


import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


class       __lowerCAmelCase       (	nn.Linear   ):






  """simple docstring"""





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




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

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



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	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    lowerCAmelCase__					(       self    :    Optional[int]      ,			_lowerCAmelCase    :    torch.tensor      ,			_lowerCAmelCase    :    float					)				->	int:




     """simple docstring"""
     # Get the mask
     if self.pruning_method == "topK":
        snake_case_         =   TopKBinarizer.apply(self.mask_scores      ,			_lowerCAmelCase					)
     elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
        snake_case_         =   "sigmoied" in self.pruning_method
        snake_case_         =   ThresholdBinarizer.apply(self.mask_scores      ,			_lowerCAmelCase      ,			_lowerCAmelCase					)
     elif self.pruning_method == "magnitude":
        snake_case_         =   MagnitudeBinarizer.apply(self.weight      ,			_lowerCAmelCase					)
     elif self.pruning_method == "l0":
        snake_case_      ,      snake_case_      ,      snake_case_         =   -0.1, 1.1, 2 / 3
        if self.training:
           snake_case_         =   torch.zeros_like(self.mask_scores					).uniform_().clamp(0.0_001      ,			0.9_999					)
           snake_case_         =   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b					)
        else:
           snake_case_         =   torch.sigmoid(self.mask_scores					)
        snake_case_         =   s * (r - l) + l
        snake_case_         =   s_bar.clamp(min=0.0      ,			max=1.0					)
     # Mask weights with computed mask
     snake_case_         =   mask * self.weight
     # Compute output (linear layer) with masked weights
     return nn.functional.linear(_lowerCAmelCase      ,			_lowerCAmelCase      ,			self.bias					)








from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE		:str											=       {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE		:Any											=       [
       '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''CvtForImageClassification''',
       '''CvtModel''',
       '''CvtPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE		:Any											=       [
       '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFCvtForImageClassification''',
       '''TFCvtModel''',
       '''TFCvtPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cvt import (
          CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CvtForImageClassification,
          CvtModel,
          CvtPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_cvt import (
          TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCvtForImageClassification,
          TFCvtModel,
          TFCvtPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE		:str											=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




