


'''simple docstring'''



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      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = True      ,					SCREAMING_SNAKE_CASE_ = "constant"      ,					SCREAMING_SNAKE_CASE_ = 0.0      ,					SCREAMING_SNAKE_CASE_ = "topK"      ,					)    ->					Tuple:
         super(SCREAMING_SNAKE_CASE_      ,					self     ).__init__(in_features=SCREAMING_SNAKE_CASE_      ,					out_features=SCREAMING_SNAKE_CASE_      ,					bias=SCREAMING_SNAKE_CASE_     )
         assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
         __lowerCamelCase       :     Optional[int]        =		pruning_method

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







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










'''simple docstring'''



from typing import TYPE_CHECKING

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


A__		: str												=  {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: List[str]												=  [
          """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """HubertForCTC""",
          """HubertForSequenceClassification""",
          """HubertModel""",
          """HubertPreTrainedModel""",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: Optional[int]												=  [
          """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFHubertForCTC""",
          """TFHubertModel""",
          """TFHubertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_hubert import (
                HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                HubertForCTC,
                HubertForSequenceClassification,
                HubertModel,
                HubertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_hubert import (
                TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFHubertForCTC,
                TFHubertModel,
                TFHubertPreTrainedModel,
            )


else:
      import sys

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






