
+ raise ValueError("The number of folds and shape of 'coeff' must match the layer's")
+ self.coeff = coeff
+
+ def _valid_parameters_shape(self, p: TorchParameter) -> bool:
+ if p.num_folds != self.num_folds:
+ return False
+ return p.shape == (self.num_output_units, self.degree + 1)
+
+ @property
+ def fold_settings(self) -> Tuple[Any, ...]:
+ return self.num_variables, self.num_channels, self.num_output_units, self.degree + 1
+
+ @property
+ def config(self) -> Dict[str, Any]:
+ return {
+ "scope_idx": self.scope_idx,
+ "num_output_units": self.num_output_units,
+ "num_channels": self.num_channels,
+ "degree": self.degree,
+ }
+
+ @property
+ def params(self) -> Dict[str, TorchParameter]:
+ return {"coeff": self.coeff}
+
+ def forward(self, x: Tensor) -> Tensor:
+ """Run forward pass.
+
+ Args:
+ x (Tensor): The input to this layer, shape (F, H=C, B, Ki=D).
+
+ Returns:
+ Tensor: The output of this layer, shape (F, B, Ko).
+ """
+ coeff = self.coeff() # shape (F, Ko, dp1)
+ return self.semiring.map_from(polyval(coeff, x), SumProductSemiring)
+
+ def integrate(self) -> Tensor:
+ raise TypeError("Cannot integrate a polynomial layer")
diff --git a/cirkit/backend/torch/parameters/nodes.py b/cirkit/backend/torch/parameters/nodes.py
index 5f711fd0..374a0ceb 100644
--- a/cirkit/backend/torch/parameters/nodes.py
+++ b/cirkit/backend/torch/parameters/nodes.py
@@ -768,3 +768,67 @@ def forward(
sq_mahalanobis = torch.square(mean1.unsqueeze(dim=2) - mean2.unsqueeze(dim=1)) * inv_var12
log_partition = -0.5 * (self._log_two_pi + torch.log(var12) + sq_mahalanobis)
return log_partition.view(-1, *self.shape) # (F, K1 * K2, C)
+
+
+class TorchPolynomialProduct(TorchBinaryParameterOp):
+ # Use default __init__
+
+ @property
+ def shape(self) -> Tuple[int, ...]:
+ return (
+ self.in_shapes[0][0] * self.in_shapes[1][0], # dim K
+ self.in_shapes[0][1] + self.in_shapes[1][1] - 1, # dim dp1
+ )
+
+ def forward(self, coeff1: Tensor, coeff2: Tensor) -> Tensor:
+ # TODO: torch typing issue.
+ fft: Callable[..., Tensor] # type: ignore[misc]
+ ifft: Callable[..., Tensor] # type: ignore[misc]
+ if coeff1.is_complex() or coeff2.is_complex():
+ fft = torch.fft.fft
+ ifft = torch.fft.ifft
+ else:
+ fft = torch.fft.rfft
+ ifft = torch.fft.irfft
+
+ degp1 = coeff1.shape[-1] + coeff2.shape[-1] - 1 # deg1p1 + deg2p1 - 1 = (deg1 + deg2) + 1.
+
+ spec1 = fft(coeff1, n=degp1, dim=-1) # shape (F, K1, dp1).
+ spec2 = fft(coeff2, n=degp1, dim=-1) # shape (F, K2, dp1).
+
+ # shape (F, K1, 1, dp1), (F, 1, K2, dp1) -> (F, K1, K2, dp1) -> (F, K1*K2, dp1).
+ spec = torch.flatten(
+ spec1.unsqueeze(dim=2) * spec2.unsqueeze(dim=1), start_dim=1, end_dim=2
+ )
+
+ return ifft(spec, n=degp1, dim=-1) # shape (F, K1*K2, dp1).
+
+
+class TorchPolynomialDifferential(TorchUnaryParameterOp):
+ def __init__(self, in_shape: Tuple[int, ...], *, num_folds: int = 1, order: int = 1) -> None:
+ if order <= 0:
+ raise ValueError("The order of differentiation must be positive.")
+ super().__init__(in_shape, num_folds=num_folds)
+ self.order = order
+
+ @property
+ def shape(self) -> Tuple[int, ...]:
+ # if dp1>order, i.e., deg>=order, then diff, else const 0.
+ return (
+ self.in_shapes[0][0],
+ self.in_shapes[0][1] - self.order if self.in_shapes[0][1] > self.order else 1,
+ )
+
+ @classmethod
+ def _diff_once(cls, x: Tensor) -> Tensor:
+ degp1 = x.shape[-1] # x shape (F, K, dp1).
+ arange = torch.arange(1, degp1).to(x) # shape (deg,).
+ return x[..., 1:] * arange # a_n x^n -> n a_n x^(n-1), with a_0 disappeared.
+
+ def forward(self, coeff: Tensor) -> Tensor:
+ if coeff.shape[-1] <= self.order:
+ return torch.zeros_like(coeff[..., :1]) # shape (F, K, 1).
+
+ for _ in range(self.order):
+ coeff = self._diff_once(coeff)
+ return coeff # shape (F, K, dp1-ord).
diff --git a/cirkit/backend/torch/rules/layers.py b/cirkit/backend/torch/rules/layers.py
index ccbd00cb..e17c8877 100644
--- a/cirkit/backend/torch/rules/layers.py
+++ b/cirkit/backend/torch/rules/layers.py
@@ -15,6 +15,7 @@
TorchGaussianLayer,
TorchInputLayer,
TorchLogPartitionLayer,
+ TorchPolynomialLayer,
)
from cirkit.symbolic.layers import (
CategoricalLayer,
@@ -25,6 +26,7 @@
KroneckerLayer,
LogPartitionLayer,
MixingLayer,
+ PolynomialLayer,
)
if TYPE_CHECKING:
@@ -69,6 +71,20 @@ def compile_gaussian_layer(compiler: "TorchCompiler", sl: GaussianLayer) -> Torc
)
+def compile_polynomial_layer(
+ compiler: "TorchCompiler", sl: PolynomialLayer
+) -> TorchPolynomialLayer:
+ coeff = compiler.compile_parameter(sl.coeff)
+ return TorchPolynomialLayer(
+ torch.tensor(tuple(sl.scope)),
+ sl.num_output_units,
+ num_channels=sl.num_channels,
+ degree=sl.degree,
+ coeff=coeff,
+ semiring=compiler.semiring,
+ )
+
+
def compile_hadamard_layer(compiler: "TorchCompiler", sl: KroneckerLayer) -> TorchHadamardLayer:
return TorchHadamardLayer(
sl.num_input_units, sl.num_output_units, arity=sl.arity, semiring=compiler.semiring
@@ -121,6 +137,7 @@ def compile_evidence_layer(compiler: "TorchCompiler", sl: EvidenceLayer) -> Torc
DEFAULT_LAYER_COMPILATION_RULES: Dict[LayerCompilationSign, LayerCompilationFunc] = { # type: ignore[misc]
CategoricalLayer: compile_categorical_layer,
GaussianLayer: compile_gaussian_layer,
+ PolynomialLayer: compile_polynomial_layer,
HadamardLayer: compile_hadamard_layer,
KroneckerLayer: compile_kronecker_layer,
DenseLayer: compile_dense_layer,
diff --git a/cirkit/backend/torch/rules/parameters.py b/cirkit/backend/torch/rules/parameters.py
index 20993f95..259d6542 100644
--- a/cirkit/backend/torch/rules/parameters.py
+++ b/cirkit/backend/torch/rules/parameters.py
@@ -18,6 +18,8 @@
TorchOuterProductParameter,
TorchOuterSumParameter,
TorchPointerParameter,
+ TorchPolynomialDifferential,
+ TorchPolynomialProduct,
TorchReduceLSEParameter,
TorchReduceProductParameter,
TorchReduceSumParameter,
@@ -44,6 +46,8 @@
LogSoftmaxParameter,
OuterProductParameter,
OuterSumParameter,
+ PolynomialDifferential,
+ PolynomialProduct,
ReduceLSEParameter,
ReduceProductParameter,
ReduceSumParameter,
@@ -234,6 +238,18 @@ def compile_gaussian_product_log_partition(
return TorchGaussianProductLogPartition(*p.in_shapes)
+def compile_polynomial_product(
+ compiler: "TorchCompiler", p: PolynomialProduct
+) -> TorchPolynomialProduct:
+ return TorchPolynomialProduct(*p.in_shapes)
+
+
+def compile_polynomial_differential(
+ compiler: "TorchCompiler", p: PolynomialDifferential
+) -> TorchPolynomialDifferential:
+ return TorchPolynomialDifferential(*p.in_shapes, order=p.order)
+
+
DEFAULT_PARAMETER_COMPILATION_RULES: Dict[ParameterCompilationSign, ParameterCompilationFunc] = { # type: ignore[misc]
TensorParameter: compile_tensor_parameter,
ConstantParameter: compile_constant_parameter,
@@ -259,4 +275,6 @@ def compile_gaussian_product_log_partition(
GaussianProductMean: compile_gaussian_product_mean,
GaussianProductStddev: compile_gaussian_product_stddev,
GaussianProductLogPartition: compile_gaussian_product_log_partition,
+ PolynomialProduct: compile_polynomial_product,
+ PolynomialDifferential: compile_polynomial_differential,
}
diff --git a/cirkit/pipeline.py b/cirkit/pipeline.py
index 8ff190fa..e4c2ae5e 100644
--- a/cirkit/pipeline.py
+++ b/cirkit/pipeline.py
@@ -112,11 +112,13 @@ def multiply(self, lhs_cc: CompiledCircuit, rhs_cc: CompiledCircuit) -> Compiled
prod_sc = SF.multiply(lhs_sc, rhs_sc, registry=self._op_registry)
return self.compile(prod_sc)
- def differentiate(self, cc: CompiledCircuit) -> CompiledCircuit:
+ def differentiate(self, cc: CompiledCircuit, *, order: int = 1) -> CompiledCircuit:
if not self._compiler.has_symbolic(cc):
raise ValueError("The given compiled circuit is not known in this pipeline")
+ if order <= 0:
+ raise ValueError("The order of differentiation must be positive.")
sc = self._compiler.get_symbolic_circuit(cc)
- diff_sc = SF.differentiate(sc, registry=self._op_registry)
+ diff_sc = SF.differentiate(sc, registry=self._op_registry, order=order)
return self.compile(diff_sc)
def conjugate(self, cc: CompiledCircuit) -> CompiledCircuit:
@@ -157,10 +159,12 @@ def multiply(
return ctx.multiply(lhs_cc, rhs_cc)
-def differentiate(cc: CompiledCircuit, ctx: Optional[PipelineContext] = None) -> CompiledCircuit:
+def differentiate(
+ cc: CompiledCircuit, ctx: Optional[PipelineContext] = None, *, order: int = 1
+) -> CompiledCircuit:
if ctx is None:
ctx = _PIPELINE_CONTEXT.get()
- return ctx.differentiate(cc)
+ return ctx.differentiate(cc, order=order)
def conjugate(cc: CompiledCircuit, ctx: Optional[PipelineContext] = None) -> CompiledCircuit:
diff --git a/cirkit/symbolic/functional.py b/cirkit/symbolic/functional.py
index dd6b7af4..f9b394b4 100644
--- a/cirkit/symbolic/functional.py
+++ b/cirkit/symbolic/functional.py
@@ -1,6 +1,7 @@
+import heapq
import itertools
from numbers import Number
-from typing import Dict, List, Optional, Sequence, Tuple, Union
+from typing import Dict, Iterable, List, NamedTuple, Optional, Sequence, Tuple, TypeVar, Union
import numpy as np
@@ -378,16 +379,186 @@ def multiply(sc1: Circuit, sc2: Circuit, *, registry: Optional[OperatorRegistry]
)
-def differentiate(sc: Circuit, *, registry: Optional[OperatorRegistry] = None) -> Circuit:
+class _ScopeVarAndBlockAndInputs(NamedTuple):
+ """The tuple of a scope variable and a circiut block for diff.
+
+ Used for differential of ProductLayer.
+ """
+
+ scope_var: int # The id of a variable in the scope of THE ProductLayer.
+ diff_block: CircuitBlock # The partial diff of THE ProductLayer w.r.t. the var.
+ diff_in_blocks: List[CircuitBlock] # The inputs to the layer of diff_block.
+
+
+_T = TypeVar("_T") # TODO: for _repeat. move together
+
+
+# TODO: this can be made public and moved to utils, might be used elsewhere.
+def _repeat(iterable: Iterable[_T], /, *, times: int) -> Iterable[_T]:
+ """Repeat each element of the given iterable by given times.
+
+ The elements are generated lazily. The iterable passed in will be iterated once.
+ This function differs from itertools in that it repeats an interable instead of only one elem.
+
+ Args:
+ iterable (Iterable[_T]): The iterable to generate the original elements.
+ times (int): The times to repeat each element.
+
+ Returns:
+ Iterable[_T]: The iterable with repeated elements.
+ """
+ return itertools.chain.from_iterable(itertools.repeat(elem, times=times) for elem in iterable)
+
+
+def differentiate(
+ sc: Circuit, registry: Optional[OperatorRegistry] = None, *, order: int = 1
+) -> Circuit:
if not sc.is_smooth or not sc.is_decomposable:
raise StructuralPropertyError(
"Only smooth and decomposable circuits can be efficiently differentiated."
)
+ if order <= 0:
+ raise ValueError("The order of differentiation must be positive.")
# Use the registry in the current context, if not specified otherwise
if registry is None:
registry = OPERATOR_REGISTRY.get()
- raise NotImplementedError()
+
+ # Mapping the symbolic circuit layers with blocks of circuit layers
+ layers_to_blocks: Dict[Layer, List[CircuitBlock]] = {}
+
+ # For each new circuit block, keep track of its inputs
+ in_blocks: Dict[CircuitBlock, Sequence[CircuitBlock]] = {}
+
+ for sl in sc.topological_ordering():
+ # "diff_blocks: List[CircuitBlock]" is the diff of sl wrt each variable and channel in order
+ # and then at the end we append a copy of sl
+ sl_params = {name: p.ref() for name, p in sl.params.items()}
+
+ if isinstance(sl, InputLayer):
+ # TODO: no type hint for func, also cannot quick jump in static analysis
+ func = registry.retrieve_rule(LayerOperator.DIFFERENTIATION, type(sl))
+ diff_blocks = [
+ func(sl, var_idx=var_idx, ch_idx=ch_idx, order=order)
+ for var_idx, ch_idx in itertools.product(
+ range(len(sl.scope)), range(sc.num_channels)
+ )
+ ]
+
+ elif isinstance(sl, SumLayer):
+ # Zip to transpose the generator into an iterable of length (num_vars * num_chs),
+ # corresponding to each var to take diff.
+ # Each item is a tuple of length arity, which are inputs to that diff.
+ # TODO: typeshed issue?
+ # ANNOTATE: zip gives Any when using *iterables.
+ zip_blocks_in: Iterable[Tuple[CircuitBlock, ...]] = zip(
+ # This is a generator of length arity, corresponding to each input of sl.
+ # Each item is a list of length (num_vars * num_chs), corresponding to the diff wrt
+ # each variable of that input.
+ # NOTE: [-1] is omitted and will be added at the end.
+ *(layers_to_blocks[sl_in][:-1] for sl_in in sc.layer_inputs(sl))
+ )
+
+ # The layers are the same for all diffs of a SumLayer. We retrieve (num_vars * num_chs)
+ # from the length of one input blocks.
+ var_ch = len(layers_to_blocks[sc.layer_inputs(sl)[0]][:-1])
+ diff_blocks = [
+ CircuitBlock.from_layer(type(sl)(**sl.config, **sl_params)) for _ in range(var_ch)
+ ]
+
+ # Connect the layers to their inputs, by zipping a length of (num_vars * num_chs).
+ in_blocks.update(zip(diff_blocks, zip_blocks_in))
+
+ elif isinstance(sl, ProductLayer):
+ # NOTE: Only the outmost level can be a generator, and inner levels must be lists,
+ # otherwise reference to locals will be broken.
+
+ # This is a generator of length arity, corresponding to each input of sl.
+ # Each item is a list of length (num_vars * num_chs) of that input, corresponding to the
+ # diff wrt each var and ch of that input.
+ all_scope_var_diff_block = (
+ # Each list is all the diffs of sl wrt each var and each channel in the scope of
+ # the cur_layer in the input of sl.
+ [
+ # Each named-tuple is a diff of sl and its inputs, where the diff is wrt the
+ # current variable and channel as in the double loop.
+ _ScopeVarAndBlockAndInputs(
+ # Label the named-tuple as the var id in the whole scope, for sorting.
+ scope_var=scope_var,
+ # The layers are the same for all diffs of a ProductLayer.
+ diff_block=CircuitBlock.from_layer(type(sl)(**sl.config, **sl_params)),
+ # The inputs to the diff is the copy of input to sl (retrieved by [-1]),
+ # only with cur_layer replaced by its diff.
+ diff_in_blocks=[
+ diff_cur_layer if sl_in == cur_layer else layers_to_blocks[sl_in][-1]
+ for sl_in in sc.layer_inputs(sl)
+ ],
+ )
+ # Loop over the (num_vars * num_chs) diffs of cur_layer, while also providing
+ # the corresponding scope_var which the current diff is wrt.
+ # We need the scope_var to label and sort the diff layers of sl. We do nnt need
+ # channel ids because they are always saved densely in order.
+ for scope_var, diff_cur_layer in zip(
+ _repeat(sc.layer_scope(cur_layer), times=sc.num_channels),
+ layers_to_blocks[cur_layer][:-1],
+ )
+ ]
+ # Loop over each input of sl for the diffs wrt vars and chs in its scope.
+ for cur_layer in sc.layer_inputs(sl)
+ )
+
+ # NOTE: This relys on the fact that Scope object is iterated in id order.
+ # Merge sort the named-tuples by the var id in the scope, so that the diffs are
+ # correctly ordered according to the scope of sl.
+ sorted_scope_var_diff_block = list(
+ heapq.merge(
+ # Unpack the generator into several lists, where each list is the named-tuples
+ # wrt the scope of each input to sl.
+ *all_scope_var_diff_block,
+ key=lambda scope_var_diff_block: scope_var_diff_block.scope_var,
+ )
+ )
+
+ # Take out the diffs of sl and save them in diff_blocks in correct order.
+ diff_blocks = [
+ scope_var_diff_block.diff_block
+ for scope_var_diff_block in sorted_scope_var_diff_block
+ ]
+
+ # Connect the diffs with its corresponding inputs as saved in the named-tuples.
+ in_blocks.update(
+ (scope_var_diff_block.diff_block, scope_var_diff_block.diff_in_blocks)
+ for scope_var_diff_block in sorted_scope_var_diff_block
+ )
+
+ else:
+ # NOTE: In the above if/elif, we made all conditions explicit to make it more readable
+ # and also easier for static analysis inside the blocks. Yet the completeness
+ # cannot be inferred and is only guaranteed by larger picture. Also, should
+ # anything really go wrong, we will hit this guard statement instead of going into
+ # a wrong branch.
+ assert False, "This should not happen."
+
+ # Save a copy of sl in the diff circuit and connect inputs. This can be accessed through
+ # diff_blocks[-1], as in the [-1] above for ProductLayer.
+ diff_blocks.append(CircuitBlock.from_layer(type(sl)(**sl.config, **sl_params)))
+ in_blocks[diff_blocks[-1]] = [layers_to_blocks[sl_in][-1] for sl_in in sc.layer_inputs(sl)]
+
+ # Save all the blocks including a copy of sl at [-1] as the diff layers of sl.
+ layers_to_blocks[sl] = diff_blocks
+
+ # Construct the integral symbolic circuit and set the integration operation metadata
+ return Circuit.from_operation(
+ sc.num_channels,
+ sum(layers_to_blocks.values(), []),
+ in_blocks, # TODO: in_blocks uses Sequence, and Sequence should work.
+ sum((layers_to_blocks[sl] for sl in sc.outputs), []),
+ operation=CircuitOperation(
+ operator=CircuitOperator.DIFFERENTIATION,
+ operands=(sc,),
+ metadata=dict(order=order),
+ ),
+ )
def conjugate(
diff --git a/cirkit/symbolic/layers.py b/cirkit/symbolic/layers.py
index 876c8543..526518f8 100644
--- a/cirkit/symbolic/layers.py
+++ b/cirkit/symbolic/layers.py
@@ -377,6 +377,47 @@ def params(self) -> Dict[str, Parameter]:
return params
+class PolynomialLayer(InputLayer):
+ def __init__(
+ self,
+ scope: Scope,
+ num_output_units: int,
+ num_channels: int,
+ *,
+ degree: int,
+ coeff: Optional[Parameter] = None,
+ coeff_factory: Optional[ParameterFactory] = None,
+ ):
+ if len(scope) != 1:
+ raise ValueError("The Polynomial layer encodes a univariate distribution")
+ if num_channels != 1:
+ raise ValueError("The Polynomial layer encodes a univariate distribution")
+ super().__init__(scope, num_output_units, num_channels)
+ self.degree = degree
+ if coeff is None:
+ if coeff_factory is None:
+ coeff = Parameter.from_leaf(
+ TensorParameter(*self._coeff_shape, initializer=NormalInitializer())
+ )
+ else:
+ coeff = coeff_factory(self._coeff_shape)
+ if coeff.shape != self._coeff_shape:
+ raise ValueError(f"Expected parameter shape {self._coeff_shape}, found {coeff.shape}")
+ self.coeff = coeff
+
+ @property
+ def _coeff_shape(self) -> Tuple[int, ...]:
+ return self.num_output_units, self.degree + 1
+
+ @property
+ def config(self) -> dict:
+ return {**super().config, "degree": self.degree}
+
+ @property
+ def params(self) -> Dict[str, Parameter]:
+ return {"coeff": self.coeff}
+
+
class LogPartitionLayer(ConstantLayer):
"""A symbolic layer computing a log-partition function."""
diff --git a/cirkit/symbolic/operators.py b/cirkit/symbolic/operators.py
index 826fdd80..a0fa5143 100644
--- a/cirkit/symbolic/operators.py
+++ b/cirkit/symbolic/operators.py
@@ -12,6 +12,7 @@
LayerOperator,
LogPartitionLayer,
MixingLayer,
+ PolynomialLayer,
)
from cirkit.symbolic.parameters import (
ConjugateParameter,
@@ -23,6 +24,8 @@
LogParameter,
OuterSumParameter,
Parameter,
+ PolynomialDifferential,
+ PolynomialProduct,
ReduceLSEParameter,
ReduceSumParameter,
SumParameter,
@@ -173,6 +176,51 @@ def multiply_gaussian_layers(sl1: GaussianLayer, sl2: GaussianLayer) -> CircuitB
return CircuitBlock.from_layer(sl)
+def multiply_polynomial_layers(sl1: PolynomialLayer, sl2: PolynomialLayer) -> CircuitBlock:
+ if sl1.scope != sl2.scope:
+ raise ValueError(
+ f"Expected Polynomial layers to have the same scope,"
+ f" but found '{sl1.scope}' and '{sl2.scope}'"
+ )
+ if sl1.num_channels != sl2.num_channels:
+ raise ValueError(
+ f"Expected Polynomial layers to have the number of channels,"
+ f"but found '{sl1.num_channels}' and '{sl2.num_channels}'"
+ )
+
+ shape1, shape2 = sl1.coeff.shape, sl2.coeff.shape
+ coeff = Parameter.from_binary(
+ PolynomialProduct(shape1, shape2),
+ sl1.coeff.ref(),
+ sl2.coeff.ref(),
+ )
+
+ sl = PolynomialLayer(
+ sl1.scope,
+ sl1.num_output_units * sl2.num_output_units,
+ num_channels=sl1.num_channels,
+ degree=sl1.degree + sl2.degree,
+ coeff=coeff,
+ )
+ return CircuitBlock.from_layer(sl)
+
+
+def differentiate_polynomial_layer(
+ sl: PolynomialLayer, *, var_idx: int, ch_idx: int, order: int = 1
+) -> CircuitBlock:
+ # PolynomialLayer is constructed univariate, but we still take the 2 idx for unified interface
+ assert (var_idx, ch_idx) == (0, 0), "This should not happen"
+ if order <= 0:
+ raise ValueError("The order of differentiation must be positive.")
+ coeff = Parameter.from_unary(
+ PolynomialDifferential(sl.coeff.shape, order=order), sl.coeff.ref()
+ )
+ sl = PolynomialLayer(
+ sl.scope, sl.num_output_units, sl.num_channels, degree=coeff.shape[-1] - 1, coeff=coeff
+ )
+ return CircuitBlock.from_layer(sl)
+
+
def conjugate_categorical_layer(sl: CategoricalLayer) -> CircuitBlock:
logits = sl.logits.ref() if sl.logits is not None else None
probs = sl.probs.ref() if sl.probs is not None else None
@@ -194,6 +242,14 @@ def conjugate_gaussian_layer(sl: GaussianLayer) -> CircuitBlock:
return CircuitBlock.from_layer(sl)
+def conjugate_polynomial_layer(sl: PolynomialLayer) -> CircuitBlock:
+ coeff = Parameter.from_unary(ConjugateParameter(sl.coeff.shape), sl.coeff.ref())
+ sl = PolynomialLayer(
+ sl.scope, sl.num_output_units, sl.num_channels, degree=sl.degree, coeff=coeff
+ )
+ return CircuitBlock.from_layer(sl)
+
+
def multiply_hadamard_layers(sl1: HadamardLayer, sl2: HadamardLayer) -> CircuitBlock:
sl = HadamardLayer(
sl1.num_input_units * sl2.num_input_units,
@@ -245,11 +301,12 @@ def __call__(self, *sl: Layer, **kwargs) -> CircuitBlock:
DEFAULT_OPERATOR_RULES: Dict[LayerOperator, List[LayerOperatorFunc]] = {
LayerOperator.INTEGRATION: [integrate_categorical_layer, integrate_gaussian_layer],
- LayerOperator.DIFFERENTIATION: [],
+ LayerOperator.DIFFERENTIATION: [differentiate_polynomial_layer],
LayerOperator.MULTIPLICATION: [
multiply_evidence_layers,
multiply_categorical_layers,
multiply_gaussian_layers,
+ multiply_polynomial_layers,
multiply_hadamard_layers,
multiply_dense_layers,
multiply_mixing_layers,
@@ -257,6 +314,7 @@ def __call__(self, *sl: Layer, **kwargs) -> CircuitBlock:
LayerOperator.CONJUGATION: [
conjugate_categorical_layer,
conjugate_gaussian_layer,
+ conjugate_polynomial_layer,
conjugate_dense_layer,
conjugate_mixing_layer,
],
diff --git a/cirkit/symbolic/parameters.py b/cirkit/symbolic/parameters.py
index 91d55445..7a8022af 100644
--- a/cirkit/symbolic/parameters.py
+++ b/cirkit/symbolic/parameters.py
@@ -550,3 +550,30 @@ def shape(self) -> Tuple[int, ...]:
self.in_shapes[0][0] * self.in_shapes[1][0],
self.in_shapes[0][1],
)
+
+
+class PolynomialProduct(BinaryParameterOp):
+ # Use default __init__
+
+ @property
+ def shape(self) -> Tuple[int, ...]:
+ return (
+ self.in_shapes[0][0] * self.in_shapes[1][0], # dim Ko
+ self.in_shapes[0][1] + self.in_shapes[1][1] - 1, # dim deg+1
+ )
+
+
+class PolynomialDifferential(UnaryParameterOp):
+ def __init__(self, in_shape: Tuple[int, ...], *, order: int = 1):
+ if order <= 0:
+ raise ValueError("The order of differentiation must be positive.")
+ super().__init__(in_shape)
+ self.order = order
+
+ @property
+ def shape(self) -> Tuple[int, ...]:
+ # if dp1>order, i.e., deg>=order, then diff, else const 0.
+ return (
+ self.in_shapes[0][0],
+ self.in_shapes[0][1] - self.order if self.in_shapes[0][1] > self.order else 1,
+ )
diff --git a/notebooks/complex-squared-npcs.ipynb b/notebooks/complex-squared-npcs.ipynb
deleted file mode 100644
index dec15b4e..00000000
--- a/notebooks/complex-squared-npcs.ipynb
+++ /dev/null
@@ -1,720 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "97f3b03e-0aa7-4fa9-ad37-21a141f4a1a1",
- "metadata": {},
- "source": [
- "# Train and evaluate a squared PC ($\\mathrm{NPC}^2$) with complex parameters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "498b64a5-86bf-45c7-b65c-6c010a9b4a82",
- "metadata": {},
- "outputs": [],
- "source": [
- "import random\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "48e26e8e-8c76-43ce-90db-87da5d8eb39a",
- "metadata": {},
- "source": [
- "Set the random seeds."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "63dd05c5-f4ea-459a-b2fb-75d976f50afe",
- "metadata": {},
- "outputs": [],
- "source": [
- "random.seed(42)\n",
- "np.random.seed(42)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9d389743-936c-49a8-9ef8-f5c55be758a5",
- "metadata": {},
- "source": [
- "# Load the MiniBooNE UCI data set"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "61b76039-c260-4b43-88e2-e3e8875206bf",
- "metadata": {},
- "outputs": [],
- "source": [
- "from datasets import load_uci_dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "b153dc84-3e0b-463a-99b0-2b7e09a388af",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of variables: 43\n"
- ]
- }
- ],
- "source": [
- "data = load_uci_dataset('miniboone', path='datasets')\n",
- "data_train, data_test = data['train'], data['test']\n",
- "num_variables = data_train.shape[1]\n",
- "print(f'Number of variables: {num_variables}')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "611257b5-8c18-4f1d-8316-3c898f2e6035",
- "metadata": {},
- "source": [
- "# Instantiating a Circuit structure Template: the Region Graph"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d1ef46af-2e7d-46cf-ad72-52db50d5f939",
- "metadata": {},
- "source": [
- "Instantiate a bunch of _random binary tree_ region graphs of maximum depth, with different seed."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "a4559103-4e61-4b6c-ac72-ff54c72e0736",
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.templates.region_graph import RandomBinaryTree\n",
- "max_depth = int(np.ceil(np.log2(num_variables)))\n",
- "region_graph = RandomBinaryTree(num_variables, depth=max_depth, seed=42)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "96c77dea-c04a-4349-80e3-d78c508339c9",
- "metadata": {},
- "source": [
- "## Constructing the Symbolic Circuit Representation"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "90db495e-1065-43fa-97f4-b3b1ad1f0b32",
- "metadata": {},
- "source": [
- "We construct a non-monotonic PC with Gaussian distributions as input."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "84505053-eab3-491f-b143-89eda6f86224",
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.utils.scope import Scope\n",
- "from cirkit.symbolic.layers import GaussianLayer, DenseLayer, HadamardLayer\n",
- "from cirkit.symbolic.parameters import Parameter, TensorParameter, ScaledSigmoidParameter\n",
- "from cirkit.symbolic.initializers import UniformInitializer, NormalInitializer\n",
- "from cirkit.symbolic.dtypes import DataType"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "1a27d1ce-3fc7-4ea0-8e4c-2b1f2cd5b351",
- "metadata": {},
- "outputs": [],
- "source": [
- "def gaussian_layer_factory(\n",
- " scope: Scope,\n",
- " num_units: int,\n",
- " num_channels: int\n",
- ") -> GaussianLayer:\n",
- " return GaussianLayer(\n",
- " scope, num_units, num_channels,\n",
- " stddev_factory=lambda shape: Parameter.from_sequence(\n",
- " TensorParameter(*shape, initializer=NormalInitializer(0.0, 1e-1)),\n",
- " ScaledSigmoidParameter(shape, vmin=1e-5, vmax=1.0)\n",
- " )\n",
- " )\n",
- "\n",
- "def hadamard_layer_factory(\n",
- " scope: Scope, num_input_units: int, arity: int\n",
- ") -> HadamardLayer:\n",
- " return HadamardLayer(scope, num_input_units, arity)\n",
- "\n",
- "def dense_layer_factory(\n",
- " scope: Scope,\n",
- " num_input_units: int,\n",
- " num_output_units: int\n",
- ") -> DenseLayer:\n",
- " return DenseLayer(\n",
- " scope, num_input_units, num_output_units,\n",
- " weight_factory=lambda shape: Parameter.from_leaf(\n",
- " TensorParameter(*shape, initializer=UniformInitializer(0.0, 1.0), dtype=DataType.COMPLEX),\n",
- " )\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "8f5c34a5-f6c1-4735-ba88-76ab00ef35d4",
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.symbolic.circuit import Circuit"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "8ecd1c60-402f-4e65-b79a-8ddbd69dd700",
- "metadata": {},
- "outputs": [],
- "source": [
- "symbolic_circuit = Circuit.from_region_graph(\n",
- " region_graph,\n",
- " num_input_units=64,\n",
- " num_sum_units=64,\n",
- " input_factory=gaussian_layer_factory,\n",
- " sum_factory=dense_layer_factory,\n",
- " prod_factory=hadamard_layer_factory\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "57ad50a2-b91b-4934-bf36-061442375583",
- "metadata": {},
- "source": [
- "We can retrieve some information about the circuit and its structural properties as follows."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "de247e7e-d5bf-44e8-adc8-f070a86ad236",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Smooth: True\n",
- "Decomposable: True\n",
- "Structured decomposable: True\n",
- "Number of variables: 43\n",
- "Number of channels per variable: 1\n"
- ]
- }
- ],
- "source": [
- "print(f'Smooth: {symbolic_circuit.is_smooth}')\n",
- "print(f'Decomposable: {symbolic_circuit.is_decomposable}')\n",
- "print(f'Structured decomposable: {symbolic_circuit.is_structured_decomposable}')\n",
- "print(f'Number of variables: {symbolic_circuit.num_variables}')\n",
- "print(f'Number of channels per variable: {symbolic_circuit.num_channels}')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e0eff399-2f7c-4836-9b65-78622f4be7d6",
- "metadata": {},
- "source": [
- "## Computing the Circuit Square"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0a841699-70ec-44f3-b90b-24d92722eabd",
- "metadata": {},
- "source": [
- "We now square the symbolic circuit representation and compile it. However, to actually train it, we will only need to compute the integral of the squared circuit.\n",
- "\n",
- "To do so, we need to set up a pipeline context first and specify a backend. Since the circuit is non-monotonic, we will use the complex log-sum-exp and sum semiring. Note that in the following we also compile the integral of the squared circuit, i.e., the partition functions."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "1a01f3c5-21d1-4677-af7d-bd6bc5d8f45b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.pipeline import PipelineContext\n",
- "from cirkit.pipeline import compile\n",
- "import cirkit.symbolic.functional as SF"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "a89681ce-efcf-4dae-acf3-9448502f21fe",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "device = torch.device('cuda') # The device to use\n",
- "torch.manual_seed(42)\n",
- "# Using float32 ...\n",
- "#torch.set_default_dtype(torch.float64)\n",
- "torch.cuda.manual_seed(42)\n",
- "# Extend the default cache limit. In the future, torch will support compilation with 'more dynamic shapes',\n",
- "# and therefore many recompilations will be avoided\n",
- "torch._dynamo.config.cache_size_limit = 16"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "78bd1d2b-018f-47fa-a2df-e3ddfc816ed8",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 405 ms, sys: 57.5 ms, total: 462 ms\n",
- "Wall time: 435 ms\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "ctx = PipelineContext(\n",
- " backend='torch', # Choose the torch compilation backend\n",
- " fold=True, # Fold the circuit, this is a backend-specific compilation flag\n",
- " semiring='complex-lse-sum', # Use the (C, +, *) semiring, where + is the complex log-sum-exp and * is the sum\n",
- " optimize=True # Optimize the circuit layers (set this flag to False to disable, which yields slower inference)\n",
- ")\n",
- "\n",
- "with ctx:\n",
- " symbolic_sq_circuit = SF.multiply(SF.conjugate(symbolic_circuit), symbolic_circuit)\n",
- " symbolic_int_sq_circuit = SF.integrate(symbolic_sq_circuit)\n",
- " int_sq_circuit = compile(symbolic_int_sq_circuit)\n",
- " # Note that compiling the integral squared circuit will also compile the symbolic circuit\n",
- " # we started from. We retrieve its compiled module as showed below.\n",
- " circuit = ctx.get_compiled_circuit(symbolic_circuit)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "acead97f-2783-45ba-b2b1-0ef164fd2481",
- "metadata": {},
- "source": [
- "Let's print the circuit and the integral squared circuit torch modules."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "8bf4de33-c03f-4a69-9103-6a6b4a5ffec4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "TorchCircuit(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchGaussianLayer(\n",
- " (mean): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " (stddev): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " (1): TorchScaledSigmoidParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (1): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (2-7): 6 x TorchCPLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- ")\n"
- ]
- }
- ],
- "source": [
- "print(circuit)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "bfc5922e-c1d8-41b7-856a-759ee9aa253b",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "TorchConstantCircuit(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchLogPartitionLayer(\n",
- " (value): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-1): 2 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (2): TorchScaledSigmoidParameter()\n",
- " (3): TorchGaussianProductLogPartition()\n",
- " (4-5): 2 x TorchReduceSumParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (1): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (1): TorchConjugateParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (2): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (3): TorchHadamardLayer()\n",
- " (4): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (1): TorchConjugateParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (5): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (6): TorchHadamardLayer()\n",
- " (7): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (1): TorchConjugateParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (8): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (9): TorchHadamardLayer()\n",
- " (10): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (1): TorchConjugateParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (11): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (12): TorchHadamardLayer()\n",
- " (13): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (1): TorchConjugateParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (14): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (15): TorchHadamardLayer()\n",
- " (16): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (1): TorchConjugateParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (17): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (18): TorchHadamardLayer()\n",
- " (19): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (1): TorchConjugateParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (20): TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- ")\n"
- ]
- }
- ],
- "source": [
- "print(int_sq_circuit)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "25f6510a-0631-4770-9002-9431f126bb9d",
- "metadata": {},
- "source": [
- "## Training and Testing"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7c3c644c-ac45-4156-b1f5-391585ea34c7",
- "metadata": {},
- "source": [
- "We are not ready to learn the parameters and do inference.\n",
- "First, we wrap our data into PyTorch data loaders by specifying the batch size.\n",
- "Then, we initialize any PyTorch optimizer, e.g., Adam.\n",
- "\n",
- "Note that the parameters of the integral squared circuit are the same parameters of the circuit itself."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "5a0f61c6-7564-4c3b-8612-e02a52160d12",
- "metadata": {},
- "outputs": [],
- "source": [
- "from torch import optim\n",
- "from torch.utils.data import DataLoader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "ea7b248b-802e-4f22-9849-2b5fe304b019",
- "metadata": {},
- "outputs": [],
- "source": [
- "train_dataloader = DataLoader(data_train, shuffle=True, batch_size=256, drop_last=True, num_workers=4)\n",
- "test_dataloader = DataLoader(data_test, shuffle=False, batch_size=256, num_workers=4)\n",
- "optimizer = optim.Adam(circuit.parameters(), lr=5e-3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "ee8840b0-617c-4881-8753-313b1491f13b",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Move circuits to device\n",
- "circuit = circuit.to(device)\n",
- "int_sq_circuit = int_sq_circuit.to(device)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "13f65113-023f-4e9f-922a-e7202de5bb43",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 4, step 500: Average NLL: 53.305\n",
- "Epoch 8, step 1000: Average NLL: 42.474\n",
- "Epoch 13, step 1500: Average NLL: 37.257\n",
- "Epoch 17, step 2000: Average NLL: 34.311\n",
- "Epoch 21, step 2500: Average NLL: 32.699\n",
- "Epoch 26, step 3000: Average NLL: 31.816\n",
- "Epoch 30, step 3500: Average NLL: 31.037\n",
- "Epoch 35, step 4000: Average NLL: 30.417\n",
- "Epoch 39, step 4500: Average NLL: 30.037\n",
- "Epoch 43, step 5000: Average NLL: 29.646\n",
- "Epoch 48, step 5500: Average NLL: 29.495\n",
- "Epoch 52, step 6000: Average NLL: 29.365\n",
- "Epoch 57, step 6500: Average NLL: 29.134\n",
- "Epoch 61, step 7000: Average NLL: 28.810\n",
- "Epoch 65, step 7500: Average NLL: 28.816\n",
- "Epoch 70, step 8000: Average NLL: 28.661\n",
- "Epoch 74, step 8500: Average NLL: 28.602\n",
- "Epoch 78, step 9000: Average NLL: 28.500\n",
- "Epoch 83, step 9500: Average NLL: 28.431\n",
- "Epoch 87, step 10000: Average NLL: 28.391\n",
- "Epoch 92, step 10500: Average NLL: 28.282\n",
- "Epoch 96, step 11000: Average NLL: 28.244\n"
- ]
- }
- ],
- "source": [
- "num_epochs = 100\n",
- "step_idx = 0\n",
- "running_loss = 0.0\n",
- "for epoch_idx in range(num_epochs):\n",
- " for i, batch in enumerate(train_dataloader):\n",
- " batch = batch.to(device).unsqueeze(dim=1) # Add a channel dimension\n",
- " log_output = circuit(batch) # Compute the log output of the circuit\n",
- " log_output = 2.0 * log_output.real # Multipliying by two squares the output\n",
- " log_pf = int_sq_circuit().real # Compute the log partition function of the circuit\n",
- " lls = log_output - log_pf # Compute the log-likelihood\n",
- " loss = -torch.mean(lls) # The loss is the negative average log-likelihood\n",
- " loss.backward()\n",
- " optimizer.step()\n",
- " optimizer.zero_grad()\n",
- " running_loss += loss.detach() * len(batch)\n",
- " step_idx += 1\n",
- " if step_idx % 500 == 0:\n",
- " print(f\"Epoch {epoch_idx}, step {step_idx}: Average NLL: {running_loss / (500 * len(batch)):.3f}\")\n",
- " running_loss = 0.0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "b712e5df-4ad2-4ae1-a7e3-f0c1df7db254",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Average test LL: -29.133\n"
- ]
- }
- ],
- "source": [
- "circuit.eval()\n",
- "int_sq_circuit.eval()\n",
- "\n",
- "with torch.no_grad():\n",
- " test_lls = 0.0\n",
- " log_pf = int_sq_circuit().real # Compute the log partition function of the circuit (just once as we are evaluating)\n",
- " for batch in test_dataloader:\n",
- " batch = batch.to(device).unsqueeze(dim=1) # Add a channel dimension\n",
- " log_output = circuit(batch) # Compute the log output of the circuit\n",
- " log_output = 2.0 * log_output.real # Multipliying by two squares the output\n",
- " lls = log_output - log_pf # Compute the log-likelihood\n",
- " test_lls += lls.sum().item()\n",
- " average_ll = test_lls / len(data_test)\n",
- " print(f\"Average test LL: {average_ll:.3f}\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5db6015a-b911-4f36-a471-c8ace9b9abdf",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "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": "3.10.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/moons.ipynb b/notebooks/moons.ipynb
deleted file mode 100644
index 8ff051be..00000000
--- a/notebooks/moons.ipynb
+++ /dev/null
@@ -1,798 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Train and Visualize a PC on the Moons Dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import random\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Set the random seeds."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "random.seed(42)\n",
- "np.random.seed(42)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate the Dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.datasets import make_moons\n",
- "from sklearn.model_selection import train_test_split\n",
- "\n",
- "data = make_moons(n_samples=3000, noise=0.1, random_state=0)\n",
- "\n",
- "X, y = data\n",
- "X_train, X_test, y_train, y_test = train_test_split(\n",
- " X, y, test_size=0.2, random_state=42\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Instantiate the PC"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "import cirkit.templates.region_graph as rg\n",
- "\n",
- "region_graph = rg.algorithms.FullyFactorized(num_vars=2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "assert region_graph.is_smooth and region_graph.is_decomposable and region_graph.is_structured_decomposable and region_graph.is_omni_compatible\n",
- "print(region_graph)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.utils.scope import Scope\n",
- "from cirkit.symbolic.parameters import SoftmaxParameter, TensorParameter, Parameter\n",
- "from cirkit.symbolic.layers import GaussianLayer, DenseLayer, HadamardLayer\n",
- "from cirkit.symbolic.initializers import NormalInitializer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "def gaussian_layer_factory(\n",
- " scope: Scope,\n",
- " num_units: int,\n",
- " num_channels: int\n",
- ") -> GaussianLayer:\n",
- " return GaussianLayer(scope, num_units, num_channels)\n",
- "\n",
- "def hadamard_layer_factory(\n",
- " scope: Scope, num_input_units: int, arity: int\n",
- ") -> HadamardLayer:\n",
- " return HadamardLayer(scope, num_input_units, arity)\n",
- "\n",
- "def dense_layer_factory(\n",
- " scope: Scope,\n",
- " num_input_units: int,\n",
- " num_output_units: int\n",
- ") -> DenseLayer:\n",
- " return DenseLayer(\n",
- " scope, num_input_units, num_output_units,\n",
- " weight_factory=lambda shape: Parameter.from_unary(\n",
- " SoftmaxParameter(shape),\n",
- " TensorParameter(*shape, initializer=NormalInitializer(0.0, 1e-2))\n",
- " )\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.symbolic.circuit import Circuit"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "symbolic_circuit = Circuit.from_region_graph(\n",
- " region_graph,\n",
- " num_input_units=32,\n",
- " num_sum_units=32,\n",
- " input_factory=gaussian_layer_factory,\n",
- " sum_factory=dense_layer_factory,\n",
- " prod_factory=hadamard_layer_factory\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "device = torch.device('cuda') # The device to use\n",
- "torch.manual_seed(42)\n",
- "if 'cuda' in device.type:\n",
- " torch.cuda.manual_seed(42)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.pipeline import PipelineContext"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "ctx = PipelineContext(\n",
- " backend='torch', # Choose the torch compilation backend\n",
- " fold=True, # Fold the circuit, this is a backend-specific compilation flag\n",
- " semiring='lse-sum' # Use the (R, +, *) semiring, where + is the log-sum-exp and * is the sum\n",
- ")\n",
- "circuit = ctx.compile(symbolic_circuit).to(device)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "TorchCircuit(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchGaussianLayer(\n",
- " (mean): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " (stddev): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " (1): TorchScaledSigmoidParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (1): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " (1): TorchSoftmaxParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (2): TorchHadamardLayer()\n",
- " (3): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " (1): TorchSoftmaxParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- ")\n"
- ]
- }
- ],
- "source": [
- "print(circuit)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "prepare the dataset for pytorch"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "from torch import optim\n",
- "from torch.utils.data import DataLoader\n",
- "train_dataloader = DataLoader(X_train, shuffle=True, batch_size=64)\n",
- "test_dataloader = DataLoader(X_test, shuffle=False, batch_size=256)\n",
- "optimizer = optim.Adam(circuit.parameters(), lr=0.05)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "setting up the data for the plots"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(160000, 2)"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x1_bounds = (-1.5,2.5)\n",
- "x2_bounds = (-1,1.5)\n",
- "num_samples = 400\n",
- "x2 = np.linspace(0, 1, num_samples)*(x2_bounds[1] - x2_bounds[0]) + x2_bounds[0]\n",
- "x1 = np.linspace(0, 1, num_samples)*(x1_bounds[1] - x1_bounds[0]) + x1_bounds[0]\n",
- "x1v, x2v = np.meshgrid(x1, x2)\n",
- "X_meshgrid_np = np.stack((x1v,x2v), axis=-1).reshape(-1,2)\n",
- "X_meshgrid = torch.from_numpy(X_meshgrid_np).float()\n",
- "X_meshgrid_np.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(tensor([-1.5000, -1.0000]), tensor([2.5000, 1.5000]))"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "X_meshgrid.min(0).values, X_meshgrid.max(0).values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "from matplotlib.pyplot import xlim, ylim\n",
- "\n",
- "def print_density(title=None, scatter=False):\n",
- " log_probs = circuit(X_meshgrid.to(device).float().unsqueeze(dim=1))\n",
- " log_probs = log_probs.reshape(num_samples, num_samples)\n",
- " plt.imshow(log_probs.cpu().detach().numpy(), extent=(*x1_bounds, *x2_bounds), origin=\"lower\")\n",
- " if title is not None:\n",
- " plt.title(title)\n",
- " if scatter:\n",
- " plt.scatter(X_train[:, 0], X_train[:, 1], alpha=0.5)\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "print_density(\"initial density\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.title(\"data samples\")\n",
- "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, edgecolors=\"k\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "losses = []"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 0: Average NLL: 2.091\n",
- "Epoch 5: Average NLL: 1.290\n",
- "Epoch 10: Average NLL: 1.179\n",
- "Epoch 15: Average NLL: 1.146\n",
- "Epoch 20: Average NLL: 1.130\n",
- "Epoch 25: Average NLL: 1.140\n",
- "Epoch 30: Average NLL: 1.151\n",
- "Epoch 35: Average NLL: 1.145\n",
- "Epoch 40: Average NLL: 1.136\n",
- "Epoch 45: Average NLL: 1.116\n",
- "Epoch 50: Average NLL: 1.114\n",
- "Epoch 55: Average NLL: 1.124\n",
- "Epoch 60: Average NLL: 1.135\n",
- "Epoch 65: Average NLL: 1.133\n",
- "Epoch 70: Average NLL: 1.104\n",
- "Epoch 75: Average NLL: 1.122\n",
- "Epoch 80: Average NLL: 1.123\n",
- "Epoch 85: Average NLL: 1.119\n",
- "Epoch 90: Average NLL: 1.134\n",
- "Epoch 95: Average NLL: 1.131\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 100: Average NLL: 1.142\n",
- "Epoch 105: Average NLL: 1.126\n",
- "Epoch 110: Average NLL: 1.136\n",
- "Epoch 115: Average NLL: 1.148\n",
- "Epoch 120: Average NLL: 1.123\n",
- "Epoch 125: Average NLL: 1.118\n",
- "Epoch 130: Average NLL: 1.132\n",
- "Epoch 135: Average NLL: 1.133\n",
- "Epoch 140: Average NLL: 1.128\n",
- "Epoch 145: Average NLL: 1.108\n",
- "Epoch 150: Average NLL: 1.104\n",
- "Epoch 155: Average NLL: 1.132\n",
- "Epoch 160: Average NLL: 1.116\n",
- "Epoch 165: Average NLL: 1.124\n",
- "Epoch 170: Average NLL: 1.122\n",
- "Epoch 175: Average NLL: 1.126\n",
- "Epoch 180: Average NLL: 1.121\n",
- "Epoch 185: Average NLL: 1.135\n",
- "Epoch 190: Average NLL: 1.146\n",
- "Epoch 195: Average NLL: 1.129\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 200: Average NLL: 1.124\n",
- "Epoch 205: Average NLL: 1.120\n",
- "Epoch 210: Average NLL: 1.131\n",
- "Epoch 215: Average NLL: 1.122\n",
- "Epoch 220: Average NLL: 1.124\n",
- "Epoch 225: Average NLL: 1.123\n",
- "Epoch 230: Average NLL: 1.129\n",
- "Epoch 235: Average NLL: 1.129\n",
- "Epoch 240: Average NLL: 1.139\n",
- "Epoch 245: Average NLL: 1.119\n",
- "Epoch 250: Average NLL: 1.117\n",
- "Epoch 255: Average NLL: 1.115\n",
- "Epoch 260: Average NLL: 1.129\n",
- "Epoch 265: Average NLL: 1.118\n",
- "Epoch 270: Average NLL: 1.128\n",
- "Epoch 275: Average NLL: 1.119\n",
- "Epoch 280: Average NLL: 1.120\n",
- "Epoch 285: Average NLL: 1.117\n",
- "Epoch 290: Average NLL: 1.131\n",
- "Epoch 295: Average NLL: 1.132\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 300: Average NLL: 1.140\n",
- "Epoch 305: Average NLL: 1.114\n",
- "Epoch 310: Average NLL: 1.123\n",
- "Epoch 315: Average NLL: 1.132\n",
- "Epoch 320: Average NLL: 1.112\n",
- "Epoch 325: Average NLL: 1.130\n",
- "Epoch 330: Average NLL: 1.128\n",
- "Epoch 335: Average NLL: 1.120\n",
- "Epoch 340: Average NLL: 1.137\n",
- "Epoch 345: Average NLL: 1.126\n",
- "Epoch 350: Average NLL: 1.129\n",
- "Epoch 355: Average NLL: 1.115\n",
- "Epoch 360: Average NLL: 1.119\n",
- "Epoch 365: Average NLL: 1.111\n",
- "Epoch 370: Average NLL: 1.128\n",
- "Epoch 375: Average NLL: 1.117\n",
- "Epoch 380: Average NLL: 1.126\n",
- "Epoch 385: Average NLL: 1.121\n",
- "Epoch 390: Average NLL: 1.151\n",
- "Epoch 395: Average NLL: 1.125\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 400: Average NLL: 1.127\n",
- "Epoch 405: Average NLL: 1.134\n",
- "Epoch 410: Average NLL: 1.147\n",
- "Epoch 415: Average NLL: 1.154\n",
- "Epoch 420: Average NLL: 1.136\n",
- "Epoch 425: Average NLL: 1.120\n",
- "Epoch 430: Average NLL: 1.124\n",
- "Epoch 435: Average NLL: 1.130\n",
- "Epoch 440: Average NLL: 1.127\n",
- "Epoch 445: Average NLL: 1.120\n",
- "Epoch 450: Average NLL: 1.136\n",
- "Epoch 455: Average NLL: 1.128\n",
- "Epoch 460: Average NLL: 1.154\n",
- "Epoch 465: Average NLL: 1.133\n",
- "Epoch 470: Average NLL: 1.128\n",
- "Epoch 475: Average NLL: 1.127\n",
- "Epoch 480: Average NLL: 1.119\n",
- "Epoch 485: Average NLL: 1.131\n",
- "Epoch 490: Average NLL: 1.109\n",
- "Epoch 495: Average NLL: 1.118\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "num_epochs = 500\n",
- "for epoch_idx in range(num_epochs):\n",
- " running_loss = 0.0\n",
- " for batch in train_dataloader:\n",
- " batch = batch.to(device).float().unsqueeze(dim=1) # Add a channel dimension\n",
- " log_probs = circuit(batch)\n",
- " loss = -torch.mean(log_probs) # The loss is the negative average log-likelihood\n",
- " optimizer.zero_grad()\n",
- " loss.backward()\n",
- " optimizer.step()\n",
- " running_loss += loss * len(batch)\n",
- "\n",
- " losses.append((running_loss / len(X_train)).cpu().detach().numpy())\n",
- " if epoch_idx % (num_epochs // 5) == 0:\n",
- " print_density(f\"Epoch: {epoch_idx}\")\n",
- " if epoch_idx % (num_epochs // 100) == 0:\n",
- " print(f\"Epoch {epoch_idx}: Average NLL: {running_loss / len(X_train):.3f}\")\n",
- "print_density(f\"final Epoch: {epoch_idx}\", scatter=False)\n",
- "print_density(f\"final Epoch: {epoch_idx} \\nwith training data-set\", scatter=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "test hold-out average log-likelihood"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "hold-out avg log-like.: 1.09 vs train: 1.08\n"
- ]
- }
- ],
- "source": [
- "test_running_loss = 0.0\n",
- "with torch.no_grad():\n",
- " for batch in test_dataloader:\n",
- " batch = batch.to(device).float().unsqueeze(dim=1) # Add a channel dimension\n",
- " log_probs = circuit(batch)\n",
- " loss = -torch.mean(log_probs) # The loss is the negative average log-likelihood\n",
- " test_running_loss += loss * len(batch)\n",
- "train_running_loss = 0.0\n",
- "with torch.no_grad():\n",
- " for batch in train_dataloader:\n",
- " batch = batch.to(device).float().unsqueeze(dim=1) # Add a channel dimension\n",
- " log_probs = circuit(batch)\n",
- " loss = -torch.mean(log_probs) # The loss is the negative average log-likelihood\n",
- " train_running_loss += loss * len(batch)\n",
- "print(f\"hold-out avg log-like.: {test_running_loss/len(X_test):.2f} vs train: {train_running_loss/len(X_train):.2f}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A 3d-plot better visualizes our resulting density"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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DlStX0N7ejkKhgG3btkEphQ0bNqC2ttZaWuO576SUuHr1KgYGBjAwMIBcLgff97F8+XJ0dnairq6uyEUIwMazuIowJa4UKW4NpESVouogZV9/fz927dqFjRs3YuvWrWhtbcUdd9wB3/cBoCyicts9efIkzpw5g7q6Oly7dg2NjY3W2mppabEuQiklpJQAYPO0yNpKXYQpUsxspHlUKaoKruwTQiAMQzz55JNYuHAhVq5cOS2CEEKgrq4OtbW12LBhAwqFAi5fvoyBgQHs2LEDQghLWh0dHchms9baCoIAhUIBQog0ZytFihmOlKhSVA2usu/SpUsoFApYt24dFixYUJFzkMUEANlsFrNnz8bs2bMhpcTg4CAGBgZw7Ngx7NmzB21tbVZF2NDQYI+TUiKXyyGXy6U5WylSzECkRJWiKuDKPiEEDh06hBMnTsD3/YqRFCHJe+15Htra2tDW1oYVK1bg+vXrVpBx+PBh1NfXW2urtbUVvu9bF2GhUEChUMD58+fR1dWF5ubmNGcrRYqbiJSoUlQcpZR969evx65du8Y9liykcgmh3P3q6+uxYMECLFiwAEEQ4MqVKxgYGMCePXsgpYzlbJGL8OTJk6ivr7cxLHIRkoowdRGmSHFjkBJVioqBavYVCgUAQBAE2LZtGwCgt7cXhUIh0fpx25iM1cJdf+Uik8mgu7sb3d3dUEphaGgIAwMDOHXqFPbt24eWlhZ0dnZCSgkhhCUuKSXGxsYghEhztlKkuIFIiSpFRcBr9gHAyMhIkbIvDEOrvKv0uacKIQRaWlrQ0tKCZcuWIZfLWRdhPp/H3r170d3dja6uLrS1tcUEGYVCAfl8Ps3ZSpGiykiJKsW0kVSzb9u2bVi0aFFM2TcV62ciVJoQamtrMW/ePMybNw8///nPsWDBAoyNjWH//v0oFAoxF2Ftba0lrTAMLUmnOVspUlQWKVGlmBZIfEDKvjNnzmDfvn1Ys2ZNkWiCBuvJuvcmQrVSAcnaWrx4MVatWoWRkREMDAzg3LlzOHDgAJqamqwgg0o/kYuQVIRpzlaKFNNHSlQppowkZd/JkyexYcMGdHZ2Fu0/EVFNZQCv5qDP2xZCoKmpCU1NTViyZAny+bzN2dq+fTs8z7Ok1d7enuZspUhRQaRElWJKKKXs6+3tRVNTU+IxnKhKYbLEUw13YjmoqanBnDlzMGfOHEgpce3aNQwMDODIkSO4fv062tvbrYuwvr4+lrM1NDSEQ4cOYd26dWnOVooUZSAlqhSTgqvso5p9Qghs3rwZNTU1JY8l66HSxFJNoiqnbc/z0N7ejvb2dqxcuRKjo6O4dOkS+vv7cejQITQ0NMTKOgHA5cuXAcDmbAHpOlspUpRCSlQpykY5yr7xQANvJZV/N8uiGg8NDQ1oaGjAwoULEQSBdRHu2rULSim0tbUB0PeBuwjDMMTo6Gias5UihYOUqFKUBbdm3+XLlxOVfeOhHNffZHGjYlRTRSaTwaxZszBr1iwopTA4OIhz585BKYV///d/R2tra6ysExdkpDlbKVJopESVYkJMRtk3HqpBVNVor1oQQqC1tRWZTAbnz59Hb28vBgYGcOnSJRw9ehS1tbXWRZjmbKVIESElqhTjYrLKvvFwq1lU1UZdXZ0t6xSGoS3rtHfvXoRhGMvZqqmpSXO2UjxjkRJVipKYirJvIlQ6pjQTY1TlwCUT3/fR1dWFrq4uKKUwPDyMgYEBnDlzBvv370dzc7MlraampjRnK8UzCilRpSjCdJR9E8HzvIqXUbrZqr9KtymEQHNzM5qbm7F06VJb1unSpUs4ceIEMpmMJS03Z2toaAi7d+/Gvffem+ZspXjaICWqFDHQbP748eNYsWLFpJV9E2E8C0gphYGBATQ2NqKhoaHs9p7u4GWdpJS4evUqBgYGcPDgQeTz+VjOlhAC+Xwevu+n62yleNogJaoUFhS0z+VyOH78ODo7O7Ft2zYsXrwYK1asqMjAVoqogiDAjh07cPXqVQRBgIaGBusKoyXlJ9NeJTATFYWe56GjowMdHR02Z2tgYAAXLlzAwYMHUV9fbxeNpPvmrrMFpDlbKW4tpESVAkBc2Uez8a1bt2Lt2rWYP39+xc6TRCy5XA59fX3wfR+9vb0AULSkPJFWR0dHkVV3K8aoKgEhBBobG9HY2IjFixejUCjgzJkzOHbsmL1vpCLs6OhAJpNJc7ZS3JJIiSqFVfbR+kvHjx8HANx9993o6uqq6LlcohoeHkZfXx/a2tqwbt06hGEIpVRsSXkqT3T48GHkcrkiV9ethmoRazabRUdHB06fPo1nPetZGBwcxMDAAI4dO4Y9e/agra0tlrNFfUlztlLMdKRE9QyHq+zbtWsXrl69CgBob2+v+Pk4UVHS8MKFC7Fy5UrbHw63PNHIyAguXbqE8+fP4+DBg2hoaLCuLqpgfiugWv2ke+t5Htra2tDW1oYVK1bg+vXrNmfr8OHDqK+vt6TV2tqa5mylmNFIieoZivGUfZs2bcJjjz0GKeW0xRMuSPV39uxZ7NmzB7fddhsWLlxo+zQRyNW1aNEiFAoFnDt3DocPH8a2bduQyWRiFcwr0fdbza1YqjJ9fX09Fi5caMs6Uc7Wnj17IKWM5Wy5ZZ14zhaPbaWCjBQ3CilRPQPhLnQ4Ojoac79VoyYfx9mzZ3H+/Hncdddd6O7unnI72WwWs2bNwuHDh/Gc5zynSA1Hg29XV9e0JPWVRrXl9BORRyaTQXd3N7q7u62kfWBgAKdOncK+ffvQ0tJiSauxsTEmyPjpT3+KO++8E83NzWnOVoobhpSonmFwa/ZdunQJ27dvjyn7aCCt9IBKsbCLFy+ip6fHVhKfDvjqwVwNR4scnj17FgcOHLAJs11dXXbwfbpiMtdGi0O2tLRg2bJlyOVy1kV47Ngx1NTUWNJqa2uDUsq6ANN1tlLcKKRE9QyCW7Pv9OnT2L9/f5Gyj4LqlbSogiDA9u3bIaXE2rVrK0JSHNySSFrkcGBgAAMDAzh+/LgdfKmmXqlBdSbK0yfCdFdPrq2txfz58zF//nyEYWit1P379yMIAkgpMTAwgLlz56K2tja2zlaas5WiWkiJ6hkCV9l38OBBnD59Ghs3bkRHR0fR/pWsIDE2NoatW7cim82isbER2Wx23P0nM9iWs19NTY1NmE2qqdfZ2WljWxP1rRK4VSpp+L5v782qVaswPDyMp556Cv39/Th27BiampqstUVCljRnK0U1kBLVMwCusm/nzp0YGhpCT0/PuKvxVmLQGxoaQl9fHzo7O3H77bfjiSeeqErsq9y+JtXU6+/vt9YlxWcqLcu/UZiuRVUKlLMFAOvXr7du40uXLuHkyZOW1NKcrRTVQEpUT2O4Cx1yZV9vb++Eq/FOl1AuXbqEbdu2YcmSJVi+fLkdqGZK9XReU2/ZsmUYGxvDpUuXbO6RUgpnz56F7/tobW2t6KB6q7oVAViymTt3LubOnWvLOpH0PZfLxXK26uvr7fFpzlaKqSAlqqcpkpR9W7ZsQXt7O9atWzehdHu6RHXmzBns3bs3Mf5VDaKqRJt1dXWx+MwTTzyBMAyxe/duKKWsxdDZ2YlMZmb+dKplUQGRCtRtv1RZp4sXL+LQoUO2HBblbPm+n5izBWglZ21tbeoiTBHDzPy1pZgWaAAIggCe5yUq+ybCVMUUSikcOXIEx48fT1yzqlq1+SrdJsVTFixYgK6uLlvl4fjx49i7d6+1GLq6uqzFcLP66rZdbYtqovYbGhqwaNEim+t2+fJlXLp0Cbt27QIAmzbQ0dERy9k6duwYrl+/jtWrV6frbKWIISWqpxnKVfZNhKm46KSU2LNnDy5duoSenh40NzcX7VMti6oa4CrC1tZWtLa2Yvny5bbKA5V1KreA7o3o940Qakym79ls1pbDUkoVEX5ra2usHBYRVLrOVgqOlKieRuCiiXKUfeNhsq6/QqGA7du3I5/Po7e3F3V1dYn7jWepUdxiMqjW8vbjwa3yQHEtt4Bue3t7oovwVrWoSrn+ykUS4ZMg4+jRo/A8D7W1tbhy5Qra2tpi1laas/XMRkpUTwNwSbCr7Ovt7bVqrclgMq6/69evo6+vD3V1dejp6Rk3flNpMQXhZpU6ymQyRQV0L126hCNHjmBsbAzt7e02tlWKvCuJarv+pjKZKIX6+nosWLAACxYssLHAsbGxorSBzs5O1NTUpDlbz2CkRHWLo5Syz/O8CZV946FcQhkcHERfXx+6u7uxdu3aCWe3t5LrD5gcAfICurTo5KVLl6yooLGxEV1dXaitra1ij2duMvF48H0ftbW1aGpqwrJlyzA8PIyBgQHruk6qLJLmbD1zkBLVLQxX2TcyMoK+vj60t7fjjjvumJZLpBzXX39/P7Zv345ly5Zh2bJlZcdmnk4W1XhwC+iSi/DkyZOQUmL//v0VLaALVN+tWE03m5TSWkSUNrB06VLkcjnrIjxx4gQymYyNa5F7Nc3ZenojJapbFEopXL58GZ7nob6+fkrKvvEwkevv1KlT2L9/P26//XbMmzdvUu2ON5jeCjGqqSCbzWLOnDmYM2eOLUnkeV5RAd3Ozs5pWVzVjlFV00Ip1ffa2lpbWURKiStXruDSpUuxe0fuVbp3STlbnuehpqYmFWTcgkiJ6hYEuTv27duH2bNnQwgxJWXfeCjl+lNK4dChQzh16tSURBoTEeBUCKdaA0612vU8D5lMBqtWrap4Ad0bEaOqFsiiGg+e59m4Fc/ZovXJyL3a1dVlK7yTtdXX14d58+Zh1qxZ6TpbtxhSorrFwJV9nufh/PnzGBoamhJpjIck15+U0i6sOF75pfHwTHL9lYNKFdB126wGZgJRcVBZp8bGRixevNi6V8m7QArMzs5OdHR0QEqJbDYLz/OK1tlKc7ZmNlKiukXgLnRI6wiFYThlZd94cC0fEmkEQYDe3t4pu6eqofqrFvlVC+P1NamA7qVLl7Bv3z4EQRBbYyupgG61XX83IkY1VXD3KldgHj16FHv27LH1CZuamorKOqU5WzMbKVHdAnCVffl8Htu2bYOUEgsWLKg4SQFxQqGFFRsaGrBhw4ZplQ+aiFT6+/sRBAG6urrKPk+14ybVQDl95gV0qXo5V8LxAroNDQ323t7KFlWl2ncVmKOjo3jqqacwNDSEX/7yl6ivr4+VdUpztmY2UqKa4RhP2UdLK1QD5Pq7du0a+vr6MGfOHKxZs6YiIo3xYl8nT55ENpvFvn37YtbDRBbcrWRRTQVJSjhyER47dgy1tbXo6upCGIZVuxfVJqpqqgobGhrgeR5Wr16NhoYGXL58GQMDA7E6jvRHpAWkOVszBSlRzWAopZDP5205JPK9UzXy/fv3WwKrNDzPw/DwMI4fP44VK1ZgyZIlFflRJokppJTYvXs3Ll++jHvvvRe1tbW2TBEFyZuamtDd3Z0oMKjUYNHo7wI8H4AepJ63uR5QFzESzK5I+4RKEIm7wCENvBcvXrSlrHg9vUrgRsnTq91+JpPBrFmzMGvWLFvWiZYr2bdvX0lLNc3ZunlIiWqGgi90yGv2cTm47/v2R1NpDA8P48qVK7jzzjsxZ86cirXrElUQBNi2bZstvZTJZBAEQSxIniQwINJqbW2dUoyq0dsJ+BkACrCDow8Iem7ak0BjzR6zSQJKYSRYP617UGn4vo/u7m50d3ejtrYWg4ODqKurq0gBXY5qy9NvBFG5+Wq8rFPSUi8kZqGcLa4iTHO2bhxSopqBcGv2HThwAGfOnClS9lVyFV6CUgoHDx7E1atXMWvWrIqSFBCPffGVf6n0EsXhOJIEBv39/dZtE4YhLl26hPr6ehvXaszuBbykQZUILWOeCkAps69g79sO60cpNYkJoDGrq4AjDDEi75rSfaimpL62thbLly+vWAFdwkxT/U227XIsQnepFxKz7N+/34pZyEXo5mw98cQTWLRokV0GJl1nq3JIiWoGIUnZN17NvkoTVRiG2LVrFwYHBzF//vyqxDrI+hkeHsaWLVvQ0dGBdevW2QFkoh+0u0JvU8NhQAjACwGc0DspBSj21VYy2g5RvF2fOeFsJa5fePpY30ejtwOQalKEdSOXoncL6JKLkArouqvyTtT2rRqjot/JZNpPErNcunTJ5rs1NTXZ95uamlAoFCwp8XW20pyt6SMlqhmCUsq+8Wr2VZKo8vk8tm7dCqUUent7cfLkSYyOjlakbQ4hBHK5HJ544gksXLgQK1eunPQPt7HukOYVISKLJzZAK/0eERE9FwCk2c895YT3kbfPDhYe4CntSpQSCAoY8e6d1PVUEuORCY/NSCntkhsk325vb7cDb1IB3Rvhmqt25feplqriYhbKd+MlsXzfRxAEGB4eRltbW6ysk5QS169ft+dPc7Ymj5SoZgBKKftca8OF7/sVEVPQ6r/Nzc1Yv369/QFV2q0I6NjX5cuXsWbNGixatGhSxzZm9wG+r4mH/7gtSZWwVGKWk2IPU7FsEgYV6oufATwPjZiYtG62hNzzPLS1taGtrc3Kt/mqvG6Fh2pL34HqEiH9TirV/5qaGsydOxdz586FlBJXr17Fjh07cPr0aRw7dsxWze/s7ER9fb2NbaU5W1NDSlQ3Ga6yj9wypOwb74tbCTK5evUqtm7dirlz5+K2226z56tGYu6JEydw7tw5tLS0lCQpmoW6aKzZb2JE/H6oElyjkslJSuiYFHuv6FwTDRSl7ok5jsQYvgf4GTSqnebcCiPyzgnargymMti5q/KStbBt2zbrAqt2CkC1Y1TVsl48z7Ox47vuugsAYlXz3bggF2SkOVvlISWqmwhS9pFv/tSpUzhw4EDZhV6nS1QXLlzAzp07sXLlSixZsqSibXOQQOP06dNYvHgxRkZGyj62CTuhslmHpNiAaUmnHGsKgCILVBiBBHtO28eDAFMGlrM/ERfQ6O8GlETjXGDJrAxGqjDuV8LqcSs8XL161aYKBEGAnTt3VqSArtvvaseoKlWhvlT7SilkMhnU1tbGquYnxQXpj1yE1Eaas5WMlKhuEspV9o2H6ZDJ8ePHcejQIaxfvx6zZxfnCU1m4cTxwOsD9vb24vLlyxgeHi7r2CbsgMpkDEEZIYSS8fiT7iw7ilx77H0ZImZJSSauIOWfe3yii492ceJhgENe44D2y3hoxG7bp0pJ3ivtniNrgfKxBgcH0dLSEiug29nZie7u7kkX0HX7TeerBqodXyPXokuG2WzWLqyplLJlnSh1oLW1NZazBcQXQk1ztjRSorrBmKyybzxQcc3Jnn///v04d+4c7r33XrS1tZVse7quHsqRKhQKtj7glStXymq3CTugPD8iKQUAoSEpFbeulEJEMKxtS0ictDiRiegxhgSZOgAox6LjA58VbpQxGNI+StrnjdndsbamSlzVTsrNZrNYsmRJrIAuJcvSOlG0xtZk+jHdZe4nArnWq4VSRMUhhLBxQUodIBfrkSNHUFdXZy3Vtra2opytgwcPIpPJYP78+c+4nK2UqG4gkpR9W7duhe/7U1qN1/f9SVk9YRhi586dGB4eRm9vr53BJWG6rr+xsTH09fWhtrYWmzZtstLniZJzG/JPAr4PJaIKEQAicrIWlYq2J5EUvY6RFJepg1la5lH3UB8XGy8dgiKiofamS1gJ2xtr9jJZffnEdSNr/bn5beQi5DlHNPBO9N2einx8Mqi264+IcDL3vr6+HgsWLMCCBQsQBAGuXLmCgYEB7N27F2EYxlyENTU1uH79Ourr66GUiq2z9UzI2UqJ6gZhqsq+8TAZMsnlcti6dSs8z0NPT8+EA8d0XH+UI9XZ2Ynbb789dm2liKpm+DE94HuOq0/KSOUnOUm55JREVlxEQZukI6pw2xPs0BIWFO+/8CIrDYiSjGOuSeezLZfMaMARvq6OYSpjIChgBBsTD7lZ1dN937eDarkFdN22gVvX9TddIsxkMra6CK2M4N6/QqGAmpoaqxYka+uZkLOVEtUNAPmbp6LsGw/lEtXIyAi2bNmC1tZW3HHHHWX9oKbq+stiO9obFV7wvFYABUBtBYLIMljcEWJxuwRGHoMgohDsDwqKSKqURaU3RI9EYrHt9JK5RhViVkoRYQGmPyg+v/CKZfCU+EvPgThpAZq4SpHWZKwvu58EsjVolDu0pSXvLt7tJkvfyy2gSyWw6HtMFkI1cCNcf5VqXwiBlpYWtLS0YNmyZcjlcrh06RKOHDmCU6dO4cKFC7E1yibK2aJQQ2dnZ0X6dzOQElWVMV1l33goh6iuXLmCrVu3YsGCBVi1alXZA8FkrLWst4tZQb4ZgFU0aPseEAZGMRcl3Coz+Aul9HalSUJICeWTpUOljcxBSQTlebF4j37b6Ttdi+v+U47QAiJuTQkRkRQnbs8hqVLiDtrsWloTElapqhimr54ApECjtw0IA4yIe81lVbfqxVSIpFQB3T179kBKic7OzqquBADcGNdftdqvra3FvHnzcPbsWSxYsADZbBYDAwM4cOAA8vm8LetEqwy4OVvf/e538clPfhJbt26tSv9uBFKiqiLCMMTx48fR1dWFmpoaq+y755570N7ePu32KeG31ABy7tw57N69G6tXr550cm05rr9sZh/0gJowwErjTqNqENx1Bxhi0vsoIQxZsWbs8cJYJTCPRHTCxJgUEDIloAzj9flccUVM+WesJykZKZJQw7wWQpOsnS2LuLsvyWIqqpLhJVha4xFWqQE7oQ6hNKWcwqeAoAClWqtqUU13MOYFdKly+cDAAM6cOQMpJbZt22bjWuPFUCeLG6H6qyYRAlqclM1mrYtVKYWRkZGiVQaItJqbm+H7PoaGhtDc3FzVvgHAZz/7WXzsYx/D+fPnceedd+LTn/40Nm3aVJG2U6KqAriyb//+/di4cSP27t1rRQyVWuiQfnguUSmlcPz4cRw+fBh33nknZs2aNaW2S83OLUF53PrgwgXF/tzXXKXHnzMlHLVI1yQZ4dn2mLBCStYXMEJS8bE9dAreKmm6wYUZgFX3CWFI0Iv64ErjQ8TJxnPIPck9SNdUyi3Iybhc+Fp8smn1IPaeaSr/uEmg0iWOeOXyrq4u7Ny5E93d3ejv77cFdGnQpSr5U8WNcP1Vm6jccwgh0NTUhKamJqvCJGt1+/btOHXqFP75n/8ZtbW106qaXw6+8Y1v4F3vehc+//nPo6enB5/85Cfx4IMP4sCBA1Maf1ykRFVhuMo+z/Owe/du1NTUTEnZNx7oh8dni1JK7Nu3DxcvXsSmTZvQ2to65bZdiyrr7QEy9ENxRAgUCyKCKElU7LVV3bH4E5GER+dAsSTd7otooA/DuGTdkpfj8qM+Jgoq9ImFUGybgPIUhCU9vZ8iV55LRDxxmM4v4iQcHecQUknLrEx3nvAAH1g79zRGsLi8YyaBags1fN+PqeBo0N21S1ern0wB3aT2Z7KYohyEYTjuddfU1MQStXft2oWf//zn+Kd/+icMDw/jwQcfxEte8hK85CUvwdKlSyvat0984hN45JFH8IY3vAEA8PnPfx7f+9738MUvfhHvec97pt1+SlQVBFf2CSEwPDyMQqGA5uZmbNy4seI/FPphEKEEQYAdO3bg+vXr6O3tndYsyiWqbGZ/tKhg0QCapMJjkFITGVlEMoxiPkRAVCXC80AWmpBKEwJ3AwLMqlGRS5DcfMKDte4C1h8iNnpOj8wFKCTFyjwTSzP9cGNWQNQ3NxZGEIJZgGF8yREiN2WuN8mCcmNu8caL3ZocnkBj/t8hcmMYbr6/RBuTx41UFE63gG5S+7dqjAqIJsDlnsPzPNx555343Oc+h/b2dvT396Onpwff/va38e53vxuPPfYYent7K9K3fD6Pvr4+/P7v/37s/Pfffz8ef/zxipwjJaoKoZSyL5vNYtGiRVWZzdGgEYYhcrkc+vr6kMlk0NPTM+1VXbmMXJMUEHfVAYkDpQKzrgxhWOWdMu43EVlTXHQBpcUHQgsFAECEgPIci4TvL4xQgwgrDBzhAuujJSZ6j4sejNZQKQChJisoEwdi8TZm2Qnm9ouRVpG4gse0HGLiuVgx2fs4JFhUPSNBoegBqqYGTUM/rhhZVdMqGS9ReSoFdF1Um0iq7VqcTvX3oaEhLFy4EO94xzvwjne8A4ODgxWN/w0MDCAMw6IKN7Nnz8b+/fsrco6UqCqA8ZR9J06cqJoSiwpZDg0NYe/evdPKyXLheR5e9MA8wHdJygGREM3wuauNEwN3vZEIQmqrQpB7TyBy/9HA7QmIMDREYPoBNtCHQfRaspiWNP3yBOAuxmj7GBpiMspCS0ICEORSVJGVx2NI3F0nPG2NRUGyiFxd0ordQtqHWV+WZOkaDWHGyMjcK2stIroGDuFBZTJoGvoRhptfgOmi2hZVuW2XKqC7fft2eJ4Xq47BvQ7TnbyNhxtBhAAm7fIEgMHBQSxfvty+bmlpqVi/bhRSopomStXsI2Xf6dOnK7IUx3ignKwVK1ZUbCBpbTymA/RFJJUQl0pU14XOPiETOZAloQdfBQWhvGibEMyy0ucTIYy4wvTF83RcivpDMR0iQSKIAJEbzRVTSKkVhzyRWJnz0yMQWZEhIyjAEJhKdNMJ5vaz8Sy3+K0VXnhRn7nlRecW7HigOKZF6j8BAE5FD88DhIemK9/HcPsLMR3crGTi8VCqgO7Bgwdj0u1CoVBVQUG1iSoIAjsxnSyGhoZKlkqrBLq6uuD7Pi5cuBDbfuHChYqtEP70LxJVJZDPmCwpKSW2b9+Oixcvore318rPK7VmVBLOnj0LKSWWLFkypQUISyEr9piZvRsHKUVS9LaMk5SxmIosGiKtsABIqYmCx62sNRZGbZr9hJTaCgqDqB2+v5R6sFdSn1fRuYJoXykhwgBCKX1uek/BnF+xR6kJkZ5TP+n6pNmPt0/3ghSMUkXExa1Syc5F75FMP2a9kpXnGUI0z71MtM0Qko3zCR9EbMr3Ad9H0+V/LvcrkIgbsQzHdEAFdFetWoXNmzfj3nvvRUtLC86fP28l8MeOHcPQ0FDFvRw3wqKaavvVlqfX1NRg48aN+Nd//Ve7TUqJf/3Xf8XmzZsrco7UopoC3HJIuVwO27ZtQyaTKVL2VYOolFI4evQojh07ZvMqKgVLUmS92Jk64rEmIO7uswN06AzYzKKypGMIj+JZ1nUVRgMvHW9zmmgfZcJF3NUGKHtOsy2gPirbluCkSp+JlcBL2FwqIZhwhNxsrnqPXHIotnBCti8be0VkRGq3HBELb4+sKu5u9BxLKjYhISGKp08QBtE5FVloUrfn+2i6+E8YnvUyTAUz0aIqBSEEGhsb0djYiCVLlmDr1q2oq6vD8PDwtAvoJuFGiDWm4vYDNFFV2933rne9C69//etxzz33YNOmTfjkJz+JkZERqwKcLlKimiSSlH3j1eybSoXz8SClxN69e9Hf349NmzZhx44d0yoey5FVO6MBGjCPpJZj21wFnUtKRVYXc/sRoRixQGRpAHpgVtFAS+67GJnIiLyoX54HQW49NlNWRqwgVJzUIpGFiCw4qzZEdC5KNqZ2wzAiDuXcFxtTI1GEYaWQJSArOp4qcoRMjEHuSjCFoIiOFYjiVTF1oIr2l0K7a6WwZKj5z1huwpDV2W9heN7LMVlUu+BttauAt7e3Y+7cudMuoJuEaospJqP446C6gVNNUykXv/Vbv4X+/n68733vw/nz53HXXXfh+9//fuISQlNBSlSTQJKyb/v27Vi2bBmWLVuW+COupEUVBAG2b9+OXC6HzZs3o66urmILHGblDiCTiYL0nCC4SIJbQkCy5USvmastvq8Z5GUILU+jgVRqK4AsAG4hccuKthOZSNY32ldKY3U5+7sI2XVYsiSLCto6coURIVBk3cSukfqqojYsQclIoCEichOGYJUH7bYTnKSYhRv7jqlYHM/u65kYG3wbV1Oep+OAfgaYYi5ftRc2vFEllKZbQDcJM9n1Nzg4eEMqU7ztbW/D2972tqq0nRJVmSBlH7koyq3ZN9mlOEqBls2oqalBT0+PdQNUgqhqCluhamoAEipYi8UhgKTB3hVUxCwntnyGCiMSi7niVNS8JSvowZ22+35EnNSuK3Rg1pUVTVh3HoMlS+ZODENNTErFlX0KjmycuSNJQMGtS4LnRcfa+8RiUbaaBuLnFcLElsyxTrFe58Y7j9Qv029LWJ75k4CMYllN5/8vhuf8B0wG1SSTG1HiKKn9qRTQLdX+THT9kUVVTTHFjUBKVGXAVfbt378fZ8+eLatmn+/7yOVy0zr/0NAQ+vr6EpfNqIRrUWUcd5/rbnMHRYo38XWZpGQuKMlIKYyr7cx72uUnIlITPiDIFcZcbrwPkvUrRhBMjGAtGxaDCoNiUhEAghC2Ujp35dlKGGDXSduYBUcSdpdEyKgKiYDMPqEypMvcnTGS8thzG8yKfx6uZRu7fv5ZIYpbmbaVJ3Xul6cJq+nMP+BI5jl2SfSJcCvFqKbafjkFdOmPy91vhOpvKu2Pjo4iDMOqu/6qjZSoxoG7Gi+VJRkZGcHmzZvLSpqbruuP3ItLly5NdC9O12LLyu2GFJIsD76NW1i0iavuQqDASIL6RCQVsEe6BBu7gm7bz+pK61Ia44UsHOUQFLNMWE5UVJWCX4NzTbFtpk1bfoksGHNuvjaVo1+wfXYtNrLoKNZnNReGfMmVSJaajTExNyv11X4uxjXqsX0SSQrMDciI3RKWia+R61J4WH793/Cdn81BW1sburu7x630cCvHqKYidnAL6NIaUSdPnsS+fftiy8jPVNff0NAQgFszd4ojJaoScJV9tBovKfvKTR6cjmvu9OnT2LdvH9auXYv58+dXvP2acBvL8TGDIT2Hay0wayr26JIBI2WSbDv7iYDk4Ap2JFcw8SIRxW+AyJqgnCmySGiAjyn5gmhg5rX/ghCmnhLrp0JsZV8pjdzbDPB+hlk2cAQMYMSQcO+pIgfAEoYVdCKzYP1nMnIhoiK87nncOJh903nOVZj2LbPNxMeU75t0AGmVgC+ddxL7GlYUVXro7u5GU1OTJaeZLk8fD9MVO7hrRLnLyCulcPbsWQCYdgHdJEzV9Tc0NIS6urqK1hi9GUiJKgGusm9oaAhbt25NdL1NhKlYVEopHD58GCdOnMCGDRvGlZ9PlahqCn1QvsfiGopZDWwQjAkVVHwwdEUKriVVNLBqYlHCgwA7norPqlAThJSwggqryjOkQSTnCR1zsX00fQ5DQAWRZUPSbdrPtYAMoanFq6M2KFfKPArKpQoDxKT17vULZjGRqEKQyy+j2wylFq1Yt5/pR9JaVLxYL9/uCipin5P7ubH9bPzKWFaemRRkM1i4cCEWLlwYq/RAEzMirWrHqKppkVSaCN1l5H/2s58hCIKKFNBNAi3xMVlcu3at6mt93QikROVgKsq+8TBZopJSYvfu3bh8+TJ6enomVOtMhahqrj8BVZONSMAaVTTgk2uJ4lV8oHfJyYkJAcUkFRSY200wKTlY/MoICei152mjLqbsU6wvCkDAhAkici8KARTy0b5uRQoTm1LL72DXS4RJlpi0Cz5qw09o1Rykdp258SB+L2IljoyFJ41ggywqx5qKV1k3liYnKbdgLQe34Ipk+E7fPAGlBASPh/k+mg7+DYZXva6o0sOVK1cwMDCAvXv3Ip/P49ixY5g7d25RjGa6qHYdwWoSIS1UuGrVKtTW1uLatWvTKqCbhDAMp3TsjcihuhFIiYrBVfadPHkSBw8exLp16zB37twptTkZoioUCti2bRuCIEBvb29ZX8wpxcAypDQzAzIvj1SqVBInIteCohhVkhuQxYVETBGoIlcfUGzpAEy9xywJwQhCQfcfKvLsEVlRgVpq1hOaoJbfod2dvByTbjwiKaqcThYYqzKhCQXR8XwftjQIFLkpYUolMUUexaxsO2CkJOLPubgjdp8SXH/2XjnuPxf02ZNF5XlAxkfTnr/G8O1vsrt5nheTcT/22GOoq6uzMZq2tjY7AE+3PFG13YoAqtY+/f5834cQYtoFdEudY6quv9SiehphOsq+8VAukVy/fh19fX2or6/Hhg0byv5STtaiqsk/FdXMi8nR4RgIzGpyYyCWfBLIhfYLCswFZuJSgMkpQlxEwXOPaICne8Znwdaq4tYFJ1TWn4DV/AsDQHpQq++OW40wrjeeh8Tdl5DRgB4oKJjcLCIuW/LJEBw92jwxc7wyVpUCbH4UveZiCLIO6ZwkoIgVvOUfVMI214py5fP8u8Ll70rf66a+z2F443+GCxroFi5ciKamJly/fh0DAwN2kcNSca1yUe0cLeDGEJWLiQrodnZ2oru7O1ZANwlTVf0NDg6mFtXTAUnKvp07d2J0dLRsZd94KIeoBgcH0dfXh1mzZmHNmjWT+kFNRp5ec/2XZpCkLRQbcgZviheRywpAZD2E0fsU/+HWFJEUYK0bESQo/2ipj9iqtyxGRfegUIgGU+6mTCIr3gcb25JQqzcY68VYO/Y4h4j5a+vmNP3iZEiEFBSic4cO0ZB4gghJSmPJmvtOJEprYAkBwQUVUjGXJ+tj7NqZVefuwwmJW4MEUv8BxqoybdaUdudx1V99ff2Eca3JlCe61S2qcgrGum5VchFSAV3uIqytrS06x3Qsqlsdz2iiqpSybzxMZPH09/dj+/btWL58OZYuXTrpmajneZZkJ0SsuoNir2kgc2bqrnqMy9FpYKP1n2i7m9QbBA5BQY+vdlshqkruMUUfL1fE41Ok5uNCC27FcFGFEFC3bUx2cdL1JiU18z9bNFZFS4rw41yrityRXiYiC4/1l1dF5zla/D5zdyj/PrhJzrzb/PNTrM+uJJ+7X6lNUlBmdIywaevnMLyh2KoqJU8vFdei8kQkLBgvrlVNoUa5RDJVTCX+5Xke2tvb0d7eHnMRnj9/HgcPHkRTU5MlraampinL01OL6hYHkdTPfvYz3H777fB9f8rKvvEwnkV16tQp7N+//4bEwGpGH9eBdD7rV6wSOJej2wHfdflx11oQf+6QVJEVRQMj5VEFZl9ewJUKyPKFEiXrr2DkEJNiUzzJ9FtKYNXdUNlMCZJix/NrpERm3l9plH5KRjE26gMnA3pthRDGeuOnjUndyXolYtJ/QpmVg4sqZLA4FW23DTPESInvz8jJHmqe8HJOJLBwoJQqyz3nxrXc3KNSca1nuvSdCuguXrwY+XzeWqhUQLdQKGB4eBhtbW2TOteNqPN3I/CMJCqu7FNK4dKlSzh27NiUlX3jgYiEz0aVUjh06BBOnTqFjRs3oqOjY8rtlxOjqhn+d5t/JKTSZeBicR1ZPDjxGBTPnXJjU6To84wbS7IirC5JcbKCUemR5BzCxKa8qJCrxywoqi7Bu2mJQkXbpYRac49W7Anoa7PddUjKVTDyayUxRsyt5pATjOVHrj1OHp6vyYrEFYKTD7vfUppCskTGwlTtCHR1de4iLOn+A6KCwPzegJEnbefuUUbWQkSxwYyPpic/jeFN/4U1p8xpy/9tJOUeUXkiN65VzaKuN4KoKqkorKmpwdy5czF37lxroe7cuRMnT57EsWPHJlVAd2hoCN3d3RXr283CM46oXGVfGIY4evQo7rjjjilbNeOBrzBKVSR27dqFq1evoqenB01NTdNqfyKiOnv2LJa0mtk4m+BHs20magAQn+kza4NXYaBBkwsPgsAO8jF3X4ycEl4HYRTfAYy7zwyY3D1FdQC5mIITg9Rtq9vvZSRFl8BdeQkkZS0NQ5B0LeSqCwK9dpa13KgNZk1xV5wybkLpAzV+vM90D5Jek/uSCEtJ0yprPyZh52BiFILi1xcRsiiStzvPjVu46Wcfx/Bz3m2a0jtNZ8AfL64lpcS5c+fg+35Flt3gmKlVI8oBrbGllMLGjRsRBMGkCugODQ3FVve9VfGMISrKpXCVfblcDkuXLq0KSQHRDzsMQ4RhiG3btkFKid7e3qKA6VTbTyIqpRSOHTuG27rPIFpTCXGLqsh9lGAx2EEd0UBqhRSRVSDCQK+WGwRxMqKBnl4DQIFZUjEXl7GOyDog1SCJKciq8hC3psxzta6HlR0S0T6uC5OuNSY4cAQhkl0zWVlBISJ4GRY/twO+iTsJoT2AClBSAZ6KyI/aJoKSiJKaGdkIlkulbJzLuAotCRvIpOuMk7qtSuF+Z+gYT2ii90RMcUnfsYotzunEtR5//HEAmFRcq1zcahZVUvuAXoa+vr6+qIAueYRqamrQ3d2Nzs5O6yK8Ga6/JUuW4MSJE7FtH/3oR/Ge97xnym0+I4iKVuMNzEDJlX3t7e1TTsIrB/QFHhkZwe7du9HY2Ij169dXJFsdSFb9KaWwb98+3DXnHKCy0eBNJBXLyXFECGQpKTYI06P7Y3cSe0UQRGTCycklKsCQldkWmjiNErreH7kSiei4LJ3iVor1TQjg9k1moDfXZUmAqRa5e8yxMmIxNi4miZETDfKmfev+oxgV3YjYgyYYS0JE9ojqGhIRk8VGhGRJi70v+Ak4IdO1s0cVWks4tmhkESErpgI0rkbT56af/CmGn//fp+T6Kxee50EIgUWLFqGtra3suFa5uBEFb28EUbnncAvoXrlyBf39/di7dy/+/u//HmfPnsWRI0eqvs5XEj74wQ/ikUcesa+nqzx82hPVRMq+PXv2WAKrBoSRHW/duhVz587FmjVrKh4D4xZVGIbYuXMnhoeHgfm0Si25lGDiHwqKW0+6o4gl9RK4uCEwqjcZLR+vPE8/p3iT3Q+RyIJvy+fpzpgKEc69CKlskTJcKQ2RmYHb4/5Lcw139GgBQinVousm48pEV9ptRRUqcmW6bkB+bGBUj4Uw6penRRH6GJ/dX8mIRkVqR+orkZNdbNG85+vnAkYMY4nNnIPqE1KF+FIgkhUojhtSH2kCQH3xfTR990O49IL/bnapjjKPyKTcuNZkEmZvZdcfoHOoPM8bl3B837f3RSmFbDaLb37zm3jsscfwX//rf8U//MM/4GUvexle+tKXYsWKFVXrK6G5uRlz5sypWHs3nmpvIIikgiAArcb7+OOPo7m5Gffccw+y2SwymUzFl4rnuHjxIpRSmD9/fsVJCoi7/vL5PJ566inkcjn82u2GEKwFRfEUPRALKXWsQjp/gH4MjXWkZGQ5cTWfQSweFQbFlhQN5rQN0ARFpFcITJwq1CQW6vMoS3BEKsw1SGQiJbD2Hl2zkK7VgseNJGIiAv4IIL7IYxg98v1C00cq1cRjaPlCzGqNVurV62gJktvbSQOzZIpcq8zatfJ0DypbA5WtAbJZXSuQ6jSOu26ViCYV8U8tbh2G5vOgBGM+IHoCqM1U3PXnopTVQ3Gtu+++G89+9rOxaNEijI6OYtu2bfjFL36BAwcO4NKlS+PGaWe66q+c9idDhEII9PT04GMf+xjmzJmDr371q3jFK16BH/zgB3jZy15mreNq4o//+I/R2dmJu+++Gx/72MembQw8bS0qt2Zff38/duzYUaTs832/ahbViRMncPDgQWSzWcyePbtqbhMpJUZHR9HX14empiasX78eGPx/bOas2AycCxSo0gKMRSIhCgWjsNbxpqi6egQbhyJ3IB/kgWLpuudpEvK8iHhI6UfWg0JkTfhmP48sBRFZJNR3IxRQWVPslbs0eZctMSUkLgMRaSSRNZyYDt07TwABEY4Z3PMFkMRbx6XIYhVRJRDrTpVMIGL6xNetsm4+D6quUd8PBdglQ5Lq/9H1WxeiKR9Fnw+5Q7lMne4Hd/PSvpz8fB/dP/pTiObnVo2oypG+TzVf6+kQo5pqqGBoaAgrVqzAvffei0cffdTmlFUTb3/727FhwwZ0dHTgF7/4BX7/938f586dwyc+8Ykpt/m0JCpeDmmimn2ZTGbaCxu6UErhwIEDOHPmDO655x7s2rVr3BnfdOB5HoIgwBNPPIHZs2djzZo1qL30Y5MoC8RiO7pzelCjOniGiARZOAQ2YNuVeImM+PLsZDnx1xSjct19+XjFCovA1PzzAHhhNMiT2zFGttRNBWx4VnQ9FiqKKcVcgIyo+WDNlYt2oJbRPQlYMnXIyMq+JitKxAmAWTtCSkNWzPVHeUu2th+RlbGgmtucKiLmelQYtcGToZ3dtKS9xASMLCm7eKVyyAuw1dWp/WymqgPcZBN+3Xyt4eFh9Pf3J8a1bkQMaSYumkhraHExxVT7+Z73vAd/8id/Mu4++/btw2233YZ3vetddtv69etRU1ODt7zlLfjoRz86ZQHZ04qouLKP8pb27duH8+fPl6zZN92FDV1QjGhoaAi9vb1obGys+Dk4hoaGkM/nsXr1aixZssT82M2gJyUbbNhMmQZIFUIvlcEGqSAwY6OJRdEglnG+4CTh5gRFVguvXJ7k9uOPhcDkEZm+BSHgG0EA5WX5LI+IvFsbn6VFChR8oz4D7BoRd6sRiUlDXO6yJLE6eaFjMbLPj8fegMgKscnLIqqyQRaicPrK18sy60JZC6ytM7Ku7PUQWXvRcx5jjL1mf5LtG4vJsXtjiZrtQ38ZX39GHvCSqz/FMO5DpVFuMnEp8OXkk+Ja2WwW2WwWg4ODVSnQGoZhRRS847U/1dV9gyCoiOrv3e9+Nx5++OFx91m2bFni9p6eHgRBgOPHj2P16tVTOv/ThqhcZR8RxujoKHp7e0vW7Kuk64+EGgDQ29trk/GqRVRnzpzB/v37IYTA0qVLAQA1F78fkZNQgDQzee4e4z7q0LiseD275IvTjzS4ExHl88zyUdGATtuBBJIiS8sIMMhtJhVQl43cgWSlWEk2tMvt3l+JSMrGZYzLi1/DRL74WGHZiLBErDAtkq0v2k5EQCWfOEGKbLQfDcLclQnErETVNQeIXZd5I4mUuCtTsf2sFWlIjdb9SipWa643usYEy8y6AT0j6qg8Kl2Lz83X2rdvH4aHh7Ft27Yp1SGcCDfCtTjVOn9AZVb3pZWOpwIqwDtr1qwpn/9pQVTTqdlXKTHFyMgI+vr60NzcjPXr18dmQJUmKqUUjh49imPHjuH222/H7t27AQC1p/9JrzPFV80FKbgQuQJjOVRIsB6Yq4/k11DxIDu3oABGWELPwPl+QDJJ0XaewBuG2roIQka4whCuAu75FYecSpARKdhIOBBbtkPF+87EDVZ4wB/dmBuYK1XK6HqF/Y/tY6wrS7im2kYmE/XF86G65kbkrDvFLsYhKVfVGCNnOCSmIum5694risupqKlQRsd5wsquGv//f4iRV34IlUQ1i8Zms1nU19ejvr4ey5cvx9WrV9Hf31/RfK2Z6vqj1X2rae25ePzxx/HEE0/g+c9/Ppqbm/H444/jne98J17zmtdMaxWKW56olFLI5/NWNDE0NIS+vj50dXWVVbOvEiRy5coVbN26FfPnz8fq1auLXAuVJCopJfbt24eLFy+ip6cH2WzWujyVbxbiUyH0yMJiM0RSsUKzDmmNF0ezA7aKLCqyoPhx+ULkwiPpNnfz8X2oD6Gx/qjorEIkvKDjKQ+pKG4jnMGdPff8OAFb8jLHlRBQ2P7GtrEFHfOF+H0EjGtSQtTUwwpDpIwIiVuHPBVg1kKomhpjtdA1JJASgHiZpBIWlmLKREoUBrQLF0pfmyuo4JOT2HvmswlNTMsT2i1bYVSi6sV4kFIik8nYKg8dHR2Jca3W1lZ0d3dPOl9rpsrfq+XqHA+1tbX4+7//e7z//e+3xRTe+c53xuJWU8EtTVRuOSRS9k2mEvl0XX/nz5/Hrl27sGrVKixevDhxn6kuF+8iDEPs2LHDujPr6+tt5fT609+Gqq2N3FlUBYGr4TgxcZECua70TtGAagUUTERBsapCPpp1A07OFLe0CtE2cvMBUXUKc0o9qHtmUESk+COCkgrY/BxmsJDrzGOvmcvMKvTIMuTfBfacrBolIULKlzLkWERg7BqtkIIN6kSwdG8zGUuQQkkoI1eH0PEoNW+pth5j0nKXgBJcclQ8VyFOLJJtY0uUCC6l90S0yGSMrFTcorKiFPbdUQrCE2j82u9j5DUfRaVQbel7GIZFNfHcuNbY2Fhsfa2GhgZLWhMN9jNV9XczlvjYsGEDfvnLX1a83VuWqFxl34kTJ3Do0CHccccdk0o0m6rrTyllz3nnnXeO63+thEWVz+fR19cHz/OsJQWwWSgF72mwtrN3RDEMHmwPwkhwUcp9JhO2J6n8uJuOFIGxihQUtyrEXyulCUsqIJMF+PpYZHkEhqye9VxLisolKZ/q6dGMXEWuQrK4PIGYcMRK3I27j/KfwkJ84CdL0rWs7P0IWT/ZMiV0fbZfAiIMdc6XDKEWrohIKmZBIXqeJHywxAFnX2ZRUc5ZLAGaTTr4c3ttKiI4uh8hIzfzp6TSE4oKghR/NzpHi6Ourg4LFizAggULEAQBLl26hP7+fmzbtg2+71vSSopr3QjX31TcdzfDoqoWbjmichc65Mq+e++9F21tbZNqbyokQiWKzp8/j02bNk2oqpkuUY2OjmLLli1oaWnBHXfcEftReJ6H31g1BOXVRW4uxUhKQv9HBKCvALZ8El+dlsc3rLVAg1qCLN21qKzVlGf7OW6/mLUVRESaK+jqDjU1evFExaw3yc7DSSpmjTgWiVX+EXGz59SmW5LIiitkNEi7pEXXauXd5pqUgi5bxBiERBRhqEnM9EMtXhmJJuw1uS4/9ugu6EhiCL4sCZ1PseNkqCuR2GMSngNxa0omnYu5AI37tOHL78How3+MSmCmlTjKZDKYPXs2Zs+eDSklrl69GsvX6ujosDX1stnsjLWoBgcHnxZLfAC3GFFNVdk3HjKZzKRcf+R+GxkZKfuc0yGqa9euoa+vD3PnzsVtt91WNDsSQuhqBVIaYmJEQzEPS1JxN040oE0Qm+KgGTgRR75Q/JoSYMnKKoRsmxftZ8soOYMUxZPygVaaPfc+S0hKGKm3nzFCP+7yE5FLLEZYpn+h2eYJQGlrUlAeEeVDARHxJCUwu7Ed606FcQciIgEiS9/T6jrfh1q8OoqzxRKBFXtu4BKUPT8nNadPPIGZjnVjkER0tiQUIyVLbHQMuy+e0MJGDxWtaTOTK0fwuNbKlSsxPDwcq0PY2tpqq99UC1MlwqfL6r7ALURUScq+vr4+1NTUTGs1Xt/3rRhhoi9zLpfD1q1brfttorVgCJ7nIW9r3JUPWv13xYoVVn7uovboN3UshFxMRFiK4g3cDaSKE2ipRl+SJRXKaLtrUeULcYvKJSESHRCSntO580ZJ5wHIIrJEAODZz42eU7FUPxMXH3D3GU9udt1nPCYWyzmCPr9V4XlRiaSk+BS3Crk1Vd8YF4lQX6Q+t1q8illSCS6/GEklTGySBA9kBcXqNBqSVYrVJnQ+39Dcc2rXjXXRHyUqS2muNbqPDV/4bxh988eK+zlJTCeHqhxUigh5XGvp0qU2rnX16lXs3r0bDQ0Ndn2tSrrcpqP6ezqs7gvcIkRVStnX3d2NtWvXTutLSF+AIAjGJZ7h4WH09fWhra0Nd9xxx6TO6RaOLQenT5/Gvn37Jl79l1fatm4849ai/Ck7AIv4YJSk9ksqakozbKXieVNAPEaVL0SiDLKoCs6ASySVN4rAHBGlLkIrClyezsoKeRkjVHDFB04/YfaPXYfrVov+lPAgPC+eTyQlUCjocwcsv4xXpigEEdmRxVEIgGxN1Ibv2/wytWKNWbkXcUuKKxYT7z1NFBJISkpNaHwf9r6V2ttajYy4Fdg1k9Wt4m5Nbl0ZglJmSRMhkFheayqo5jL01H41XHN1dXWYP38+Dh48iHvuuccuJ095Q0Ra083Xmo7rLyWqGwRS9tGsayrKvvFAX+DxXHOXL1/Gtm3bsHDhQqxcuXLS55yM608phSNHjuD48ePYsGEDOjs7S+5be/DvtNuPiwe4yo+7fZSKhAkUjxKIXD+ctJKsKk5OQEKMyrEyAB13IsuKy9RzQfQoBFQQ6v7XZqKKDUIA9z3PkJW2pJSXJD5wXGfFd5S5w8L4fsJDJIGj+2YsK4GIdLk1VWAKusAIF+w6USYPKpRRcmwYQq1aZ0iKWTBU7X08cuKuWXehR8XIiVx43O3H19HiFpKr5LOFdktYVPa+8P5Js74WUP8Xv4urb/zwtHJ1ZrLrbyLQBLS2thbNzc3jxrUo0Xiy3p/pWFRdXV2TPm4mYkYTVaWUfeNBCDEukZw7dw67d+/GbbfdhoULF07pHOUSlZQSe/fuRX9/P3p6eib2L8dWsqUYVRJJsbiNZIrAyaIQVVG31lkgI3diyLaTGIFbUEBkYRnSUhSHMu0rkwcmXvCrsEtdeJkEkuJQ8c1JldK5pBuIu8psFQdE1qcVGBjrlFx8BJeYqXSSVACNKVJCrVgTVTrnEwjrVkN8GydhLkEvRVL06BTXtav4Wteusy8nQXqP9ou5PMPIsiJRhdK/G2We/+IXv0Bzc7O1IBobGxM+o9K4EWKKarWftFZUqbgWrcrb2tpq71U5+VrTiVE9HVb3BWYoUZGy7+DBg1BKYenSpdNS9k2EJEEFrZB75MgR3HXXXVMuHwKUR1RBEGDHjh24fv06Nm/ePOFijrV7vwrUZOODW6I1RYM4czXxQdJaVCiOY3DrSjqiB560y+NV+QJT+oVxS4pIyrj9FMWm8qZSQyiBjFaVWUWf55vniLvNSsElqaJl12V0zQGzXGIxHhJmGLIiOT4th0EITGFfIiduwVBsJ5s15MtiVnxyAfczYu5LSy4l4lUx6ymBnLklSBXs+aKP1hoL9R/FrTzP5J+5hGjuuwctTPEE4Cn8+s7v4OhDv4uBgQEcP34cdXV1diBuaWmZ0ANxI2JU1VLlTVRVo1Rca2BgAEeOHCkrrjWdPKpU9VclcGVfGIa4fv06tm/fjuvXr09Z2TcRXCJxqz9M1887UYyKRBq+78dypMZvlNWOIyUbDSRc0cetKbdCAoG7g1zwQbBQiCf92uoTXERhfmgOKSFnHo1lpcjCIosqXwCUB2RDiAd+FaAK40U5UtQvFQ34thoHvx5DaDQwxyxI9n5oYjKx/CciaGUXcixaf4pA1lQgAeFBhSFERrsw1foN0Xn4uen0fDu3cux1uHEpxwpyLUYguQQU3S8bb5LRd8USmdBeUDdWR8cEIVRg7ou1Ss17QmDevHmYN28egiDA5cuXMTAwgB07dsRiNYs6TsQnGoZkl3ZILG2TELl/t+t+DTf/KiqFarr+yNopNxyQlK+VFNdqa2uz48ZUiTZV/VUJrrIP0Mq31tbW8gfwKYATFbdsqPrDdJG0XDyBagS2tLRg/fr1k/tBJSX46jeYNUXWlmADH5z4lDmGq/yov7SNV0+nBQ4JtLQHEBETEKn/coyUAB2T4peRDyGypv28EVN4wgonFBW89YUT3+EDOw2AgqVUKfYeDPEYEYS1Hs1/dG+49RIbfIxlyK+bVvUl4YQ5RoUhcOdGQCkooRLianD6z6wnKE26pYxGTlCAIVpDUnRfY4VmEX2WnqeXLSGiCrgghLXJxRTUjmLbAwkVxgm39k/eidzv/S9kMhnMmjULs2bNgpQSzZndgMgDOAMgw76XxrKkRSYBqFBqYYuSaBr6V31dhQDDXS8qcTPKQ7Vdf1Ntu1S+1oEDB1AoFNDR0WHr46WqvxkCV9k3ODiIkydPwvd9bNy4saquAXL9jY2N2WK2lSTGUq6/q1evYuvWrZg3b15ijcBSqN3zN2bpCzO4CuhlypWM3Etcks4tKhJTFMVISoBcUeTe40QRSuYWK0TPc8w9Ziqjq3wBwvO0JUVERoo/KKh8CNRm4L3shcblZ1R+1rXG6vXZvpdyAxJZGQGDIleeiTcRYlYHjZaIBmCKtUlVHJMiUDKvhLkfIXDXRtMN6ofzudgT8ROa57FajE5siq+XJbwYSXGrSgs6mLye/3kCKDgWlY1DqYh46S/Q+yqq9SeVXX1Z8colzkjS6O8221hiM4l9aEkXKy6JJlwKCvA8U5tQAJ5A08B3NWHN/Q+YLGgJkWq5/iqV7FsqrnXmzBkAwM6dO60YoxyvklIqTfitNEop+2bNmoWRkZGqkhSgiWRkZAQHDx5ER0cH1q1bV9FzJhHVxYsXsWPHDqxcuRJLliyZXIMUmHcl57bCBHdjORYXr+dGFhVZCeQe4jEqGvBslQgzaNNSHIBZr8g8J6XfeAmQUlnXoBVThBL+S16grSeSoduCs8bSmZBYBXskS5Jch8ZCk4aszCArYko5Ig3FLDIR1fXj9f/IdanA1tAynwuX1ZOLMlYZg1tu1E8v7uKLPQJFcSqHpATd79DI1V3XHnfj0WsqUMvfDxlpkYjCExCe0JOJwFhXobk2xqvZ//kO1LzvTZFSM8b65pEmGTFXJ30+Alq9CicmqeNmTWe+BeQLGF76qoTPPhnVrMwOVKd8Eo9rdXd3Y8uWLZg1a9ak4lrA08uiqi4DlIEwDG1hWUAv375jxw7ccccdmDdvXtUWHHT7cOzYMcyfP3/SOVLlwHX9nTp1yl7jZEmqdscXEQvC29JHki0Rz2bgQJRHAyAWZOfuHY4kyXS+EI/NSBW95s9pX4DFowrmkcnSoV1++rUhrUwmsqbM7FvZwZ36puKPdkRjsIsXskfhA/BM23SIcxy1yeNRsVgYIuEBgT5XBU1od98D+myUzWGjHYBYwViOpBqDriXFhROu0IHadtvg+3Ahhd2HEbBLWub7ofJB9NlBF6aNzgkgVMj+7htR84E36wmN0Le6ON+Nf9fY9zdWoUO/p2gfS1jQn2PGR9PRvyu+fyVAv7tqytNvRPmkBQsW4K677sJznvMcLFmyBGNjY9i+fTt+/vOfY//+/RgYGIiNMUmr+1YaH/7wh/GsZz0LDQ0NJQVuJ0+exItf/GI0NDRg1qxZ+G//7b9NqYrHTbOo3Jp9ALB///6Ysu/KlStVLU0CAGfPnsXVq1fR3d2NlStXVuUcPCh65MgRnDhxAhs3bkRHR8fkG7OuL49ZAIjcJhLRgKMUAC5PVvGxwlX80YBKLi+ytvjARCWTrOIvKP0cgLIiijASVSAiKZULdYrAb79MF5u1VSesNC5ynbmIJcyWgtL3ygOgKI7kAwghZCGyGojAqbQQX4FXIm6lAqywrnme8YB7N0WDqiU4Zj3wz4vHqmxXDQElCUfs+zyWZCYnLvG5a3+Zfe2jtZR4BQ42aXGTfcntlw+N64+1A8D/3Tfpz44sIMtN0XezKAG7aH7ESDR2nxC1Yes8Ak0HvwpcH8PwnY8AABr/8cP2PQAY+c33msu99Swqt32u+CsV1zp48KCNax09ehTr16+v2Oq+pZDP5/HQQw9h8+bN+Ou//uvEvr/4xS/GnDlz8Itf/ALnzp3D6173OmSzWXzkIx+Z1LluGlFxkqL6eWNjYzFl32Tr8E0GPLF2KrkfkwF9kXfv3o3Lly+XlyOVgNqtfxVX+zmSZ6Gg41T0PoBobSLowdpKs8c5EZ+B2xl3GAkGgKgaBT231SmM9JzcezTAAXGSIlcSAJWXeqCLVZ0AbCyGELtelvfkWl0ctmQUjLXm6UrtSjK5uWMh0P2i756AySEz9yRgg25oXInPez4bpFX8GlyrzN5nQ0qJeVHMquMWFbni6DPh4NtcIYXr9iNFoyWsAvusDTGRTJ0k7ID+XPPSGkTeO37H3GdmPVmSYgSb6MY0+xQRlGL3kFyMoTmh6ZfvAbU1aNrxBagj/ZGHUep73fgtvbhjo1Lwup5btcoXN3PRRDeuNTIygosXL+LjH/84du7cCQD40pe+hIceeqgq+VQf+MAHAABf/vKXE9//4Q9/iL179+LHP/4xZs+ejbvuugsf+tCH8Hu/93t4//vfX3YJOuAmuv6orH8ul8OTTz4JpRR6enpigUJagkPxWWcFIKXE7t27cfr0afT09KCxsbGqlhv1f3BwEL29vdOQjNKAp6IfNg2EARvIKKYCsEC547ICovJHlPsUBNHzfKE4zsS3SaVJKVeIBkBLRFxMYWbhuQBKqoicxqJ9Mq/7D9EA5Nbws9Yhf+QzdGaZkFstSeoNmMHUSN79rC53RBYcr8whpVbHUbtBiBjTUG4YJ6xsJhKZUD+VGVxpsLWyd8pZQiSPpxWTpYy7ahMg+OdoE3sTSIon/dJr+qxilhSb1NDxBSdWFchIWOIJeG9+bTFJxdycDknRx0ExQo6EyhdCmc8gJnRhVilZrhkfYnFH/HtPld7N34sv/LTkvZwuqil9p/bLIUIhBJqamrBs2TI89thj+Na3voVMJoN/+7d/w5o1a3D77bfj61//etX6mYTHH38cd9xxB2bPnm23PfjggxgcHMSePXsm1dZNs6hI2bd169aSNfvI5J1qwlsSgiDAtm3bkM/n0dvbi7q6OmQyGeRyuYq07yKXy6Gvrw8AcOedd06YyFsKNb/8CyDrRz9cPrgKmNk5zBjhxC0Uoh+4jakkDORJ29xq6VLpfCqaiQNWhq64Oo6sKY580jbTjsdcfjHLqYzH8WAHOsfl5vtQyELU1MWtKjuzN/fDWmMwIhJyh3rRAP6Sl0THUtFZEmLE3H50fkaKvE/ufbfXkBCPkiRoEXGSmkiiHrC2XPcekZi1BlX0J2CVf94bXwNFilhOUu61cJJSzufAY23WCxDa76k1QmOfRYiYWIg+eg8Qi9qgjl+JLFqj9qNjG/72fRh99QeL7/M0MVOX+GhtbUV7ezt+8IMfYGhoCN///vdjhHEjcP78+aJz0uvz589Pqq2bZlENDw/jySefxJIlS0ouGU8fUKWsnbGxMTzxxBMQQqCnp8eSRiWXiucYGRnBL3/5SzQ2NsLzvOm5H6igqTTEE5MQ0+BEcRYVvbZLVzABBYGsKLKkCvRIgxkbwHJBpHSj93JRfFHlmKVl86bC+Ht2X7P9eoDM635Diyh83yj9mMsndlCJ10WWlHNv+L6x1/o8KpMF6hsiF6Fbboi7FSkxONQWhyoEwAMv0O9ZNZ+MjnMVhU6ZIxuTClmdPq7m42tOKW1liDDQ+3DiSSIpmjRw6Tm5Z8Mwfj2Kfc703Lyv7HcAEJ6A94bXapIilZ6bD8bdeS5J2ftPfWafKSdtd3Ih2TUq9pqIznw+Yn4zVKDrECr+3ZUKCBUavvoHqDRuputvPPBk35aWFrzyla/E8573vAmPe8973mO9XaX+9u/fP+n+TBc3zaJqbGyccNFBqsNXKBSmbIkQBgcHS1Zcr0Ys7OrVq+jr68OCBQuwatUq/Nu//RsmW0E9BhogrXvJcYcI6Npr5KfnM/mYCCDKXYrVvqO2AL2d4hCuNUW5QpRTE4SRCozlSKm83q6u0/pUkXiCP8asKBKDwFyH5+sl3EnqTDEmkuWTq4lbadxVRNfCkfAZKM+HaGrRlmI+H1lSNCngsSpO9KHS8S5rRQkz9dP3XCj9XCVJ0s17RX12FH1CSihPmFWIZbz/JADhZarcWBQRkyUxM7Bz4QS5zFzCovurFFQo4b3+P0L5fvz+J2FCknImFPy5+bxKXq/93gOR5W2sK0/Am9MIeW44eh/sUQANX34vRh/+cHK/pwApZcW8PUmY7jL0k50cv/vd78bDDz887j7Lli0rq605c+bgySefjG27cOGCfW8yuKmuv3IUKZUgESpRsnTpUixbtqzow6u0RUU5UqtWrcLixYunfY6aX3xWiw+UFkzECIVcMsK8pmRK0MzYvObLVXDyoirotD0mPSaLLR6fAACVD+x9VPkwcvXlGFkx8YTNwwGgCvp82de9LMq5obW0fCNNJ7eZEJqsyNqyA7qKW5lRp5Ofc/l1DKYd3wda2oBcTp87JmyAJiXK6zKfgXjJixlJwahZmOsSCsp+Do6bzy3rZD4rEQSWmOhdm3ZAkytLUOY1Jyle8R1gVSkEW2U5iH8HYuSkLxMhYtaU9/r/qC1fILoHdD2uoo8T00Qkxau8m/2FS2D2c1DRI03CaNVh+k17gNdVB3lhNE5UNDEQAvX/+z0YesP/rAjBhGE4KVHAVNqfikU11SU+uru7p1XXlGPz5s348Ic/jIsXL2LWrFkAgB/96EdoaWnB2rVrJ9XWTU34FUJMKJSYLlHRuk6333475s2bl7iP7/sVs6hOnjyJAwcOFFV4nzYZmtmuIsc8zSJpNkzPaabLYxNchUaEpaI2ATDrIYyOd6Xp0OQjMnqAVPkgkbwUi0VZhd+YtuRUXkYOZyEia4mTE12jG68CzHivImJD1FYRiKjpfgHJZEW7ZTJAeydw8TwsWZFVkgvsIKlM6SRhrVJK8DXHsDiJMOdXRXEq6iO59qLtNFALTk4Ac9/K+GsgcvfRdqDYkuJkxa0mTwAhouPpu0HznFf/FkvAhiXh2MRhXDeeAy724PG3kP8GnfZoKRLuXuUuQvoNCAH4HkRzFupa3l6TzWkzE4fmv34vHu/5bXR3d6Orq2vKy5TcCNffVMq4DQ4OVr3O38mTJ3H58mWcPHkSYRhi+/btAIAVK1agqakJDzzwANauXYvXvva1+NM//VOcP38ef/AHf4BHH3100vd7RlSmGA+k/JsslFI4fPgwTpw4MeG6TlM9h3u+Q4cO4dSpU7jnnntsjS7CVImq5id/DtT4cfcbtyp4uSQbwAcii8qABrWk5FkXMbVXGJVOMtUpVD6IiyVyQRExxeJZCci+5iVRTT/hxd2RVnpunhN50XNOXFwGHoO1R+L3y15j3MJSnoAIodf36ugEzp+LkziHEBAPvIBZUzIiTiIpAHzmL6hPiiWzAhD2NXT8SUEP3nxhShkW94Hyp2wpJUZSNrHXuW5TODdOVoiTlZ34aCtFvOL/B/gZW4UqTkzO98cVewAolqZL9j67vy4B07ExlyC3zqL7GXOjGsISNZ4JC8a3a8tY797R0YHz58/j4MGDtgpEV1fXpFJVZqqY4kZUTn/f+96Hr3zlK/b13XffDQD4yU9+gvvuuw++7+O73/0u3vrWt2Lz5s1obGzE61//enzwg5MXtTwtLSqSn1++fBm9vb1oamoad//pWjv8fD09PYnnm/I5qBAruVtosIi5NVQ8fsJnj2EY/yED0eJ/MdEFc62EDgnA/OCJkOg8ZC3xQL3dDqictp7UWKg5Jq/bVWOSSdG9qJ+8TJNPMRBy/ykoXq+HrCoer+N9tqTkfr8YyekLix8rhJatx5b5YIH8QEL82vPNtSq2n3RiWI4FRYnESpk6djToGhLjVgYQSdS5dZwUtwlNigGVrQpDQ5qMpLjSk3/udH3muiLrVU+8xEtfopcpiVlRSfc0uhZ9Tud7XhRrCuP3nYjI87SbM7YQZBgRL53aqgXpHpjnYRibKIgWH+qKjE+8BPR3MpBY9e1PY9F/+Rjy+TwGBgbQ39+PY8eOoa6uzpLWRMuUzFQxxY1Y3ffLX/5yyRwqwuLFi/HP//zP0z7XLWFRTYaoCoUCtm3bhiAIsHnz5rJMzOm4/pLk7knwPG/SYoqaf/k40GhkwJY8zJvKGXDs4MfcZUEY7V8gl984s2B3Ow0KfPAPTDyK3H+WvBDV75MKyEmT1KvblzmaLSvUvOHF2u3n+dHg6WeYEUQDP1lQnk5ylVLXArSWlrlm6z5EdH3WQmMWF1f0WbenKD7W84B584Fjx+KuKSkhnrVZiyN4jIfuMVm7dmJgPgeryovKECUO7q6rj8AJhsQQSsZrDxJJAXFFILl+STRB10dWFb1nY2+m27/+60Btnak4IaJrBLtnMeFKAkkl5bPF5OgyIiSwWBy9Zy0qQ1SURpC0cjE/D7ldfQGvyYe8FkQfsVRAQVn3c93/ejfwzo8XLVNC9UY9z7OklbSk/I2wqJ7pq/sCtwhR8TJL4+H69evo6+tDfX09NmzYULbJnMlkIKXUs8hJqGQoRyqbzaKnp2fc803JosqKqOApzeAV4pYQi4cAMO4NQ0pEVi45xQYpY0ERKXFBBcl8KTk0tnCia0FpS0vRcvcEqaDMwKDyUisE3XtMA6+iCzCvPfaeNK4oM5ApQYM6Fy8kwBI7kRmLg3FhhLVG2WC+ZAkwlgOOHtOG3YYNUT1CXuKJBno3n8gq6RwCooGVq+PIIqXKIUmCAvu5O67A2HOzD5EUwbr3ZCRAsK4/RPeQLPbaOs3xvMCubSuJgBKsVLpOTiZurUGeQ+W6/rib0Ip/CpF7kluc9D2n7775asADRFZBjrJJhFTazQmFWAV4oGiZkqtXr6K/v98uKU8VzDs7O+24MRNdf8PDw2Wr824F3HTX30Qo16K6du0atm7dilmzZmHNmjWTyhanL9pkvhTDw8Po6+tDe3t7WdXWp0RUNHhIaSwDM/DZGbyMD/I00HHhBO1vRRT0hziB2QFDS86tyi/jRW4TckcJp0QSoGv2UWgpZ1R+Y2FRpl7Nax80AW+eMwXYQYwsJRuH8uIEQPsLl6DYd4mI0l6e0iV3bFULMys3gRdBIgEqh0QHeh5QWwusWgn4WZM/hDj5MIVfnFBKWBoxpR8jKsAZoBOIoEjtx2JTPCZFnzvV8iNLilcs4etqWcvcPH/Zy6BspRB+I7nlyb87XHbP3ZTM8iE3Hlcu8rW0rGAiQJHFZPvO23YI0J4HlowVXW9WaCs9UCzvmCYRQO2fvgu5//4JuOAlilatWoWhoSH09/fj+PHj2Lt3Lzo6OmIFtauB6bj+Kr0S+s3ELWFRTVQ1or+/H9u3b8eKFSuwZMmSSecO0BchCIKyiOrKlSvYunUrFi5ciJUrV5Z1vskSVc33/0wP8pyM7CDJBgou16UCC3agYhYVvXYTfqn4rFsxgkBqNyIkciFRW8yC4oo+G48iF+AYs5B8z1oxyjODizKDiVKaxGycisgDsFNks16REsIowsk9RXEvRmYEXnIpDI3CjFmWSmhlW+hMijwBwCQku7Ea+kwo9sHdfjGFH9tuySlhksAhVfKADcTVfnR8wIiQlmDhJMWtboXIouJuPyjgpb/BLKmk70SCZaXY85hYIgFWkeiQVJEVxUlJRlYVTRCovBJ5BIik7GRO2Bi4ENqqUvTRkiVpL8OZFCRACIGWlha0tLRg+fLlGB0dxcDAAC5duoSdO3eipaXFSrsruQr5dFx/T5fVfYFbhKhGRkZKvk9y8HXr1mHu3LlTOofneeOuwstx4cIF7Ny5E6tXr8aiRYsmdY4piSl4/AmIz+jJWuI/UBX9UK2Lj+CKKIxEWSml1VD8Rx8qG4eChJZkM8JCPoQqhBAZDyoX6lypjKetKF9bXCrQg4QckxAeUPvqX2Mk4mmCoQGJ3HhA5GakeAzNwsnaUgoQHgTM6r/Cgy1o64nIurSEAth1qYDIopNZIAygCjmIgtJE6RmSpIFeQRMrEWCSctIm0DoWR8wNaB5jVoYz4eCuslIuw4B9DnbtKEZGXI4essGbqd1isU3q+/PuA+obtCVlyX4cS2oikrKluxzLirv7gOKcqfFKR5FVRcRCknWuzrTVWMyf+VrphYSVyQ8z3FxQ5rlC9kPvQOEPP4ly0dDQgEWLFuHIkSO4++67MTo6iv7+fhw9ehT19fWWtKaSdEugFSamqvp7uqxFBdzCrj+lFA4ePIjTp08nysEni3IEFUSK69evn3TdrMlYVNl/+BOgMSIJQEUEBfNoxQHMwqFB3C5RoZLjVHxQtdvYD56QD6BCs/4QvZfTQgqahcqxEMIX0XMPkfXEEJLSz/eiAUwhEkyQa9MeSKOJmfXT9dFaUxCMnEqQFD23rlIiKi+yjkyyr8rWQoyNAIW8ua+IYjakPrT5UoCNdVmyZRMKIgdQf4Gi5eVJtcYXvnShlK6WQfeA9hOISMreYPb5KTCSoq6wftF26vsLHoDiy6vE8qTAGqGn45BUKSQo/4pIKjF25TwCkcvTTnIEy7Gi34Bx/dmYHADPXEOoYh5DJQFM0P3kS9Jx7fr6erS1tRWJMbZv3z6hGKOc9idrUd2ItahuNG4Ji8olkDAMsWvXLly7dg29vb0VWaJjvFyqSpCi7/vI5/MT7heGoVZHK2UDvtoFaAYOW4XCkBW3jrjSj0QQRGY0wAZh5LunHzVZUDSTJ0UfJUvmnMTesUDvT2NwQixKuwEF5Jh+rH/tfZF1YIiEygPZskm03LxniIjiUwKw5EQWlvB0sjC5qGIkhfhzguv6JBIR0OTb0AyEAcTwoG6TavC5S43oelUsdsasJy5aAABPMlGDilyt+gYb76Mqtqj4AM1zxWiA9gTUpuc77jkVDfBhCPH/fmQH7SLSAoBnPxeqtg6xGosxS4pfs2NJxQjE+S7y/Cd+Pcy6EkXEk+TyU46FJYtdf3z1ZVdkYshZmWsWvraqrNKd7r0hcP+9/xXhh/8c5YJiU5xIJivGGA80HqWuv1vQosrn89i2bRuklOjt7Z1yRrmLUhYP5UhduXKlZI7UdNrnKBQK2Lp1K55tZ7/Q7iiyqmJCCMcFAjildCL3h7JxKtMOwNwlzqAE6Dp81AaPTRUkRIa9zsuI1IiYclrZp6TeZl1/WbbWlCFdRdZVrE8KgO8MjopZQIakyIqynWZP6Pm4s3x23SR5933ddmuHHuivDkSGRaIrzAySXBBiZ/WG4PjilNYydrpCib6lwJbkUPfc58TgeJ+EdnEqTfrq+Q9G7jNaot5+r2jSwKpqUJyNX4t9KaPt491Xt/Buwv6JMSkgHr/i73GyChJysIj8OQlzFyCfmJn9VNJPcbzPIAETEUk5YgwirqRxLAz1oqKTXUZEKZWKKW40OFGNjo6ir68PTU1NWL9+fUVloUmuP8qRKhQK0ybFiYhqbGwMW7ZswfPP/gyoo1gIvWsGuFhcSkXPC+aH6nvxGbuVnTOLCtBVIxRrj8WjtBvEkKORm5NrD9DuvZg1dT205CXHJIQPY0WZ/fNEpl5EqORy8v1o0OGS8TCMFmkUzJICIsGEdatxMmMuu3GRMCLZgVtBW3IeVOccQEmISxej+81dfjy+xNsmK5V3g6vaaFDlgoMkNxhrV931HJYk7RBj0XWZe8GSjG2cja7BWql0/aK4DZXQbpErTkXk5FqDQPzzlWF8wcek2JQlp7D4dSxWpeKrBghE1eIDaeKubMIWSmOg67ipCgXgATKvtyUp7icCrUVVzoQ7SYzR399vK2O0tLSgq6srJsYgxd9kY1zXr1+v+uq+Nxq3DFFdvXoVW7duxdy5c3HbbbdNOUA53nk4kYyNjaGvrw+1tbXYtGnTtAtYjpfwOzw8jC1btugEvYvQBCGUGYcNKYSMrGxukTMwmaReRSIAxfLCiJysyy86v7aOAiaoiIgLgC6L5ORH6Zwo/TxGTGPKVKHQ/VYKaHwjc1ERQWZMZXQlouuyognHpUauQIqj2H2kM8CK6Bh7X9zZKL/w8QiLNvhQsxdE91sGmrh4O3wSwc9BFhcN4rTSMV+gkA/43LVFW+/8FYegSo2m442yNFkgIkexJW3vc+yLwY53LSNOUE7/pYRbgFa43/3xSCqMXITFrj9uMfHXKrKiPKEnb9aiYscIRC7tgH2fA22M4l3/FfhEee6/6Sya2NDQgMWLF2Px4sXI5XIYGBjAwMAAjh49ioaGBnR1daGurm5K7Q8NDQFA6vqrFCbj+nvqqaewcuVKLFmypCp94RYVEUdnZ2fJtbKm0n6SRUVS90WLFmHFihXAvm/rNwSstFYFevC1y2mEZsZoBkbhwVpSioLKJl5F/vlEOITFYd1/NoYEqJxe60d4ACQgjaoPgHX9xZofU1HiJbn8SI4uWbyJlyGiZSTIQhLaHafdctzVR1YiIyy3/h8QdxNNCG5hePHtwjPklYWav4RdZHQDxekjZpu5xpjFRVYOWQIq/rnE4og6NqbuenZ075IIxLWCYsSC+DH2/ihoKb4oJquislO8fzL5Oe2XYAUmHifjBFZsSQWR649KQ+ULThtum4yglIinXZBFRfGqULuvZV5bUSoAlDKu6lDZjINyUKmqFLW1tZg/fz7mz58fE2OcOnUKSikcOHAA3d3daGtrK2ssGhoaQm1t7bSXRppJmPEW1blz5wBg3OrnlQBZVC5xVMpySyIqWg6EpO6Zv/0IkIVxrekfHq/6rChvSajI/SYEVGj2oR+nQLSUOPVfRlW/rbXCLSilDAkR8yhEsnRl3XuQCrIQKQFlIXpP5XSfQmNVAUDDa54ddyHZMZAN3ED03A7e5N4DIw3F/jf7cJIiV2CMmJ3BuMjCYm2V2mYJmMeGDNg4pRavip+TLLDjB/SmmNuPucqs1aW3qzt6I2IW7jUycnItuCRiYYncUU6ackjKdfsh6lvseqhN7pZTxeRB1hS3pIikCDxxuRRJ0Tb+SO9xa4pvF0JP1shqIuEJl/mFEgICMhSxW0SPwaPvQOazn8REqEb5JC7GOHfuHE6cOAEA2Lt3L6SU6OzsRHd3Nzo6Okp6eahyeqW9TjcTM5aolFLYv38/zp49C0BXOq4mfN/HtWvXcPTo0UnnSJXbPicqWn4kvhyI1NYLpfzQIGatBxW9hiYSO3jFCnOyGTgtn84Hz6LBHDY2pQrGasrEv+Tyuoxtk47rT2RM14wVJfOGEyg2RVUmuGUBHVhX9IOiH73nW9GElZ7TJdCl0XWTZRCTeZtrS0pijllY/H1VbEXF2hBxgnLIMw66Hn28WrEu+iyDAsTh3U6fTBtrNuq8MLciRCwWJ+P9KGX10L7WlcjIjOJUvI1YoV7XeuMTDDcJmRFV0XusX27FCSAiJhKdlCKppDiV3ccIhcg1TY+e0GKfUNnvjgpV5EJXCsJTCHOeuUR9TyYTq6p2+SSlFGpra7F69eqYGOPYsWPYs2dPSTHGjShI++EPfxjf+973sH37dtTU1ODq1atF+yQR5d/93d/hVa961aTPNyNdf2EYYufOnRgaGsLmzZvx85//vOIr8LoYGRnBlStXcPfdd9tFvioJIiqlFI4ePYpjx47Flh/x//dHgAb2QwKKJN82Z0ogyp2iIDI9KhiLiwYYx71koPKhGZuTPwOVM4ONL6IZZ17aun2QMJaVfi8ci8ZuIqmG394MO8hZ60BFcTZTQUEopWXqVtFoBlIvgQwUuz9KAtLEb9z4Ca8gbo9n5MPk9vG2x7Gs4jsn7EbWqCzeRnE2UQN1212IYoXGyrE5TGQVkkUpYHOwPKd9bqnapwmuTimj87hWVQnOi7kRaWLgiimSqk+wz8Gq+5LchlIyYmIkxcs9BdwCC+OP9BmSXJ/ypdzntq90z8y9NXFUIdgab5PEjVzioxwxRmdnJ0ZHR6e8uu9kkM/n8dBDD2Hz5s3467/+65L7felLX8ILX/hC+3qqSsSbblG5S33k83ls3boVANDb24uampqqLBVPoBypq1evoqurqyokBUSVKfbt24cLFy5g06ZNsVmPyGoCUgAEufXIpUd5VIoNLjTQBoa4AuY6YlJ0HeMia0vvo2gwMBYUoPRsU6qYwg8A1JixsKjahFJQ+Wg8DccUPPMtknkVH+cpvmIHFRlJwYUfEZYQppwSXS8JGpxBwAyyepilQZwNvPa8InnAZm5Qulfmw2HnUMX78iU9+H7uQMAl9UXn9qLrtRaXialEO0XHqzA6xhKvQ8axR/acu/3sel7OdnuIe694G861xJSJjmKRu/uSpOd8/5CRVyhLkJSpWckLUgemUn+eFpBk/Qu0i1qFUv8WrPgn+m7TqsVKarISdEkBoCS3phTGfuddqPurT2A83MwlPpLEGDt27MBrXvMatLa2oq6uDk899RTuueeeqhDWBz7wAQCYcJmPtra2SS87n4TpqwQqiJGREfzyl79EXV0d7r33XrvEc7WISkqJnTt34vz581i8eHFVv3RCCARBgIGBAfT09CSY5tqaElCRP53WCKIfHv0wCzS7NMRDarLQqQZdkPo9mnkC7JGdWUbbVV5C5aSWoTPIsfjAb2Xn0NYUtRPkTJdz0JJoIKoMDkRWkx2cjFxZ6NpsVlxhpevG6ih6jsi1xRNh+fbYwCqjY3kbENEkYCLEjkPCMaXa4ATGiVBFj7H93NcwVhX1nX02tC2muHM/XOf6uRsxSdmXpE5N2mbvf1h8XJLlZWssGgTsNbeYAmdbIYxIrOBYWfTHrWPK/SuQZWXICia2yr/aJT53VfKzjDAd1V85KJcISYzxohe9CEeOHMF9990HKSXuv/9+LFy4EI8++qiNdd1oPProo+jq6sKmTZvwxS9+EROtP1gKM8aiIhHDggULsGrVqtgsoBpExdet6u3txYULF6yss9IoFArYs2cPAKCnp6coH8v//IeBWv1DUmQxFaT1pVsEbJAxM32V01UhyDKy+/EERwHr6uMVKZQtN6MSpyzyuowVZlB5FVP6yXzkOuICijAn0PbGeyN3Fo8fcXm5IHcUXSft78f3pVvAXFW0+KCylooTmOfH80FTwJzHi1tA3PJwn5P6kfoSc5excyfCsXL4dls+iu1H/SslCom5F1V0b9zgSqIVJZhVx4jdtUjtRIATmvOck6O5N4KTpbWYgsjNy2NUpdx6LkkRQfFK8YUg6k8obTkpK6IIpJ7s8QkaFUY291qFxP3KpHhFFpWUAsLj9z4ZM3F13/b2dqxZswbZbBZf+cpX8NOf/hT/9//+35sirPjgBz+IX/3VX0VDQwN++MMf4j//5/+M4eFhvP3tb590WzedqADg/Pnz2LVrV0kRQ6WJipJr+bpVlViOPgm5XA5btmyx1mHyF5t+/NA/LB+GsJThKjOo2GRfc1jIfszk2qPBWJg2ePkct8afkacrCf3jDvU2kRWxfaTJMdGWlzGAaIyVjA/yiAiPSFZKvXKvvTbjahJGhm5dYVRg1hlEeeymKOHW3Du6CFfsIEPEXG0wl2/FHXxfGe3nqTgh8HiXGw+LtcUHe4wDZiXFPlDethM8srOFBOusSETiuAmLrCqn/aSqE/Z8jJjsuSLLS8gw5naOnde1moG4IEKGcUJyScpV/HFyK8R/q5yktJtPMVeisqSlFIymh00MLAcLa0nJUGDode9G8998HKVwI1x/UykycO3aNbS2tqKmpgYPPPAAHnjggbKOe8973oM/+ZM/GXefffv24bbbbiurvT/8wz+0z++++26MjIzgYx/72K1JVEePHsWhQ4dw5513lowPVZKohoaG0NfXh66uLqxdu9aa7tNdjj4JIyMj2LJlC9rb27F27Vr8+Mc/Tp4l0fIWPDYl9KOigYnGFfK9C+gxmkossbWGFCU70jEx2TciS6oE5Jg+Xvh8G8nX9eswryxZWSJTAmEO6Hjd3RFRkTUlEBGCWeYDEhEpCBN7okYp8OXGWcjqMgNuNFM01hWUHqjDIKrKwK2XWKxHsH0Qt8BiyccyPnADyRZc0TpY1LWE71UiuRFxJTSStD+9pmAL9Z+UdEVWlWKPfMKS5NYL49uTYlMc7r5uGSTAUe0F8ed0P4tIihEcj0/Z58xqSgDFpFRB6t9WHlA5BRXw3CkFGXgIrVw9IqzxcCMWTbyRq/u++93vxsMPPzzuPtNZjLGnpwcf+tCHkMvlJk3AN52ohBDYtGnTuOU+KkVUly9fxtatW7FkyRIsX768qu7Fa9euoa+vD/Pnz8eqVavsdrc6hfcpcvvRDma2SomyAKwMnRMOl61LI3gQhqT4b0zybcaCItegydi3wWYmpFAS0TII7FsS5iPPHLegwpyIe8BCCWQzzP3ICFconfhLRWcB7Zoha0oxclCIBmH644Mys6SEkvoypTNA043gpAREJASUcPc57kE6hs7LXwPFFmspJIoamJXkqvEQvRV/4pKZOT99f9x4le0j4veBqwi5gedaas59FZS868YCXZLilhPg5EQxq2k8kuKPznNlhBRkTWlBhXEFBnq78IT5PqsizhdCsY+EkZVK+hAiTHUJjnJR7vp4Lqa6ui8tTVItbN++He3t7VOyEm86US1btmxCS6YSJHLu3Dns3r0ba9aswYIFC4rer6RFNTAwgG3bthVV0kg+hyYNpZRW+1FQO6DBHZG7xhIXm0VKMziHSs8CjQcrRk4A4tLvCMrU9VPGKlOB4/qDUfaxsnxhDvqHrwCZAzxft1nICf1cwKmEbvphycf4X0iunuQ/J4ICEBu1KeahwNyEiOIkdLm2ArveT1BMTnA3moSt0sBl4q7lFXPvsZykmKUHe65ExaH9HOyNZ2/RgO5MWEqCk4wq3q5k5PYiwuX3MiZVN4cpdz9VfB+khCCpPJGgcD4b1/3nWk6Jz5lYoqT7L5ms3EUPlREe2ckXoCdBAQkqzCUVEGlT7O+k+HvY/1vvQfc3/rhoO3BjYlRTtaiqXefv5MmTuHz5Mk6ePIkwDLF9+3YAwIoVK9DU1ITvfOc7uHDhAnp7e1FXV4cf/ehH+MhHPoLf/d3fndL5bjpRlYNyVvkdD8ePH8ehQ4dw1113lZwxlLMeVTk4e/Ys9uzZk1hJI5GoBPOfm9mwyNAYoqLcWIo38e+tazlQQJncgtYtxA4xK++OF1slN18ROfmwpCa5t4ZZU+2vWg+b52VcfEpKCLsGlGMRUmIvEQ9ZSwqwxGEJj70GXZ8hDulFr5WEsK9h2vYgmHBB8RytUL9v17zSd8F8Pl5swLOThqI4F++nC1XCvSYjkrDbHIKSqphA3LaTnrtkwa1DisfReT1mudpjzaAeFmzLwlX3uQsiutc4HkmRK8+uTMzIdSKS4iWVgCguxT8nW5nCJLEHMKXIjLVkboG9DKXjUuT+04d78MaxaGYyUVW7zt/73vc+fOUrX7Gv7777bgDAT37yE9x3333IZrP47Gc/i3e+851QSmHFihX4xCc+gUceeWRK57vpRFVuvb/xVvktBaqTdfbs2bLci5SQO1WFzLFjx3D48GHcfffdiT5il6jEn30EqFFmaaPIbaQCM5iQqIJcdZx0KPGXD74FsrLM9RcMGVAQGdH7MmTvGfWTiJGgniCrUFnXX5gTZhKt3SdhQRRZUzaBmIdalFEP0hLzlC8VU/oJmwAcVwaKiLhiM3jFYlUK1rVnhSkhIzwYF6WI3IQmbqYMwwrTR70vdxEyF59k1phkgzxX6CW6/0q42ZJiX3b05LMLZr3ESA1xRaWSZuViGbeklIwKs9oROk6egixz9ppfj7DPHbjkNKH15MjTaVvBIS7aL1/aooqVSrLuPyo1ZiZtVkjBLh9m/hGy7xcAys2TEmjqaIaCgJICw2/+CMJAofWL741d6o0QU0zF9Tc4OFh1i+rLX/7yuDlUL3zhC2OJvtPFTSeqcjAV1x/lSA0ODqK3t9eWzi8F+sJNJUBaLiEWW1TSuKLig5ySerkMFLS/3RIIT/ZlCkDFhBQ2JiWiH2fUNorGUJtKIwFpYlJeNr5POBZpGwBNUEJEBOUbsur8rXWmQ3ROIlNHvk0uOL70h5SsOro7qDJrils0sTgSHPJA3DoJjVSRRB0AoASEJ23bwpqPMlqUMebyA3SZbSa0KMvSSXhtzxUUb4NDSPZ9xzIj8uTH0u8k5v5zyJOseDCCVtKEEWX8PjPrShNgGH9OZDieu49vB4pFFQVmKXneOJaU3q5TMQyopqV7qyg+xeacSiooI0PXt0sAIguRyUAVQjTN1pPLMDcGpSTygyOQ0oNU2tK6/PqPoOMr/8O2NxMtqqfj6r7ADCCq6SxHXwq0ACEtrkjS8InOAYyfDZ4Ed2HF8VYb5kt9XH3zp9C+WltB2tVnBhMRWVV2PCULyxNRWERA/xCt+4gIizoWP3fk9jBqPTbeughzsIURlBI2gdfzlf2R61wTvX8h70UWYcxyUxERSwWFECJjigLygqSehPUz0pgqyIoUEXnRSrQAIBnBkcuNkbVz481NkNCVIMLIEiIXIVf5QQ/gEWFR3MoRUqho/7jV5LyOv8GsYP4Wd7sJWCVfElyhR2w/Fd/Pbo62i1C7PomYBSfLopgW6y/FBznc1XldwnL34blSSfvmC5Gij1tYBecx74wHRulKKw0A0PmFgfEYBAqqAD0HCJX5XusJkJ/14dXUQ4V5hPkAwhPID40AMBMyJeB5CvnrI9i1a5ddWr6aqj+l1Ix2/d1o3HSiKgeTIarr16+jr68P9fX1uPPOO8s2nYWpjDAZQUUQBNi+fTvy+XxZCyuSRXX1zZ9C65IR7VbzgSg5F1Ech//obJFao1gi95l155hAsTLPJbS4grYheWE4u9SBWd6A1HzamxYthcBjUdb1pwSCPOB55nlAZECNG4IiUYUQmkCkBDIZfaG8QjipDq3VKIsHxSSlnbVMwqijnnEJUVyLt1WqTe72M/spel+yY4RDbEVkUoZlRZYLtefKyEn5lyjKQPG+bj8skWqiFwmEJUK2je5VkloyNBXfpYz6zWNSSWtrjSeaIFCMqpCQ5Jt33H4uSdGxPMnXtisjgVCof0PKNCM82hZZVQp0HQHCvALgIT80Cm6KjQ2NIFACNfVNaPujb+LE770Y+/btgxACly9fRnNzc1mT4cmAxqHJuv5Si+omolyiGhoawpYtW9Dd3R3LkSoHQohJKf9yuRz6+vqQzWbLXlgxKkwL2BiNISRu3aiCfp9cfoqKvxqyAsWjeFyEkncVTCklgHJhbRIuGT1Jk30AYQFaWMD7ogRkQcVILAg96+4rFDx4Apj3qpXmB2/iUD4i1xiRVcbXMUBqnKxITmSU3Gs7ELnm7IBnyw8xa8fuC20lKRm3ltgsW18sIzIe+5Gw+wuKGYJZW8T4oYxIC4gTTkzkkUQ23PpyyMGNQbnb+L7W8in+QC05cbeoACxxx7qjNBkTCblJzeOBVuwlS2wikuIydLcc0niP5Pazr0sk/Cats4VoMiYDwT4mBRmEUAi0C9jLoDCqSSo3PKq5WQk0zV2tfwcSKFy+iJ6eHoyOjuLJJ5/E5cuXcfr0abS0tFh5d319fXn3bhxMtMx9KYyNjaFQKKREVWlUyvV36dIlbNu2DUuXLsWyZcumJIgolxBHR0exZcsWtLS0YP369WUTou/7aHz//zWhaf1rUQoQof4haQsk+pHZdaaUST+kHyBbqiBy+0E/sSsB610lc4VoyS70eZSAkiQ+MPtL7eZTZEmx30iQi4QTAJDPe/CMmzJf8DQxmcK1QhgrRHjR+Yy1JDwRxaZ4eSWfsWkYwha05X/0vmCsGSIaXLmLT8o4QVuRgrk/pPbzM9FgTkRE1oYhQiFlVN2d9qPzRCeIWznEYW7CMX3oQFzsQMdzt5szo+CCBy7+KLl6rk0HoNciijHFBCM0aaJ+8OeMCOk5WU9uzGwyJEVJvrFt4z9akgr4OcOYq1flQ+t1UAFZUDDP2X7kcQD09xUeCiNjgBAYG7oOAGics1K7CEE/Q4G65lk499rPYu5XHwUArFu3DplMBgMDA7h48SKOHDmCxsZGS1qNjY1TGouojuBkawkODg4CQNWX+bjRuOlEVQ6IQEop8ibKkSoX5VhUg4OD6Ovrw5w5c3DbbbdN6ku48NP/DgmgfeF165oQHqA8UyopVLqKuIpKJ8VqlsrIhafdb+Y96+KjCT9TAAKWzGLxK4qXh7ABZhuDMktMBHnPbNP+/EJewPeiUSEIdGxq/stWGlNL6RgUufKUBLJsxV4gTrBESJ6RgHswRJLkniMFH7fAJJA1yg9FBEeExYiFCynofsCQZBjAWlV8UBCM2KAgSMJOVlbR516iGgS58Tx3O5KrN/B4kbF+hJK2YkdkjUrz3XAIxVUREmlTe7SN3MbWOjOfGQkkyqlewe6PlcBzFyAnvCRRBcWjgNIkVXBIKs9ek+KvQKRZbFGpQvz7ROQlA2FvRzAWQkH/1saGdRpMXfdyqwIcOn/cZId4CKVAfftiKKVsjIqv0FsoFOyy8idOnEBtba0lrZaWlrLHi8nGyglPx9V9gVuIqIDiTHClFI4fP47Dhw+PmyNVLibKpSKrbdmyZVi6dOmkZ0pK6eCsEHopDmFiSyLUk92Yy43GZkr8JZecBABDUqG+B/o9E9PSb0dtOF4QawRIhZgkCrrqhJ2Em+W5A+MqscKJggfPFOzUXORB1BmrTABUIUNbQYgGD+pHTH5OfdEWlQh1sVGVyUSWmL5xcbEE/wEXClF7NMAWCrptn329iRyLhBTmhpGsm7cjA0Ze/EYqxBJ/7SY+tZfxw+yCluya+Q4xgQa9H8WXBGScLLjFyK0eTkyeFw345CIlsUZRTMpD0cKG1nIyHSuyqBDvk2tFuSTFhRRcFOHmVLkkZWNWzmtPQF2PJpYyR30yVhTdUhOnkqFZDdt88WQgTRf1Z5kbCSAEUNOx1Fyzh5H+4/CEgGRVKgYvnUL+1f8beGRFEZlks1nMnTsXc+fORRiGdln5HTt2wPM8S1oTLSs/VSHF03F1X2AGEFU5N5Q+MJ5XQCsAnzt3bsIcqXIxXmFaKpw7Vatt7J3ftSVZlFLabWbGB7NRB33JLaVMfpVJsrXjEFlhIezgq6QZ0+mHSeNNoOySBkoBSurBXkmSl5v+GO8TKfzCghmjfSIkAVWIXsfISgrjSgQi4YKnxR58hkt5TAQlAfiRu0s3pgdzO6gLFFlXtB/MfaJKF0lxFRqUgXgirlXNMRdfyAmMtcEJRkRvCMH6YC+J95VZLG7CbszqcS1faSqUAJH0Uka7uWQVs4ic67eTLhEVMKbjXMuPDg0D1iVnllMKYYCihQrJwhqPpLi4gudLeQLIadWfJSVa0sYKK4xoAjBL2cS7pALjHTAuP2l/W5qPVagQBgIyVAhDadW42bZFEEJiZOBM0WV6QiF0JnfjkYnv+5aYpJS4evUq+vv77bLyXV1ddll5t52plmd6Oir+gBlAVIAmq/HWKfE8L2bt8BWAy8mRKhelXH8nTpzAwYMHxy2cOxGuXT4BIYC2OTlYZZ4bujCWUbyic/QcJiFfBdBtMLef9XaoZEtKx6Ci7frHKiH8DEJTcobHoIB4Qi8QydCl1HklQigsevlSZv4BVkjB405kzcTKKSEiJgEttTf761iMimJTZnZr2wISySMiRc+cj7mv7GVQm047ABv0uWXE+q34/uw4E9sS0OdXbNkL5eRa6Wvj5Bz1Ubg5UfScCMlaoKqYwLnl5JKSvT8ee8+Lnyd0PAkkRefxMumQkV1nyiFP26cySIrWk+LbckY44ZJUEJEUQcXiVUwV60B4egKmQqHdftDEw5ypCFGDjFIYHThfdLyLq5dPYtFfZiGeX57l4nkeOjo60NHRgVWrVmFwcBD9/f04fPgwcrkcOjs70d3djc7OTmSz2Wm5/lKL6iaC4lT5fB7btm2DUqrsHKly4br+lFI4dOgQTp06hXvvvXfKyyjrtgz7kDiCJsuGfOz3isb8EJawimL3IduHhxIk9CyTtkkzAaBzg+/nbIMmIiUBz5Na5aSAMK9JyfeVCQMZslICYUBiAJiBjPppRBO27FMm7mW01SnAiMjETYi43B9azLIRzCKAEWawQZgMKRuaISvNWEzcLUhVLQi2ZJKKv+YybCDuztQN2VmHIAUdlW2yFpxp0gghbEknm2TrDLCuXJz2tf1m79GMp+BYcNyiix1D76vIGkuKMbmfAREWt4JJgBKzqBx3HpBMUoRSMSm7OKjr/uNkpYr6KgP9fZQBkZOxrEIiLZ1SoRN6PYTKQ01TK65fuQAXe3btRkEKSCEQiixWrbwdno5qTYkQhBBobW1Fa2srli9fjpGREfT39+PkyZPYt28f2tvbkc1mp9Q2LfHxdMMtRVQjIzrhrrGxEXfeeWfFk+24609KiT179uDSpUvo6elBU1PTlNs999q/gBDAyaP70DF3kSapjB4MaYySRlegDAHFUoVom0nU1R0075lK47ZikIwGPL18QeSXJ0uLq5AFBJTIQhnGVBIIZKToI998vuDB96QOAYUefFpYTkDL4T3oZexJSCEBZJjV4HmxwTo22FlRhW+sq1Bb2T6LZ5F7T7+AlabT8QijbW48ihKMY64+5hbjN9tH5JqjG0LXFEuydWJU9kNR0WAeK78UJx1BVhsnHtetx9+324koWJ+DMG4F0mSLrplciUJELjl73wBIEScoOn9RfIqB4lGSfZmoL0lqv1IklUBWMeEEd//likmKKwBVCYtKeMxDrIAwEAilB5t24Ncik81ibPAqXOzbtbdoW2gqrVy5dBS7X/FVrPuH1xbtUy6EEGhqakJTUxOWLl2K69evo7+/H6dPn8bY2Bj6+vpsgnE5nqPU9VdFTOT6o33279+POXPmYO3atVUxbcmiomrA169ft9V/pwMdohG4Y9NKKDUWueGMVUPhHS4Jt9YSjdVKW19kfOidYAblKPakzCxdk1J0jygOBQhI9kMOxiS8mnhQVymdK6XDNtG++YJvVX/5wMfylywy01RmQQnowUUaVxYXT9gBVhOn3SbMDQgDfU+oOoRU2k3Gj/eZNJ1cQDyfKjRtkZVFbknuDlMqLsiIXbwsHpQpVsDjNx4bxOMNsH0YkXILzE0iLqn6Y2BJrvoYFXfz8bgTvU/nhWKuQQ8lXYNEZHQdsUfo91yLivcvye0n5cQkFYRQpiKEoooUrnCiQO4/aUVFiuVSWSERvS7A1LDUk0AZCvbH9jOFLHMjuhIFfS0P7z+IIHS+Bwb7j+zD0iW3AwAuDhxL3GeqqK+vx6JFixAEAUZHR9He3o7+/n4re6e4VlNTU+IYODQ09LSTpgMxndnMxaVLlzAyMoKOjo6qkRSgLap8Po+nnnoKQRCgp6dn2iR1+j9+3ooouHsMYfRDUoF5LQGZF9ZqUobMJMWkpN4Pih0raQE4bUGpQJjnwi6vnbSujpLa/QEAUnoIAg+FfPHXIQh0rTOyrHKBH7XnST1FFdH5Y1WsqR6hz9ol1yTVKzTLiMcsDhX9xZY3V1Ir+gpBfCauZNQOP08Q6H2TEIbmL4idz/YrdhMKxQQmpTmWmag8sEh9shJ0sx9/zc8vZZykuGIuYNUhoKJjCoX4+TiJ2b6H8XsVJqjxSPxA7drjAr2/7QOie0Pb+Hux/iLZtedWQGelkIrcezZmRa/JDSjZc6XjmwBkXpk/0yDlaXsA1aa05woVZKgQ5EMUxvKx944fPg5h/pVC6NavrDDCMERNTQ3mz5+Pu+66C895znOwaNEijIyMoK+vD48//jgOHTqEa9euxSb5g4ODVSWq48eP401vehOWLl2K+vp6LF++HH/0R3+EfD5+D3fu3InnPOc5qKurw8KFC/Gnf/qn0zrvjLGoSoGWzWhubkZ7e3tVg4RSSvT396OzsxPr16+viGuRPFZHj+7FxllLAVN53P4GzCROKWaRQPvWo0IIwoRNWPxJAQqR2w8AVBgF/VWoIq9NAMhQ+z50mENEsSipf45ethZhkENgVjr1fKnVfkrnj1BcSimBghRY8WvLADUGuyoxjeE+ud6MuhDRa4sw1MvTB4FZCsQMsrackrk/5iYIBe0G5O4usp54/lMYGqm+F7eYKL4Vy40ycCsZeDKucDH30MonuTsPiKxEa82Zz4nHiYQhEluLirnf3IKxrjQ8dg1OnlaS6o8O4Yo7sqJ4rIruCScZmjC47sYJvB32msjqA+IVJdzlOxySsgSFKKZVRFJJsSmWK6jy+rkVSQbakopbVPq55l5hq614QiE0k6+zJ89OfK0A9h/bjZVL7gAAfPc5H8ZLfvbeCY6YHNxl6DOZDObMmYM5c+Ykyt5zuRyuXbuGq1evTlnwVQ72798PKSX+8i//EitWrMDu3bvxyCOPYGRkBH/2Z38GQJPlAw88gPvvvx+f//znsWvXLrzxjW9EW1sb3vzmN0/pvDOCqJKglMKxY8dw9OhR3H333Th79mxFV+B1MTQ0hFOnTiGTyeCuu+6qGCGSiOKuDWsAdd2EPHR9PCszF0DkMtIVnvUSTcoOZFxcoV17QudjhcISEl9iXkvNVUw0Ia2LEJBSQBoC8kw+UaahGbmhYQDazack4HtST/6NZeUZwhI1eUAkDGBKAQG0yzLrCAQUVYbncnDowYmsLhs8i99/YUhI+aTo41aqGXy57JtIQ0B3JkaWMhJT8GRcwLjkmEuR4mHSISiaRQjKVWJxIGWsHnfxRddi4nEowQnTiQlRv5OsOs/E2wIwIiIS9yLL0QolqJ+IXIAUQytyA5L1FX037X6ueIJAlhjP4bLiCo/J0MmVFxdNAIaUPCRaUnafnH4u2TbhAeEYreA7zu/XzG0KeSAIJXK5EPl8gDHHspoIOZuHV3mMl0eVJHv/x3/8R3z4wx/G1atXcfvtt6O3txcvfOELxy2SPRW4y3csW7YMBw4cwF/8xV9Yovr617+OfD6PL37xi6ipqcHtt9+O7du34xOf+MSUiWpGuv6UUti3bx9OnDiBTZs2oaurq+JLxXNcuXIFTz75JDo6OlBfX18xkjr2qi9AKoGjx/YCMKVdEC2FbSfHgYgKZRorRgYATEUIGZhKz+ThMS492q4z7Y2lpCKXn35MXlKbSsIEgYf89QKk9PUaVQ5yQfRjkSp6LdiaUqoQufuUhIkPseexk0ooGjClig9mRDjk1qKBks3wRRBq0uLHhmw/674zbZGLjV7Tn3X9hXH3YyyWZdriLj7ltO1Kya3YgrnolIyuhbsxY18CFe3LSTV07o8rEedxM+tGdI6l53Qf+Eq8QaDvtetq5Nv4FyAJ1E7SWlPcTTsRSfHaf2WQFN9Gbj97uQXyJIjYXxgKyNB4DUz8VQgPotRSAuNgx5HtUOMR4jRQbsIvyd5/53d+B4cPH8aGDRvQ3d2N//E//ge6urrw8pe/vGIrl5fCtWvX0NHRYV8//vjjeO5znxtTZD/44IM4cOAArly5MqVzzAii4sRAQgZS25G/tVpEdeHCBWzZsgWrVq3CvHnzIN0f5zRg64TBWDdKWfccEU1s9V1DWPp9J8ZEYgm7no55PwRU6DGCEiY8ElWIloFAUBAICx4KBd+692J9hZbqerUtyAWmPVL8BfEfzKr7F5nB3mzwEBEPs+rsIor2PQkbn6KbwImCCIcPiO5AbSwoEYZa3k2ldNz4GHdjhWExURR9WIwgbJ8YoVhy5eQl4+cADAkwsqJjOclR/+gcAWuLq+lKkRSPT0kVv4e0P10vvxageGLA73PgDGhkHQWhmTgE2m1Hca8gLF5tl15LGY8PBvHz2/wneswHenLDBRJJ7r58MknRSCbzgCyURx6F0Ecgo++270+edApSexl++qK/n/Sx42Eqiyb6vo9sNov/+B//I/bv34++vj685CUvqeqaWYcPH8anP/1pvOUtb7Hbzp8/j9mzZ8f2o9fnz0+co5aEGUFUBBIy5HI59PT0xOSYlARXSZw6dQo7d+7E+vXrsXDhwootR08gq+XuO9fpYK6ZbfMKzjIQ9o/H9AFExGR87Hack4L532m7dvFJI6Sw1pUUsXFfT7j1e/m8h3xBCyl4Ym9tYwsKRJiGsHIh+7KLEHb9LAkbzIZ1uTCFGxB/bgZfJWU0o6aO8X1oQLaEo+KDqxmEhdKkpbulrS1RKNjnsQ+D8oCU0gKJ0AgICoX4YB67WTKyRGibJaOEvgLR+3wbR+w6VMI2fi+CuAWkVPG5uTXEiYNfPycVIhlqx5WUk3XE77/bf44gNMt1MMFE7PlEJGW258gKS7YAXJKS+aimpcwDIfPcyUCLhcKCFiPJAMaa0lZVKPUfACiRRSC0aEqUUQSWJwlvPdoHADh35diEx00G01mLimoKrl27Fm94wxvKOu4973kPaKmjUn/79++PHXPmzBm88IUvxEMPPTTlJebLxYyIUQkhMDo6ir6+PjQ1NSUKGSpJIkopHDlyBMePH8fGjRut2TpeCaXJ4tBDfwVA4MTJPejsWGUTb8kLJwMRr5PKjAydlERuJD3o0zgtAEip27IxLTMmCpMrpccRcz5jxPDxzUUoBYav5pGtj/zZdU3NGB4cRobL00MfPhGuVfspG8AmlXksQVaZi5JC99kX0YUIYWJWvh68TKxOuAViw9BeK4CoLp+VZwuIIIgLK8iqo1k95XGFIVRJabqK3Fe0DxdgyILug28WeSzI6JxSIVYlIwyiPCbed7opbjzIxtm4lF5Fh/E4ExA9Jq2wSwTmxqWoX0CUSyY9di8NOXHXN5erKziWnYrOY++RjD57kpoDEUmF5nN2Scpx/7nWlH20sanomqRDULboCMWAx4GChwA+PK+A66IJYTA67v5HC7WYlYkTdSFWOqsyuNGr+7773e/Gww8/PO4+y5Yts8/Pnj2L5z//+XjWs56FL3zhC7H95syZgwsX4onT9HrOnDmT7hswQ4jq2rVreOKJJzB79mysWbMmMUZUKdefUgp79+7FxYt6XRmeHDeZ9agmPk/8BSn9ZKgHbOEZsrJJUiaWpAQEpMl6JwLQZYtYYQuQNaNkFA+SpoafkrSCqTm3EVboTHx9fMaXUTzMjDU1tQq5MS0gUEqgvqkJw4MjCKRA1tc/ztXPWwiIPKgQrpYMQltT9LviLhTK87KllaD/Y4IKFRaMmCI6ThWM4o8v/0HfC1Ks0QDMhRVkNfl+ca6PGcwFEFU/jyUdi/g5ALOWlrEehbMvnZNIws2XshfDyIUn/1LlBrfqBYeb8+SKKagvbsIw4Ej4Wac8oa1Iq1YMEcvrcnOirOijxEwnX9DiCrqXXOmXjxSOyqr/HFIKInISzP1HuVFFJBXEScpevqOFCAvGDS49HaMyHobIotL9ElBQXg18bwxjNW24FuZwXeUxHBQwTwwBAHbnaiGEh4wPDKhatCBnz1NQAr4AvvuCb+AlP/qt5Hs0SUzF9QdMXZ5O4oxycObMGTz/+c/Hxo0b8aUvfamouO7mzZvx3ve+F4VCAVmzusGPfvQjrF69Gu3t7ZPuGzBDXH++72Pp0qUlSQqoDFFR/Ovy5cvo7e0tyuCuJBkS7lx7NwCpi5gaeTkPVej9hfOa3HdI/lPatafLvzABBZQZp/UPklyPpPDjyBc8FEL9B8A+1tZJTYoGre36Sz8W+DpmRdfhqVifbRKzXT6CxaV4zEbr3fVgSDN0QMczzL6KDZIqDPUfi0XFxBhANGBzcKEEERiDULp6vXDdZ1YsUcLtx91uNi7luM6s2EKynKIg2pcLLOgYdy0nvk4TwY1fccGFjT2pyK1H/S5yAbI8p5gyz5zTUWrG3qMYVcG04+Zs8SXig+iztcVk8/FHK5YoIUOfiKQAbejKPJtHGJdfOQilsNJ0L6sXPPTNxMEXwNFCA/bl6s1Ayc7NLvupY1sAAGevHS/rnGX16wZbVOXizJkzuO+++7Bo0SL82Z/9Gfr7+3H+/PlY7OnVr341ampq8KY3vQl79uzBN77xDfz5n/853vWud035vDPCompubp5wGffpkkihUMDWrVshpURPT09ijUDf1yvQSiknvWAZQSmF/a/4IjwhcOTkPvTeuVpvN8QBCO2RCXUsR0Do3KpQGOWRhJKelp5T2SPBVyU1RGjHIWXGdwFhrSg21ihtjZH7j1yA/IeWD3z4vsLAxTw6u+tQU+thbEyfsBAAHZ2t6B8YQk56oFVv7bIjZPX50DN0LxKI6CU/SIIuo+RfslJ8oUnLuAHtc32bYFf8NdaUYlaOfW4GGQHEXY7885PGnPXYwM4mRMKURlJkWnIJesyaUpHkm+dREcHwMkK2MoaKk5XnWGic7aXSvisO1+1JsJUq6HiSmJsvhjTHUhmpmDUmIoLyBEvmNf+RtFxKxNyP9KXiIhParxDq3DjaZi1Tdn0OWalCnKQit9847j4mmgCMN5ZuVSE+4QsKZhKn9O8tlNqqovhUKIWeoAkiLA+1je2QtQIjuVMohcaWBbg2eBY+IjdhLkFZO1VIqau5T9aiuhGr+/7oRz/C4cOHcfjw4aJVJGiC3traih/+8Id49NFHsXHjRnR1deF973vflKXpwAwhqnIwnfjR2NgYtmzZgvr6etx1113j5icA2uyeSrFbKSV2796NjEJU5ci4/UJQUNIZm0LtbhKGcKzrj0jHuv3IPahAcSspFZTy9f4SkMoz7aiYNUVtSKl/kHzxQ5KuB2E0sAuhUMvIKpAe2jtasWBtExTy2qiCk+okYVhQRK89pQnKF/HqFJQ3VQhK5E+pyJ8DmKRgw7KxNZa8aGCmz5TKNkkFG6Cg5TsCxOXySZMRO5jTuf0oZ0rKKA5FLjwiNu7Oo36A4lQqaq9Q0G2GYdSWJTov2YVnJwfMauLHBYyM3Dp+9D71jW8X7F5Se1LqvLIiAYUh3NC5To58ITp/IYAKpC6LFMjI0jbHW3efqZRuSSonjfpPk1JSTEqVICn+vFQ8NgmBFCgYj4OE/j01tS3A6MUjRbWRa5rnQymFltaFGLl0wG4vsDnWdDHVZeiHhrSbspqVKR5++OEJY1kAsH79evzsZz+r2HlvKaIab5XfUhgeHsaWLVvQ2dmJ22+/fVxLib4YUyFEciuOjY2hVQl4ukqdnqkrZWJHOp4klSnQQmEOM9OD0v5yiIiQLHkJWn9HWCuJYvJCCGOMRNZTPJ4VQSnt5qNYFRWXDSXxQXRva2o8jBlXfCZbB+GF0fUYIwVgj1RjDzDWllma3qcYlYhIiogpH+hVgAXicSIlIxKLDaaMvMJoAFZs5h7VrmV90TcatnIFtcusEgEYoUS0VEd84UEVEQL1kyf2eiIiQ7rvMQuJBmtWRokTHF/xl1eWSEJSvAqIuyGJ1AUiS5CfV7BzkyuW3rfXruJWEaFALlPzPr9fLO6m+AhOMSc3b4qT1ARwSYor/VyLCgCCQFj3OMWlQukhMC6/wPwmhVCWrJTIAsijuWMJRi8eKepDbV077l60FnJ5D0IJ/N3jX7Xuw795zjfwup9NL041HaKqqal52q3uC8wQoiqHeDKZDJRSmMyCYlevXkVfXx8WLlyIlStXTngeIcSUBBWFQgF9fX26EvLHDgACOHT6AHrWroPCGGprr+hBwQwA+bz+IpG0nNchs/lLUkbkBoptCRv+0Dvr/yjfCkxf4KqfoZTNi6L8Lj1JFoZDFArSx9kLBcydFVmTmYxAIIFZS+oAcd3M+GGFE0qZ8S4bFaJVSuiCtPQ7I6tKISIpw7m2AoIybjtahFEIPcjzNa1iKjoXpkGawZP3zjOWG7ni9I2Pv+Yw7jkRaLJQ3MXnVkF31X72Ped7FlfWRC7KMIxeh8YVyJeEj/XLsXw4kr6vQagtWldJyAUcFC+kfpGF5V6ri6R6iBSvIoLKM+ILVZTkS4WEeazKvV3GenKtKYpDETm5JBW7/EL5rnsuVQ+kgIRAJlsLBKNoaF+MMVZ49rlrnhcViFECUim8fNNr8cVffh0bFt2Lc0Mnyj5vyf6Y+NRkCw9cu3ZtUsvd30qYEUQFYMIK6kRO5aph+vv7sX37dqxatQqLFy8uux+TjYWRW7GhoQF33nkndmOPXstQCTQ1nAMQWlc/oC2rbGYEgEKBERZYCcxoDSnyGilIpQdpRTlKxqrS1pmK5Od2Ox1nLC8lSo47AJALPXgeOyfDcNAFIUzVd4iIgCiOBBOXIuGeNC5AQ1CKPHpkWSkVxausdVKiY4G5eT5zU3FritxoENYasnGVWJxKwcZrAEMMCjZpmWTn/EcudVFc5ft6DSkeuwIia4osQSKBjB+/iWRtUT1DKfV18XgVtRcistBcUuavuYuQx+ZcpV9MVcj7RPdJsbbCePvUXkzZKKOJBllUzK2rgtDcW7DPmqwrIidT42+MFaSlahMmL4oIjZMUAIQ56JhSCZIKAt0Xz9O/gwJLXrcxKvM8UPqPjtbv6eND5aGmtgl+cA21rYuQv3ICv775taAJUah8w78SUim8dtOrsfXsIVQC01008emIGaH6Kwee58HzvLJI5PTp09i+fTvWrVs3KZICJidRHxkZwRNPPIHW1lYb+1LGzfBrGztig5UwloKk/M0Q8P0xvfggYF0TND5pYZdgCbrmkSpPmIReEmmERnZLbo7oD9E2aALLBZ7+C+OV0aW57uPnEyYMNn9KD1JUHcNK04mMA0RxJjdQEMi4Os0q7YyZmA/MzFxGSjarrGOmZBhZp7GKDPYYqf8KAVQh0KpBqaDc/riggZWqhtOll4oNWVUj6y8QH9xpMOeVIqwQwS0Iy67DTRam6yQFo+v2I5cbv0dFSkBm+ckS95D6T+q+2HWXuH/5IJ68DUQxKQi9bDwJKMYmJilJllQuTlLW3Rcpw+MkxZ4XAs+SFnksShkagSEtyociNx6Ej/r6dgDAA5tfa7wQPgoyg0AK5EOFgtR/oRRYN3s1MkJPXqeD6ST7Ph1X9wVmkEVVDiYSVPBCths2bEBnZ+ekz1EuUQ0ODmLLli2YN28eVq9eDSEEnnrJl+B7wPK5l6xnS4XKGBBC18ejvpoxoyY7inyhThsWgIlPwU52zYVFsSOljNTcLEUPaQkKhrCgWJV0qJg63HWbFaRnXH8SofIQmpIyfBxcsVJnUSopACkgfBV5zSSzsDzYckoqAEQWkVVlrCjh6zWHdH4Um3GTGxCISMkTkWsQ0JYKDbAZPyIK2g6nHQJZUwCU0gnGFjafirn4CDzxVgqzPpYXkQpZSlZYIezuJkAZnYOsnqJcJ7dQLd3LBDdj7LiE7yiXpJOohGJU1A+rImT9I5gyRnHLS0bvAZGrkOJSUurPViqowNQd5ItXsrZIOBEjqXwySYXXtRCjiKQSLClS9wFRTIrgCWCs4JsJGcx3XCAwj6EyQgoySMmTAQ8KPvxMLTbfdhv0781HoDzkQyAIQ1wbG0JtphZCZHWyvTn28ccfR1NTE7q7uzFr1qyyFjzkmEx4g6PaS3zcTMwYoipn8cTx3HJKKezfvx/nzp3Dpk2bpvyBleP6u3z5MrZu3Yrly5dj6dKlsfeWzroWxYuELlkpQ6GLKYTGbSeiHCQhgIw/hrystfJ1Ii2pjLiCJ+8iStLV98tIqxWsBRVzAxqloP3xKtg2+F9O+sgYsgKAIxd8LJ8dmj6awLuQEJQ/RcQqAUiKT5nXSulvFi3JrgSQNZYjxY+k2W4JT8FWY+dkY6XcIj44W5GAiAf7ibRCaSpYMNm0gZW2m3QA4fuMzMwAy0mRJSeLMITisnIuACH1HZd0Q39EtjqHO1twY0jkUnPB851cuBaRe4x7H12XYsj6FHP5IXLz2TaiPtjcNi6mALQEjr6aUoFXRo+vP6VdtpTYG1lShqxyekKUVLuPk5S9XHL7mQr/hdCLYsATyMfJqtK3Q0AJD0JkMLdtHgpBCAkPodJxY6kEcu9sxcX3nkBbfSfqsh58kQUEcPvs27Drv1/AK769Gv39/Th27BgaGhosaTU2Nk5o8aSuv2LMGKIqB5lMBoVCoWi7lBI7d+7E4OAgent7Jz2D4ZjIorpw4QJ27tyJNWvWFOURLJ8zAk0lMLFwynw3Vo9xj3mesPlIYairSWS8MeSDOv2DMoOgkjo2RcnCYehr9R8jHktqkrYpK0mnHBJunUklkDc5JVkvPujlpA+lAni+FlMoBbTNmQelrutB1uR1CR+anMCEIMaKUFLp5T1c2Dwq9p5SWglG8SoOslq4RJ0uJuNH5JQhSbfZHsvhMSQVhrD1qkLFLC69rwIRMl0TkaJy1HKm8jut7utaYTEBgioe4Ok+xZ9EYg+AfKooqoIRc/U5llPsvjkuQy7n51Bsf+oruRDJAmQWfVHg0kUo9b3VN1AfFiir21b5+OQvqj5hukEkNcY+P/cUFKcah6QAvUSNC/IchNJDaNzd5O4rKGHnS6ESkPABkYHnZaBUCCF8jOSl5ngIPPuffhM//elPcT0I0AIfo4UCGmtqTVqIwPnRk5g3bx7mzZuHIAgwMDCA/v5+bNmyBbW1tZa0Srnppur6GxwcrGoO1c3EjCGqcpV/rrUTBAG2bduGQqGAnp6eCROHJ8J4NQVPnz6Nffv2Yf369UXVgX/xoi/jtrl60KVKOoCwQgkoT8u7oX9gnmcICCT+Esj6mqz0GBRZUZLiUUoZo0NYzwqgouKaxlriSj8KJOsx3xxhSC4X+lrAJ6T10vBP4chAAzbOyxtpPAAPEFJoixCAlyF3oHHzEQIF1Gj1nfAFy6USWuXla5ekLpFEPhdpLA9EnVAqIjBrBTHLyvOi2brHLCvPSxAQhPFZPx8IDAEqs12R75WUi9LUI+Slm6wFxV5DJgyyjnVF7VIcz94zN8bjqPySJk8lJZ7UhoznPVmLD2zmwlx5brvUL4WoGghNCuiRC0UA0GSGf5nUmC7XpK4XzHLzRFJmspUgSydXH1lT5ZJUIdBrrCklEISeme9QuohZT40ESQYeNEGFykMIk4sofLQ2dCCUITb+02tifaPJ8kghj9HCGNrqOlEIFTzhIZAKm+assfsmLXh48eJFbNu2DZlMxpYuamtrs2PgjS6fdCtgxhBVOXCJKpfLoa+vD9lsFps2bZrSh5t0jiSL6ujRozh69GisiC3H8lljZmIs4Hl6nSchwtjAL0OdgOuJUD96Aoolfug6ZOaHYn7npACUlENliIjyoABuLQFA5B4EjBuDW2C0LwRoPNFLHSgIKITKQyEIkM2QRN0MSn7kHlJWaSi0u89YU5AKKAAqA4gC4mQFWJLS8RPTOllafJxX5ip8Ly6iyHjRgOoq/yLmtnEyfQLm5qNtnoDNb+KWHLVp7iO3RpQKo22SEVIgI5IU3JXpkBZZJTyepRC54SiGxXO77PkdAnJJrYigjAoy1gbi7QPReXkAUyqd1+Sx93nzXFhh/lQQ7SO4xZwPoUzFdnXdPFKppJBIivoGyFH93CUp+qjGI6kgiE8QCiyBHaBJmTBGPItTKYGC8uCZC/CgIEUGQmRQCPN49j8XV1Og8UEbkFnkwwKyXi0CJRBKhYRl3QAUL3h45coVXLx4Ebt37wYQ1dubjutvKnH5WwG3jOoPiJPIyMgIfvnLX6KxsREbN26sCEkBxa4/in0dP34cmzZtSiQpAPCMdSQlbApMaJRHtAYUgeruBYEHGXqw60cpoMa/bhV7euJq1ICGoArSQzS5jcttQ6kTGKkCRSEkd4cwhCRiWfgcEgIF5dtYvoSPO29rh7U0WCxK35ho7SxzQKxStQpUPMGTLB8a6AIV5dIE0ir5FI9n0EDIi5lapRo9NyQRMKvD1vmT8WOS4KrtpIq5+iLCoz7w9yRT+xl3HFfk0T6kxrNKQBWdh7s3C2wtJ16Hj67d3Ub95+tDxWr3yeI2+DXztbyUMvfK2YejEOp9SizDYW9TqKKKE0AkoigiqQDEcHJE72PJiSwp55GDflMFljMVhF5s/bSC9FGQ4w/6HhRCaGsqgA8JH76fLbl/GIbwPA//ffvvYyQ/hpFCAXkptRIXPqQCvvbc74x/Ts9DZ2cn1qxZg1/5lV/BunXr4Hke9u3bh1OnTuHq1avo7++fVE4nLfHxdMSMsagm4/q7du0a+vr6Yoq7SoG7/qgk0pUrV9DT01NyWedzr/srQImo4IFxP4jYVFShUPChlETG15XMPc/kWEHvH4ZCx6KUZwqzmpiTISUtnFN2ZqgQ5UwZg8RaI6GKrCq9XVkXoES0TpVPNQXNpDhU+kc9Vgity09rB4Sp8GOsKM8o/6gyOsH14gQqUgUCiILviNxogJ6eZowL09T8U2TNURIvAJFh7r4MSx6m19yF5RlTzR10M+aDEoKVXjLHqTCykJTS7xMRUTkkutE8jiSZu9GtMOwJJihQkQVKr9mDtehipCYjlx2/llKJcZycpXN+9xirzKF9zWaaPNh9VPQIpdV6MSIjsziyHFWuYFVBtq5fjKRMl0a02rDI3ZdETmRVJZAUEZQnFHKBHyllzbZ8mNElEKWnH+GhwFx+9CEIoZdxSbKmgHgMaeB6PxprWvWEEtqiCkp8LKXgeR7a29vR3t6OlStXYseOHQjDEIcOHUKhUEBnZye6u7vR2dk57oR8ePj/Y+/LA+uqqvW/fc69mZMmbZqhQ9omnec2SQeGAoqCPgcUQQYfIJOzzwEUfaI/BfT5QFABxQFFCj6HhwrCk1lAaEubqXPTJm3TNsnNPA/33nP2/v2x99rnnJubObkN0KUl95575nvu/vZa61vf6n7bAtVbzqPq7u7Gzp07sWDBAixdunTCawbIa7NtG+Xl5ejs7BwSpP6y5Rc6fCfDca6whG2ofwxh1ywvpH5cnMuuurJ0x9nOFP2whSFDgdwAV6810Lg8J6qDcth+THtacuxR2ynYjPQtwsL0eFjUWnvTytlybeb0uxKUC6PGjNRF2FbeFI09pHEadgYzEeIQFslAKaNZv/ayXO8jvTCtEefah3sw1dsPwoBzW6QXZdsRNV4uz8KtUh5ZV+Rm21Hxm9tjovgsFcnCNeBr1fYI74pHXLOIuD5ax0Oj557aMc/107Vpj9B1zAiPSnAOEeYOmy/yXkUzAm46HRsyB9XvDfl5bn9fBEhBNhMFoP+6zQp5w3vRQCpsG5rYE4zoSG1xE2ER3auSOspC5bBMxRQC3vXCFwe9ZDdQzUicA8AEF6b6CpgCw0E3H9JIHWfmzJnYvHkz1q9fj6SkJBw7dgyvv/469uzZg/r6+qikssnMUR0/fhw33HADFixYgMTERBQUFOA73/kOQqGQZ51oDRd37Ngx7uNPGY9qJNbb24vm5masWrUKs2bNmpRjmKaJcDiMkpISCCGwceNG3VMlmm3IiwMQVikKOePmnGLe3qfVsg0tCGvZBgxm61wPFCmCCnh9CMKCXxEpVPjNVZwrxweHmh7iBgzGpXaZcADJmQhTHQmDqRLKeuIO6UkJMBjgsKD07oRw+ugxSJq9wQCf6wDKhAWwOPkXhiJ90G0LcSc3ZTKIEAfzSx1AMCH3T7U3agYON8kC8JIlTEOHA5nP8A6kbiIGJQsMBqiZvCcnZZM3Jpx6KE0TF46HZJquXBmBEPPmfNx5Kg+BwuUxRZIPmOvzSHCJZPl5KPWIPhLSNu5wH4MEKYs7kkpcritAbHXh2p97IkGTDBdtH+q+EiFkiKmu6JWDmAjLaxY286hW8W7lSVlOHZO+BRFeVThkgBkyKsGYGABSzmtX2E94varVL/xw8JMdhXHONVCd6jqFOanzAL8PpuFX1RkGuOD4+Yan8JmdHxr1/t0SSqmpqUhNTUV+fj56enrQ1NSEU6dO4dChQ8jIyNCeVkJCwqSG/g4dOgTOOX7xi19g4cKF2LdvH2666Sb09PTgnnvu8az74osvYsWKFfr9ROTNpgxQDecZHT9+HI2NjUhLS5s0kAJkTqq1tRXTp08fUmkdAP587i9QNDsOBmNOHl5IZhEACBia0WcachZp2SY4F/CZXIEVlEfGnJk7aEJtQAiuf2yaWCGcsJ9Qg4cAYCkKrlxX/rVIPd01MQ5SHysAZgSY2vBh/eJ5AHOq6+X4LAt9uc1gGALMkPsVKm/PDAestIUEhGT6ujwJOUALl8yOUH+1zJI8cTq6Q03nwgnvqdcyJMjk/80ogziBAtVS6RvhAjHhWg+QA77pAkChgMNduEvNBt31UwxOGM3wfp/6WvQ5cAcII0FHA45rHX1PogBUpKfn+cwLeMKywUxDe6WazKMLdNV1hizdvdm9vV6X9seZZPIxJj1py4YIhtXzAfB+WxZ5hyWokTfFu8MAmMeD4spTigQpS4NVFNq5AqmQ5QxlVkROatlzdw+8L+MwylEBNBfygQsTwmawhVABgWG8+iFsMNZfcnIykpOTMX/+fPT19eleUF/60pdQX1+PkydPIhgMRtnj+O3iiy/GxRdfrN/n5+ejsrISP//5zwcA1YwZM8bcyXcwm/KhPyEEKisrUV1djfz8/DH3iRqJ9fb24tixYzAMA+vWrRsSpP50zi884xB1DZXnzGBxI6LTggnLFWLjeqCQIUBSzKFaVak0EZb5KkjAkZNhR7cMgKKjO6E+OXGXr8PccMJ/YHpb4QoLhoQPNnxyP5AyS6ZhKU5ghOdEs1POwC2Ah4WjtkHjnSVfCxfDX4QkM8yjbE2z8ggTlgoR2i5ihTsMGO01hQVt7mwfSaJwSxjZrpCX/tz1GQAtQeSmhkeGAN0ST/qLc/2jnA7th47hDrlpuaWIf+66o5DlBRw6thWxL31v5bmIsO0AkouoItwhRE8oUIFstCTLEKGsAdJUAuC9oQHrEUjZPVHCgcOAlNvImyKQsmxTTw7dIMV++mksefaeAduP19yhP0tID8rmDGHOJeuPM8XjGRtYjaSOKjExEXl5eSgsLMQ999yDD37wg+ju7sY111yDjRs34oc//CGqqqrGdPyRWkdHR1Ry2Yc+9CFkZWXhnHPOwVNPPTUhx5oyQBXNo+KcY+/evQgEAti0aRPS0tImpANvNOvs7MSOHTuQnp6OuLi4EQFi4RxZWCzZdeqHZpsR4yM9cHLQD6sfJBemGtsMNUZQCwLXfRD043PTamlcYapwEYpYrvJWasyhAkc3K5D0/uSumd5OgCmwioNgccTz03lxwR15GNIapHMTHJr9p4HbNaiRErY7VwUupEdluf8pcFGv9faWWlfeCG/eyt3nKAK0AKgCX4383hyY/oIov2NH7Nv2HodAQee0BjIW3fkez/ryQYCLrulax72MO+vQNgRY3LVP97LI/B6BkWv5ANalG5zoPGjiQOxLd9hPXb8I0T8OERZqUsIAGCo3ZelwH4TQdHNhMXAlm2R3hfSDQmDE7egg5bZIGrobpJyv0hX2u/emSZvUuoHk7oPfQFuwC31WUOeoLE7hv/HvfyQ2b948fO1rX4NhGPjXv/6Fm2++Ga+99hp++MOJCXVGs6qqKtx///341Kc+pZelpKTgRz/6Ef785z/jmWeewTnnnINLLrlkQsBqyoT+Is2yLFRUVCAYDGLjxo1ISEhAMBicFKAiSaQFCxYgPT1d1zUMZn84+xcAZNiEQ8BgUkFZCAMGOMK26cwAGIelCBMmkz/csG3CZDa4MGVNlTCkB6PyTRBMj1c2qZ6ziNCfEACYGpeFSp2oH74ev1QNFaQnBgjlZUnPymSuQV1Z4cJsMIRV7ZUk7JEMjWQAyr0JW4b0hMVg+KA6FKvz8qn1bagdqH82B0youhsBoQt8iUEIb+hPqGujEB8HmE8x2Bi8tUzycp3QoOFI80CFKYEo4UF3jZbel4CWZhLCYftFFvnSeu66KDjfh2NcL5ZvXetrdid9RpME4RyTmIDu8OQQtPsB3qQb+Ci3RAXWbvkkLuQkhXJtGkTh3ZfOr3kvkfRWRG9ITYEFhG1G7aZihwmc5Ofagwo6eaxIhl8kSLktZDtDWd03LgFvaEBKSgra2to8xbQTYZFAwoWJ1mAXpsfFgwEIcyG9KQb8YPVf8Y09HxnV/sdS8NvX14dQKIQFCxZg8+bNuOGGGzCcJB0A3HbbbcMC2sGDB7F06VL9vra2FhdffDEuu+wy3HTTTXp5Zmamp918cXEx6urqcPfdd+NDHxp9rs5tUxKoQqEQSktLYZomNmzYoMkM421HH80aGxuxe/duLF26FHPnzkVHR8ewtQscQOHsNAAhWLZqJ6+q3m3hDIqRZqkaKIPZsIQJA7YCNRu6fQdkjxuitpsIwRJ+T5EtARV5TMQ/gPqclGyIkq5eAmAapAQAGyYEDJWgNwBwJ+clGMBl7kHT000Z19MKP6rpnFAyUMISYH4V9mMOjkjAEGAKpJiJgQMdmSY4qJMmYAIk5VlJLjGfIo24SRVg0NRxGpyJ6ADXPi3n+9GFxZYLuIigQaQOy3YAkEGuSy1K1Hnp3BWF8TRwCe+xAefcdC5KOMSMAffDBS7k8UR5vLR+oQYod24rYr9CqO67tK1Q37HaNwf9x2tuT9lmgGmCwVShXguiPwTRE5Qaf/1cSW1BUtENBrsrJIVmLVORc1T4LCjPVZ8P94ITY5IpazDhASnypgik8v/vXgDAfM5RXl4OIYSedGZlZWkFiPF6WgOAigOp/jT0WSGp+QeAC6HrF8e7/5FYtO6+IwHnr371q8N27M3Pz9ev6+rqcMEFF+Css87CL3/5y2H3v3HjRrzwwgvDrjecTRmgopva29uLkpISpKWlYfXq1Z6HaqxdfgczkkRatWqVTv4NJaEEAI+f9UsQ8cHizDPe2lxJtHApakkkNqGkjEymCBXchGnYsLj0pBzvB1qVAkKG8ohyawsGQ4GYzHe7c1LuSa7Qk2ACKZogD0wlUCiQwWAMq/LmwDBC6pgq9KMGLmaGVasM6UExU31O9VUEXhaUIC15gAqw1DpgEsgYqUfIG+R4V8obYVRTpUCF+ZjjBfkMDX4AFKNMOPtU62hzMQfd+RTtXblvDIMHGPUxqRhWi+VaDuHCUDeKBnK3Nh95TZ5+VOrctfcEb8jP9Q3pZZSH486zL9wTqshtPYK06q/h8rYI9MnzIXUJXZcmnPIC9T0K931har9DjKc8bDjeUUeImN/yMwUynjYdwegeVChKE8SQ5dO1UYADUoCsS/L5fJgxYwZmz56N9vZ2NDY24sCBAxBCIDMzE1lZWcjIyBgTaLlZfwDw7b3X4PYVjyLVnyrVXaTzP6YcFVdq9KP1qMba3ZfUMEZitbW1uOCCC1BYWIjf/va3I7p3FRUVyM3NHdU5RbMpA1SAvNklJSXIzs7GsmXLBoARdfmNfFDGYseOHUN1dfWAdiA+n08/LFHzZq7IixziARn0MMAgwcdQIqey2aH0oAChPuNqe7meHJ9kCFDosZfp1ItcZgHwq8m0pDkw4TD5bOEwzGzhzOSFYE6Ji5A5Lg6mQjQMhm4mJc0wZKhLyjxJlhZDCNqLMhW7z2aAX4CHDRh+6Ia5wgJgynMTIQbmFwqwoMAHDjAxSLq0ckCZoQZK9UQKHfoD4FODKhcq9Cf0cpAnKmRehblDeAza69OhM0CDmCAVcA2OivYuiPauBmqf4YT8bO4AlG073pc7zOJmjRAwaQ8Par9wPC8CJM+45nhfBK5MgZ2g70xvD+8x3MZdAOPeLwFWxATB++DBdV6ufVO00meq+jkLoi8I3heUHlOvrScaPGh5Qn922IzqTduDhPncRgW94UE8Kc9lc670NJkupl28eDE6OjrQ2NiIQ4cOwbZtDVrTp08fMWhF83jCnKM12IE033QwJsP1lvODHrGNtQ19Z2fnpPaiqq2txfnnn4958+bhnnvuQVNTk/6MJvm/+93vEBcXh3Xr1gEA/vKXv+A3v/kNfv3rX4/7+FMGqCzLQklJCfLy8pCfnx/1hru7/I4VqIQQOHz4MGpra1FcXDxAbZj2G60nzKObpau7Jnc6hAiqMYgebq7AiitlCZmTEoAiMsi6qTBnWm5JTnrdFHQ1TVVhA8bkX5MJ1dXC27nX1oDk8qCEGnghSRQy6+C9l24SBVgcDMOHpbNmAiKs1/D5+uS5EKOCQfU3lAocPGzC8Alw24BhcHCLwSBgspjTDoQ8KgoDupySqGYJPUbqGbjONbnCiD5DLif1dgIfUmMHInJT7lAYd8J0nm2pVonp11pJXed1XHkqIiKQDaihgvo+nTuvw3zkCrsAiVYZMLgpQHJyTy6w8QjCOqbzcwM8LeE9RwIi4Wos6VaioNVCwvEIDQMwTF0G5sZlu9dS4T1DKqYzwOqSdHU77JOSi7Y3LzUUSBH5yLKJ6afChYo4EQ2kAAeo3MYYQ3p6OtLT07Fo0SJ0dnaiqakJhw8fRjgc9oDWUOOLbduIi4vzLNvW/DKKMs5HPwvBYD6YTEY/LAHcuuRPuLvy8kH35zaK5ozW05vsFh8vvPACqqqqUFVVNaBrhDsXdscdd6CmpgY+nw9Lly7FH//4R3zsYx8b9/GnDFD5fD5s3rx5SNfV3eV3LCrpnHPs378fLS0tg6pNaNpplISm8zu2AQaEbKnKTErpQkEVfXFcEAg53haDrTwtG2AGOOeKTAEwSMCi791WNVBhbsCEhbDwqTAihfVcrTxAERoBuD0pOJ9zlU2LHNZSk9Kl5BOTXprf6Jf74ZBeFU3auQHDlM0DmUYgePZJQCRBSoUuTWgNOFC9lSnULSAQdA2eKjclbPJ01Am4PCk3IHm8LAKuaLkpSt7R4B5Zs0X5KSJRKBIH8zHpaSmZJ8aU2K4hlzuTKhdIRgKO672w5DaC0zcCV0gyEugw0Ftyv3ctF1bEOm6L1FpkrjCgkPfUiSEzwFbAZXkBS7nz8ng2AFt5U73BAaQJ3RLGdRNs2w/GBKyQkg2LACm3DQdSQ9lweR7GGKZNm4Zp06ahoKAAXV1daGpqQlVVFUKhEGbMmIGsrCzMmDFjwH6i7fu1pkfx+YW/BqfIBhfgELA4h+Wpyxj+vH0+36g9I1KlmCyP6rrrrhs2l3Xttdfi2muvnZTjTxmgAoCkpCTwwfTLlI2VUGHbNnbv3o3e3l5s2rRpUEAkMIxGqOCuwabfYlJsgTFFdHBNlOEIeAv9O2cq/6TCbcIEFzat6coxMZ0q0L2elFncDQxCESlUCFCdmoCbTOFEacJCEiYEM6TCOzPADB+S4tOQnRoPg4UhAMT5+hyGHwBuMzBmgAlKuBtqVuwDsfU4GJjflqFAny3JFabQOQweUuAFACE4hAtAe10UgmL0RNKgazoDsB4ILcjBMiKPpUkWzg3TL4ULtKgYVd4o1z2mliR0k9VArscZxqQiuM+QA7khl9HEhDE4VHoCHDdY2o4HJNQDIih8NxhQuQFJ59qcjz3gFLlNZD0UUc41MHs/E5ZQ18AdrwrkMbkeJgBy6PAem/dZgEnelGztYXWGwUzAthz2nx1RM+UGKc3siwCpaDaYNwVE96gGM8YY0tLSkJaWhvz8fHR3d6OpqQlHjx7FgQMHtNZeZmamlliLBoJcCLSG2pHsy4CPmbCFgD0C5p3bxtOL6u2q8wdMMaAaiY0FqMLhMMrKyiCEwIYNGwa47ZEWjVDx8MZfKjBhg3QBp3yJ/OseA2VaxmHbmcwAF+RZEZnCcBEvoBBOrm9xBp8qrLWpt46gKngCKwlfxATUs3dBpbvMNdlnEDBgGPFISUjUoch4fx/AhOzVp0KS1LyWCQbGiTosCRVuE7YBZnKZv2ISdRmBlPrdiTAc4sUgEz+nZcTAfJQuImYRn4G5clsRoAa41CmY40m4jEWTa6Ivj7bVEklqPQoF+gw9XgsPyBBYCPma9uOmitPMJgoAIQqYCC40yA4I7ZHw7JCUdbVDmknpz4SeVQlFhKGor/tYkvlpAIYfIG+qN6gnFwM9KHk82/Kr/pN+z/ciBPNMxiJBKlJk1mBSDZ0BmPvkD2HbNhhjUQFpNEDltmiyRY2NjaipqcGhQ4cwffp09PX1Rd32ZPAEFiYtgyU4wkJI9j84+Cg8qjPdfaPblAKqsTZPHMqCwaDurLl27doRsWmi9aQiVnDhrBmwuaUm31Jfz2AU0oPz41aAIgFKqMmoBBU5BimPSlCei2sKuvaqXGNO2GY6/Ed5eFCvKXjDf5EhP3eOSpYFxcHvT0BKfBKS4+RnCf4gIAS4YuQNbKfEvODCyLviSlaJy8JfU3pTwmIytKdewxTQjD9BeE5Irk6aPBMCNst5T4XDUdmAgEMCoPM1IrwrwJWboi+a9uUMJMx0AYq76SOBk63AieqQ3B6YPrj6jxu0dMjN5T25JZI8hIuIvJP7pRtk3eAXJVelvTt3WJXwL0QuM7wzKgvgQa5LCTSw0T45pLq4E1uG3dnvSCZZJni/BcMHWJ2S6edpWBxmnvxUOGxIzI8AKbeRyGxIhf7mP303OOf6N0pg5QatsXomkZacnIwFCxZgwYIFWmuvtbUVhw8fRnNzs2bN+f1+/P3UHbh6wU+R6c+GgEAQtnq0Rx/6G629nVt8AFMMqEZiowEqorpPmzYNq1atGvEMa7B29IwBQUuocYqpMY/qniBDV0zIMBmoPYf6p9YzmZJCUi1zJSDp2JL2siRcKSIG1OSdOS0/5OGoWymF/5geJKkVCJgkVYAZYDD1PUjwJyDBHwcGDtMQgGIgShk7otnLXBQzBAyDCoANcG6C2RzMJ7QnJcN+ikVnMeVRCYA6AquBiJkOyUKuJK+Ucllye9c9N9V7ARkG1IDEVMExHMCDF5w0/dpNSTcQJTcFL2gp9qDOQ4WUT0o0eG5L5l8k8NHjpXJxet+eEJnjMXrCehGgNkCWyEV60IBJ98lFjRekMDGgw7Hbq6J9us6KclEU7gsL72nT+n6//k55T1BOKgRg91naW9b7FIAdHuhNhUPyt+OmnxtMIKQEZwE3w0+FAFVeasnzP5Gnopi5tm1HBa2xelRDGWntBQIB5OXlIRwOo66uDpWVlUhPT0dWVhZscPTyXvgMmUMPMRsQHNct+g0eOXL9sMcYq0d1JvQ3xWykQEVU95ycnFG3A4kM/f286JcSONQPlxrVyggRhS+EDv1QKkKCFHXc5Zr4QIXBVHMhGQaAIxAqw4cOgMnB3BJM789W+wagQorCNe45UkluD49uQUJ8GvymH371e0iMk0KW1PXUYEKFBuXxqRWJ6VPK8CYghAFuCximAGwDzLRVKxCZpwIAHmKS0q6OSyDlZgG6LRKgAMiEPVe5K5o7+FzrM3WlGmjgrdHiThgU2vvi3kHYZPCw4ajeyHT6YOmQIVO1WNx2vC95g52BP9IdVvtUXw40O9CdM4osEI4GVGRhh50IwMkhRa5ncQc4BtDfFRjRDGsQD46H6FAMwjDBDJ+cAIVJEqlf3W+ACx94MAxmMoTaQzqkyhEnn3WXtxQKGTAM4YT5wq42OIOAlNsIhEzTHABawWDQA2CDhQfHarZtIykpCenp6Zg3b55HILYxfAqmmQA/AB98sGDDYhbECL2q8eSo3q7dfYEpBlQTFfpra2tDaWkpFixYMCjVfbhjuD0q+o2vys6CxS3XZFVSzakNBxX46m1orKS8E1xeknKZBKhVPAGhqymiUOw62idko8Ne2+dqeKh+AiqfBQVkejwEA6f8F/MDzITP8MFnmLp3oRBCtz5yukgwndcyDBmeZBwwTBocGcC4rKEylHyUCZmn4nSlaiB27VdYDELlvWA4g6dMqzEwn7xxBFrMDUr0Xueq4DzBrvyVvtcDQn+KxKAOOiA35WYXUg6MU6hPOKE+pZYhVJsVuTMXIKrvgSnwES62i5spKDjg6T+lw3P0wo0YLs9I3U9qRug2h1wxyLaUj4ryGUDApOSvnOSbszfOAFV6oFtc9dgwaGLhfobcXlTYhGkKWGHmCBlHsUiQcht5U5HmBq1wOIw9e/bo1hdDhQfHapFgQgKxeXl5+Ef1Slyz4lewuAWfKb0qG/aI81TjCf0tWLBg1Nu9VWxKAdVIbDigIkmkJUuWIC8vb0zHGEydQhIdJB3dFgaYAin3hJaiPoJqmFTbD67/Ko+HBjDQWCfLhIlMIZTnZSnwgoDqJcW1R0ZOAwEbdfUlb4oIHoKZYMyAyYDEuFT4DB/8pgxTpiYEYdtOmyahBGwpBMOFIT0aRaIApIdlmkKiSwQzUYYABcC4Lg5mhmyMx1zJc2Ezxd6TN08pzyhAktfLfAKCZvTRAAsALNcgylzLaV3DGWy1pxVBvACEbEWhwl/MYA4t24ByoaluC7K/FoGWBijXLIUO6VanUC/0dy6/UK9uIODNYQFRyBHCYSJGhPiijoVWRBgx0quy4CoNEPpS5D5Bzrw0M1EeIxyG6A/C7g5qz9e2fbD7LRgmEOwIyTwnA4SIh6GOH1aU9HBIhvhCIRPMEE4jRNv7FxgY8hvKSMSaMYY1a9bIyUCU8CAA3e9pIpQp3BYfH49T/ZXITJwHAzb8LA5hhMFho6SkREs5JSYmRt1+rKG/t3N3X+AtClTRulsCsnr6wIEDHkmksR6DHuqfrv8luBDwGQxhJUQQtg34TQIpyk95PR/379uZNDvekwYUTaRwlhOZIkx5fJfyRFjljWxhyB+hcPYtjWnihi1UzY+QShOJcakwDRM+hUpS6UdoL4/BgOA2GJNhPVncq2q7uKlDeIYpr8kAES8cUgVR2yEMCTRcgp1hqqJmBlWb5R0xddiPvCpaxuV7T1hQ56ac95796LHH1U/JU/Ar9Ljs7E+9cZEICLwYgZKcoTgkBAOOPlUkecJ9eQa8n5ENqIWSxxURxBCPRRImyEMyvNcHYGCei9bXQOo6TwYntKrXpbMywEyT0omOgj4cbypSmIO5rtmyDf3sWBG081DY9Gj4jRWkhBDYv38/QqEQCgsLPYN9tPDgWEFrJMo4gnGEEYIfBsIIwWJhcHDMmjULjY2NqK6uRkpKCmbOnImsrCwkJSXpbcca+jvD+ouhjTT0F40eevz4cRw5cgTr1q1DZmbmuM7DTaag37rflO0vwpxpUoPJHHAh7VQaL2TYj8HmQrP9hIYvrktSaP+UqxKCK09MkbBcIEUm+11RcM0V/oMX6LSOn2GCMZ+smDdIEVYgJd7S5AyTqTyXYjIy9ZqUKIg4AlrHUPfHViBkq6Qdk2FAGYJUqvCur5XbDIwLJ9SnBjQBBsNPZAzo8KFhwiFiKADjijFmqCoDN2jJD7zgJW9wFI/DzS4UKlRoCzlg+1RoTcDRITSEzjExVXQsMWogldwNVm59Pv3ekIMeLBUOjPR6ogEb4JAmIouAI/t9uU3XrA0OgMKCBioh5HH0Y8fUJbrCem6zbZ9e3t8RgmEwWHY8hBkPRt5U2FCkHceigRNZyJZ09JGC1OHDh9HR0YHi4uKoHbmHymmNBrRGInG07eSDeM+i7wLMgAEfLIQhmMAn3vUQXj70PYTDYTQ3N6OxsRHHjh1DcnKyBi3btsckZnCG9TfFLDL0J4TAkSNHcPLkSWzYsGGAJNJYzB36k+MSw5zUDABKj8+QYTm3dIhb3IDCfFrPVMgJPZGqmGIA6kiLkkNiCqBsFbLTmoEqFCbg1FEZDAhxF51duQhcjdgSJGSfKSYY4vyJYMxUVHoiewgn6uRSmZChPwOmq1MxDZhCKQQKwcA5AzM4bEsSLwRXChuMyzyE8jqELZsyShKFOl/V3dydr5CAJOR+TEXkUGMIAZYQDgjxkDxvGiQHeFaAtzYpIjToVlHXnhSXnhQN/I7iO/PkqQSYFsNVcVqHwEEPgssk0cZZT4D2R6xC17lGIT4Ag3lIA9eDzolFN0lQERqQ1AmqY0TbH8ASEwEO2EEV9usNwqRwrWDg/aGotXG27WLyhQ3NKvV87yokrLv1Khp607cvg3HkCLKzs4fUsTt27BgCgQCKi4tHNMi7QUueoz1i0KLPh/O+gugFIHPYFsJwf6F+vx+5ubnIzc2FZVkatGpqarRiRnd3N5KTk0c0eRdCoLOzE+np6cOu+1a1KQVUoyVTkLvf3NyMjRs3IiUlZULOg7y2u9f80lP7GbIAv6mAhUtPgbmmv9Tah2pDibYuBGCDSbFWoQYtSADydIrgEgWF/p/0TLzektyXjwEhEHip5ZSTUj4cFCgBgMF8AKQqBWNAWoKNsOXt0C7Pm+luqUTmkAxkA7Ytcwxg5FVxGZozKNQEeU8YDT7S42KMQ1gGBJMFxMIV+tOtyNXYbPgp7BfhRdlwhQEp5hcdnDxFpUa0dWhUdue61DLDCf2R3BMUcUGHApla30UG0SA1FDVdqPW42nYAW4+AmtZ3bTsAQFxgRF8egauLJs4MSJ2+EZigovEQ5d2kRiUME0zpN7ovJ9Rla7DinMm8JYAwj4fb4bAsQz8jYUtS04mKrkOAlspbKZBa/Oy9aGlpQUNDA0pKShAXF4fs7OwBoHXq1CnU1NSgqKjIE0IbjZmmqUGLcw7LsgaAlluxZiRhwvb+k0hJyIJh+OX68MHGwJSFz+dDTk4OcnJyYFkWSktLEQ6HUVJSgoSEBO1ppaSkDDk+vt09qoktNIiBEVDZto2Kigq0tbVh06ZNEwZSQGToTyDRF6/BRYfuBGBxgTCXoBTmBFoqeqQ8I7cYNb2XreShC4Gp9MUSBizhBR6bWnVQO3n1Pgyf05SVelARmUJ7agCYD35/KgxmOqwn5U0BDrtPvjb1XxLb5cKQ75V8kxBMDy7cNvRY7KYe25YBbjNwW/6lui+5DZMdX9U6Qr3Xn4cMVSysjmd5P6dCYue9+5933ejrePNdtMx57+4oDAkGFDpTA76QpTFyW/UlCPXFCfWa/unCWFqHaqQ49LoQikVH29A/G85r7vyja9ARR0uem8cbUvuLClIc4BYgws594+o1hVVlHsrRndTFvb1Bz64sKw7hXjkA97aFndwVEiLWc56PYITyRKQnBcjfYFZWFlatWoXzzjsPixcvRn9/P0pKSvDGG2/gyJEjOHbsGCorK7F27doJy88YhqHbZcTHx2vdPQKw/v5+HT4cyvac/D0sEYIlggiLEEKiDxYxgwYxn88H0zQxb948nHvuucjPz0d/fz/Kysqwfft2VFVVobOz0xPJIZtsoPrQhz6EvLw8JCQkIDc3F//+7/+Ouro6zzp79uzBueeei4SEBMydOxf//d//PWHHn1Ie1UiMyBSlpaWwbRsbN24cVhJptEahP1txyA1mIGwL+A0iRHBPmIO7gMUNAAYjFXQAOvekBjsByPbx8j3tTo6RlIOSx6eaK1rRFkq5GgxhlcNyWi0y9U/25JBECgr5ya2mJajzAmDZJM9kqE7Fhs5PwXAEVwUVJ3PANGRNjGnKsB9MKAYkk2E7JckkqDDUorCfVzLHbdxmerZuUK8rWw6Whl8WFEd+RiaY44kB5DUR88NZrtuM0DoKZTU5A9DEBxFySBb0OWMSEKhpIyUSKewqPGQEOVtwwEMA1AMK3jCeW6tQfgbA7bpE3jIPK9D7kbCFDqt6lru1FSMschl5VtJ8gJkIQMDusyAEEOoMgods2fg4YtuwSJR5KQMIWyZs24BpcDk5swztrZNJaSQpvEy27AVvXopAi3I4LS0tOHnyJFpbWxEXF4empiYYhjHhoqzkRfn9fu1hNTU1ISEhwaNyTv8iLci74GfJkMF+VWw2jBHrL/KaW1tb0dTUhIqKCs9naWlp6O/vRygUmpC0x2B2wQUX4Jvf/CZyc3NRW1uLW265BR/72Mewbds2ALKO673vfS8uvPBCPPTQQ9i7dy+uv/56pKen4+abbx738acUUI3kIeOco7+/H8nJySguLh5TzcFw5vP58Nx1pchOlrO09PhUcGHBEgxxzNLZGi4c0hdTpAYiSFAxr/Oe6XVtHRKkEBupmsvYGYUMyZuSDUSEPi7dpQSfD91hS6ngMMXqJuoGNDAZhuNNAVLZ2WDCyYlxOToLRlqEdB0yT6XzUSrHZHM5+JAJQaQKobUBBZfbGD4FoQwQtgHBhMxh6QGeaWCj8BC3VY6O8lQKpKQShgJiBt28EYgALtuVt3KHASNIF45Uk/Nev2YKQJhaRgQQFRp0HlVXOMyA08BRfeR9IaIsU+/cTLlopIcIMKTja68qDOixkEoNbO/20aYI2oMSclupms/UJEP9I+aP53wZwiIOpsEhOENvZxCmYoOyuESpR6m8cArrAdJ7YkwgaBkeoiJZJEhFmmmaiI+PR0dHB5YtW4b4+Hg0NDSgrKwMPp9PhwcnA7ROnDiBpqYmFBYW6r519I/WcYOWJYJgwqd+hwZ41ISi16LVUZmmqaWaOOcatHbv3o277roLWVlZABC1G8RE2Ze//GX9et68ebjttttwySWXIBwOw+/34/HHH0coFMJvfvMbxMXFYcWKFaioqMC99947IUA15UJ/Qz1cvb292L9/PwBg/fr1kwJSgKxJyExMAUn2h11uPkWFQrZDoLC5fE0hP7ke80RtbAUkxLKTAtWk6SdDiFyF+iz1l0P+k2MIU0QJAxwGBEwIZsLitC7tG0o+iQHwIc6XrGZzMuSXGi/vr8UZLO79+nUOjBsOyYObyotk6r2hBzzLNhSIGWp7J2zEuVyfWwY4N3QuikKCtmVARLC9hG1I4oUCJjtsKNCiz+W27vcydGhEDfsJOOFAHfoLu/fnhAQBFdqj166/7tcUDpThPxWyE+4wHnQxrFBhPh3WG7AM+i84hd4UonD3PxUetJ1lwgY45ehduSq937B7uetabPkZDzP9nVBLFrmu6/5w71+7N4Rwlzd8FeqLUm/IGSzV1JCeKXfoj0J+QfVdBlXIbziQAoCenh6Ul5ejoKAAc+bMwcyZM7Fy5Uqcd955WLp0KUKhEMrKyvD6669rJmC0UNlorb6+HkePHsW6deuQkpICv9+P+Ph4HR4k2SbLshAKhWBZFvZW/wE2D8LiIdgiBFuEsXTB0L2ZhqOnG4aBzMxMLFu2DOeccw4+85nPoKenBwCwePFifPazn8XLL788pg4TI7XW1lY8/vjjOOusszTDcvv27diyZYsnunXRRRehsrISbW1t4z7mlAOqwayrqwtvvvkmpk+fPqnHqaurw0tX7dRqElXtxwDIHJTMM3FPi+loJS8SuIT+XEoeCS1uLXNTzANsMlfl9aa4VlyncB6BFINQgD4tMUk1SJRUdi1Qy0z4zXgYzASRKKQJhGzAHWKn49jC0EBkK2o6IAGIK6/KuW4XYHAHpGwCJhegcJupZQMnIdxmsC0Tdtjw5Ll42NkHjwAobstBNtr+CJg4/Q2ziJAcPMCll4Ucr4xSCTSwgwstJaRBxRbOPmxXLkp+EQ5zj4CFwEqBjQN+Qv/VxcAWgR+cfBT9484/2g+Nw8JiEmhc3xMPqXsV5bOB34P8juyw/A4FfBDMD8EZ7H7nJoZ6KXwt99XTGQIXQJB7i1gt20DYNrSDSADlNgr5jQSkKF8ze/ZszJs3z/OZYRhDglZlZSXa29vHBFotLS04cOAA1qxZMyAPRKHBSNASQsCyLATtblg8iJDdC5sPnaOibUZaR2UYBi655BJ861vfwvTp0/H4448DAK666ir86le/GvV1Dmdf//rXkZycjBkzZuDEiRN48skn9WeBQADZ2dme9el9IBAY97HfEkDV1taGnTt3Yu7cuVi+fDkATMqM4cSJE9i/f78EEiFwoLUSgBOiM1XtkBACFhcu0oQEH/KswtozUX+F4z0569NyaHAJc4dAQV6SDfKyJEBxGJDMQB8E/GBgmJ6YosCKqVySKWnszAcwUy7XoOP8UOUxDQcQo6RFZHjTPbgxrf1n25IJSGaFie7rgJZwD5rc0IDl/PMCmG2ZEV6U43nxsKH/6X3arsHVtc9II9KGsJjTyBjSwxIhN1mD/rrBSd27sGsdNwgJF5DYLq+G3uuWHNCg5SFVUA1UhJel9+UCJrj3TZ+FvN4PtyDBKeQFJfdnBOJ0z/Q6FELWz4vjGQnuPEeWiIfVH9b71d+fkYSw7Y10hG1Dl00EdRt5xbIbIW4Q6MyYMQMLFy4cct1ooBUOh1FeXj5q0Ors7MTu3buxfPnyYbX03KAVFxcHn8+HkNUJm4dg8xAsHkTY7huUiEHLRxspIiLFu971LvzsZz9DbW0trr/++mG3u+2228AYG/LfoUOH9Pq33norysvL8fzzz8M0TVxzzTUT4q2OxKZUjgqAlj0howSiWxJpPF1+o5kQAkePHsWxY8dQVFSEw3haf7YofTnCtkAcU4KyUFRzyFwPOSoEPiSZRPVOcpDn6ppUbyjh5Jmotgpw0gvkZRkK3BhjADMUSEFnqxgzISBgGiYyk6ahsbdLq/KYzNQKFXIfQEqc9Oxk1wqmGihK7478NpszWYCqzs00FMVZMD04m4ZTGAzImbNhUJGwJExwrmpIbJmLkHkphcxRJvUSXIQmS9DgSXkobnnJFNwyPOK2Tr5KDbBub4sGcdf2Iiyvh5YJi14TWEkiiAg5uSum6eoSYGQzSKE9KRJn1Qdz/+Xut0Iy+tw5JYEBOSWiq2s2PXf24zYNcuTRuY7LlbaeBjLNwBQD9xG5X/gVMFqw+8OwgyFJoHCJHtP+QkiCz7XPkGV68phBy4BP5amCtleNYsWLPx54cJdZloWKigokJydj2bJlo8o9EWhRfqelpQWNjY0oLy+HaZo6pzVt2rQB++3t7UV5eTny8/ORm5s74mPScQ3DQNjuBWMyHEa/28GIGCMpJo5mnZ2dHsq+m24/lH31q18dtmtvfn6+fp2ZmYnMzEwsXrwYy5Ytw9y5c7Fjxw5s3rwZOTk5aGho8GxL78ejEkQ25YDKbXV1ddi/fz9WrlzpeVDG2uU3mgkhUFlZibq6OmzcuBF3b/g9ZiYmuRQmGDgT8BnSX7G4UAMu1W4q0HDtk1oYhdVg7RDXOdwCtfIAapxz5bI0zR2UszN0rajSH4JgUmHCYIpsYTBkJ6cj0N0JMB9MIwGACQEDJjOQ5FdAyakrrzprwcAEYDCmAFhohQ2H8KAIHwpnNMtNeVeM2ZoaLxPqsv5Lpsa4BC8YAONgUpIBWnFeyJoqrdRORAr13o4EKAIgxmD6XIQOla/S4rdKzonqsMiisQu5DZB0E1cARvVazBBOLZVFoKX0CxWYUH2RiAQp13cMzZBTZAs5f1Hn6txrT6sM934igYhWGYRIRh6QXIc5G9J+CGhcnphQ60oANgBhep4BIRhC/W55QYa+riB8ygOzjGT4YDvrWqaaEBFAOVJKQVUIvOal+6JfAF0259izZw8Mw8DKlSvHRZBwg9ayZcvQ2tqKhoaGqKBFzVZzcnIwf/78MR8z0FSK3KxNMJhP5aNtTcSgUB+dWygUGpP+4FhbfNC9GIuR9xcMynKFzZs34z//8z81uQIAXnjhBSxZsgQZGRljOobbpixQDSWJNFFAFVkwnJycjCXpBWgN1qOyTYb9wpzDNBgYZG7KZK4uETRQwSns5ULoZqu2EDDhtOsgZiD1ppInwSBUXZbjXSnNPMF0bNZghkNTYwYM5uSQ6NgAkJs6DQDQGVKUdK04IanwA6SeIK+NzltozwpycIUEANNgzvkpr0qrZMBdb2No2oetvDZNSRdMqhIwFcR0sfY4l6ApBIPps2G78hmGyTWAgWEAiLmZfe5ZPABFGPAWqzI36AnhErx1ipdlixIFVqYLtGwoyr06LnPVZVEDSC3k4UIV/bgquaQItrK+fVTobLieD+1tRaKUdxGBDreY00mZvEBX+FdOENjAc4R6jgRzmINRgNBGAqz+UFTWXpj7YXOm5MXkMcMukeOwPZCiPpgJIbBv3z6EQiEUFRVNSCNEMiIlEDGBQKuiokITI1JTU7Fo0aJxH8vmIQjGQRJqOdnr0dK6V1PeCbSoRsuyrEEp79Fssmuo3nzzTezatQvnnHMOMjIyUF1djdtvvx0FBQXYvHkzAJkX++53v4sbbrgBX//617Fv3z785Cc/wX33DT0RGalNSaA6cuQITpw4geLi4qiyIBMBVJxz7N69G93d3di0aRMSEmSBosWFhywByFojW0g9vxAXWjvC/UOljgsUuqOeVFIaiUgS7p5RBDBe4CLvitHgzxgMladiAjAN6SU5zRO9RoCRGsfhYxYY44gzfLCFCRMMIZup7umqjb0K/Rmuc2Jwhy+ZQ2sXSl0DTrgPTOaqDINrQVoZKiQvgclGi8xWbGdn5LNtinlRyEIWANmWJHSYmq4e4VVptqJDYdffqyvk5558eyR7bI3STjdiLmuxhCU1DxmTYTPDlF4WM1U9mCEHGw1QJF3CodqGuIBLMI96u/dE4YCa/sv0ex0c1h6Qc58A4cn9DbpMyELeSOO2oQBJ3kOP9yUAwQzAlrV63OIQlgUraMEKyYkHnVt3VwgGGPpZsgz7uQ4ftE2Y6rvmYLKeisnnhUJ/Q3lTFOno6uqatDIUMjdoWZaFkpIShEIhdHZ24vXXX0dWVhays7ORnp4+Jo/OsrvB4Fe9vDi4+lLcYBQMBlFVVYXs7OwBntZwoDXZQJWUlIS//OUv+M53voOenh7k5ubi4osvxre+9S2dfpk2bRqef/55fO5zn0NhYSEyMzPx7W9/e0Ko6cAUBKrKykrU1tYOKYk0WBuOkZplWSgvL4dlWZ6C4dsWP4RlGQUIcQFbcPRassAxIQ6ADvs5+5FsOO9AKYkQUh0vrGaWgBMmctdagRh6aq7lqKc7hb9Q+QCDGbCFT4bfwLQYuBdTnZNzj3vkDYVs4fQOFAZMZkMIVcpLoSdXWMctH0WgJa/FyU9JsOFK/w/ggsKU3AE+JgBhIOM3XxnwXbTf8H3ne1GARF6RrZL3PtPlQTF4Qn7cNlS+SIVgbXnRAwAsbKgQnAQa+lzYhtL0496wny3BiFNYzlZfhw6jKW+MvidIL8v5DpQHx533WoCXU95IiRVz5nhXXO3CjgjZURGuC1Sci3MBm1rXDnr19Cjk6SHMeAp7Xcs5IJuQOXkoIllwFg8rGFITKQNgtpxo+ZJVsbfQ6ikhF1gBCrzUOa19+d4Bx3Xb0aNH0djYiOLi4gkv6B/MhBCaPHDWWWfBMAy0tbWhoaEBu3fvBmNMhwdHA1qtbYeRkb5IijELDh6hUEHtSZKSkrBs2TK4BXNHAlqT3d131apVePnll4ddb/Xq1fjXv/41Kecw5YBq9uzZmDNnzqD9WoDxeVSkamEYhmem9rVFDwFwaOWWEAjxMEK2jHVRkS6D036DMa67PlDuxqcGI6ottQVTg4zj+nPllXAFfE4OQP7lajtHsdwAVyQI2ZPKYT+7m8zSoEM/IJKAI7knv6kknTiThZoKmEidh8J1jElvkdsy9wXOdDdgi8vQjcEksPgUqIQs2e9Ki84KqbCe+ciXhvw+0h/+pn7ddv1dAKCZhAQm8r07b6UKSNU44Q4hkpEX5ihhCBjugduSRa2mXyKQUAM5MwV4yFGEF5xJgVxuAAaRPYjwIz0mGZZzAYMLjPR7SK/NrXHIDCfUJ0+WTtXxpjy5JFdeywEwAhw463nug+szMXC5oBo4tX8JPnJKwjlgBW3dq0zdRXAYMNWJhI1kFTqWJROCG7A40wAVtE1YgsHnem9EAUe3nTx5UkdUhhoHJtqqqqrQ3t7uGRdmzJiBGTNmYOnSpVFBKysrCxkZGcOCFuchACEZi3HRTim8ads21q1bp9l2BEaR4cFooNXV1TUheaCpbFMOqNLS0oYFIb/fPyagCgaD2LVrF5KSkrBmzZoBMW8uBOp7pX6VzYGVGYXQrTrU5NP9PNpKIshtYS40ANhCtQIhiBPSd3K3kadZrm6hIRyQoroqGAIm8+nwo2YFMqfo2C3eTeBjQOa2LC4QZzJ9HK02AYC5Ri8CYnkvmKeFk1CUeZ8CBYsb2msakHCBPM7MR/5jxN8NAGT85j8BAG2fVICl8lQmKVRQyI9BC6ASyMgoqeJV2TLUJwCYLjIFtxmgBmjDJ5P+doQ0E4X3aDljAkKpKUgVeKgRX4VN3UK9LtCRYVAHZHTDSGLywc3Ic+fQmJNbcjBW/rFds5oIMoR87QUlreihjuEBLfd2yrPS6iPqXgm1Uqjfhm07+VIA6OlxqaX7U0HfP3lTQdth+cn30rsSAlj/yo8wmAUCARw5cgTr16+fUP3O4aympga1tbWDKrAbhhEVtPbs2QPGmA4PDgZaXPSDEn9dXfV6Oen3bdiwIWoOzu1BDQZa9fX1mDVr1kTchilrUw6oRmJj8ah6e3tRUlKC9PR0rFy5coD7TB6T2ypaduLdc94NwIKt+mFQiw1q9nr169fp9YlKH/j2UafPnsovWC52oJRSYkrKyMlpu4d6qq8yGQnbOrRyQHVHVxuYDNqzA3PyYxSujKMurFyqpQtF8HDCh1RkLPdlgICWKaCWszync7vMN3DBwJkELAmUkrafs/Vzo/puIi3jt/+pX7de933pUcnIk/aKbFWHo0N+HvByzLYUkKiJgvTShKS7K2BgJge3VMjSkLqCTpsRRWwBMQAZqPGj/kyH/wAob1nA1c2Ye9+7wWiAZ8Wc/dBntA/5wvkbWcLiCeup58QNzrSOA0bS/dedoIVs7yJgIDLEDQA2S4IdCqrDG9qTMgHY8MOyHU+KgSFIeSllQT60N9Xc3Iz9+/djzZo1MW1ZEQgEUF1djcLCwhHJEI0FtLq6GpCSnIHunja9n5MnT2pwHEl4MxpoBQIBvPrqq29r5XQAYCJWFVsjNM75oB18ySorK2FZFlasWDGifXZ3d2PXrl3Izs4etA7jKwU/h8FMJPvjUNMtGX+LUldjZnIC4n02hBIxMhgHNd64Ztsn9PaBQAB79+7VVPpHNj2kvCahiBKqdYeQsyHG4OS8hPD8fAUoRyTzU37DhMF8+r3JnPGKQn8+wwkFygGHwW/IME6CKavl/YYBvwml88dhMhtccBi6zks4Az+kV2caDKZhwGfIwcxnyBCayTgFMmGqGirGBOY+PjHJ00hr/eT3QdqF6gRhGMIDVO6/8jV5VdQRUP8HgAQl8nMMQ2iCgabOq7Cf2yOSnpdw7UroY2kTkLkwyUyQxIso7DnmWc48ZAS5H1qmztImDT7uugwCQzcYMX3+mhlKXnSEmgcf4FnJZ4ZzAW5xcCss288IDmb4EA6F0dvTD0OEYRmJMP2JYOCwYYJz+SyajMPmTMp0CaZYgByWYNj4yt0DbwSA9vZ2lJWVYdmyZaOuWRqPtba2ory8HGvWrBl3w1XOOdrb29HQ0IDGxkYAGNTTamxsxL59+7B+/foxg3JPTw8uvvhiLFq0CL///e9HTWt/K9lb1qPq7+8f0bodHR0oKSlBXl4eFi5cOGgs2RYc3BXrbwt1orT1Dbwv5V0qTyUHRZmbYrhu+9V63draWi2xQgKR1+34NB7e8HNApstBTQoZc/T9AEfUlgYeSkUADuHCdoUSiUlIxbjkuZE8k5DOh6rjAnyGVGKPU8+wTYl8eOu5yKNiwqmhAiKS9NqE9Ko06UHOnPN+f9OIvpPRWjAYxKGbzkdqairm3OsUY1MoMFKR3efz+KaShCGgwmsusVtXUbGkukvAotYkhuEmVRiaJs8UeEjQoKOoL1GdCvMAU0SeScMj8xIr3ErvpFpvE9WceRUgLENdBxskT+WAFJ2ze53IewjI/BRjqr2HvntSDghmPMKh0EAsBQOHHwDXxwipvJSJCG9qEFp6d3c3KioqsHDhwpiCVFdXF3bv3o1ly5aNG6QA6fFMnz4d06dP93hae/fuBeCAlmEY2LdvH1asWDFmkLIsC9dffz2SkpLwyCOPvK1BCpiCQDXa5olDWUtLC8rKyrBo0aJhi/ZCXKAgeT7arYAO061OPxeWLb0eQwEAFww37LhKb3fixAlUVlZi/fr1AyRWbtj5Gfyy+EG6MnBwVTQrPKFGklpSASopWsCYzhNJsoOTt6LOu1RYbKvx0a1qQYAj9yvJDZKtJz08WtdgUmmDC6EKN+V7mfNRs2FDzo5luJHBAKmqq1yWITDvf24c9vsYi4VCIZSWliI1NVUWfP52lefztuv/a8A2tvYanHCb4aqv4sozMXWeSrWGVIDlLj5mKqQpAUy2MOHCcFQ2FKGCwogarAwAA8DDAUvAFcWzJeAJF6C4o4k87DQelOAqz9fTL4ryWJ5jKs8qAqDcArGR21Ce0wpLxmNkrVRfv6RBWkYq3GKJFleyXbC1KHJQeFl/m14Z2J+or68PZWVlmDNnjlaeiYXRcefPnz8p+R3GWFTQ2rNnD8LhMKZNm6YLf0cLMkIIfOMb30BlZSW2bdumS2vezjblgGokNhKgoodi2bJlmDNnzpDr3px3P0yDoTUc0D9MSwAZCXEqzEfeD8Ondl2htzt69CiOHj2KoqKiQVk3N+/6HB4qelB5UAxc5QQkUDh5K3dXdIMRbV0Sv5mQSuymCn7ZinVmMKd+yzSgOwz7DIeZaDBq1gjPbJ6GD5nbEvo1nZt8L8OPtgIp2o4JtS6ToJn/h+F1xcZioVAIJSUlSE1NxYoVK6JOYjJ+c1vUbTtu/L7nPeeGpkfKei0iawgNAnbY1F6T9KikqgYoH2UI5XUI7bW4SRMEXELVm0E4+9Z1b0R3dwGX26sigJLLXOdPpA339QjhWRad/SeXWSpXF82r0vuD0ot0udqWbYKZfljhCG+KMRj+NNjCgAEvYNHTxQCEuOnxrNxG+n0zZ85EQUFB1HUmw+i42dnZ41KdGKkRaKWkpKClpQUZGRmIj4/Hvn37IITwhAeHAy0hBB588EH86U9/wrZt2ybEE3wr2FsSqIZj/VEobvXq1QMUfQezMOdajYFIClJ8lsNvAmFbaJquEAJHjhzBqVOnsGHDhiETmUII3PTmp/Gz4p8rn0qFAeHyfuBKhCuQkvkmVZjJDFA9FXORJQiATOZ4V4B8TeQHWwCm0o8QQgKiDF+qgZQ5lPRIVqOupYJUZgd3yAxceXaL/vTJEd3f0VokSI121jnt1w7t3bZtNDc3I/FbUlHaUb1QoT7Vf0mH/bgMq1F4jauCZvKoBBggHJKFvHESxPQXyeR/tCK7znM5YTVAeVMQDhXecDws7d7AIWSQl0TRYqe1ivf63S1XRMQyAEogGPpchSLCOJwQb9KPAejvt2XFnJkM5ktQF6XUKISchgEClqDQn5wQhISJc179gef8qJYxNTUVS5cuHVMh7VjMtm193CVLlsT0uBUVFUhNTcXq1avBGMOSJUt0TmukoPX3v/8d3/ve9/Dss89OiGrGW8WmHFCN5MEZquC3pqYGhw8fjhqKG8zCgsMk9QXuKJwTxSFkC/gM4NO7roAQAgcPHkRDQwM2bNgwJIWWCvdkfkqouiYnR8XUXxImcAMXeVWMGQAMHd6jQYcL6Z1Rbsmdu3K8H8nIor0SH01AKr0bkMl0BhdIUUTLMDTLkIFJDT8wcFvmwUzVQHEyjMJ9KSkpYwKpSCMdN/zqW7AsC01NTUi+/WEAskZLs/0UWAHQIUBLeVnEHpSMOHUfCawUa84J3rrM5Z1w2wnheWqqdEiWweAOqUNAsR0jPCE6LueGAlgepZBXHdMVErQ5HQfwoCWgWrjIa5DALcDhg2lKAoUsQGAQZiIE5zDBYMMPKDBijAq26akEQsIHA/aAe0KqMD6fb9z6faMx0g00TXNQD30yjGqlAHiulzGGjIwMZGRkDAlaqampiIuLQ0lJCW666SY8/PDDOOecc2Jy7lPFpmQGbrgHKFroTwiBqqoqVFVVobi4eMQgRZbqSwWHQFWPrEw/J/NCcOHIKVmcgXOOffv2oampaUjlDDofAinGGD5f8lkAMtxncwlc1FjR3ZeKap1kBwhvp14hFJByZ339lzthQJJwsrhMkAv1NWsCB4UVo+S2bU5tRhwqPOUcbNesnQuGFU/8+4jv70iNQCo5OTlqGcF4zefzITc3F2m//hYSfvZ1vZxIBbZlaoUMWy9TRbFKoYGaQsraJEO9l0AklHdGdUt6GwWI0mOT98/msk2K4Mzz11YNKaW0EXQrDmrSyDmDZbn7hTnnRufHudRVpMaWmlgxwKsyNNU/mnHFPOxV/agYAMOfCA5TlkIIn3wNyNyVkHJdBFZh+HHOq04YVgiBvXv3wrIsrFmzJmYkAJpg9vf3R62hnEw7fPgwuru7sXbt2kGPS6C1dOlSbNmyRd+b559/HvPmzcOHP/xhfOhDH8I3v/lNXH755TE796liU86jGolFAhVJnwQCAWzYsAGpqamj2h918PW023DF/m0OfKn8MuzevRs9PT3YuHHjkAlMtwSKuwX8F0s/h3vXPwDS8bOFo0xBHR5oTk4pc8YMFQpk3sS2LLSSoTv1lpiBzOVVUS0UXR/t2y1MSxqqblIWFR17r4tBqNDjur9djYm2yQapSIuLi0Pcw7fDsix03/wD2JZsm8KYkPqFJne1LXFyRJ5iXggtHeQhLrhCg/Rc6Zo5BWzEHhxoQon3umqeOJOqRi5JJqqDEnBavQ8s5IUX0JTnRIDlfc4ZOJfPr1DelGXJ3BRjDMJIAjN9cPQpqdCbAEpmUAUYbPhgMBtMMGzbtk1LD504cQI9PT0oKiqaVP2+SKuurkZrayuKi4u1uncsrKamBoFAYFRSUG5Pa9GiRYiLi8PnP/95WJaFe+65B1VVVbjsssvw7ne/O6bXcjptSnpUw5nPJ38snHPt5TQ2NmLjxo2jBikyoo8LAXSEu8GFQMim5ogMZWVl6Ovrw4YNG8YEUmS2kOoVlitPpVvW630ASldCzrxdHhD9I3IZFw5r0NYJfqa8KKmg7rDRpCcX1l4itGdH10l/ybhQ9TDuOp2olPXxWaxBioxyFkc//296GckvkSdFHgl5NZZlKm/IadKovRryqJTnI9TkwO01aaks7vV4pHdmOO9dnpE8V++x3B7xAA+OG85rtS55jQRSnDPJ1uOGJ4wbSbjo77fAjQT9GYf0mgCAw4QlyKMCbNfc10IcNr/4beTn56Orqws7duxAXV2dFn+NlZ08eRKnTp3C+vXrY8qQa2hoQHV1NdauXYukpKQx7cOyLPz617/G2WefjdbWVjz11FNITU3Fpz/96Qlp8f5WsSnpUUU2T4w0mon19/fj0KFD6O3tHdbLGcosCHAayNUyVWkEzoHzH54H27aHnY25wTMaSAHAreWfx3+tuV97TqQyDtAAwuBjpsfL0r2KGIGYAAl36xyEWpfyVMIA/HCH/by6gNx1f6lticGcTIu8EbLQ16GtywFxw1NXjvzmjsBON0gZhiHDQY+sBwC0XHOHVoS3bAOm4bTFIMV5N+tPAoDQNAnSWCTvyTF3PgsgGrskPLiWkTEHlLTHpMCQ3tuK/edeh0cAjdyHA1LklZFqPhl1cxYwYBg+hMJhhIKWMy0x42ALw7lO0HUDnPkAYYMxBs7iwAQDuOy9lJOTg1AohLa2NsyfPx/t7e3Ytm0bUlJSkJOTg+zs7EnT9GtoaNCSTCNRnZgoa29vx/79+7Fq1SpMmzZtTPvgnOPzn/88Ojs78fe//x3x8fE4++yzcfbZZ+O+++6LWY5tKtiUBKrhjOK8u3fvBgBs2LBhzArLH5/9I2T40sAFUNNXCQMMa9O2SOFYLiCUQOS6deuGDFUQSNm2PShIkRGrT9M1BJQyOXMV9sp6JR2igeP+kicVmWOi2ivAkD2iYMBUpAyLCyl0oEJ3fsNhDQ71uNMAagsGHxNvK5CqqKiAEALr1q3z5A5mPHo7mv/9Tt3KxNK1VYpsoKji1MnYTagwVH2ZHQFQWsbJWST/2ESa8ZIzDAYPaJHH45AhXGE9PfmI0nMKzmdyP7KHGLWGp3ycfA6JZeHsR4BBGPFgzOd4gsLQzy8XpsphCoD5JUOV22DMwJZ/Sims+vp6VFVVobCwUA/a4XAYTU1NCAQCqKqqQmpqKrKzs5GTkzNhXk9ra6sGi1hKMvX09KCiogKLFi0ac2NCIQR+8IMf4LXXXsOOHTsGMIvfSSAFvEWBijphAhh3rxoS0bFUcJ9DwG8wBG2OBB/w/scXDZv0HS7cF2nf3PMF3Lnqp1qFggsotRzpChkGDVqA5H4RWAhdSyqPC1WQKnT+iJTPIZwwEVmIO20+LAHttcoxkSmFdpnTcvcdoiF209/fHiBFrDPbtrF+/fqoCe7Mrd9C4yfuhG2ZihGoM3nao5AhNsApLBZO2FTo/2g6OwANZJrF7mL0uYGFgC6S8ccBT76Mc7e35M45OXmoaO3nhQfUIsyIg2WFEAxZjsfFKNxouLxyQwIeMyQ9HyYY5zDMBGx67hYAUv/ywIEDWLt2rcez8Pv9mDVrFmbNmoVQKITGxkY0NDSgqqoKaWlpyMnJQVZW1phBi1QnlixZMmawGIsFg0GUl5dj9uzZmDt37pj389hjj+GBBx7Aa6+9htmzZ0/gGb41bUoC1VADfX9/P0pKSsAYw8KFC8edkJUFuPK1LQS67R5YgsOvPI+JBikyd4GvM2AYMBgD1IxVq1FwQLg07SwBpSThtAxh6lrADTBDESRU7RUXTh8t6uRru8gUVDemVdfVctMAwqQ2PsETuNMNUuFwGOvXrx/y+cl67FtouOouBQxyWsEULd/dAJIpSrcGLKFCx8JbpAshi7XJw3KDhZtyHgle8jlwwBFQtW9wwo50lIHg5Pa85fFC3HCFkZkCRQHqe+b+JmwWB5OZqluzA4ICKvSnq5J9MA0fOOMo/sdXAMjw1969e7FixYohWbhxcXGYM2cO5syZo0ErEAjg8OHDmDZtmgataKrm0ayvrw/l5eWYN29eTAd58tKnTZuGhQsXjnk/r7zyCr7yla/giSeewJo1aybwDN+6NiWBajDr7e3Frl27MH36dACORzBW++Ds7yMOfu1VUd6GgiFhjhGBlBBiVCAFAP9v3xfxnRU/UTVUqpsuoxm5gMF8CqCYAiHHg9KgA6GBRSpGEKgYOpRIg1TYFlJ6iSmKvAAMQb5UdAvbkj3IBXDBP64YZK3RG4FUUlJSzEFq7969CAaDKCwsHBFjKvv3/4n6K+8CtCKFanaovBgGJ68p30PnrwBvbpFATIKDrGnyPMGC2IDMsx0AHarTXporr+UO+XEBnVMTEeE/Lc3keu/OZ9GrUMhCMBTWmo9cTXjCXE6gHG9fbcckQ9DmQoNUV1cXysvLsWjRIuTk5Ax7n8ncoBUMBjVoVVZWIj09XYPWYKH+UCiE8vJyzJw5EwsWLBjxccdrE1WjdeDAAVx99dW47777cPHFF0/wWb51bUoCVbQvuaurCyUlJcjJycHSpUuxc+fOCWEOJRqSjSPzU8Di1I0ICw5whnf9LGPQ7cbqSblN10Apph4pplN3X1KWoB5UBpTHAxA7XW6vXuhCX0Ft5lUewuU9hbiiVzBHwokGRQOKyq7CfgKyW/F7JgmkVq1aFVOQ2rdvH3p7e0cMUmS5//OfqLvi+1oF3FagRRMLDSFEjNE5SPpA/vE4V0LWqzltQtyfORu6qeWAAxzujdwgBbjzY24Ac5JeQjBY3CFF0DaCmTANHwDZa4qzOBjMVEAFj2dnCT8YEzBNGZpb8/cv6n2Rjl5eXt64wl/x8fGYO3cu5s6di/7+fjQ2NqKuU77w+gAAablJREFUrg6HDh1CRkaGBi36LsmjSUpKiqnahRAClZWV6OvrQ3Fx8Zif6UAggEsvvRSf/vSncdNNkyPw/Fa1KQlUkdbe3o7S0lLMnz8f+fn5YIyNq8svWWR+qsPuQj58EJAdfokgEfngTQRIAcBnX7wMD7z7zyrvpJLYkNJJbpUKG3oirhUuyJOiWTcpqPsMSaJgzJDK7BDwGarAGEpxXcjjDXbWkqovdfCS/jMXb7zxBnJzc5GTkzMu5tTpAikhBA4cOIDu7m4UFRWNiXgz6w/fxKmPf98zwOvXzAEJNgChXB6Nei09Mjo3r5fjPXHvN0TeltzYGxK0ueM9CyGFgrnHm4Lexm22i+Y+kqfYdhWQ0/DhBqlgMIjS0lJkZ2cjPz9/BHscmSUkJCAvLw95eXno6+tDQ0MDTp06hYMHD2L69OnIzs5GQ0MDGGNYtWpVTMkGx48fR2NjIzZs2DDmuqaenh5cdtll2Lx5M+666653HFliOJvyQNXc3Izy8nIsXrwY8+bN08t9Pt+wfauGM4tZ4BCwYMMWss+UDQ5LAHEq9m5ZlmdgmyiQOnXqFA4fPuy0slfeDBcCYEKLzPoUAMmWHkJLJREJAxqkBHyGqpcRMnlvuGShSKeQvKuw7aWjMwHYjPQCpSf14Zev1jp5gUAAO3bsQHJyMnJyckbNzjrdINXZ2YnCwsIxs0MBYM4fv4mTl39felRCsf4EQ0QaykVTN5zckuu/5Om4SRlu1p8O56rVaR9cM/yYBjtbRH/+3KxDDqYP7YQLHRIGeW7MjEM4HEQwFAYz4sBgIqwLm5k+P8AAM0wYhh9r/v4FfZxwOIzy8nJMmzZtUnX0EhMTMX/+fMyfPx99fX26K3A4HMaMGTPQ2NiImTNnxqSguL6+HseOHUNxcfGYKfbvtJYdY7EpCVT0gFMzwhUrVgyQ4h+vR/XuObeD0sZcRvMxN8VpIXFP9RV47rnnYNu2XjZRIFVTU4OjR49i/fr1eNehd+FrS3+qBxKDyXYcTKtOODNmea5wDWRC5aec8JDbT3L6VjleGOBM1AUYPvH60DJIpJOXnZ2tKcVEN6acQXZ29pAzSVKrPh0gdfDgQbS3t6OwsHDEyfihbO6fvomay37gqW9yM/QkCJHXJTxA4q2XA4jOLsOwSlFfkRZsF+3dC0YKtFzgA9czossZXLc4MjToACIdS5IitJ6f2ne0x5urhmem4f2+bdvG7t27ERcXF1MdvcTERP17LCoqQnt7O44fP44DBw5gxowZyMnJQWZm5qSAVmtrq+5DNx6hgXday46x2JQEKgDarXc3I3Sbz+fzgMhYzYKNur4jYGAwjTiEYYEJE4wxmKapj0GFvGMhTrjt2LFjOH78ONavX+/QdQWBjRzcqIMvA4XzXNR0NYCQd+Xkn1SCW9Xz2IJJsDMoTCUPRIWo177xCYzW3JTiYDCIhoYGnTOYMWMGcnNzMXPmTA/dOxwOo6ysDImJiTEHqcrKSrS2tqKoqGhCBwCbGH4QuviWGHjkORnMy7yTiyUwyNoruZ4bhMLq8zA3HIINqCjcXbLg5CqpuaY6QMQ5Rhzena8SUutcgyyAYCiMUDgMw/ABzCfr7NSn8smU1Xw+MwFcAOuf/rzcnyKpcM6xbt26mHoEp06dwokTJ1BcXIyUlBRkZGRgwYIF6OnpQSAQwNGjR7F//35kZmZq0JoInb/u7m7s3r0bS5cuHXOrjXdqy46x2JQEqubmZhw6dGhIBXSfz4dgMDjmY1gsjHSWibDSfxAQ4EzAEAyW+nmS1+YGKcbYmEBKCIHq6mqcOnUKRUVFnhnYf1d+Ebcsvl/mlYQcAuU26lxd9HFbCDAXkYLWY8yrB0es4bBNnpfyvgTD9dvGXw8VHx+vcwa9vb16UDhw4ABmzpyJnJwcpKWloby8/LSA1OHDh9HU1DThIAUA+U/chqMf/S9JNafQrSuUpycKFBN0eT5hKtyNyF9x7Z1JogZtb6v92sKQYV64SBNQrEO9f6aUJZRXNcCDc06HR3hVMPwQPKzXN1mEpJfaBWNykCeQIq+1t7cXxcXFMRV7bWxsxOHDh7Fu3boBAtHJyckoKChAQUEBuru7dWGxG7RmzJgxpvPt7+9HeXk58vLyxkV/p5Ydzz333DuqZcdYbEoCVWZmJjZv3jxk4n48ob+amhrPexs2+ngPBBMICQtbj8tutaZpIhwOa69qPCB15MgR1NfXo6ioKKrq+j2Hv4CvLn5Q1qxQDgqqgy+1hBBOOw9SsAAAUvaDS/1ANlr0pum5YLhxx8dHff7DWVJSEvLz87FgwQI9KBw8eBChUAgJCQmYO3duTBlYVVVVaGhoQFFR0aRJ8+T/5TYc+cgPJR0c3vozkrmiGKsjf+QANXlSDK6eVorYIMGIucgUDmOPvC0n50XhvoGUDIs7/cTInDororVLfT4zAsy4OkdS3JBOuwnT9Ib8qqqqTovYa1tbG/bt24eVK1cO2rSULCUlBQsXLvSA1uHDhxEKhXQrjRkzZoxoImVZFioqKjB9+vRxkUV27dqFm266Cb/5zW9w9tlnj3k/7xSbkkDFGBuWXTYWoBJC4OjRozh+/DgshGHBgk/dAhv2gHVN00R/f78+p7GCVGVlJZqamlBcXDykOCWDCepZ5ekxBQFDOPkpykQJtS5TOQ4IBgtSXUIICgsK3bn30zsntz0AYwypqalISEhAc3MzEhISkJKSoutLiISRkpIyacBVXV2Nuro6FBUVjVkIdKS26K9fR+UlP9ThM1s4oMQJlFzuSHS1dIbIuiRbMI8X5m5gKQTAXcw/Ws3NHrTdBAj9ufeoTo5L7t8OhxEMh2EYfjCYntOkGj2mqOrFz3wOgGS71dbWori4OKa5le7ubi1RFC0tMJjR85mamoqFCxeiq6sLDQ0NOHToECzL0qA1ffr0qKBFtVJ+vx/Lli0bV/j/8ssvx+23347LLrtsTPt4p9mUBarhbLRAReGg2tpafOOTfwUjVh8sWLCRnbYYABBkYQhFTc/MzMSBAwdw6tQp5ObmIjs7e1SsMWKctbW1jWh2f8/hTwMAbl38KxV2cRhgJEIrBHXlddh+BnOS8lSuTJ8LAfhMTDpIkYXDYZSWliIxMRGrV6+GYRhYunQpWlpaEAgEsHPnTiQmJmq6+0R6PEePHkVtbS2KiopiJkDqIVIIFhECBHThr/6PCsu51uHCKw7rBhdihErvxgklElY5+nve86K3pOXII8KMmunHAMP0wbZCct0oJAouZLmD30WgqKurw7Fjx1BYWBhTsVd32G08NVqMMaSlpSEtLQ0LFy5EZ2cnAoEADhw4AM75gE67FOIMBoPjqpVqa2vDpZdeio985CO45ZZbztDQR2hMjFfeYZJsuPxTS0sL9u/fjy1btgy7L3rIGhsbUVxcjAuWfR25icsQ6u9AoPcwTOFDeloekiGFH/9y5AvgqkeVZVloaGhAIBBAR0fHoKSBSOOcY//+/ZoWPdoZ5y2Lf6VfM8Ajg+Qw/iibRfp8JvyGDwmmH3Gm7K0UbzJ8oTQ2szYCqYSEBA1SkWbbtlYbaGlp0bpu2dnZ42LlHTt2DDU1NYOGVifLuru7UXPlz7UihASGwX5S3uVu5YkBa7o8qmi0dfcx6CfsLgoWgFK/cAgR3v1q6o5UlbBDCIaCMA0TNvO7tpA5L8YMmL54bPq/z6GpqQl79+7F2rVrtUpMLCwcDqOkpATTpk0bl0czlAkh0NHRgUAggIaGBlCnXc45WlpaxtWlIRgM4sMf/jBSUlLw5JNPvmN6SU2ETUmPCiAZmcExdKQeFSkStLe3Y+PGjUhMTISFEADgZO9e+BEPm8n9hFkYfz/8FQ1ShmEgLi5OV8cTaaC6uhoHDhzQas/Tp0/3/GiIBdXb24uioqIxDcD3HJaV6bcs/pUkUMAhTTiMP1k7JZSYrLpzsmCZO7VYsbCRgBQg8365ubnIzc3Vum719fU4fPgwpk+frtUGRkMnrqmpQU1NDQoLC2MKUr29vSgtLcXs+z6Cni/9Vbf00I03BbXSoJxSJAtwqL17awkc9XNokgURK2g3BgQsF82dzkU3zvR4VeoFA2CH0BsMgRl+gA2cfHEY8JsJAATa2tp0yUgsQYpUJxISEiZVdYIxhvT0dKSnp+v28NXV1Whra4Pf78fx48eRnZ2N9PT0UZ0Dtezo6urC008/fQakRmlTFqiGs5EAFQmQ9vT0eBoecsHRg07Mmr4O9a17ELS7MY1ZiFPbANE1/tykga6uLtTX12Pfvn1gjCEnJwe5ublITEz06MmNp8AUcADry4t+Kb0oYnwRoUKHAIE4UxEpuIBpSmD7Utnke1MjBalIc+u69ff3IxAI4MSJEzh48CAyMzORm5s7LDPrxIkTOHr0KAoLC8dcyzIW6+vrQ2lpKXJyclBQUAD29K3Y/YG7dXgvLJjUVgQkeDAv4cJturAX0NqPnLQFVd7LYe4JBxCZcLwlIZTgrTcMSKr6A1t+yJVM0w/bCrnWp8mQ0jJkTv5sxR+vQUlJCRYvXozs7Oyx3rpRmxAC+/btgxBiVM/XeI0xBs45Ojo6sH79ejDGEAgEsHv3bhiGoesLp02bNiRoDdey44wNb1MWqEbiUbmbFEYaNcULhUKD9qviikBhIwwTPgTRB2BoIVo6N4pvL1q0CK2trQgEAti1a5c+t3Xr1o0bpNx235Gb8R8Lf6FzV4AUlbUhYELOoi2l4+czSaFiwg4/qFGd1GhBKtISEhK02oCbmRUOh5GVlRXVcz158iSqq6uxfv36mP74SSYoMzMTixcv1udEXXep5slyMfts1wSDbEALDyrwVZkpYuUxN0iBjqHKEVQeUyvfq/U4nQ+khqPkXjBVFC7PCjBgqPfS8/NptRO5nTTDTASHQFlZGebPn485c+ZM9C0d1IQQOHTokG5fH0v6e1dXF/bs2YPly5frMpnp06dj6dKlaGtrQyAQQHl5OXw+nwattLS0AaD12GOP4cEHH8Srr746aWrur732Gu6++26Ulpaivr4ef/3rX3HJJZcMuv4rr7yCCy64YMDy+vr6UYkIx8qmLFANZxQaipQ4ApzBE5BNFd1hpBVzPwEDBsIIoc9qg4ANAY4g+vBK5R2jDikYhoHMzEykp6ejt7cXlmUhPj4eb775JqZNm6ZJGBPh6v+k6lMAgC8UPKTYX0I1SyQCinvWLPCf+yaeiu42us9xcXETOtN104kpyb1v3z4A0MzBrq4u3bl1rB1Ux2IkBZWenj4gBLX2mVtQ8r4fRQnpuWqqXCa01+KE+YiMQd13CfgAqtUiQBJamJhYebagvJPanSIcaiq8cLwpLhgMgyFohREOh8AM5zeimyoK2XiTCcD41mrkzJyJ+fPnj/3mjcGOHTumGbMTOfEbzoi0MX/+fOTm5no+MwwDM2bMwIwZM7Bs2TK0tLSgoaEBZWVl8Pv9yM7O1l7Ya6+9FpOWHT09PVizZg2uv/56fPSjHx3xdpWVlZ5J3mhYlLG0tyxQkTpEJFCFQiGUlJQgLi5uQOdWMluE0RVuQGn1g8ibcT6y0pYDGBnbMJqRxpnP50NhYSFM00QoFEIgEEBtbS0OHTqki2Bnzpw57gH9/upP4/P5DymtN6GS8hwwpNSsxQW+e3DiFM+jmRukhuvZNVZjjGHatGmYNm0aFi1apGexJSUl4JwjNzc3prF+uuaUlBQsX7486vPizkXpeioAboFZobNG0J8ZLiCzVajQFuSNMZeXI/Q6AsQYFJoabwnvOTHlVTFAhwaJyu4zfLCtMDiYLsGzVPsR7loXANLT0z3eYyystrZWE2Qmqx4umtHvOTMzc1hgNgwDM2fOxMyZM8E5R3NzMw4fPoxLLrkEqamp6OjowNe//nVcdNFFk3rO73vf+/C+971v1NtlZWXFtPvxWG3Kqh8O94OIpqDe39+v6c+DdW61hROPp0JewYCw6B/TedIM2+/3Y+3atfqYcXFxyMvLw8aNG7F582akpKTgyJEjePXVV3HgwAG0trYOGdoczh44+ml8418f1CEeAaqd4ghzPuz247FYgFSk0SyWEvj5+fmwbRs7duzAjh07cPz4cV3zNhlmWRbKy8sRHx8/ZA+t4n98VSuZS+/G3TLF2zXZ/c9Wmn5yOwaLO0QJUpug/lFchRWJns4FA1eFv85+pEqFpdaxI9anmiiqzRPMpz0vN+mCmQkQwKDAPFnW1NSEQ4cOjUtHbyxGee2xkDYMw0BWVhbOOeccvPnmm7AsC/Pnz8c999yD5cuX4zvf+Q7q6+sn8exHb2vXrkVubi7e85734I033jjdpzOoTVmgGom5gaq3txc7d+5EWlrakIPnoVN/khrpwtbECWBsQBUMBlFSUoKkpKQhj0lyLmeffbbWQtuzZw9ef/11HDlyBN3d3aM+9qlTp1BZWYk7S7z1UWFu4fuVV496fyO10wFSZA0NDTh48CDWrl2LgoICrFmzBlu2bEFeXh5aWlrw+uuvo6SkBKdOnRq3sr7biHFmmuaIQpybnvuKAxaufwRWdgSguP/ZwpFBkusY+jWg2nKo41gKoAiELCJZ0L4VeOm/CuC06gSAYCgIMJ8DYmrfss281DaZ8aNz0dbW5vm9TKZRZ+CVK1fGlFlIdY+WZY1L8qu7uxvXXnst3vOe9+DAgQNobGzEd7/7XV2uMhUsNzcXDz30EJ544gk88cQTmDt3Ls4//3ydMplqNmXrqGzbHpbV98Ybb2DRokVISkpCSUkJsrKyhq2viKaAvnr+Ndhz/NFRnV9/fz9KS0sxbdo0LF++fNQPNdVl1NfXo6mpCUlJSboIdrg6jZMnT+LIkSNYt27dsPIxE2mnE6QaGxuxb98+rFq1CjNnzoy6DgnlBgIBdHZ2jrjmbSjjnKOiogK2bQ/qpUezf73nPgCImpsaQFNX5rTzIA0/p+uz0zqE9iBcDEGHTAEqCFe5KBIsJo9b7oPJ705whKwgGDO9OSrBACbb0ptmPOY+cAECgQCEELokY7T07JFaT08Pdu3ahfz8fOTl5U34/oeyqqoq1NfXY8OGDWOu6bMsC1dddRU6OjrwwgsvnBY1dMbYsGSKaHbeeechLy8PW7dunZwTG4e9ZXNUgPSourq6sG/fPsyePXvYGPpgbTpGC1JETZ4+ffqYCw/dsW3LsnQ9UVVVVdTupWQnTpzQTLdYxpZPJ0hRgelQIAWMTCh3pJpugFMPFw6Hde5xpHbuC1/Gq++5T+emoh4x4rFxSyfJWix4PCk3WFEvMZ2TUghGLTr0NRABA5RvkgDnM/yw7aAm4hjCCfkxALYw4TPjAQgsWbIEixcv1jlComdToXY0pttYrL+/H2VlZZg9e3bMQerUqVM4efLkuEBKCIHbbrsNhw8ffku27NiwYQNef/31030aUe0tDVSkSE4qycOtOxG9pHp6elBaWoqsrKwJaw7n8/k87TMCgQBOnjzpIWFkZmbi5MmTuo9VLJlupxOkmpubdRhoNIykSKHc+vp6HDp0CLZtj8grEEJg//79umh7LP2Mznvhy/jnhT8G4CoC9hyE6boltcBRM9csPrcn5dRJUW6LGIBc6wVSzZUAQZtT8Cv3wZipiRhgPoCZKjyojsCc9S96Qcp6McYwffp0Tc+mkozS0lLExcV5dBzHYkRgyMjIwMKFC8e0j7FaU1MTKisrsX79+jHLQVHLjj//+c9v2ZYdFRUVAxiOU8WmLFANBwAtLS3o6OhAZmZmzECqu7sbpaWlmDVrFhYuXDgpoY/4+HjMmzcP8+bN0/VElZWV2Lt3L4QQWLp0aUxrhmgAOR0g1draij179mDZsmVjLjB1C5EuWrQI7e3t2isYTCiXJLc6OztRVFQ0LmahG5ooJzUQsKjYV6lKKM/KXehL7D/yqizF9vN4VUKCk61AjQoWBHOU0gEg3u9HMBxE0ArCYD4YwiF4SA9s6OeaSjIyMzNh27bWcXzzzTeRlJSkJwIjFQUmAkN8fHzMSRsdHR1aaWM8YfTT3bKju7sbVVVV+v2xY8e0ynteXh6+8Y1voLa2Fo8+KqNHP/7xj7FgwQKsWLEC/f39+PWvf42XX34Zzz//fMzPfSQ2ZYFqKGtsbMTu3buRkZExLCNICAHLssbd8LCrqwulpaWYO3cu8vPzY/JjonoiwzBw/PhxZGZm4siRIzh+/LhWwphMQVACKb/fH1NFAECKd1ZUVGDp0qUTNstjjCEjIwMZGRlYsmRJVKHc7OxsnDhxQjdcHG9X4He9+CW8+O4fy7CbemRI/shtpN3Pmau/lHCAkwp9qSZLtudgHq+KMckWdPpYqet2hRPlsRwPCkzVV7nYiAwG4n3xg+oQus00TWRlZSErKwuWZaGpqUmHXFNTU3V4cLAwGKlO2LYd86aLfX19qKioQH5+/riKXKdCy46SkhJPAe9XvvIVAMC1116LRx55BPX19Thx4oT+PBQK4atf/Spqa2uRlJSE1atX48UXX4xaBDwVbMqSKTjnUZlbJFu0atUqdHR0wLZtLF++POo+JsqT6ujo0FX5CxYsGNM+xmrV1dU4efKklgiiWo36+no0NzcjOTlZkzAmotU6mWVZKCsrg8/nw5o1a2KqCNDe3o6ysjIsWbJk0ir53eYWym1ubgZjDAsWLMDcuXMnpMj0uXf9JOpyhmhK5fSZ0GFBIle4mzE6un5UVyVDi872Tn6L5JsAgfg4GZoLh3vRH+6H3xevKfMyX2UAYIj3xeP9L39qzNccDof1PW1ra8O0adM0aNE9pRY4LS0tMS/oDYfD2LlzJ2bMmDGuEP6xY8fwrne9C7fccssZNfRJtCkLVEIIhEIhzzLK26xduxYzZ85EdXU1enp6sHr16qjbTwRI0cy+oKAgpgleyr/V1tYOKrZKg0F9fT3a29vHLOoaaacTpGhSsGjRophK9QCyTciJEycwd+5ctLa2oqOjY8Lu6bMXuMHK8a64AHwqt6RIexBwFPJJiYKMK0CidQFV6MuZSwjX4WnopoyQxeHJ8akIhnvQH+qHYfhk23nXugwMYAbifPH4wMs3j/l63RYMBjVo0T3Nzs5GX18famtrsWHDhpgW9Nq2rVUk1qxZM66x4d3vfjfOP/98/OxnP4upN/hOs7cMUB0/fhxVVVVYv369rq2oqalBS0sL1q9f79mWNADHC1ItLS3YvXs3Fi9eHHN9s6qqKtTV1Y1YEZxEXevr69Hb26v18UbDcgNOL0h1dnaitLQ05pMCQD5L1F+Jwsl0TwOBAHp6ejzElrEMSk+f/9OILsCAC0fkW40wQgMOVytrCrpcQeo7CodRKNcXOkQoqe0GZKsQuWFyQhr6Q13oC/XBMPwwDZ/2qOiE4vwSND78z4kBKrf19fWhoaEBJ0+eRH9/PzIyMjBnzpxxlRCMxoQQ2Lt3L/r7+0fN5HTbmZYdsbUpm6NyJ7arq6tRU1OD4uJiD9stmoI6AdR4c1JEiV66dClmzZo19gsZpVHb+kAgMKoGgG5R166uLt0OnnOu81nD0YhPJ0h1dXWhrKzstNTPuNmU7pznYEK5+/fvH1QodygTAMKqPbxnofuNgAc2pIySKvLlVCPlLJO5Kqdey3YRI0j+iFiCqQnytyMFcwVMAY+0E2My9CcEcMkrEw9SAJCYmIiUlBSEQiEsX74cwWAQR48exf79+8c9ERiJHTlyBJ2dndiwYcO4aus+97nPnWnZEUObsh4VIGe0lZWVqKurQ3Fx8QDiRENDA6qrq3HWWWcBkA8QySKNtXU8IMkaRImOdTuDw4cPo6GhYUJaqQsh+wfV19ejsbERfr9f94KK3PfpBKnu7m6UlJRg3rx5Mc8B1tXV4dChQyOuSxNCoLOzE/X19WhoaADgCOWOpJ7ob+fdD2BwSRgR+V/hXiJfuduCAMqrclHTiZTuEC8EBASS4zPQF+oAAHT1dyPenwyo5ptCrW8wBr8Zj4++OjlA1dHRgdLSUixfvlwTGIQQ6O7u1sXapJg/VFv4sRi1hNmwYcOYf1tCCNx1113YunUrduzYEZMc6hmbwh4VyZk0NTVh48aNUT0Lt0c1USBVX1+PgwcPYtWqVTFVEqbEclNT04SAFDCw9oVIGMeOHdOddXNycmAYxmkDKapLy8vLizlINTQ0aD25kRZPu4Vy3UWwlPMgYstgnjCBDBX0DjDhgJLMRzGZvdLyRkyH97izifayAFUcrOp+mdb4EwAzwAXQ2d8FvxmvVNddhb4M8Cltv8mwnp4elJeXo6CgwMOyc5cQuBXzqS38RKhhNDY26tTBeH5bjz32GH72s59NasuOMzbQpqxHRfpqBQUFgyZaaXZ2/vnnTwhI1dbWorKyEqtXr45pwR713Glubo6JUnQ4HEZDQwPq6+vR0dEB0zSRkJAwIY0eR2M9PT0oKSnB7NmzT0uR5549e7B69eoh1S5GarZto7m5WTMHk5OT9UQgkpr9xJYHADhECnePMeHxjqDJFJJy7oQFqccVgZ3FndeCWoTQ2sJGerK8xq6+FrT3dcBnxiPOjNOeFAAwZiLeF4/LXrtp3Pcj0oLBIHbt2oWsrCwsXrx4RNtQRKChoQENDQ26WeFIvVcyYpGOtmg80l555RVcdtlleOKJJ3DxxRePeT9nbPQ2ZYEKkAPqUEKY3d3d2LZtm+b+jydEQPp5a9eujbkQ5sGDB9Ha2orCwsKYsp8sy0JJSQksywJjDMFgEFlZWcjNzR1V7mUs1tvbi5KSEuTm5k5a8fRgRiSZFStWTEpoNxwOo6mpCfX19Whra0N6erqmZtfV1ckeS7fv0wE6rzmARe8ZcwgV3AVW3saYwqVFAQAGwOTzJYTAjJRsdPY2AgBae9vg8yUizoyHw0CU2n7xvnhcPsFARc9ZSkoKVqxYMabvmnOO1tZWNDQ06DD2SNQwSKx6vLnPAwcO4D3veQ/++7//GzfdNPFAfsaGtrcsUAkhEAwG8a9//QvJycmYNWsWcnJyxpTYrKmpwdGjR7Fu3bqY6udReLO9vR2FhYUx1QajthWGYWDt2rUwDANdXV2or69HIBAAAE3CSE1NnVAg6evr0yLCse5x1NbWhvLy8piRZCKFcgHZomTnNf/nWY+mWJSRIqFZuAFqgFcFr1flYgBS7okLQAiO7LRZaOsJoLmnBT4zEfG+BMUwVLksAHE+OUm66vWJG4g55ygvLwdjTD9n4zW3GkZTUxMSExM1aLnDeqFQCDt37hyVFxfNAoEALrjgAlx99dW46667ztRKnQZ7SwKVEEKz+9y1RJ2dnZg5cyZyc3NHzByi2plYtzMnLbmOjo7TDlKROSkhBFpbWzUJIyEhQYPWeD2+/v5+lJSUIDMzc8K0Ekdqp7NGq6GhAfv27UNOTg46OjrQ39+PhtsPAJAhOqaVzh3vicKAthCeol2PVxVxHCd0yLSH5TcTkJGUgdaeegS6mhDnS0KiP1E3eWSQ4fJ4n3wGr54goCLVCWojP546tMGM1DAaGhrQ3NyMlJQU3aB0//79SEhIwKpVq8Ylm3bxxRdjyZIlePzxx8/USp0mm9JAZVmWzj2RUSEvnbb7wenp6UF9fT3q6+u1+OisWbOixrOJ9n7q1ClP7UwsjHOO/fv3o6urC4WFhROqKDGcDQdSkWbbtg5jtbS0RFUYGKkRSI1HdX6sRhJYCxYswLx582J2XEAK6+7evVvnw4jlVl9fj12ffEGtFVmsKyLkkbxARcYYYCu0oq7ABmMuYAOyUqXn2N5Tj9rOAOJ8yUj0J8l8LlROS+Wn/v2NGyfsug8fPozGxkYUFxfH5Bl3q2G0trbCNE0UFBQgNzd3TLnXqdCy44xJm7Ksv2jmBqlopInk5GQsXLgQBQUFaG9vR319vVb9Jlp2YmKihwZeXFw8qXp5kcY598wyY0leGC1IAfAIt4ZCIU3CqKysRGZmpvZeh9tXMBhEaWkpMjIyYg5SPT09KCsrQ15eXsxBqq2tDXv27MHy5cs1acPNctshXlDxPqaV0uU7h/3nKepV9HMdAnSxCN1TTqqnslREoq3H21nWqceS5xPni59Qtt/x48dRV1c3rrYZozW/349Zs2ahq6sL/f39mD17NhobG3HkyJEhW+dEs7d6y463m71lPKqxSiJF8wgYY7p9w0TQwEdq1N+ot7c35gy7sYDUUNbb26vzWaFQSJMwMjIyBnw3oVAIJSUlSEtLG3MyfazW19eHXbt2ITc3N+aq1qS0MVyo8ZHND6pXbiKF14uikJ6lPCzApQuoQAcgQDMUR0LAZyYhI3Ea2noDqOuoAwBMT852qVHI0F+8yk99cvv4PSoq8SgsLIxpOxpAAmRNTY1HlokURhoaGtDV1YXMzExkZ2dj5syZUcORQgg88MADuOeee7Bt27ZJe25ee+013H333SgtLUV9ff2Imh2+8sor+MpXvoL9+/dj7ty5+Na3voXrrrtuUs5vKtmU9qjc6hRj1e1zewTBYBAVFRXo6uoCYwxVVVXIzc0dtczQWIxzjj179qC/v3/crSNGaxMNUoDs91RQUID8/HxdALtnzx7dUI9IGKFQCKWlpUhJSYl5CwcKNWZlZcWc/k5e3IIFC4bNhxHIUCBOYZMrFMjAhRjgYelCX/UBKaozJsC5rKXyAbDUAcKcI96fAos8KTggJwBcPwEg1dLSggMHDmDNmjUxBylSbY8s8XArjFBDzWPHjg1oqEm/i7///e+44447Jr1lR09PD9asWYPrr78eH/3oR4dd/9ixY/i3f/s3fPrTn8bjjz+Ol156CTfeeCNyc3Nx0UUXTdp5TgWb0h6VbduaUGHb9rgkkdx5ofXr18OyLJ3PEkJMGsONrmPPnj0IhUJYv379Wx6kBjOiEBMJIzExEeFwGKmpqRPG+BqpBYNBlJSUnJZQI7Eac3JyRjzQ/Wrjgy5quTSu2soL4VKjcHlVUibJWd9gTOeoiJAxIykLfsNAe38jatpOwO9LRlr8NJ2fAmPwG3HwGT7cuGN8QNXZ2YmSkpKYy44BDptzuC7QbiNZrEAggKeeegrHjx9HUVER7r77bvz2t7/F5ZdfPsln7dhI2sd//etfxzPPPIN9+/bpZVdccQXa29vx7LPPxuAsT59NaQoLsfsmAqT27t2L7u5uFBUVISEhASkpKVi0aBHOPfdcrFq1CuFwGCUlJdi+fTuOHTuG/v7+CbkG27axe/duhMPhtzVIAU5DvVWrVuGss87S+cTW1laUlZWhtrY2auuWibZQKISysjKkpaXFHKSCwSDKysqQmZk5Ki/upjc/B1t19bWFBCMuJFGCPB4OuRzqr+167f7MFnLgI7Bq6W2AzYXaRlZvyf2RzJJf57TGar29vSgvL0d+fn7MQaq7uxsVFRVYvHjxqIq3qd/b2WefjY9+9KPw+Xz43ve+B8YYXn75Zbz66qtD1nHG2rZv344LL7zQs+yiiy7C9u3bT9MZxc6mdOjvYx/7GBITE3H11VfjvPPOG9NA6/ZmooXc3DJD1Jeovr4e1dXVyMjIQG5u7phbPJC6hm3bWL9+/aTQc4c6dixBym2WZWHv3r1ISUnBmjVrtBLGqVOncOjQIQ8JY6K9rHA4jLKyMiQlJcU8H0bHnjZtGpYuXTrqYxMgAXIG6ZZSchf5uuupuGtbADBVKI+2CXOOsGusnZaQLtdVdVpCMIhxfgU0McjJyYk5WSUYDKK8vBxz584dc8kBYwxLlizBoUOHcPPNN+OKK67AH//4R3zsYx/D/PnzsWvXrgk+67FZIBAYUKCenZ2Nzs5O9PX1xVQsINY2pYHq9ttvxyOPPIJPfvKTSEhIwOWXX46rrrpqxINAJFAM582YpqnZgZSArampwcGDB5GdnT0qxQYCCiHEaQMpKrKMJUjRsU3T1F2B4+PjkZeXh7y8PF1CQCrkdF/Ho+PmPnZFRQXi4uKwatWqmIYayXtNTEwccy7us7s+hweKpLySBXcvKkc2lrvEADWBQlPUBYTBVCGwN4x4svMk/GaSDA2CugwzTWn/3K4bxnLZ+rrT0tJiXrxNx87IyEBBQcGY9xMMBnHllVeioKAADzzwAHw+H84//3z89Kc/xdGjRyfwjM/YWG1KA1VhYSEKCwvxox/9CP/3f/+HrVu34uyzz8bKlStx5ZVX4rLLLsOMGTOi/jjoIWaMjQkoIttmUGdhxpgnnxXNIo99OoACANatW3dajj0UQLpLCDo6OlBfX4/du3dr0ktubu6I+m9FO3ZFRQUYY1izZk1MQYpzjt27d8MwjHEDpFMjxWCrHBWBlK1Ai+vlikBBZAow2Nzp6CsAWCrkx2HI0CIHBHPaf8SbiSNqOR/1XBVByOfzYeXKlTEFKQrn+/3+cZF0Ilt2uMcJv9+PJUuWTNQpj9tycnK0Yj9ZQ0MD0tLS3tbeFDDFc1RkcXFxuOSSS/DEE0+gtrYW119/PZ544gksWrQIV1xxBZ588kkEg0G9flNTE9544w2Ypol169aN25tJTU3F4sWLce655+oeOjt37sSOHTtQU1PjObY7L3S6gAI4PSC1e/duCCFGdGzGGNLT07Fs2TJs2bIFS5cuRV9fH9588019X0eaJ6QB07btmHuQNGBaljUhx/5i6edVjko2TuRCCs5aOm8ll7tzWZSjAhRYKeBJj89EW3+rWm6AK7UKDqWszkxwMEe8dhRG8l/BYDDmEwMSce7v79de+1j38/3vfx//+te/8PTTT8dUmWYstnnzZrz00kueZS+88AI2b958ms4odjalWX9DGRXtPvroo3j88cfR1dWFSy+9FO9973tx22234YILLsB99903aT8gy7LQ2NiIuro63QY+KysLtbW1usX1OwWkyKMgwsh4JgZ0X0nQdbg8IQFFX18fCgsLY0pWIRmsrq6uCS05+NG6B+gIWvuP6qjc4rSAQy83GDUAccSVZiRkoi3YCpMB9d31MI0EpMVNA/WdMphPCdMCXyq9flTnSM09Y1nQS3bs2DGcPHkSGzZsGFch7qOPPorbbrsNr776KtasWTOBZzgy6+7uRlVVFQD5m7333ntxwQUXYPr06cjLy8M3vvEN1NbW4tFHHwUgr3vlypX43Oc+h+uvvx4vv/wyvvjFL+KZZ545Q09/K5ht23jttdfw0EMP4X//93+RkJCAL3/5y/jEJz6BuXPnTnpIoq+vD7W1taipqdFU91mzZkUtfp0MO90gtWfPHgSDwQlnNUZrBe+ueyOg6OzsjLnKB/UPa25unhSJoLvX3S/rqIRT+MuYUJJK0pyGiTIEyJgEICEALjhmJM5EZ6gFANDQ04CMhExVk0VAxZDgS8L5D6/X+ngjeXaoAWGsVV0AWUx86NAhFBUVjUv27J///Ccuv/zy09qy45VXXtGdH9x27bXX4pFHHsF1112H48eP45VXXvFs8+UvfxkHDhzAnDlzcPvtt78jCn7fFkAFyDYd7373u1FUVISLLroIjz/+OP75z3/i7LPPxpVXXolLLrlk0vT8iPUUHx+PBQsW6MHVTc6YrB/06QapWHkzpI0XCAS0jmN/fz96enpipiXnturqatTW1qK4uHhS8gP/tUZ2AiaPShIrHG/KnaMCCLAMWWfFZUffzMQsdARb0NjbAL+ZgGRfMhiTXpfJTPgNP/yGHx/+3/MQCATQ39+PrKws5OTkDNpZlxoang7VidbWVpSXl2Pt2rWYMWPGmPdzpmXHW8/eFkBl2zZWr16Nc845Bz//+c/1bLu2thaPP/44tm7diuPHj+NDH/oQrrrqKmzZsmXCWHikvJCUlORJpHPO0dLSgvr6ejQ1NSElJUV3f52omX8kszCWICWEwN69e9HT0xNTOShqpldZWYnu7m4kJCToFi+xmt3X1NTg2LFjKCoqGhPxY6T2/dX3QygRWgr7uWWVDEW4AGSRL2Do1iC2EMhKykZHsBmNvQ0wDAeoDMbAYCDRJ+/XLeWf9AjlBgIB3Vk3NzdXy44RUExUs8nRWFdXF0pKSrBkyZJx1Wmdadnx1rS3BVABcpY0WHGnEAKlpaV49NFH8Yc//AFxcXGa6j6eglACqeTkZKxcuXLQfFhkR93RiLkOZsRy45yfFpA6XSE3AKiqqkJdXR3Wr1+vNQepxUNubi6ys7MnzcOqra3F4cOHUVhYOOnJ9ztX/9RhAXpAimqgHH9LhvzoORbITJSt3jtDjQj0SI8q0UwBY4DBDMQZ8TCYnKzdttubnxJCaFHnxsZGmKaJ6dOno6Gh4bSoTvT392Pnzp2YPXv2uGjo1LJj6dKleOyxx8607HgL2dsGqEZq4XAY//jHP/Doo4/i6aefxooVKzTVPTMzc8SgRWrgqampWLFixYgfehpY6+vrEQ6HdSsSmrWOxNwgNRGsxtGYu9ljUVHRaUmk19TUDMiP0GQgEAhocguRMCYKxBsaGrB//36sW7cOGRkZE7LP4ey7K3860KtSnxHTz2AMELKbgC0kry87aRba+mVH3/reADLiVX4KgAEGv5kAH/Phm3uGJlFwznVeSAiB5ORkrZ0ZC0q0ZVnYtWsX0tLSxkVDp5YdnZ2deP7558+oob/F7B0HVG5rbW3FH//4R2zduhWlpaV473vfiyuvvBLve9/7hhyA+/v7UVpaimnTpo1Z/UAIoeuIAoEA/H6/zmcNpeh+ukHq4MGDaG1t1VJUsbQTJ06gurp62ER6X18fAoEA6uvrR5R3GYlF9pSKlX1nxU8HhvyYo1ohBInZSqCSbT0EcpNmoy3UgLqeOsQZSUjyJ4OpdvMGDMSZEmRu3zt0oW8oFMKuXbswY8YMFBQUoLm5GYFAAC0tLUhLS9Me7GR41RPVHVgIgVtvvRUvvvgitm/fPq781hk7PfaOBioyIQSOHDmiqe4dHR342Mc+hquuugpFRUWeH0hfXx/KysqQnp4+YWrgnHPdiqS5uRnTpk3TA4CboHC6QYpYbqcDpE6dOoUjR45g/fr1I07iR+ZdiJGZk5MTtZnmYEaCp8uXL0dOTs54LmPU1tfXhzsLfwkAHkKFAFzVTyr0BwoHCmQnzkFHOKCBKt5Mhsmo/YeBJJ+cDA0FVLZto7S0FPHx8Vi9erXnflFvskAggI6ODkyfPl33e5qI55LCy6TPOdZ9xqplxxmbXDsDVBHGOcdrr72GRx99FE888QSys7Nx5ZVX4oorrkAoFMLVV1+Nu+++G1u2bJmURKy7OSH1zqE+T3v27DltIEXdWiNbKMTCqL/ReEJuRMKgvAs108zJyRnSgx1pT6nJMPJmnrmxxONVuVvWSxIFUzVU8qeckyjPszVUj1PdtYg3k5DsSwHAYDIDBjPhN2TE4Lv7oyumU22cZVnD5kD7+vr0M9vb26tbZ4xHy7G6unpCGi8+9dRTuPHGG/Hcc8/h7LPPHvN+ztjptTNANYT19PTgb3/7Gx599FG89NJLME0Tq1atwlNPPYX09PSYHJ/yWcFgEH6/H6tWrYpZfRYgB/iqqirU19fHvNEk4OSF1qxZM2EhG9u20dzcrD3YwUJYPT092LVrl5bSiqVZloXS0lIkJiZi1apV+Pqyn8gPhNT7Y5CAJQTTXhJ5XLOT56Cxvw4mY6jpOok4MwnpcdOcNvUwEGfIycadBwcCFeUhiSwzmrIDd+uMcDg8ZEPNwYwIK8XFxeNiVe7atQv/9m//ht/85jcxbdlxxibezgDVCKy6uhrnnXce8vLy0NnZiWPHjmmq+3nnnTep3g1R0KmvU2NjI+Lj4zUle7K9m+rqapw6dQpFRUUxL+5samrCnj17JjUvFBnCmjFjhtYbLCsrw6xZs2LedJFyM4CsjTMMA7cs+TFEBPOPgMpUtVEMAmHOMTd5HlpCtQCAU92nkB6XDcNgKpclPZwkMykqSAHQE5Pi4uIxh3iFELqhJunTjSTsSrnAdevWYfr06WM6NgAcPXoU73rXu3DrrbfilltuOUNDf4vbGaAaxqqqqnDBBRfg0ksvxX333QcAKCsr01R3v98/IVT3aBYtJ2Xbts5ntbS0ID09XXsDEw2YR48excmTJ1FYWDip9ULRrLW1FRUVFTHNC/X19aG+vh51dXXo6+tDUlISli5dOmLF/IkwIYTuBF1YWOj5Tr+86D4tOCvp6fSJJEkIcAghMDtJAtXJ7hPwm8lIMlNVyE/WTxnMgN+Ix38dGljsevLkSU1YmajvnHOOtrY23Q4+Pj5eg5Z78kONF5ctW4bc3NwxH6+1tRUXXnghLrjgAjz44INnaOhvAzsDVMNYbW0tHnvsMXzta18bMFgR1X3r1q14+umnsWzZMlx55ZW4/PLLR0V1j2Yk8mrb9qA5qWAwqNltPT09OswyHnYbGdHAJ7uoNZq1t7ejrKzstNTshMNh7Nq1C4mJiUhMTERDQ4NHMT8lJWXSQItYlUT9j2TS/cciOVGSuSko/T/pJZEiRaKZgulxM9BuncKxruPwG8lI80+DwQzJ+oOTn7r78M2e/VOYdf369ZMW2qawayAQQHNzM5KTk3VR8e7du5GXl4cFCxaMef/BYBAf/vCHkZqaiieffDKmudwzNnl2BqgmyFpbW/GnP/0JW7duxa5duzxU99GGTwikKJE9kh8bsdvq6+s9eoNjGVhJeaGwsHDSZKcGMyIvLFy4EHPnzo3psS3LQllZGeLi4rQqN3kDRMJISEjQJIyJDrsOF3L7fMG9ALzelJRDcsgUOQnzYDKgJXwK1V1HEWckY3rcdEBR2E3GEK/yU26gImbjypUrkZWVNaHXNZiFw2GPAHF8fDzy8/MHsF1Hapxz3HzzzTh06BBeffXVmD+7Z2zy7AxQTbAR1X3r1q14/PHH0d7ejksvvRRXXXUViouLh/V0xgJSkcdvbW3VA2tiYqIeWEcCmCdPnkRVVVVMlBcirbu7GyUlJaeFvEBhVgCDtuugDtBURzRYGcFYbKRCr5/J/5EGKmL8GcwAF7L8d3bifLSETgEAjvdIoJrmnw7ypwAgzkzEfUcckCJ5otPBbOSco6ysDIwxzJgxAw0NDZrtOhqhXCEE7rrrLjz22GPYvn07Zs+ePann/eCDD+Luu+9GIBDAmjVrcP/992PDhg1R16Xmr26Lj48fcRubM3YGqCbViOq+detW/O///i+ysrJw5ZVX4sorr0ReXt4AT2e8IBVp0VpmzJo1a1C1hrHUKk2U9fT0oKSkBHPmzBmXTM5YjBTgQ6HQiO97KBTS7LbOzk4PJXu0ShhEvx+J0OunFvxIv6bnx4DMP4W5hTmJ+WgJn8Sx7moAQFb8HLmG8rpMZsJk8fhp9acATJw80VhMCIF9+/aht7cXRUVF+r719vbqkHYwGBxRwTa17HjttdewevXqST3vP/7xj7jmmmvw0EMPYePGjfjxj3+MP//5z6isrIzqjT7yyCP4j//4D1RWVupljLEBbeXP2OB2BqhiZER137p1K15++WVs2rQJV111FS655BKkpaWhp6cHDz74IM4///xJaV1PLTPq6uq0WgPlsxhjqK2tRWVl5aTmJwazvr4+7Nq1C7m5uVi4cGFMGVoT0VOqp6dHD6wki5WTkzMiSjYxG0ejCP6pBT9SFHVJNReqVX2CkYKMuEy0WydR3VUFn5GEzDhiS8rzSDClt3Z/9ad0Po4aWMaaGXfkyBE0NDRgw4YNUZUthBDo6urSE4JoQrlA7Ft2bNy4EcXFxXjgAdk3jHOOuXPn4gtf+AJuu+22Aes/8sgj+NKXvoT29vZJP7e3q50BqhibEAJ1dXX4/e9/j61bt6K6uhrvf//7dZv7V199dVJp4PTjJ7UGxhhSU1PR2to6bkrwWKy/vx8lJSXIzMzEkiVLYg5SE9lTyk3JpjYvbhJGpBFpZMWKFaOaXd84/x4IwBPO4xAAOOYkFqAtfBJHuyVQpfszVc2VJF0kmrIO7seHb0RZWZlu8hlrkCJ24Uh7WkUK5b700ktob2/HOeecg6985Ssxa9kRCoWQlJSE//3f/8Ull1yil1977bVob2/Hk08+OWCbRx55BDfeeCNmz56tRaS///3vY8WKFZN+vm8XO8PbjLExxjB79mzceuut2L17N1544QXs2rULVVVVaG1txV133YUDBw5gsuYPjDGkpaVhyZIlOPfcc5Gbm4uWlhatPlFTU4NgMDgpx4406uOVkZERc5ACZI1YY2MjCgsLJ0RclzGGadOmYenSpdiyZQuWLl2K/v5+vPnmm9i+fTuOHz+u8xJdXV0oLy/HokWLRh0C+vXxW8CF0K3oOQBL2MiNL0BT6KRqT89hCQ5b2Opz2frD5vLf3r17AQCrVq2K+X1vamrCkSNHsHbt2hFPyhhjyMjIwPLly7FlyxZs3LgR1dXVuOGGG+D3+9HY2Ihjx45N8pnLOi/qh+a27OxsBAKBqNssWbIEv/nNb/Dkk0/iscceA+ccZ511Fk6dOjXp5/t2sTNAdRotGAzizjvvRE5ODurr6/GrX/0KdXV1OPfcc3HOOefggQceQGNj46SBVlNTE06ePIm1a9fivPPOw9y5c9HU1IR//etfKC8v100KJ8PC4TBKS0uRkpIyYZqJo7Hjx4/j1KlTWL9+/aQUTRuGgZkzZ2LVqlU477zzMG/ePLS2tuL111/Hzp07sWvXLsyZM2fMzEauAIqDwxYcDAZsAdiqGlgClQ3AgC2oM7AflhD4/DNb0NvbOyhpZDKto6MDe/fuxYoVK8YcYjYMA+effz46Ozvx0Y9+FPfeey+2b9+OJUuW4OKLL56038tYbfPmzbjmmmv07+wvf/kLZs6ciV/84hen+9TeMnamyOA02h/+8Ae0tLTg+eefx7Rp0/DBD34QH/zgB9HW1qap7t/61rfwnve8B1deeSXe//73T5gYbGNjI/bv349Vq1YhMzMTADB79mzMnj1bF75WV1fj4MGDyMrKwqxZs5Cenj4hgEI08MTERKxcuTLmIHXq1ClNv49FjZjP58OsWbMwa9YsdHZ2oqysDIZh4MSJE+jr6xuTLt7vTtyKf8+7GwZMcHBw4UwoDnYegMkMTPdnqcaKkpZuSdlaNDc3Y8OGDZPakTma9fb2oqKiAgUFBeMiEliWhU9+8pNISUnBY489hoSEBHziE59Ac3Mz9uzZM6nPE5FlSG2DrKGhYcSF6X6/H+vWrUNVVdVknOLb0s7kqE6jCSHQ398/6IyedPaI6t7a2qqp7hs2bBhzUW9TUxP27t07bM1MtJwLtSIZax7Ntm2UlZXBNM1xtW4Yq1Er9Vj2lCIjL5IabbpJGBROiiQKDGX/nnePkkUCQjyMeYlL0BquQXXPIZhGIqb7Z2olCgYDCSo/9ZN918S8iJsEdikXOVYTQuCWW27Byy+/jG3btp2Wlh0bN27Ehg0bcP/99wOQZIq8vDx8/vOfj0qmiDTbtrFixQq8//3vx7333jvZp/u2sDNA9RYxzjn+9a9/aar7zJkztar7vHnzRjyLbGlpwe7du0edwOecayHXpqamMfUiOp1diQGHYbdmzRrtRcbKSLPRMIwBAB2tNxmRMIabEHwi70cQKheVl7gEzaHjqOo5CJ+RgMy4XEW6MOBjfviUIsXvaj43mZc6wIZqFzIao5YdP/rRj7Bt27aYazCS/fGPf8S1116LX/ziF9iwYQN+/OMf409/+hMOHTqE7OxsXHPNNZg9ezZ+8IMfAAC+973vYdOmTVi4cCHa29tx9913429/+xtKS0uxfPny03INbzU7E/p7i5hhGDjvvPNw3nnn4ac//ammuv/Xf/0XNm3ahCuvvBIf+chHhizSbW1txe7du7Fs2bJRh14Mw0BWVhaysrJ0N926ujpUVlbqViQzZ84c1EOiWiXbtk8LSLW1tWkvMtYgxTnH3r17tWZj5D1ijCE9PR3p6elYsmSJlhjasWOHlhjKycmJSvgIcQsCNvISl6ExdNyTdLYEh8EM2BAALDDENtQHOLVSAMYd5v373/+OO+64A88999xpAykA+PjHP46mpiZ8+9vfRiAQwNq1a/Hss8/q39SJEyc833FbWxtuuukmBAIBZGRkoLCwENu2bTsDUqOwMx7VW9iEEKivr9dU9yNHjuCDH/wgrrrqKlxwwQWeWiwK9020fl5vb6+WbgqHw9oTcIevhJAss97eXhQWFsY8N0KyTIsXL550xYJIo5YZHR0dKC4uHtW1k8RQIBBAW1vboM0JPzL7TuQnrUBL6DgA4EjPPpgsAZlxsyAAmEyum2Am4bGaL0zk5Q1rbvr/eLoAn2nZ8c62KQtUd911F5555hlUVFQgLi5uRMVy1113HX73u995ll100UV49tlnJ+ksp44JIVBeXo6tW7fif/7nf2AYBi6//HJcffXVqKurw2c/+1k8/fTT48oPDHf8jo4O1NXVoaGhAX6/X3sCx44d072NJqNl+VBGskwLFizAvHnzYnpsQBa1BgKBcbXMAJyCbWpOSGoNM2bMwKVzv48kfwYMHsThnr3wMQPpcXPgYz4YMADG4DPiYTIf/lDzHxN4dUMbyUJt2LBhXH3MzrTsOGNTFqi+853vID09HadOncLDDz88YqBqaGjAb3/7W70sPj4+5knz023hcBjPPfcctm7dir/97W+wLAvvfe978fOf/xwzZ86c9B86KWTX1dWhubkZhmGgoKAAs2fPjqk3RYoXp6OnFCAp8MePHx9xUetIjdQa6uvrwTlHTk4O7rj4JQ1UAJAZnwcwAyZkR1+fKUHyf2u+MmHnMZSREvtIZKGGsjMtO84YMIVzVN/97ncByKru0Rj1unknm9/vxwc+8AHMmjULzz33HN773veivr4eixcvxoUXXqip7pPVdNE0TWRlZaGjowNdXV2YPXs2GhoaUF1djZkzZyI3NxczZsyY1EEnGAyitLQUWVlZMdewA4C6ujpNgZ9opZHU1FSkpqZi4cKFus/Tjb9djv/46C8Qx+LATD/AGAQTsGDDBAPD5NTDRbP29nZd+jAekAoGg7jqqqtQUFCA+++//wxIvYPtbffNv/LKK8jKysKSJUvwmc98Bi0tLaf7lE6L7dmzB+95z3tw++23409/+hNee+01HDhwAMXFxfjud7+LhQsX4gtf+AK2b98OzvmEH//o0aO6fX1BQQE2btyITZs2ISkpCYcOHcJrr72GQ4cOoaOjY8ILNMPhMMrKyjQ54XQoLxw6dAhr1qyZVAV6xhimT5+u1RrcFmYWODitCAD4a82tk3YuZD09PaioqMCiRYvG1ZWZc47Pfvaz6O7uxh/+8IczfaXe4TZlQ39koxF0/MMf/oCkpCQsWLAA1dXV+OY3v4mUlBRs37495iyz0221tbV4+umn8alPfWrAZ5xzvP7665rqPmPGDE11nz9//rgHdgp5DdZ0UQihezw1NDToHk+5ubnjLmi2LMtDhY71LJz6Oo2W/j8RtjrjClkzZfoxI34OTBiwGUeckQiT+fC36lsn9XcQCoWwc+dOZGdnY9GiRWPejxACd955Jx5//PGYtOw4Y1PfYgpUt912G374wx8Ouc7BgwexdOlS/X48ysNHjx5FQUEBXnzxRbz73e8e9fbvBOvt7cWTTz6JrVu34sUXX8TGjRs11X0sYZsTJ06gurp6xP2sqMdTfX09WltbkZGRgdzc3AHMtpGYu6dUNBr4ZNvp7OsESKACAPgMZMbnQTDAZCb8pgzx3vrwWVo1PyMjY0Lvj23bKCkpQWJi4rj1A2PZsuOMvTUspkDV1NQ0bCguPz/fwwwbr0T+zJkzceedd0b1LM6YY0IIBAIBPP7443jsscdw+PBhfOADH8BVV12Fd73rXSMCjfG2CgkGg5ok0NPTowfVGTNmDDvwuXtKFRYWxtyD7u3txa5du8bdSn2sVltbi8OHD+M/PvoLZKTMgSFM6uyBBJ/sdPunPf+hSwkA6FKC1NTUcQGLEAIVFRW6Rm48AEgtO/7yl7/goosuGvN+ztjby2Ia+J05c+a44tajtVOnTqGlpQW5ubkxO+Zb1RhjyM3NxS233IKvfvWrqKiowNatW/GZz3wGADTVfcWKFVEHtfr6elRWVmLt2rVjFhuNj4/HvHnzMG/ePN2KZP/+/QCgQ4PR2otTT6m+vj5PA75YWTAYRFlZGXJycmLemRhwcmLr1q1D2BdGWITgZ3FSlYL5YMEC4JAwFi1apLtAl5SUICEhQYPWaAk2QggcOnQIfX19I+pgPZQdOHAAn/jEJ/CTn/zkDEidMY9NWTLFiRMnUFFRgRMnTuiQTkVFBbq7u/U6S5cuxV//+lcAsl7m1ltvxY4dO3D8+HG89NJL+PCHP4yFCxcOeOjvuusunHXWWUhKShrxoCqEwLe//W39Y77wwgtx5MiRCbveqWSMMaxbtw733nsvTp48iV//+tdoaGjA+eefj7POOgs//elP0dDQoEkQTz31FEpKSrB69eoJ62eVmpqKxYsXY8uWLVi5cqXOf1C7DGpFQgNlR0cH1q9fH/NiYjdxY/HixTEnbrjVyOneCwZYsCGYgDABi4XxwrH/p7ehtu8rV67Eeeedh/z8fHR0dOCNN97Arl27cPLkSYRCoREdv6amBo2NjVi3bt247n0gEMCll16Kz3zmM7jhhhvGvJ8z9va0KUumiFa8C8jQwPnnnw9A/uB++9vf4rrrrkNfXx8uueQSlJeXo729HbNmzcJ73/te3HHHHQOS2mOp0frhD3+IH/zgB/jd736HBQsW4Pbbb8fevXtx4MCBCVM0n+rW3t6OP/3pT3jsscewY8cOvPvd70Z+fj4efvhhPPzww/jIRz4yqce3LEvns0ipwTAMdHR0YMOGDZNGtx/MSGDX5/NhzZo1Mc+J9fT0YNeuXcjPz0deXh4AYOMc6QEzQ3qV8WYKDGbin0fvGnZ/oVAIDQ0NqK+vR2dnp5bGIsXwSKuvr8fBgwdRVFQ0LnZjd3c3Lr74YixduhSPPfbYGRr6GRtgUxaoYmEjzX8JITBr1ix89atfxS233AJAzmSzs7PxyCOP4IorrojB2U4dE0Lg6NGjuOOOO/Doo48iISEBV1xxBa666ips2rQpJgNNf38/Dhw4gJaWFhiGoUNXI2n/PhHGOcfu3bthWdZp0S4MBoPYuXMncnJyBjDsNuR9BgICJvPBMPwwWRz+dXRoElOk9fb26nxhKBTykDAYY2htbUV5efm4BX4ty8KVV16Jrq4uPP/88++YSd8ZG52dKU4YgR07dgyBQAAXXnihXjZt2jRs3LgR27dvf8cBFWMMzc3N+Otf/4pf/vKXWLJkCbZu3YrLLrsM06dPx5VXXokrr7xyQqjug1lzc7P2pBhjqK+vx549e4Zt/z4RRvp9/f39pyUnRuHGjIyMqIobtspJAYDB4mAjPOpjJCUlIT8/HwsWLEBnZycCgQD27t2rw4YNDQ1YunTpuEBKCIGvf/3rqKqqwrZt286A1Bkb1M742CMwajE9mvbTb2fr7+/HpZdeirvuugs33ngjzj33XPzyl79EbW0t7rrrLpSXl2PdunW46KKL8Mgjj6Cjo2NCjx8IBHD48GGsXbsW06ZNQ1paGpYsWaLbv/f19eHNN9/Em2++iRMnTow43zISE0LgyJEjaG9vPy05MWI3xsfHD9oZufTEryAgICZgjsAYw7Rp07BkyRKce+65WLhwIQKBADjnOHHiBI4dO4b+/v5R75dadjzxxBN45plnTktfqTP21rG3DVDddtttYIwN+e/QoUOn+zTfFpaQkIAdO3bg85//vGd5UlISrrjiCjz99NOoqanBRz7yEfzqV79Cfn4+rr32Wjz33HMIh0c/u3dbU1MT9u/fj9WrVw/QcKT276tXr8aWLVu0dNNrr72G8vJyNDQ0wLbHJyV0/Phx1NfXY/369VHbbkymUcsMy7KGLWYuO/FrJaEUxrajE9Ocj8ApOzsb5513HubPn4+2tja8/vrrKCkpwalTp0b8/T711FO444478Le//S0mOowPPvgg5s+fj4SEBGzcuBE7d+4ccv0///nPWLp0KRISErBq1Sr83//936Sf4xkb3N42OarJrNGiwuHy8nKsXbtWLz/vvPOwdu1a/OQnPxnPqb+tTQiB3bt3a1V3IQQuu+wyXH311aPuT9Ta2oqKiopRqz709fXp+qFQKKQ76aanp4/q+FSrVFRUFJUmP5kmhMDhw4fR1NSEDRs2xFyFnnOOiooKCCEGFFMHg0FNwuju7vaQMKKB6c6dO/GBD3wAv/3tb3HZZZdN+rn/8Y9/xDXXXIOHHnoIGzduxI9//GP8+c9/RmVlZdQO19u2bcOWLVvwgx/8AB/4wAfw+9//Hj/84Q9RVlaGlStXTvr5nrGB9rYBqrHYaMkUVGMEyB5HWVlZ70gyxVjNsiw8//zz2Lp1K5588kksXLgQV111FS6//HJkZ2cPCRodHR0oKysbV08pdyfdhoYG+Hw+XZ81XBuKxsZG7Nu3D2vXrp0wCv5o7Pjx46ipqUFxcfG4WmaMxSgn19nZieLi4iGLv3t6ejQJIxwO60lBWloaTNPULTu+9rWv4atf/WpMiC8bN25EcXExHnjgAQASdOfOnYsvfOELUVvHf/zjH0dPTw+efvppvWzTpk1Yu3YtHnrooUk/3zM20N42ob/R2GhrtBhj+NKXvoQ777wTTz31FPbu3YtrrrkGs2bNwiWXXHKaruKtZz6fD+9///vxP//zP6irq8MXv/hF/OMf/8DSpUtx6aWX4s9//jP6+voGbNfd3Y3y8nLk5+ePS/eNOukuW7YMW7ZsweLFi9Hd3Y1t27Zh586dOHnyZNTQVWtrK/bt24eVK1eeFpCqq6vD0aNHsW7dupiDFCDJRC0tLVi3bt2wCiXJyckoKCjA2WefjXXr1oExpicln/rUp/CBD3wAl156Kb7yla/EBKRCoRBKS0s9RCjDMHDhhRdi+/btUbfZvn27Z31A9rUbbP0zNvn2jgSqb3/721i3bh2+853voLu7G+vWrcO6detQUlKi16msrPSQAL72ta/hC1/4Am6++WYUFxeju7sbzz777ACmUmtrK66++mqkpaUhPT0dN9xwgwcAo9n5558/IJ/26U9/emIveopZeno6brzxRrzyyis4ePAgNm3ahDvvvBMFBQX43Oc+hzfeeAOccxw8eBDXXXcdcnJyJrTxoWEYyMrKwpo1a7Blyxbk5uaivr4er776Knbv3o3GxkZwztHZ2Yndu3djyZIlUcNEk23Nzc04ePDgpCuxD2Z1dXWoqanBunXrRsXKc08KrrjiCnz3u9/FM888g5MnT2L79u247777UFdXN4lnLq25uRm2bY+KCBUIBM4Qp6aYvSOB6pFHHoEQYsA/KiQGZLjjuuuu0+8ZY/je976HQCCA/v5+vPjii1i8ePGAfV999dXYv38/XnjhBTz99NN47bXXcPPNNw97TjfddJPOo9TX1+O///u/J+JSp7wxxlBQUID/9//+HyorK/H0008jLi4OH//4x7F8+XKcf/758Pl841LjHs7i4uIwd+5cbNiwAZs3b0ZycjIqKyvx6quvYteuXcjNzcWsWbMm7fiDWUdHB/bs2YPly5efFlZcS0uLBsnx5OTi4uLw6quvYv78+Thx4gS+9KUv4dlnn8W8efPwz3/+cwLP+Iy9Xe0dCVSTZQcPHsSzzz6LX//619i4cSPOOecc3H///fjDH/4w7OwxKSkJOTk5+t/pmD2fbjMMA+eccw5+8YtfYM+ePRBCIC0tDf/4xz9w8cUX47e//e2YxYlHasnJyVi4cCGKiopgGAaSkpJQX1+Pbdu24ejRo1FDk5Nhvb29KC8vR0FBwWnRquzq6sKePXuwbNmycYU7hRC466678MYbb+Dvf/87Zs+ejeuuuw4vvPACjh8/js2bN0/gWQ80UtVoaGjwLG9oaBi0wWpOTs6o1j9jk29ngGoCbfv27UhPT0dRUZFeduGFF8IwDLz55ptDbvv4448jMzMTK1euxDe+8Q309vZO9ulOWevq6sJHP/pRbNiwAcePH8fJkydx6aWX4uGHH0ZBQQGuueaaCaG6D2bhcBgVFRXIzMzEpk2bsGXLFhQUFHj08Gprayft+CRyO2vWrAkNd47U+vv7UV5ejnnz5o3bk9y6dSt+/vOf4+mnnx6QX5w9e/akF/nGxcWhsLAQL730kl7GOcdLL700KEhu3rzZsz4AvPDCC5MOqmdscDujTDGBFggEBuQxfD4fpk+fPmR8+6qrrtKDwp49e/D1r38dlZWV+Mtf/jLZpzwlbceOHcjKysLvf/97/P/27jwqyvP6A/h3RDaVReoAA25sCiiEkUVBKrQQUGojLSYIJprE0BPFBMIkJmShGheOGk+19rQ0icpegwmIYsQlGBAhGBSURUa2iAgDtmyCGGDm+f3hYX4hCALDLMj9nDN/MLzL847Lnfd5n3uvuro6jI2NERERgXfeeQclJSWIi4tDaGgoJBKJdKm7rD2Q+onFYhQVFUFbWxs2NjbgcDjSahfGxsbo6emBSCTC3bt3UVFRAS6XK21FMh6lo/r6+lBUVAQ9PT25TncOpbe3F0VFRZg1a5bM7UqysrIgEAiQmpqq1L5SERER2LhxI5ycnODi4oKDBw+iq6sLr732GgBgw4YNMDU1RXR0NAAgLCwMHh4eOHDgAP7whz/g+PHjKCwsxOeff660a5jsKFCNwEgbPo7VL59h2dnZgcfjwcvLC9XV1bCwsBjzcSeq559/Ht7e3oMCD4fDgb29PQ4cOIC9e/fi4sWLiI+Ph5eXFywsLBAUFITAwMCnLnUfSn/9Pg6HAzs7uycGHg0NDcydOxdz585FZ2entDArY0zm/k7951dXVx+ynYo89Ve90NLSgrW1tUznLysrU5mWHYGBgbh//z6ioqIgEong4OCAzMxM6YKJurq6AX/Wbm5uSE5Oxscff4wPP/wQVlZWOHnyJOVQKdGkzqMaqZEmEycmJkIgEKC1tVX6fl9fH7S0tHDixIkRVxfv6urCjBkzkJmZqfR/5BNBe3s7vv76a8THxyM/Px+/+93vEBwcjNWrV4+4onp/1Yeuri44OjqOqjQSYwytra1oaGhAc3MztLS0YGJiAmNj4xFPbf3y/E5OTqPubiyr/p5enZ2dMp9fJBLB09MTr7zyCnbt2qXwgEuePRSoxtGtW7dga2uLwsJCODo6AgDOnz+PlStXor6+fsTz/VeuXIG7uztu3LhBrbhHgTGG2tpaJCQkICkpCc3NzfD390dwcDDc3NyGnJpjjEEoFOK///0vnJ2dZSqNJBaLpa1IWlpaMHPmTPB4PBgaGg77n//t27fR3Nws8/nHqqqqCo2NjXBxcZHp/NSyg8gDBapxtmrVKjQ1NSEmJga9vb147bXX4OTkhOTkZACPy/B4eXkhPj4eLi4uqK6uRnJyMvz8/PCb3/wGN2/exDvvvIPZs2cjOztbyVczcUkkEuTn5yM+Ph4nTpyAnp6etKq7ubn5gG/5lZWVaGhoGPeqD48ePZJWaXj48KG0SoOBgcGA89+5cwe1tbVwdnbG9OnTx+38I1VfX4/Kyko4OzvLVHGeWnYQeaGvO+MsKSkJ1tbW8PLygp+fH9zd3Qc8hO3t7YVQKJSu6tPQ0MDFixfh4+MDa2trCAQCBAQE4PTp01RIUwZTpkzB8uXL8e9//xv37t3D3r17UVJSAkdHR/j4+ODo0aNoa2vD/v37sXnzZixZsmTcqz5oaWlh/vz5cHV1ldbnKy0txeXLl3H79m08ePAAIpEI1dXV4PP5SglS9+/fh1AohIODg0xB6pctO9LS0ihIkXFFd1Qqigppjj/GGJqbm5GcnIzExESUlJRALBYjMjIS7733nkJadkgkErS0tEjrDTLGYGpqCgsLC4VP+XV0dKCwsBC2trYy5Qj1t+w4cOAA8vLyFFINnUwuFKhUFBXSlK8LFy7ghRdewMqVK3H16lX09fXhpZdeQnBwMOzt7eW+AKA/SBgZGaG7uxttbW0wMDCAiYkJuFyu3Jsxdnd34+rVq5g3bx7mz58v07HS09MREhKC8+fPw83NbXwGSMgv0NSfCqJCmvL1448/IiAgAF988QXS0tJw584dxMfHo6WlBd7e3nB1dcWhQ4fQ2NgIeXyP6+7uRlFREczMzLBo0SI4OTlh+fLlmDlzJmpqapCdnY2ysjK0tLTI5fz9uVJGRkYyJxRfvXoVISEhOHbsGAUpIjcUqFQQFdKUr5aWFuzZswcvv/wygMdJ2b6+vkhKSkJDQwPCwsJw7tw52NjY4E9/+hNSUlLGrVJIT08Prl+/DiMjowF3Mtra2jAzM4OrqyscHR2hpqaGmzdvIjc3F1VVVejq6hqX8/d3C5g2bRoWLlwo051jTU0NXnrpJWzfvh1r164dl/ER8iQUqMik4+vrO6g7cT89PT1s2rQJly5dglAohLu7O/bs2QMLCwts2bIFly9fhkQiGdN5+6tO6OjoDBkk+lu/W1tbY8WKFbC2tsbDhw/xww8/oKCgAHV1dejp6RnT+ftzpRhjMlfyaGlpQUBAANauXauwlh1k8qJApYKokKbycTgcmJmZISoqChUVFTh79iy0tLQQFBQEOzs77Ny5E1VVVSOemuuv+qCmpjbizsZTpkwBl8uFvb09VqxYAVNTUzQ1NSEnJwfFxcVoamoaVdCsrKxER0cHHBwcZHoG9vPPPyM4OBiWlpb4+9//TrlSRO7ob5gKokKaqmXKlClwc3NDTEwM7t27h3379qGsrAxOTk7w9vbGkSNHBlQj+bX+Drk///wznnvuuTH9x66uro7Zs2fD2dkZbm5u0NHRQWVlJbKzs1FeXo62trZhg2ZdXR0aGhrA5/NlamMvkUiwZcsWdHZ24vjx4wqvoEEmJwpUKioiIgJffPEF4uLicOvWLWzevHlQIc3IyEjp9mFhYcjMzMSBAwdQUVGB7du3o7CwcMgprtHkaMXGxg5q7DhZ82S0tbXx4osvIj09HfX19QgMDERcXBwsLCzw8ssv4+zZs4OqqhcXF6O1tRV8Pn9clsBPmzZtUBfdoqIiXLlyBdXV1YOepzU3N6OqqgoODg4y5Wr9umWHLD2qCBkN+jqkouRZSPOrr75CRETEgBwtX1/fIXO0AEBXVxdCoVD682R/JsHhcGBoaIjw8HCEhYWhtLQU8fHxePvtt9HX14cXX3wRwcHBOHPmDI4fP468vLxxD+79XXT19fWxcOFC3L9/H42NjaitrYWenh54PB60tbVRWlqKxYsXQ19fX6bz9bfsyMnJGdSygxB5ojyqSWi0OVqxsbEIDw+Xe9PCZ0FfXx+ysrIQHx+Pr7/+Gj09Pdi4cSM++ugj8Hg8hQT4np4eNDU1ob6+Hp2dnZgxYwYsLCwwa9asMT9PysrKQmBgIFJTU6lQMlE4mvqbZMaSowU8LjY6b948zJkzB2vWrEFZWZkihjvhTJ06FT4+PnjjjTegpqaGt956Cz/99BNsbGzg7++Pr776Su5NMTU0NGBkZASxWCxNIBYKhcjJyUFFRQXa29tHlZ+l7JYdLS0tWL9+PXR1daGvr49Nmzahs7Nz2H08PT0HTVe/+eabChoxGW8UqCaZseRoLVy4EEePHkV6ejoSExMhkUjg5uaG+vp6RQx5wrl58yb8/f1x+PBhHDp0CFlZWbh9+7a0xJWFhQU2b96My5cvQywWj/v5+3OldHV1YWtrC0tLS7i7u8Pe3h5isRjXrl1Dfn4+amtr0d3dPeyxRCIRAgICEBoaik2bNo37WEdi/fr1KCsrw4ULF5CRkYGcnJwBPdyGEhISgsbGRulr3759ChgtkQea+ptkGhoaYGpqiry8vAErArdt24bs7GwUFBQ89Ri9vb2wsbFBUFAQdu7cKc/hTkiVlZW4cOECtmzZMuh3EokEBQUFiIuLQ0pKCvT09BAYGIigoCBYWlrKPDXIGMPNmzfR09ODJUuWPHEZulgslj7P+t///gd9fX3weDwYGRkNWMXX37LDxsYGCQkJSlmG3t8658cff4STkxMAIDMzE35+fsO2zvH09ISDgwMOHjyowNESeaE7qklmLDlav6aurg4+n4+qqip5DHHCs7KyemKQAh5Ps7q6uiImJgYNDQ3Yv38/ysvL4eLiAm9vb3z55ZfDLnUfTn9frc7OzmFzpdTU1GBsbAw+n4/f/va34HK5uHv3LrKzs5GamooTJ07g4cOHePXVV6Gjo4OjR48qLVcqPz8f+vr60iAFAN7e3pgyZcpTv1QlJSVh1qxZWLx4MSIjI+U+5UrkhwLVJDOWHK1fE4vFKCkpAY/Hk9cwJwUtLS2sXbsW6enpuHv3LtatW4eEhARYWFhg/fr1OHPmzKiqUNTV1aGpqQlLliwZ8TJ4TU1NzJs3D8uWLcPSpUvR2NgIgUCA2bNn44cffsCOHTtkyruSlUgkGrQSderUqTAwMBi2PFhwcDASExNx6dIlREZGIiEhQVoyi0xAjEw6x48fZ5qamiw2NpaVl5ezv/zlL0xfX5+JRCLGGGOvvPIK++CDD6Tb79ixg507d45VV1eza9eusXXr1jEtLS1WVlamrEt4ZkkkElZaWsree+89ZmpqyrhcLtuyZQvLzc1lDx48YF1dXU981dTUsNOnT7OGhoYhtxnJq7Ozk+3Zs4fp6uoyf39/Nn36dLZ48WK2b98+1tnZOW7X+f777zMAw75u3brFdu/ezRYsWDBofy6Xy/75z3+O+HzfffcdA8CqqqrG7RqI4tAd1SQUGBiIzz77DFFRUXBwcEBxcfGgHK3Gxkbp9q2trQgJCYGNjQ38/PzQ0dGBvLw82NraPvH4OTk5+OMf/wgTExNwOBycPHnyqWP6/vvvsWTJEmhqasLS0hKxsbHjcakTDofDwaJFi7Bv3z7cuXMHSUlJaG9vh6+vL5YtW4a//e1vaGhoGLBqr6amBmVlZbCzs4Oenp5M5z916hR2796Ns2fPIi0tDSKRCO+//z7y8vLGtQqFQCDArVu3hn2Zm5vD2NgYzc3NA/bt6+tDS0vLqMqDLV26FABounqiUnakJM+eb7/9ln300UcsNTWVAWBpaWnDbl9TU8OmTZvGIiIiWHl5OTt8+DBTU1NjmZmZihnwBNDe3s6OHj3KPD092dSpU5m3tzc7cuQIu3jxItPV1WUnT56U6U6qq6uLXbp0iU2fPp2lpKQo+3KlysvLGQBWWFgofe/cuXOMw+Gwe/fujfg4ubm5DAC7ceOGPIZJ5IwCFZGrkQSqbdu2sUWLFg14LzAwkPn6+spxZBOTRCJhtbW1bOfOnczc3JxxOBxmZWXFzp49yzo6OsYcpEpKShiXy2X79+9nEolE2Zc5wMqVKxmfz2cFBQUsNzeXWVlZsaCgIOnv6+vr2cKFC1lBQQFjjLGqqir26aefssLCQlZbW8vS09OZubk5W7FihbIugciIpv6I0lHTx5HjcDiYP38+wsPDMXPmTPj4+OD3v/891q9fDzs7O+zYsQO3b98eVUKvqrfsSEpKgrW1Nby8vODn5wd3d3d8/vnn0t/39vZCKBRKV/VpaGjg4sWL8PHxgbW1NQQCAQICAnD69GllXQKREdX6I0o3VNPHjo4OdHd3Q1tbW0kjU019fX0IDAyErq4uTp06BQ0NDRw6dAgZGRmIj4/H0qVLwefzERQUhICAABgYGAx5rEePHql8yw4DAwMkJycP+fv58+cPCMxz5sxBdna2IoZGFET1/lYSQp7K1dUVqamp0qXjmpqaCAgIwMmTJ1FfX4/g4GAkJSXB0tIS69evR0ZGxqCl7tSyg0wUFKiI0g3V9FFXV5fupp5g6tSp+Pjjj59YDZ3D4YDL5eLtt99GQUEBrl27hgULFkAgEMDKygrvvvsuioqKIJFIsGvXLuTn5yMjI4NadhCVRoGKKB01fZSP/qXue/fuxU8//YT//Oc/ePDgAXx9fbFw4UIcPnwYp0+fHrIMESGqgu71ybjr7OwckK9SW1uL4uJiGBgYYO7cuYiMjMS9e/cQHx8PAHjzzTfxj3/8A9u2bcPrr7+OrKwspKSk4MyZM8q6hGeOmpoavL294e3tjQcPHmD79u2YNWsW7O3tlT00Qp5O2csOybPn0qVLT6w0sHHjRsYYYxs3bmQeHh6D9nFwcGAaGhrM3NycHTt2bMjjZ2dns9WrVzMejzei5e9DjaexsVG2CyWEKATdUZFx5+npOezy6CdVnfD09ERRUdGIjt/V1YXnnnsOr7/+Ov785z+PeFxCoRC6urrSn4fqZkwIUS0UqMiEs2rVKqxatWrU+xkaGsrcjp0Qoni0mIJMGg4ODuDxeHj++edx5coVZQ+HEDJCFKjIM4/H4yEmJgbffPMNvvnmG8yZMweenp64fv26sodGCBkB6vBLJjQOh4O0tDT4+/uPaj8PDw/MnTsXCQkJ8hkYIWTc0B0VmZRcXFyo5QMhEwQFKjIpFRcXU4diQiYIWvVHJpzRJhQfPHgQZmZmWLRoER49eoQvv/wSWVlZOH/+vLIugRAyCnRHRSacwsJC8Pl88Pl8AEBERAT4fD6ioqIAAI2Njairq5Nu39PTA4FAADs7O3h4eODGjRu4ePEivLy8Bh07Ojoazs7O0NHRgaGhIfz9/SEUCp86phMnTsDa2hpaWlqws7PDt99+O05XO7Hs3r0bbm5umDZt2ohTARhjiIqKAo/Hg7a2Nry9vVFZWSnfgZIJhRZTEPILK1euxLp16+Ds7Iy+vj58+OGHKC0tRXl5OaZPn/7EffLy8rBixQpER0dj9erVSE5Oxt69e3H9+nUsXrxYwVegXH/961+hr6+P+vp6HDlyBG1tbU/dZ+/evYiOjkZcXBzMzMzwySefoKSkBOXl5dDS0pL/oInKo0BFyDDu378PQ0NDZGdnY8WKFU/cJjAwEF1dXcjIyJC+t2zZMjg4OCAmJkZRQ1UpsbGxCA8Pf2qgYozBxMQEAoEA7777LgCgvb0dRkZGiI2Nxbp16xQwWqLqaOqPkGG0t7cDwLDNB6lD8djV1tZCJBIN+Pz09PSwdOlS+vyIFAUqQoYgkUgQHh6O5cuXDzuFN1SHYpFIJO8hTnj9nxF9fmQ4FKgIGUJoaChKS0tx/PhxZQ9FqT744ANwOJxhXxUVFcoeJnmG0fJ0Qp5g69atyMjIQE5ODmbPnj3stkN1KDY2NpbnEBVGIBDg1VdfHXYbc3PzMR27/zNqamoakNfW1NQEBweHMR2TPHsoUBHyC4wxvPXWW0hLS8P3338PMzOzp+7T36E4PDxc+t6z1KGYy+WCy+XK5dhmZmYwNjbGd999Jw1MHR0dKCgowObNm+VyTjLx0NQfIb8QGhqKxMREJCcnQ0dHByKRCCKRCN3d3dJtNmzYgMjISOnPYWFhyMzMxIEDB1BRUYHt27ejsLAQW7duVcYlKFVdXR2Ki4tRV1cHsViM4uJiFBcXo7OzU7qNtbU10tLSADyu1RgeHo5du3bh1KlTKCkpwYYNG2BiYjLq+o3kGaasjo2EqCI8oRMwgAEdhz08PKTdivulpKSwBQsWMA0NDbZo0SJ25syZIc+xZ88e5uTkxGbMmMG4XC5bs2YNq6ioGHZcx44dGzQmTU1NWS5VLjZu3PjEz+/SpUvSbX79eUokEvbJJ58wIyMjpqmpyby8vJhQKFT84InKojwqQhRsLEnFsbGxCAsLG1Alg8PhDFotR8iziJ5REaJgmZmZA36OjY2FoaEhrl27NmRSMfA4MD0rCzQIGQ16RkWIko0kqRh4XIx33rx5mDNnDtasWYOysjJFDI8QpaOpP0KUSCKR4IUXXkBbWxtyc3OH3C4/Px+VlZWwt7dHe3s7PvvsM+Tk5KCsrOypy+cJmegoUBGiRJs3b8bZs2eRm5s7qoDT29sLGxsbBAUFYefOnXIcISHKR8+oCFGS0SQV/5q6ujr4fD51KSaTAj2jIkTBGGPYunUr0tLSkJWVNaKk4l8Ti8UoKSmhLsVkUqA7KkIULDQ0FMnJyUhPT5cmFQOPq4Zra2sDeJxUbGpqiujoaADAp59+imXLlsHS0hJtbW3Yv38/7ty5gzfeeENp10GIolCgIkTB/vWvfwEAPD09B7x/7NgxaU29uro6TJny/xMera2tCAkJgUgkwsyZM+Ho6Ii8vDzY2toqatiEKA0tpiCEEKLS6BkVIYQQlUaBihBCiEqjQEUIIUSlUaAihBCi0ihQEUIIUWkUqAghhKg0ClSEEEJUGgUqQgghKo0CFSGEEJVGgYoQQohKo0BFCCFEpVGgIoQQotL+DwWnK9pCeMUTAAAAAElFTkSuQmCC",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from matplotlib import cbook, cm\n",
- "from matplotlib.colors import LightSource\n",
- "from matplotlib import colormaps\n",
- "\n",
- "\n",
- "log_probs = circuit(X_meshgrid.to(device).float().unsqueeze(dim=1)).detach().cpu()\n",
- "log_probs = log_probs.reshape(num_samples, num_samples).numpy()\n",
- "nrows, ncols = log_probs.shape\n",
- "\n",
- "# Set up plot\n",
- "fig, ax = plt.subplots(figsize=(15,5), subplot_kw=dict(projection='3d'))\n",
- "ax.set_title(\"3D surface plot\")\n",
- "ls = LightSource(270, 25)\n",
- "\n",
- "# To use a custom hillshading mode, override the built-in shading and pass\n",
- "# in the rgb colors of the shaded surface calculated from \"shade\".\n",
- "rgb = ls.shade(log_probs, cmap=colormaps[\"magma\"], vert_exag=1.0, blend_mode='soft')\n",
- "ax.view_init(elev=35.)\n",
- "surf = ax.plot_surface(x1v, x2v, log_probs, rstride=1, cstride=1, facecolors=rgb,\n",
- " linewidth=0, antialiased=False, shade=False)\n",
- "\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "our log-likelihood over iterations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.title(\"loss over iterations\")\n",
- "plt.plot(losses)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "our circuit is normalized by construction, which we can verify like this:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.pipeline import integrate\n",
- "with ctx:\n",
- " pc_pf = integrate(circuit).to(device)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [],
- "source": [
- "log_pf = pc_pf()\n",
- "assert torch.allclose(log_pf, torch.tensor(0.), atol=1e-6)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "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": "3.10.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/real-squared-npcs.ipynb b/notebooks/real-squared-npcs.ipynb
deleted file mode 100644
index d6e3392d..00000000
--- a/notebooks/real-squared-npcs.ipynb
+++ /dev/null
@@ -1,649 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "97f3b03e-0aa7-4fa9-ad37-21a141f4a1a1",
- "metadata": {},
- "source": [
- "# Train and evaluate a squared PC ($\\mathrm{NPC}^2$) with real parameters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "498b64a5-86bf-45c7-b65c-6c010a9b4a82",
- "metadata": {},
- "outputs": [],
- "source": [
- "import random\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "48e26e8e-8c76-43ce-90db-87da5d8eb39a",
- "metadata": {},
- "source": [
- "Set the random seeds."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "63dd05c5-f4ea-459a-b2fb-75d976f50afe",
- "metadata": {},
- "outputs": [],
- "source": [
- "random.seed(42)\n",
- "np.random.seed(42)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9d389743-936c-49a8-9ef8-f5c55be758a5",
- "metadata": {},
- "source": [
- "# Load the MiniBooNE UCI data set"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "61b76039-c260-4b43-88e2-e3e8875206bf",
- "metadata": {},
- "outputs": [],
- "source": [
- "from datasets import load_uci_dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "b153dc84-3e0b-463a-99b0-2b7e09a388af",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of variables: 43\n"
- ]
- }
- ],
- "source": [
- "data = load_uci_dataset('miniboone', path='datasets')\n",
- "data_train, data_test = data['train'], data['test']\n",
- "num_variables = data_train.shape[1]\n",
- "print(f'Number of variables: {num_variables}')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "611257b5-8c18-4f1d-8316-3c898f2e6035",
- "metadata": {},
- "source": [
- "# Instantiating a Circuit structure Template: the Region Graph"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d1ef46af-2e7d-46cf-ad72-52db50d5f939",
- "metadata": {},
- "source": [
- "Instantiate a bunch of _random binary tree_ region graphs of maximum depth, with different seed."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "a4559103-4e61-4b6c-ac72-ff54c72e0736",
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.templates.region_graph import RandomBinaryTree\n",
- "max_depth = int(np.ceil(np.log2(num_variables)))\n",
- "region_graph = RandomBinaryTree(num_variables, depth=max_depth, seed=42)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "96c77dea-c04a-4349-80e3-d78c508339c9",
- "metadata": {},
- "source": [
- "## Constructing the Symbolic Circuit Representation"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "90db495e-1065-43fa-97f4-b3b1ad1f0b32",
- "metadata": {},
- "source": [
- "We construct a non-monotonic PC with Gaussian distributions as input."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "84505053-eab3-491f-b143-89eda6f86224",
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.utils.scope import Scope\n",
- "from cirkit.symbolic.layers import GaussianLayer, DenseLayer, HadamardLayer\n",
- "from cirkit.symbolic.parameters import Parameter, TensorParameter, ScaledSigmoidParameter\n",
- "from cirkit.symbolic.initializers import UniformInitializer, NormalInitializer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "1a27d1ce-3fc7-4ea0-8e4c-2b1f2cd5b351",
- "metadata": {},
- "outputs": [],
- "source": [
- "def gaussian_layer_factory(\n",
- " scope: Scope,\n",
- " num_units: int,\n",
- " num_channels: int\n",
- ") -> GaussianLayer:\n",
- " return GaussianLayer(\n",
- " scope, num_units, num_channels,\n",
- " stddev_factory=lambda shape: Parameter.from_sequence(\n",
- " TensorParameter(*shape, initializer=NormalInitializer(0.0, 1e-1)),\n",
- " ScaledSigmoidParameter(shape, vmin=1e-5, vmax=1.0)\n",
- " )\n",
- " )\n",
- "\n",
- "def hadamard_layer_factory(\n",
- " scope: Scope, num_input_units: int, arity: int\n",
- ") -> HadamardLayer:\n",
- " return HadamardLayer(scope, num_input_units, arity)\n",
- "\n",
- "def dense_layer_factory(\n",
- " scope: Scope,\n",
- " num_input_units: int,\n",
- " num_output_units: int\n",
- ") -> DenseLayer:\n",
- " return DenseLayer(\n",
- " scope, num_input_units, num_output_units,\n",
- " weight_factory=lambda shape: Parameter.from_leaf(\n",
- " TensorParameter(*shape, initializer=UniformInitializer(0.0, 1.0)),\n",
- " )\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "8f5c34a5-f6c1-4735-ba88-76ab00ef35d4",
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.symbolic.circuit import Circuit"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "8ecd1c60-402f-4e65-b79a-8ddbd69dd700",
- "metadata": {},
- "outputs": [],
- "source": [
- "symbolic_circuit = Circuit.from_region_graph(\n",
- " region_graph,\n",
- " num_input_units=64,\n",
- " num_sum_units=64,\n",
- " input_factory=gaussian_layer_factory,\n",
- " sum_factory=dense_layer_factory,\n",
- " prod_factory=hadamard_layer_factory\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "57ad50a2-b91b-4934-bf36-061442375583",
- "metadata": {},
- "source": [
- "We can retrieve some information about the circuit and its structural properties as follows."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "de247e7e-d5bf-44e8-adc8-f070a86ad236",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Smooth: True\n",
- "Decomposable: True\n",
- "Structured decomposable: True\n",
- "Number of variables: 43\n",
- "Number of channels per variable: 1\n"
- ]
- }
- ],
- "source": [
- "print(f'Smooth: {symbolic_circuit.is_smooth}')\n",
- "print(f'Decomposable: {symbolic_circuit.is_decomposable}')\n",
- "print(f'Structured decomposable: {symbolic_circuit.is_structured_decomposable}')\n",
- "print(f'Number of variables: {symbolic_circuit.num_variables}')\n",
- "print(f'Number of channels per variable: {symbolic_circuit.num_channels}')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e0eff399-2f7c-4836-9b65-78622f4be7d6",
- "metadata": {},
- "source": [
- "## Computing the Circuit Square"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0a841699-70ec-44f3-b90b-24d92722eabd",
- "metadata": {},
- "source": [
- "We now square the symbolic circuit representation and compile it. However, to actually train it, we will only need to compute the integral of the squared circuit.\n",
- "\n",
- "To do so, we need to set up a pipeline context first and specify a backend. Since the circuit is non-monotonic, we will use the complex log-sum-exp and sum semiring. Note that in the following we also compile the integral of the squared circuit, i.e., the partition functions."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "1a01f3c5-21d1-4677-af7d-bd6bc5d8f45b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.pipeline import PipelineContext\n",
- "from cirkit.pipeline import compile\n",
- "import cirkit.symbolic.functional as SF"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "a89681ce-efcf-4dae-acf3-9448502f21fe",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "device = torch.device('cuda') # The device to use\n",
- "torch.manual_seed(42)\n",
- "# Using float32 ...\n",
- "#torch.set_default_dtype(torch.float64)\n",
- "torch.cuda.manual_seed(42)\n",
- "# Extend the default cache limit. In the future, torch will support compilation with 'more dynamic shapes',\n",
- "# and therefore many recompilations will be avoided\n",
- "torch._dynamo.config.cache_size_limit = 16"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "78bd1d2b-018f-47fa-a2df-e3ddfc816ed8",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 315 ms, sys: 28.7 ms, total: 343 ms\n",
- "Wall time: 345 ms\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "ctx = PipelineContext(\n",
- " backend='torch', # Choose the torch compilation backend\n",
- " fold=True, # Fold the circuit, this is a backend-specific compilation flag\n",
- " semiring='complex-lse-sum', # Use the (C, +, *) semiring, where + is the complex log-sum-exp and * is the sum\n",
- " optimize=True # Optimize the circuit layers (set this flag to False to disable, which yields slower inference)\n",
- ")\n",
- "\n",
- "with ctx:\n",
- " symbolic_sq_circuit = SF.multiply(symbolic_circuit, symbolic_circuit)\n",
- " symbolic_int_sq_circuit = SF.integrate(symbolic_sq_circuit)\n",
- " int_sq_circuit = compile(symbolic_int_sq_circuit)\n",
- " # Note that compiling the integral squared circuit will also compile the symbolic circuit\n",
- " # we started from. We retrieve its compiled module as showed below.\n",
- " circuit = ctx.get_compiled_circuit(symbolic_circuit)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "acead97f-2783-45ba-b2b1-0ef164fd2481",
- "metadata": {},
- "source": [
- "Let's print the circuit and the integral squared circuit torch modules."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "8bf4de33-c03f-4a69-9103-6a6b4a5ffec4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "TorchCircuit(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchGaussianLayer(\n",
- " (mean): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " (stddev): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " (1): TorchScaledSigmoidParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (1): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (2-7): 6 x TorchCPLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- ")\n"
- ]
- }
- ],
- "source": [
- "print(circuit)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "bfc5922e-c1d8-41b7-856a-759ee9aa253b",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "TorchConstantCircuit(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchLogPartitionLayer(\n",
- " (value): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-1): 2 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (2): TorchScaledSigmoidParameter()\n",
- " (3): TorchGaussianProductLogPartition()\n",
- " (4-5): 2 x TorchReduceSumParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (1-2): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (3): TorchHadamardLayer()\n",
- " (4-5): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (6): TorchHadamardLayer()\n",
- " (7-8): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (9): TorchHadamardLayer()\n",
- " (10-11): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (12): TorchHadamardLayer()\n",
- " (13-14): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (15): TorchHadamardLayer()\n",
- " (16-17): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (18): TorchHadamardLayer()\n",
- " (19-20): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- ")\n"
- ]
- }
- ],
- "source": [
- "print(int_sq_circuit)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "25f6510a-0631-4770-9002-9431f126bb9d",
- "metadata": {},
- "source": [
- "## Training and Testing"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7c3c644c-ac45-4156-b1f5-391585ea34c7",
- "metadata": {},
- "source": [
- "We are not ready to learn the parameters and do inference.\n",
- "First, we wrap our data into PyTorch data loaders by specifying the batch size.\n",
- "Then, we initialize any PyTorch optimizer, e.g., Adam.\n",
- "\n",
- "Note that the parameters of the integral squared circuit are the same parameters of the circuit itself."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "5a0f61c6-7564-4c3b-8612-e02a52160d12",
- "metadata": {},
- "outputs": [],
- "source": [
- "from torch import optim\n",
- "from torch.utils.data import DataLoader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "ea7b248b-802e-4f22-9849-2b5fe304b019",
- "metadata": {},
- "outputs": [],
- "source": [
- "train_dataloader = DataLoader(data_train, shuffle=True, batch_size=256, drop_last=True, num_workers=4)\n",
- "test_dataloader = DataLoader(data_test, shuffle=False, batch_size=256, num_workers=4)\n",
- "optimizer = optim.Adam(circuit.parameters(), lr=1e-3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "ee8840b0-617c-4881-8753-313b1491f13b",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Move circuits to device\n",
- "circuit = circuit.to(device)\n",
- "int_sq_circuit = int_sq_circuit.to(device)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "13f65113-023f-4e9f-922a-e7202de5bb43",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 4, step 500: Average NLL: 63.504\n",
- "Epoch 8, step 1000: Average NLL: 55.648\n",
- "Epoch 13, step 1500: Average NLL: 52.484\n",
- "Epoch 17, step 2000: Average NLL: 50.936\n",
- "Epoch 21, step 2500: Average NLL: 49.458\n",
- "Epoch 26, step 3000: Average NLL: 48.287\n",
- "Epoch 30, step 3500: Average NLL: 49.778\n",
- "Epoch 35, step 4000: Average NLL: 49.997\n",
- "Epoch 39, step 4500: Average NLL: 48.489\n",
- "Epoch 43, step 5000: Average NLL: 48.381\n",
- "Epoch 48, step 5500: Average NLL: 48.535\n",
- "Epoch 52, step 6000: Average NLL: 47.784\n",
- "Epoch 57, step 6500: Average NLL: 47.131\n",
- "Epoch 61, step 7000: Average NLL: 47.028\n",
- "Epoch 65, step 7500: Average NLL: 46.472\n",
- "Epoch 70, step 8000: Average NLL: 46.241\n",
- "Epoch 74, step 8500: Average NLL: 46.224\n",
- "Epoch 78, step 9000: Average NLL: 45.836\n",
- "Epoch 83, step 9500: Average NLL: 46.752\n",
- "Epoch 87, step 10000: Average NLL: 46.923\n",
- "Epoch 92, step 10500: Average NLL: 46.105\n",
- "Epoch 96, step 11000: Average NLL: 45.679\n"
- ]
- }
- ],
- "source": [
- "num_epochs = 100\n",
- "step_idx = 0\n",
- "running_loss = 0.0\n",
- "for epoch_idx in range(num_epochs):\n",
- " for i, batch in enumerate(train_dataloader):\n",
- " batch = batch.to(device).unsqueeze(dim=1) # Add a channel dimension\n",
- " log_output = circuit(batch) # Compute the log output of the circuit\n",
- " log_output = 2.0 * log_output.real # Multipliying by two squares the output\n",
- " log_pf = int_sq_circuit().real # Compute the log partition function of the circuit\n",
- " lls = log_output - log_pf # Compute the log-likelihood\n",
- " loss = -torch.mean(lls) # The loss is the negative average log-likelihood\n",
- " loss.backward()\n",
- " optimizer.step()\n",
- " optimizer.zero_grad()\n",
- " running_loss += loss.detach() * len(batch)\n",
- " step_idx += 1\n",
- " if step_idx % 500 == 0:\n",
- " print(f\"Epoch {epoch_idx}, step {step_idx}: Average NLL: {running_loss / (500 * len(batch)):.3f}\")\n",
- " running_loss = 0.0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "b712e5df-4ad2-4ae1-a7e3-f0c1df7db254",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Average test LL: -45.314\n"
- ]
- }
- ],
- "source": [
- "circuit.eval()\n",
- "int_sq_circuit.eval()\n",
- "\n",
- "with torch.no_grad():\n",
- " test_lls = 0.0\n",
- " log_pf = int_sq_circuit().real # Compute the log partition function of the circuit (just once as we are evaluating)\n",
- " for batch in test_dataloader:\n",
- " batch = batch.to(device).unsqueeze(dim=1) # Add a channel dimension\n",
- " log_output = circuit(batch) # Compute the log output of the circuit\n",
- " log_output = 2.0 * log_output.real # Multipliying by two squares the output\n",
- " lls = log_output - log_pf # Compute the log-likelihood\n",
- " test_lls += lls.sum().item()\n",
- " average_ll = test_lls / len(data_test)\n",
- " print(f\"Average test LL: {average_ll:.3f}\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5db6015a-b911-4f36-a471-c8ace9b9abdf",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "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": "3.10.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/sos-npcs.ipynb b/notebooks/sos-npcs.ipynb
deleted file mode 100644
index 3dfc5d08..00000000
--- a/notebooks/sos-npcs.ipynb
+++ /dev/null
@@ -1,943 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "97f3b03e-0aa7-4fa9-ad37-21a141f4a1a1",
- "metadata": {},
- "source": [
- "# Train and Evaluate a Sum-of-Squares non-monotonic PC"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "498b64a5-86bf-45c7-b65c-6c010a9b4a82",
- "metadata": {},
- "outputs": [],
- "source": [
- "import time\n",
- "import random\n",
- "import numpy as np"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "48e26e8e-8c76-43ce-90db-87da5d8eb39a",
- "metadata": {},
- "source": [
- "Set the random seeds."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "63dd05c5-f4ea-459a-b2fb-75d976f50afe",
- "metadata": {},
- "outputs": [],
- "source": [
- "random.seed(42)\n",
- "np.random.seed(42)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9d389743-936c-49a8-9ef8-f5c55be758a5",
- "metadata": {},
- "source": [
- "# Load the MiniBooNE UCI data set"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "61b76039-c260-4b43-88e2-e3e8875206bf",
- "metadata": {},
- "outputs": [],
- "source": [
- "from datasets import load_uci_dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "b153dc84-3e0b-463a-99b0-2b7e09a388af",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of variables: 43\n"
- ]
- }
- ],
- "source": [
- "data = load_uci_dataset('miniboone', path='datasets')\n",
- "data_train, data_test = data['train'], data['test']\n",
- "num_variables = data_train.shape[1]\n",
- "print(f'Number of variables: {num_variables}')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "611257b5-8c18-4f1d-8316-3c898f2e6035",
- "metadata": {},
- "source": [
- "# Create the Sum-of-Squares PC class"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "47d881c2-76a8-414e-b793-ec1579a4c302",
- "metadata": {},
- "outputs": [],
- "source": [
- "from typing import Tuple"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "2c130bf8-f85f-49ae-b76a-137af08b5053",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "from torch import nn, Tensor\n",
- "device = torch.device('cuda') # The device to use\n",
- "torch.manual_seed(42)\n",
- "torch.cuda.manual_seed(42)\n",
- "# Extend the default cache limit. In the future, torch will support compilation with 'more dynamic shapes',\n",
- "# and therefore many recompilations will be avoided\n",
- "torch._dynamo.config.cache_size_limit = 32"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "56710714-ada3-4ace-b816-02788afe7a8d",
- "metadata": {},
- "outputs": [],
- "source": [
- "from cirkit.templates.region_graph import RegionGraph, RandomBinaryTree\n",
- "from cirkit.pipeline import PipelineContext\n",
- "from cirkit.symbolic.circuit import Circuit\n",
- "from cirkit.utils.scope import Scope\n",
- "from cirkit.symbolic.layers import GaussianLayer, DenseLayer, HadamardLayer\n",
- "from cirkit.symbolic.parameters import Parameter, SoftmaxParameter, ScaledSigmoidParameter, TensorParameter\n",
- "from cirkit.symbolic.initializers import UniformInitializer, NormalInitializer\n",
- "import cirkit.symbolic.functional as SF\n",
- "from cirkit.backend.torch.circuits import TorchCircuit\n",
- "from cirkit.backend.torch.layers.inner import TorchMixingLayer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "2ed44de1-052a-491b-98d7-2c638d7e090a",
- "metadata": {},
- "outputs": [],
- "source": [
- "class SOS(nn.Module):\n",
- " def __init__(\n",
- " self,\n",
- " num_variables: int,\n",
- " *,\n",
- " num_input_units: int,\n",
- " num_sum_units: int,\n",
- " num_squares: int = 2,\n",
- " seed: int = 42,\n",
- " optimize: bool = False\n",
- " ) -> None:\n",
- " assert num_variables > 1\n",
- " assert num_squares > 1\n",
- " super().__init__()\n",
- " max_depth = int(np.ceil(np.log2(num_variables)))\n",
- " self._rgs = [\n",
- " SOS._build_region_graph(num_variables, max_depth, seed=seed + i * 123)\n",
- " for i in range(num_squares)\n",
- " ]\n",
- " self._ctx = PipelineContext(backend='torch', semiring='complex-lse-sum', fold=True, optimize=optimize)\n",
- " circuit, int_sq_circuit = self.build_circuits(\n",
- " num_input_units=num_input_units, num_sum_units=num_sum_units\n",
- " )\n",
- " self._circuit = circuit\n",
- " self._int_sq_circuit = int_sq_circuit\n",
- " self.register_buffer('_mixing_log_weight', -torch.log(torch.tensor(num_squares)))\n",
- " self.__cache_log_z: Optional[Tensor] = None\n",
- "\n",
- " def train(self, mode: bool = True):\n",
- " if mode:\n",
- " self.__cache_log_z = None\n",
- " else:\n",
- " with torch.no_grad():\n",
- " self.__cache_log_z = self._int_sq_circuit().real\n",
- " super().train(mode)\n",
- " \n",
- " def forward(self, x: Tensor) -> Tensor:\n",
- " log_score = 2.0 * self._circuit(x).real\n",
- " return torch.logsumexp(self._mixing_log_weight + log_score, dim=1)\n",
- "\n",
- " def log_likelihood(self, x: Tensor) -> Tensor:\n",
- " if self.__cache_log_z is None:\n",
- " log_z = self._int_sq_circuit().real\n",
- " else:\n",
- " log_z = self.__cache_log_z\n",
- " log_z = torch.logsumexp(self._mixing_log_weight + log_z, dim=0)\n",
- " log_score = self.forward(x)\n",
- " return log_score - log_z\n",
- "\n",
- " def build_circuits(\n",
- " self, num_input_units: int, num_sum_units: int,\n",
- " ) -> Tuple[TorchCircuit, TorchCircuit, TorchMixingLayer]:\n",
- " # Build one symbolic circuit for each region graph\n",
- " symbolic_circuits = [\n",
- " SOS._build_symbolic_circuit(rg, num_input_units=num_input_units, num_sum_units=num_sum_units)\n",
- " for rg in self._rgs\n",
- " ]\n",
- "\n",
- " # Merge the symbolic circuits into a single one having multiple outputs\n",
- " symbolic_circuit = SF.merge(symbolic_circuits)\n",
- "\n",
- " # Square each symbolic circuit and merge them into a single one having multiple outputs\n",
- " symbolic_sq_circuit = SF.merge([\n",
- " SF.multiply(sc, sc) for sc in symbolic_circuits\n",
- " ])\n",
- "\n",
- " # Integrate the squared circuits (by integrating the merged symbolic representation)\n",
- " symbolic_int_sq_circuit = SF.integrate(symbolic_sq_circuit)\n",
- "\n",
- " # Compile the symbolic circuits\n",
- " circuit = self._ctx.compile(symbolic_circuit)\n",
- " int_sq_circuit = self._ctx.compile(symbolic_int_sq_circuit)\n",
- "\n",
- " return circuit, int_sq_circuit\n",
- " \n",
- " def _build_region_graph(num_variables:int, depth: int, seed: int = 42) -> RegionGraph:\n",
- " return RandomBinaryTree(num_variables, depth=depth, seed=seed)\n",
- "\n",
- " def _build_symbolic_circuit(rg: RegionGraph, *, num_input_units: int, num_sum_units: int) -> Circuit:\n",
- " def gaussian_layer_factory(\n",
- " scope: Scope,\n",
- " num_units: int,\n",
- " num_channels: int\n",
- " ) -> GaussianLayer:\n",
- " return GaussianLayer(\n",
- " scope, num_units, num_channels,\n",
- " stddev_factory=lambda shape: Parameter.from_sequence(\n",
- " TensorParameter(*shape, initializer=NormalInitializer(0.0, 1e-1)),\n",
- " ScaledSigmoidParameter(shape, vmin=1e-5, vmax=1.0)\n",
- " )\n",
- " )\n",
- "\n",
- " def hadamard_layer_factory(\n",
- " scope: Scope, num_input_units: int, arity: int\n",
- " ) -> HadamardLayer:\n",
- " return HadamardLayer(scope, num_input_units, arity)\n",
- "\n",
- " def dense_layer_factory(\n",
- " scope: Scope,\n",
- " num_input_units: int,\n",
- " num_output_units: int\n",
- " ) -> DenseLayer:\n",
- " return DenseLayer(\n",
- " scope, num_input_units, num_output_units,\n",
- " weight_factory=lambda shape: Parameter.from_leaf(\n",
- " TensorParameter(*shape, initializer=UniformInitializer(0.0, 1.0))\n",
- " )\n",
- " )\n",
- "\n",
- " return Circuit.from_region_graph(\n",
- " rg,\n",
- " num_input_units=num_input_units,\n",
- " num_sum_units=num_sum_units,\n",
- " input_factory=gaussian_layer_factory,\n",
- " sum_factory=dense_layer_factory,\n",
- " prod_factory=hadamard_layer_factory\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "bf0bd994-1181-4724-acbe-f12bce3d1875",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "sos = SOS(\n",
- " num_variables,\n",
- " num_input_units=32,\n",
- " num_sum_units=32,\n",
- " num_squares=8\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "4b83e425-d154-474e-af6e-86d34295690c",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "SOS(\n",
- " (_circuit): TorchCircuit(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchGaussianLayer(\n",
- " (mean): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (stddev): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (8): TorchScaledSigmoidParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (1): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (2): TorchHadamardLayer()\n",
- " (3): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (4): TorchHadamardLayer()\n",
- " (5): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (6): TorchHadamardLayer()\n",
- " (7): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (8): TorchHadamardLayer()\n",
- " (9): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (10): TorchHadamardLayer()\n",
- " (11): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (12): TorchHadamardLayer()\n",
- " (13): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (_int_sq_circuit): TorchConstantCircuit(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchLogPartitionLayer(\n",
- " (value): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-15): 16 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (16): TorchScaledSigmoidParameter()\n",
- " (17): TorchGaussianProductLogPartition()\n",
- " (18-19): 2 x TorchReduceSumParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (1): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (8): TorchKroneckerParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (2): TorchHadamardLayer()\n",
- " (3): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (8): TorchKroneckerParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (4): TorchHadamardLayer()\n",
- " (5): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (8): TorchKroneckerParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (6): TorchHadamardLayer()\n",
- " (7): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (8): TorchKroneckerParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (8): TorchHadamardLayer()\n",
- " (9): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (8): TorchKroneckerParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (10): TorchHadamardLayer()\n",
- " (11): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (8): TorchKroneckerParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (12): TorchHadamardLayer()\n",
- " (13): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (8): TorchKroneckerParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- ")\n"
- ]
- }
- ],
- "source": [
- "print(sos)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "25f6510a-0631-4770-9002-9431f126bb9d",
- "metadata": {},
- "source": [
- "## Training and Testing"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7c3c644c-ac45-4156-b1f5-391585ea34c7",
- "metadata": {},
- "source": [
- "We are not ready to learn the parameters and do inference.\n",
- "First, we wrap our data into PyTorch data loaders by specifying the batch size.\n",
- "Then, we initialize any PyTorch optimizer, e.g., Adam.\n",
- "\n",
- "Note that the parameters of the integral squared circuit are the same parameters of the circuit itself."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "5a0f61c6-7564-4c3b-8612-e02a52160d12",
- "metadata": {},
- "outputs": [],
- "source": [
- "from torch import optim\n",
- "from torch.utils.data import DataLoader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "ea7b248b-802e-4f22-9849-2b5fe304b019",
- "metadata": {},
- "outputs": [],
- "source": [
- "train_dataloader = DataLoader(data_train, shuffle=True, batch_size=512, drop_last=True, num_workers=4)\n",
- "test_dataloader = DataLoader(data_test, shuffle=False, batch_size=512, num_workers=4)\n",
- "optimizer = optim.Adam(sos.parameters(), lr=1e-2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "ee8840b0-617c-4881-8753-313b1491f13b",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Move circuit to device\n",
- "sos = sos.to(device)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "7ecae2ef-9417-4cc8-8171-805d3e508b97",
- "metadata": {},
- "outputs": [],
- "source": [
- "import time"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "13f65113-023f-4e9f-922a-e7202de5bb43",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 5, step 300: Average NLL: 47.161\n",
- "Epoch 10, step 600: Average NLL: 39.575\n",
- "Epoch 15, step 900: Average NLL: 38.443\n",
- "Epoch 21, step 1200: Average NLL: 37.760\n",
- "Training time: 224.2 seconds\n"
- ]
- }
- ],
- "source": [
- "sos.train()\n",
- "start_time = time.perf_counter()\n",
- "num_epochs = 25\n",
- "step_idx = 0\n",
- "running_loss = 0.0\n",
- "for epoch_idx in range(num_epochs):\n",
- " for i, batch in enumerate(train_dataloader):\n",
- " batch = batch.to(device).unsqueeze(dim=1) # Add a channel dimension\n",
- " lls = sos.log_likelihood(batch) # Compute the log-likelihood\n",
- " loss = -torch.mean(lls) # The loss is the negative average log-likelihood\n",
- " loss.backward()\n",
- " optimizer.step()\n",
- " optimizer.zero_grad()\n",
- " running_loss += loss.detach() * len(batch)\n",
- " step_idx += 1\n",
- " if step_idx % 300 == 0:\n",
- " print(f\"Epoch {epoch_idx}, step {step_idx}: Average NLL: {running_loss / (300 * len(batch)):.3f}\")\n",
- " running_loss = 0.0\n",
- "end_time = time.perf_counter()\n",
- "print(f\"Training time: {end_time - start_time:.1f} seconds\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9d0dcab9-dbde-4a22-9aa3-f235ca98fe58",
- "metadata": {},
- "source": [
- "Next, we check the likelihood on the test data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "b712e5df-4ad2-4ae1-a7e3-f0c1df7db254",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Average test LL: -37.120\n"
- ]
- }
- ],
- "source": [
- "sos.eval()\n",
- "with torch.no_grad():\n",
- " test_lls = 0.0\n",
- " for batch in test_dataloader:\n",
- " batch = batch.to(device).unsqueeze(dim=1) # Add a channel dimension\n",
- " lls = sos.log_likelihood(batch) # Compute the log-likelihood\n",
- " test_lls += lls.sum().item()\n",
- " average_ll = test_lls / len(data_test)\n",
- " print(f\"Average test LL: {average_ll:.3f}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "91b0d955-a7c8-45fb-80f5-a448602c435a",
- "metadata": {},
- "source": [
- "# Bonus: Optimize your Circuit"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "5a0905dc-c7a6-4b37-a364-8824489542b3",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "from torch import nn, Tensor\n",
- "device = torch.device('cuda') # The device to use\n",
- "torch.manual_seed(42)\n",
- "torch.cuda.manual_seed(42)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "f7827e48-9a62-4ac6-8b06-3f81c8f7d1b8",
- "metadata": {},
- "outputs": [],
- "source": [
- "sos = SOS(\n",
- " num_variables,\n",
- " num_input_units=32,\n",
- " num_sum_units=32,\n",
- " num_squares=8,\n",
- " optimize=True\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "8116b368-fbfe-4507-8113-0d643d025b62",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "SOS(\n",
- " (_circuit): TorchCircuit(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchGaussianLayer(\n",
- " (mean): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (stddev): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (8): TorchScaledSigmoidParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (1): TorchDenseLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (2-7): 6 x TorchCPLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (_int_sq_circuit): TorchConstantCircuit(\n",
- " (_nodes): ModuleList(\n",
- " (0): TorchLogPartitionLayer(\n",
- " (value): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-15): 16 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " (16): TorchScaledSigmoidParameter()\n",
- " (17): TorchGaussianProductLogPartition()\n",
- " (18-19): 2 x TorchReduceSumParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " (1-2): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (3): TorchHadamardLayer()\n",
- " (4-5): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (6): TorchHadamardLayer()\n",
- " (7-8): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (9): TorchHadamardLayer()\n",
- " (10-11): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (12): TorchHadamardLayer()\n",
- " (13-14): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (15): TorchHadamardLayer()\n",
- " (16-17): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " (18): TorchHadamardLayer()\n",
- " (19-20): 2 x TorchTensorDotLayer(\n",
- " (weight): TorchParameter(\n",
- " (_nodes): ModuleList(\n",
- " (0-7): 8 x TorchPointerParameter(\n",
- " (_parameter): TorchTensorParameter()\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- ")\n"
- ]
- }
- ],
- "source": [
- "print(sos)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "5ddb1351-2192-4a90-acbf-aa34627bf496",
- "metadata": {},
- "outputs": [],
- "source": [
- "from torch import optim\n",
- "from torch.utils.data import DataLoader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "e9cb8eb6-34b5-409f-b86a-2faaf644f619",
- "metadata": {},
- "outputs": [],
- "source": [
- "train_dataloader = DataLoader(data_train, shuffle=True, batch_size=512, drop_last=True, num_workers=4)\n",
- "test_dataloader = DataLoader(data_test, shuffle=False, batch_size=512, num_workers=4)\n",
- "optimizer = optim.Adam(sos.parameters(), lr=1e-2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "36287b60-d018-4018-b72e-a96553f6f70f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Move circuit to device\n",
- "sos = sos.to(device)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "57411f8a-e5ce-4d6e-a67c-21352d49b4f8",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 5, step 300: Average NLL: 47.162\n",
- "Epoch 10, step 600: Average NLL: 39.652\n",
- "Epoch 15, step 900: Average NLL: 38.197\n",
- "Epoch 21, step 1200: Average NLL: 37.610\n",
- "Training time: 63.0 seconds\n"
- ]
- }
- ],
- "source": [
- "sos.train()\n",
- "start_time = time.perf_counter()\n",
- "num_epochs = 25\n",
- "step_idx = 0\n",
- "running_loss = 0.0\n",
- "for epoch_idx in range(num_epochs):\n",
- " for i, batch in enumerate(train_dataloader):\n",
- " batch = batch.to(device).unsqueeze(dim=1) # Add a channel dimension\n",
- " lls = sos.log_likelihood(batch) # Compute the log-likelihood\n",
- " loss = -torch.mean(lls) # The loss is the negative average log-likelihood\n",
- " loss.backward()\n",
- " optimizer.step()\n",
- " optimizer.zero_grad()\n",
- " running_loss += loss.detach() * len(batch)\n",
- " step_idx += 1\n",
- " if step_idx % 300 == 0:\n",
- " print(f\"Epoch {epoch_idx}, step {step_idx}: Average NLL: {running_loss / (300 * len(batch)):.3f}\")\n",
- " running_loss = 0.0\n",
- "end_time = time.perf_counter()\n",
- "print(f\"Training time: {end_time - start_time:.1f} seconds\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "87ced9cf-182d-4aa3-a22a-f1452d387f58",
- "metadata": {},
- "source": [
- "Note that enabling optimizations at compile time provided us a ~3.5x speed up during training.\n",
- "Next, we check the likelihood on the test data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "7712a50b-499e-4c5f-a1b9-242de67077c5",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Average test LL: -37.058\n"
- ]
- }
- ],
- "source": [
- "sos.eval()\n",
- "with torch.no_grad():\n",
- " test_lls = 0.0\n",
- " for batch in test_dataloader:\n",
- " batch = batch.to(device).unsqueeze(dim=1) # Add a channel dimension\n",
- " lls = sos.log_likelihood(batch) # Compute the log-likelihood\n",
- " test_lls += lls.sum().item()\n",
- " average_ll = test_lls / len(data_test)\n",
- " print(f\"Average test LL: {average_ll:.3f}\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3fdcf666-3610-4036-acf3-99bf06e2430a",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "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": "3.10.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pyproject.toml b/pyproject.toml
index 1c64366d..b21968c1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -177,9 +177,7 @@ default-docstring-type = "google"
# type checking
[tool.mypy]
python_version = "3.8"
-mypy_path = "3rd-party-stubs" # TODO: do we still need this?
follow_imports = "silent"
-follow_imports_for_stubs = true
# https://mypy.readthedocs.io/en/stable/config_file.html#miscellaneous
warn_unused_configs = true
# https://mypy.readthedocs.io/en/stable/config_file.html#disallow-dynamic-typing
diff --git a/tests/backend/torch/test_compile_circuit_operators.py b/tests/backend/torch/test_compile_circuit_operators.py
index 477e9b53..928528ed 100644
--- a/tests/backend/torch/test_compile_circuit_operators.py
+++ b/tests/backend/torch/test_compile_circuit_operators.py
@@ -1,4 +1,6 @@
+import functools
import itertools
+from typing import cast
import numpy as np
import pytest
@@ -10,6 +12,7 @@
from cirkit.backend.torch.compiler import TorchCompiler
from cirkit.backend.torch.layers.input import TorchEvidenceLayer
from cirkit.backend.torch.semiring import SumProductSemiring
+from cirkit.symbolic.layers import PolynomialLayer
from tests.floats import allclose, isclose
from tests.symbolic.test_utils import (
build_bivariate_monotonic_structured_cpt_pc,
@@ -137,3 +140,95 @@ def test_compile_product_integrate_pc_gaussian():
int_a, int_b = -np.inf, np.inf
ig, err = integrate.dblquad(df, int_a, int_b, int_a, int_b)
assert np.isclose(ig, torch.exp(z).item(), atol=1e-15)
+
+
+@pytest.mark.parametrize(
+ "semiring,fold,optimize,num_products",
+ itertools.product(["sum-product", "complex-lse-sum"], [False, True], [False, True], [2, 3, 4]),
+)
+def test_compile_product_pc_polynomial(
+ semiring: str, fold: bool, optimize: bool, num_products: int
+) -> None:
+ compiler = TorchCompiler(semiring=semiring, fold=fold, optimize=optimize)
+ scs, tcs = [], []
+ for i in range(num_products):
+ sci = build_multivariate_monotonic_structured_cpt_pc(
+ num_units=2 + i, input_layer="polynomial"
+ )
+ tci = compiler.compile(sci)
+ scs.append(sci)
+ tcs.append(tci)
+
+ sc = functools.reduce(SF.multiply, scs)
+ num_variables = sc.num_variables
+ degp1 = cast(PolynomialLayer, next(sc.inputs)).degree + 1
+ tc: TorchCircuit = compiler.compile(sc)
+
+ inputs = (
+ torch.tensor(0.0) # Get default float dtype.
+ .new_tensor( # degp1**D should be able to determine the coeffs.
+ list(itertools.product(range(degp1), repeat=num_variables)) # type: ignore[misc]
+ )
+ .unsqueeze(dim=-2)
+ .requires_grad_()
+ ) # shape (B, C=1, D=num_variables).
+
+ zs = torch.stack([tci(inputs) for tci in tcs], dim=0)
+ # shape num_prod * (B, num_out=1, num_cls=1).
+ assert zs.shape == (num_products, inputs.shape[0], 1, 1)
+ zs = zs.squeeze() # shape (num_prod, B).
+ zs = compiler.semiring.prod(zs, dim=0) # shape (B,).
+
+ # Test the partition function value
+ z = tc(inputs)
+ assert z.shape == (inputs.shape[0], 1, 1) # shape (B, num_out=1, num_cls=1).
+ z = z.squeeze() # shape (B,).
+
+ if semiring == "sum-product":
+ assert allclose(zs, z)
+ elif semiring == "complex-lse-sum":
+ # Take exp to ingore +-pi
+ assert allclose(torch.exp(zs), torch.exp(z))
+
+
+# TODO: test high-order?
+@pytest.mark.parametrize(
+ "semiring,fold,optimize",
+ itertools.product(["sum-product", "complex-lse-sum"], [False, True], [False, True]),
+)
+def test_compile_differentiate_pc_polynomial(semiring: str, fold: bool, optimize: bool) -> None:
+ compiler = TorchCompiler(semiring=semiring, fold=fold, optimize=optimize)
+ sc = build_multivariate_monotonic_structured_cpt_pc(input_layer="polynomial")
+ num_variables = sc.num_variables
+
+ diff_sc = SF.differentiate(sc, order=1)
+ diff_tc: TorchCircuit = compiler.compile(diff_sc)
+ assert isinstance(diff_tc, TorchCircuit)
+ tc: TorchCircuit = compiler.get_compiled_circuit(sc)
+ assert isinstance(tc, TorchCircuit)
+
+ inputs = (
+ torch.tensor([[0.0] * num_variables, range(num_variables)]) # type: ignore[misc]
+ .unsqueeze(dim=-2)
+ .requires_grad_()
+ ) # shape (B=2, C=1, D=num_variables).
+
+ with torch.enable_grad():
+ output = tc(inputs)
+ assert output.shape == (2, 1, 1) # shape (B=2, num_out=1, num_cls=1).
+ (grad_autodiff,) = torch.autograd.grad(
+ output, inputs, torch.ones_like(output)
+ ) # shape (B=2, C=1, D=num_variables).
+
+ grad = diff_tc(inputs)
+ assert grad.shape == (2, num_variables + 1, 1) # shape (B=2, num_out=1*(D*C+1), num_cls=1).
+ # shape (B=2, num_out=D, num_cls=1) -> (B=2, C=1, D=num_variables).
+ grad = grad[:, :-1, :].movedim(1, 2)
+ # TODO: what if num_cls!=1?
+ if semiring == "sum-product":
+ assert allclose(grad, grad_autodiff)
+ elif semiring == "complex-lse-sum":
+ # NOTE: grad = log ∂ C; grad_autodiff = ∂ log C = ∂ C / C = ∂ C / exp(output)
+ assert allclose(torch.exp(grad), grad_autodiff * torch.exp(output))
+ else:
+ assert False
diff --git a/tests/conftest.py b/tests/conftest.py
index 85d0060d..2a120973 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -7,13 +7,17 @@
@pytest.fixture(autouse=True)
-def _setup_reproducible_global_state() -> None:
+def _setup_global_state() -> None:
+ # Seed all RNGs.
seed = 42
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
- torch.set_grad_enabled(False)
- torch.set_default_dtype(torch.float64) # type: ignore[no-untyped-call]
+ # Set deterministic algorithms.
torch.use_deterministic_algorithms(True, warn_only=True)
torch.backends.cudnn.benchmark = False
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
+ # Use high precision computation.
+ torch.set_default_dtype(torch.float64) # type: ignore[no-untyped-call]
+ # Disable autograd because we do not need it in most cases.
+ torch.set_grad_enabled(False)
diff --git a/tests/floats.py b/tests/floats.py
index 9c9e7263..6fb5b992 100644
--- a/tests/floats.py
+++ b/tests/floats.py
@@ -4,12 +4,17 @@
import torch
from numpy.typing import NDArray
+DEFAULT_RTOL = 1e-8
+DEFAULT_ATOL = 1e-12
+
def isclose(
a: Union[float, NDArray[Union[np.float16, np.float32, np.float64]], torch.Tensor],
b: Union[float, NDArray[Union[np.float16, np.float32, np.float64]], torch.Tensor],
- rtol: float = 1e-8,
- atol: float = 1e-12,
+ /,
+ *,
+ rtol: float = DEFAULT_RTOL,
+ atol: float = DEFAULT_ATOL,
) -> NDArray[np.bool_]:
"""Proxy torch.isclose/np.isclose with a different global default rtol and atol.
@@ -38,8 +43,10 @@ def isclose(
def allclose(
a: Union[float, NDArray[Union[np.float16, np.float32, np.float64]], torch.Tensor],
b: Union[float, NDArray[Union[np.float16, np.float32, np.float64]], torch.Tensor],
- rtol: float = 1e-8,
- atol: float = 1e-12,
+ /,
+ *,
+ rtol: float = DEFAULT_RTOL,
+ atol: float = DEFAULT_ATOL,
) -> bool:
"""Proxy torch.allclose/np.allclose with a different global default rtol and atol.
diff --git a/tests/symbolic/test_circuit_operators.py b/tests/symbolic/test_circuit_operators.py
index 88bfc58d..87d71b5c 100644
--- a/tests/symbolic/test_circuit_operators.py
+++ b/tests/symbolic/test_circuit_operators.py
@@ -1,4 +1,5 @@
import itertools
+from typing import Iterable, Tuple, TypeVar
import pytest
@@ -11,8 +12,14 @@
GaussianLayer,
HadamardLayer,
LogPartitionLayer,
+ PolynomialLayer,
+)
+from cirkit.symbolic.parameters import (
+ ConjugateParameter,
+ KroneckerParameter,
+ PolynomialDifferential,
+ ReferenceParameter,
)
-from cirkit.symbolic.parameters import ConjugateParameter, KroneckerParameter, ReferenceParameter
from cirkit.utils.scope import Scope
from tests.symbolic.test_utils import build_multivariate_monotonic_structured_cpt_pc
@@ -68,7 +75,7 @@ def test_integrate_circuit(num_units: int, input_layer: str):
@pytest.mark.parametrize(
"num_units,input_layer",
- itertools.product([1, 3], ["bernoulli", "gaussian"]),
+ itertools.product([1, 3], ["bernoulli", "gaussian", "polynomial"]),
)
def test_multiply_circuits(num_units: int, input_layer: str):
sc1 = build_multivariate_monotonic_structured_cpt_pc(
@@ -97,6 +104,13 @@ def test_multiply_circuits(num_units: int, input_layer: str):
assert all(isinstance(l, HadamardLayer) for l in prod_layers)
assert all(l.num_input_units == prod_num_units for l in dense_layers if sc.layer_outputs(l))
assert all(l.num_output_units == prod_num_units for l in dense_layers if sc.layer_outputs(l))
+ # Additional check of degree for polynomial
+ if input_layer == "polynomial":
+ for in_1, in_2, in_prod in zip(sc1.inputs, sc2.inputs, sc.inputs):
+ assert isinstance(in_1, PolynomialLayer)
+ assert isinstance(in_2, PolynomialLayer)
+ assert isinstance(in_prod, PolynomialLayer)
+ assert in_prod.degree == in_1.degree + in_2.degree
@pytest.mark.parametrize(
@@ -132,7 +146,7 @@ def test_multiply_integrate_circuits(num_units: int, input_layer: str):
@pytest.mark.parametrize(
"num_units,input_layer",
- itertools.product([1, 3], ["bernoulli", "gaussian"]),
+ itertools.product([1, 3], ["bernoulli", "gaussian", "polynomial"]),
)
def test_conjugate_circuit(num_units: int, input_layer: str):
sc1 = build_multivariate_monotonic_structured_cpt_pc(
@@ -150,3 +164,43 @@ def test_conjugate_circuit(num_units: int, input_layer: str):
assert all(isinstance(l, HadamardLayer) for l in prod_layers)
assert all(l.num_input_units == num_units for l in dense_layers if sc.layer_outputs(l))
assert all(l.num_output_units == num_units for l in dense_layers if sc.layer_outputs(l))
+
+
+_T_co = TypeVar("_T_co", covariant=True) # TODO: for _batched. move together
+
+
+# TODO: this can be made public and moved to utils, might be used elsewhere.
+# itertools.batched introduced in 3.12
+def _batched(iterable: Iterable[_T_co], n: int) -> Iterable[Tuple[_T_co, ...]]:
+ if n < 1:
+ raise ValueError("n must be at least one")
+ iterator = iter(iterable)
+ while batch := tuple(itertools.islice(iterator, n)):
+ yield batch
+
+
+@pytest.mark.parametrize("num_units", [1, 3])
+def test_differentiate_circuit(num_units: int) -> None:
+ sc = build_multivariate_monotonic_structured_cpt_pc(
+ num_units=num_units, input_layer="polynomial"
+ )
+ diff_sc = SF.differentiate(sc)
+ assert diff_sc.is_smooth
+ assert diff_sc.is_decomposable
+ assert diff_sc.is_structured_decomposable
+ assert not diff_sc.is_omni_compatible
+ sc_inputs = list(sc.inputs)
+ diff_inputs = list(diff_sc.inputs)
+ sc_inner = list(sc.inner_layers)
+ diff_inner = list(diff_sc.inner_layers)
+ assert len(diff_inputs) == len(sc_inputs) * 2 # diff and self
+ assert all(
+ isinstance(dl.coeff.output, PolynomialDifferential)
+ and isinstance(sl.coeff.output, ReferenceParameter)
+ for dl, sl in _batched(diff_inputs, 2)
+ )
+ assert len(diff_inner) == sum(len(sc.layer_scope(l)) for l in sc_inner) + len(sc_inner)
+ dense_layers = list(filter(lambda l: isinstance(l, DenseLayer), diff_sc.inner_layers))
+ assert dense_layers
+ assert all(isinstance(r, ReferenceParameter) for l in dense_layers for r in l.weight.inputs)
+ # TODO: should we keep more info for diff layer ordering? i.e. testing order wrt each var
diff --git a/tests/symbolic/test_utils.py b/tests/symbolic/test_utils.py
index a14e362a..d8b419e8 100644
--- a/tests/symbolic/test_utils.py
+++ b/tests/symbolic/test_utils.py
@@ -10,7 +10,14 @@
NormalInitializer,
UniformInitializer,
)
-from cirkit.symbolic.layers import CategoricalLayer, DenseLayer, GaussianLayer, HadamardLayer, Layer
+from cirkit.symbolic.layers import (
+ CategoricalLayer,
+ DenseLayer,
+ GaussianLayer,
+ HadamardLayer,
+ Layer,
+ PolynomialLayer,
+)
from cirkit.symbolic.parameters import (
ExpParameter,
LogSoftmaxParameter,
@@ -180,6 +187,23 @@ def build_multivariate_monotonic_structured_cpt_pc(
)
for vid in range(5)
}
+ elif input_layer == "polynomial":
+ if parameterize:
+ coeff_factory = None
+ else:
+ coeff_factory = lambda shape: Parameter.from_leaf(
+ TensorParameter(*shape, initializer=UniformInitializer())
+ )
+ input_layers = {
+ (vid,): PolynomialLayer(
+ Scope([vid]),
+ num_output_units=num_units,
+ num_channels=1,
+ degree=2, # TODO: currently hard-coded
+ coeff_factory=coeff_factory,
+ )
+ for vid in range(5)
+ }
else:
raise NotImplementedError()