Merge branch 'yves' of https://github.com/GT4SD/gt4sd-core into yves

src/gt4sd/frameworks/enzeptional/__init__.py

@@ -27,4 +27,3 @@
 """
 
 from .core import EnzymeOptimizer # noqa: F401
-from .core import EnzymeOptimizer # noqa: F401

src/gt4sd/frameworks/enzeptional/processing.py

@@ -23,10 +23,8 @@
 #
 from abc import ABC
 import torch
-import torch
 import numpy as np
 from typing import Any, Dict, List, Optional, Tuple, Union
-from typing import Any, Dict, List, Optional, Tuple, Union
 from tape.datasets import pad_sequences
 from tape.registry import registry
 from tape.tokenizers import TAPETokenizer
@@ -61,23 +59,12 @@ def __init__(self):
  """
  self.cache = {}
 
- def get(self, key):
- """
- Retrieves a model from the cache using the given key.
- """
- def __init__(self):
- """
- Initializes the cache as an empty dictionary.
- """
- self.cache = {}
-
  def get(self, key):
  """
  Retrieves a model from the cache using the given key.
 
  Args:
  key: The key used to store the model.
- key: The key used to store the model.
 
  Returns:
  The model associated with the key, or None if not found.
@@ -116,18 +103,10 @@ def embed(self, samples: List[str]) -> np.ndarray:
 
  Raises:
  NotImplementedError: If the method is not implemented in the subclass.
-
- Returns:
- np.ndarray: The resulting embeddings as a NumPy array.
  """
  raise NotImplementedError
 
 
-class HFandTAPEModelUtility(StringEmbedding):
- """
- Utility class for handling both Hugging Face and TAPE models for embedding
- and unmasking tasks.
- """
 class HFandTAPEModelUtility(StringEmbedding):
  """
  Utility class for handling both Hugging Face and TAPE models for embedding
@@ -140,13 +119,8 @@ def __init__(
  tokenizer_path: str,
  unmasking_model_path: Optional[str] = None,
  is_tape_model: bool = False,
- embedding_model_path: str,
- tokenizer_path: str,
- unmasking_model_path: Optional[str] = None,
- is_tape_model: bool = False,
  device: Optional[Union[torch.device, str]] = None,
  cache_dir: Optional[str] = None,
- cache_dir: Optional[str] = None,
  ) -> None:
  """Initializes the utility with specified model and tokenizer paths.
 
@@ -259,7 +233,6 @@ def _embed_tape(self, samples: List[str]) -> np.ndarray:
 
  Args:
  samples (List[str]): List of strings to be embedded.
- samples (List[str]): List of strings to be embedded.
 
  Returns:
  np.ndarray: The resulting embeddings.
@@ -296,7 +269,6 @@ def _embed_huggingface(self, samples: List[str]) -> np.ndarray:
 
  Args:
  samples (List[str]): List of strings to be embedded.
- samples (List[str]): List of strings to be embedded.
 
  Returns:
  np.ndarray: The resulting embeddings.
@@ -315,25 +287,9 @@ def _embed_huggingface(self, samples: List[str]) -> np.ndarray:
 
  sequence_lengths = inputs["attention_mask"].sum(1)
 
- inputs = self.tokenizer(
- samples,
- add_special_tokens=True,
- padding=True,
- return_tensors="pt",
- )
- inputs = {k: v.to(self.device) for k, v in inputs.items()}
-
- with torch.no_grad():
- outputs = self.embedding_model(**inputs)
- sequence_embeddings = outputs[0].cpu().detach().numpy()
-
- sequence_lengths = inputs["attention_mask"].sum(1)
-
  return np.array(
  [
  sequence_embedding[:sequence_length].mean(0)
- for sequence_embedding, sequence_length in zip(
- sequence_embeddings, sequence_lengths
  for sequence_embedding, sequence_length in zip(
  sequence_embeddings, sequence_lengths
  )
@@ -363,15 +319,6 @@ def unmask(self, sequence: str, top_k: int = 2) -> List[str]:
  except (KeyError, NotImplementedError) as e:
  logger.warning(f"{e} Standard unmasking failed ")
  raise KeyError("Check the unmasking model you want to use")
- if self.is_tape_model:
- logger.error("Unmasking is not supported for TAPE models.")
- raise NotImplementedError("Unmasking is not supported for TAPE models.")
-
- try:
- return self._unmask_with_model(sequence, top_k)
- except (KeyError, NotImplementedError) as e:
- logger.warning(f"{e} Standard unmasking failed ")
- raise KeyError("Check the unmasking model you want to use")
 
  def _unmask_with_model(self, sequence: str, top_k: int) -> List[str]:
  """Unmasks a sequence using the model, providing top-k predictions.
@@ -440,12 +387,9 @@ def mutate_sequence_with_variant(sequence: str, variant: str) -> str:
  Args:
  sequence (str): The original amino acid sequence.
  variant (str): The variant to apply, formatted as a string.
- sequence (str): The original amino acid sequence.
- variant (str): The variant to apply, formatted as a string.
 
  Returns:
  str: The mutated amino acid sequence.
- str: The mutated amino acid sequence.
  """
  mutated_sequence = list(sequence)
  for variant_string in variant.split("/"):
@@ -460,20 +404,12 @@ def sanitize_intervals(intervals: List[Tuple[int, int]]) -> List[Tuple[int, int]
  Args:
  intervals (List[Tuple[int, int]]): A list of
  start and end points of intervals.
- intervals (List[Tuple[int, int]]): A list of
- start and end points of intervals.
 
  Returns:
  List[Tuple[int, int]]: A list of merged intervals.
- List[Tuple[int, int]]: A list of merged intervals.
  """
  intervals.sort()
  merged: List[Tuple[int, int]] = []
- for start, end in intervals:
- if not merged or merged[-1][1] < start:
- merged.append((start, end))
- intervals.sort()
- merged: List[Tuple[int, int]] = []
  for start, end in intervals:
  if not merged or merged[-1][1] < start:
  merged.append((start, end))
@@ -551,19 +487,12 @@ def reconstruct_sequence_with_mutation_range(
  mutated_sequence_range (str): The range of the sequence to be mutated.
  intervals (List[Tuple[int, int]]): The intervals where
  mutations are applied.
- sequence (str): The original sequence.
- mutated_sequence_range (str): The range of the sequence to be mutated.
- intervals (List[Tuple[int, int]]): The intervals where
- mutations are applied.
 
  Returns:
  str: The reconstructed sequence with mutations.
- str: The reconstructed sequence with mutations.
  """
  mutated_sequence = list(sequence)
  range_index = 0
- mutated_sequence = list(sequence)
- range_index = 0
  for start, end in intervals:
  size_fragment = end - start
  mutated_sequence[start:end] = list(