code: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  _UpperCamelCase       = {
      '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _UpperCamelCase       = ['''DetaImageProcessor''']
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _UpperCamelCase       = [
           '''DETA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''DetaForObjectDetection''',
           '''DetaModel''',
           '''DetaPreTrainedModel''',
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
       try:
            if not is_vision_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .image_processing_deta import DetaImageProcessor
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_deta import (
                DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                DetaForObjectDetection,
                DetaModel,
                DetaPreTrainedModel,
            )
  
  else:
       import sys
  
       _UpperCamelCase       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
style_context: 
  
  
  
  
  
  import torch
  from torch import autograd
  
  
  
  
  class 				_lowerCamelCase (    autograd.Function    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Dict        =       inputs.numel()
          __snake_case   :				Optional[int]        =       int(0.005 * nb_elems   ) + 1
          if sigmoid:
            __snake_case   :				Any        =       (torch.sigmoid(UpperCAmelCase   ) > threshold).type(inputs.type()   )
          else:
            __snake_case   :				Optional[int]        =       (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
            __snake_case   :				List[str]        =       inputs.flatten().kthvalue(max(nb_elems - nb_min				,						1   )   ).values
            __snake_case   :				Union[str, Any]        =       (inputs > k_threshold).type(inputs.type()   )
          return mask
  
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return gradOutput, None, None
  
  
  
  
  class 				_lowerCamelCase (    autograd.Function    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[int]        =       inputs.clone()
          __snake_case    ,   __snake_case   :				int        =       inputs.flatten().sort(descending=UpperCAmelCase   )
          __snake_case   :				Tuple        =       int(threshold * inputs.numel()   )
  
          # flat_out and mask access the same memory.
          __snake_case   :				Optional[int]        =       mask.flatten()
          __snake_case   :				Union[str, Any]        =       0
          __snake_case   :				Any        =       1
          return mask
  
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return gradOutput, None
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Union[str, Any]        =       inputs.clone()
          __snake_case    ,   __snake_case   :				Optional[Any]        =       inputs.abs().flatten().sort(descending=UpperCAmelCase   )
          __snake_case   :				List[Any]        =       int(threshold * inputs.numel()   )
  
          # flat_out and mask access the same memory.
          __snake_case   :				Optional[Any]        =       mask.flatten()
          __snake_case   :				Tuple        =       0
          __snake_case   :				Any        =       1
          return mask
  
  