code: 
  
  """simple docstring"""
  import torch
  from torch import autograd
  
  class 			lowerCamelCase  (					autograd.Function      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @staticmethod
    def    _a			(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						inputs.numel()
          UpperCAmelCase__ :       Dict						=						int(0.005 * nb_elems  ) + 1
          if sigmoid:
                UpperCAmelCase__ :       Any						=						(torch.sigmoid(_lowerCamelCase  ) > threshold).type(inputs.type()  )
          else:
                UpperCAmelCase__ :       Any						=						(inputs > threshold).type(inputs.type()  )
          if mask.sum() < nb_min:
                # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
                UpperCAmelCase__ :       Optional[int]						=						inputs.flatten().kthvalue(max(nb_elems - nb_min       ,					1  )  ).values
                UpperCAmelCase__ :       Any						=						(inputs > k_threshold).type(inputs.type()  )
          return mask
  
  
  
  
    @staticmethod
    def    _a			(_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return gradOutput, None, None
  
  
  
  
  class 			lowerCamelCase  (					autograd.Function      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @staticmethod
    def    _a			(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						inputs.clone()
          UpperCAmelCase__				,		UpperCAmelCase__ :       Dict						=						inputs.flatten().sort(descending=_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						int(threshold * inputs.numel()  )
  
          # flat_out and mask access the same memory.
          UpperCAmelCase__ :       Optional[int]						=						mask.flatten()
          UpperCAmelCase__ :       str						=						0
          UpperCAmelCase__ :       Any						=						1
          return mask
  
  
  
  
    @staticmethod
    def    _a			(_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return gradOutput, None
  
  
  
  
  class 			lowerCamelCase  (					A_      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @staticmethod
    def    _a			(_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						inputs.clone()
          UpperCAmelCase__				,		UpperCAmelCase__ :       List[Any]						=						inputs.abs().flatten().sort(descending=_lowerCamelCase  )
          UpperCAmelCase__ :       int						=						int(threshold * inputs.numel()  )
  
          # flat_out and mask access the same memory.
          UpperCAmelCase__ :       List[str]						=						mask.flatten()
          UpperCAmelCase__ :       int						=						0
          UpperCAmelCase__ :       Union[str, Any]						=						1
          return mask
  
  
style_context: 
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A						=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""GPT2TokenizerFast"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]
  
  try:
      if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""TFGPT2Tokenizer"""]
  
  try:
      if not is_flax_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_fast import GPTaTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )
  
      try:
          if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_tf import TFGPTaTokenizer
  
      try:
          if not is_flax_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
      import sys
  
      _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  