




import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class 		__UpperCAmelCase(		nn.Linear ):





     """simple docstring"""



     def __init__(      self :       List[Any]						,				A :       int						,				A :       int						,				A :       bool = True						,				A :       str = "constant"						,				A :       float = 0.0						,				A :       str = "topK"						,				)			->			str:






         """simple docstring"""
         super(A						,				self    ).__init__(in_features=A						,				out_features=A						,				bias=A    )
         assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
         a__      =       pruning_method

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

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






         """simple docstring"""
         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 				__SCREAMING_SNAKE_CASE		(      self :       str						,				A :       torch.tensor						,				A :       float    )			->			Dict:






         """simple docstring"""
         # Get the mask
         if self.pruning_method == "topK":
             a__      =       TopKBinarizer.apply(self.mask_scores						,				A    )
         elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
             a__      =       """sigmoied""" in self.pruning_method
             a__      =       ThresholdBinarizer.apply(self.mask_scores						,				A						,				A    )
         elif self.pruning_method == "magnitude":
             a__      =       MagnitudeBinarizer.apply(self.weight						,				A    )
         elif self.pruning_method == "l0":
             a__ ,		a__ ,		a__      =       -0.1, 1.1, 2 / 3
             if self.training:
                 a__      =       torch.zeros_like(self.mask_scores    ).uniform_().clamp(0.0_0_0_1						,				0.9_9_9_9    )
                 a__      =       torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b    )
             else:
                 a__      =       torch.sigmoid(self.mask_scores    )
             a__      =       s * (r - l) + l
             a__      =       s_bar.clamp(min=0.0						,				max=1.0    )
         # Mask weights with computed mask
         a__      =       mask * self.weight
         # Compute output (linear layer) with masked weights
         return nn.functional.linear(A						,				A						,				self.bias    )












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_						:					str			   =       {
    '''configuration_wav2vec2_conformer''': [
        '''WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Wav2Vec2ConformerConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       [
     '''WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Wav2Vec2ConformerForAudioFrameClassification''',
     '''Wav2Vec2ConformerForCTC''',
     '''Wav2Vec2ConformerForPreTraining''',
     '''Wav2Vec2ConformerForSequenceClassification''',
     '''Wav2Vec2ConformerForXVector''',
     '''Wav2Vec2ConformerModel''',
     '''Wav2Vec2ConformerPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_wavaveca_conformer import (
     WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     WavaVecaConformerConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavaveca_conformer import (
      WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavaVecaConformerForAudioFrameClassification,
      WavaVecaConformerForCTC,
      WavaVecaConformerForPreTraining,
      WavaVecaConformerForSequenceClassification,
      WavaVecaConformerForXVector,
      WavaVecaConformerModel,
      WavaVecaConformerPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






