





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__)








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

