





'''simple docstring'''

import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


class  a_					(    nn.Linear	):

    '''simple docstring'''


    def __init__(		self			,		A			,		A			,		A = True			,		A = "constant"			,		A = 0.0			,		A = "topK"			,		)		->       Optional[Any]:
        super(__A			,		self	).__init__(in_features=__A			,		out_features=__A			,		bias=__A	)
        assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
        _SCREAMING_SNAKE_CASE     =      pruning_method

        if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
            _SCREAMING_SNAKE_CASE     =      mask_scale
            _SCREAMING_SNAKE_CASE     =      mask_init
            _SCREAMING_SNAKE_CASE     =      nn.Parameter(torch.empty(self.weight.size()	)	)
            self.init_mask()


    def       snake_case_(		self	)		->       List[str]:





        if self.mask_init == "constant":
            init.constant_(self.mask_scores			,		val=self.mask_scale	)
        elif self.mask_init == "uniform":
            init.uniform_(self.mask_scores			,		a=-self.mask_scale			,		b=self.mask_scale	)
        elif self.mask_init == "kaiming":
            init.kaiming_uniform_(self.mask_scores			,		a=math.sqrt(5	)	)





    def       snake_case_(		self			,		A			,		A	)		->       List[Any]:





        if self.pruning_method == "topK":
            _SCREAMING_SNAKE_CASE     =      TopKBinarizer.apply(self.mask_scores			,		__A	)
        elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
            _SCREAMING_SNAKE_CASE     =      "sigmoied" in self.pruning_method
            _SCREAMING_SNAKE_CASE     =      ThresholdBinarizer.apply(self.mask_scores			,		__A			,		__A	)
        elif self.pruning_method == "magnitude":
            _SCREAMING_SNAKE_CASE     =      MagnitudeBinarizer.apply(self.weight			,		__A	)
        elif self.pruning_method == "l0":
            _SCREAMING_SNAKE_CASE     =      -0.1, 1.1, 2 / 3
            if self.training:
                _SCREAMING_SNAKE_CASE     =      torch.zeros_like(self.mask_scores	).uniform_().clamp(0.0001			,		0.9999	)
                _SCREAMING_SNAKE_CASE     =      torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b	)
            else:
                _SCREAMING_SNAKE_CASE     =      torch.sigmoid(self.mask_scores	)
            _SCREAMING_SNAKE_CASE     =      s * (r - l) + l
            _SCREAMING_SNAKE_CASE     =      s_bar.clamp(min=0.0			,		max=1.0	)
        # Mask weights with computed mask
        _SCREAMING_SNAKE_CASE     =      mask * self.weight
        # Compute output (linear layer) with masked weights
        return nn.functional.linear(__A			,		__A			,		self.bias	)











'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase_         =			{
    """configuration_esm""": ["""ESM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """EsmConfig"""],
    """tokenization_esm""": ["""EsmTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EsmForMaskedLM""",
          """EsmForSequenceClassification""",
          """EsmForTokenClassification""",
          """EsmModel""",
          """EsmPreTrainedModel""",
      ]
      lowercase_         =			["""EsmForProteinFolding""", """EsmFoldPreTrainedModel"""]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEsmForMaskedLM""",
          """TFEsmForSequenceClassification""",
          """TFEsmForTokenClassification""",
          """TFEsmModel""",
          """TFEsmPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
      from .tokenization_esm import EsmTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_esm import (
                ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                EsmForMaskedLM,
                EsmForSequenceClassification,
                EsmForTokenClassification,
                EsmModel,
                EsmPreTrainedModel,
            )
            from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_esm import (
                TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEsmForMaskedLM,
                TFEsmForSequenceClassification,
                TFEsmForTokenClassification,
                TFEsmModel,
                TFEsmPreTrainedModel,
            )


else:
      import sys

      lowercase_         =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



