code: 
  
  
  import inspect
  from typing import Callable, List, Optional, Set, Tuple, Union
  
  import torch
  from packaging import version
  from safetensors.torch import storage_ptr, storage_size
  from torch import nn
  
  from .utils import logging
  
  
  lowerCAmelCase__   	=				[nn.LayerNorm]
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  lowerCAmelCase__   	=				version.parse(version.parse(torch.__version__).base_version)
  
  lowerCAmelCase__   	=				parsed_torch_version_base >= version.parse('2.0')
  lowerCAmelCase__   	=				parsed_torch_version_base >= version.parse('1.12')
  lowerCAmelCase__   	=				parsed_torch_version_base >= version.parse('1.11')
  lowerCAmelCase__   	=				parsed_torch_version_base < version.parse('1.11')
  def 							_UpperCAmelCase       (UpperCamelCase__  :						int       ,							UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						Tuple       ,							UpperCamelCase__  :						List[str]       ,							self  :						Optional[Any]		)     ->   Optional[Any]:
         from torch import _softmax_backward_data
  
         if is_torch_less_than_1_11:
                return _softmax_backward_data(UpperCamelCase__       ,							UpperCamelCase__       ,							parent.dim       ,							self		)
         else:
                return _softmax_backward_data(UpperCamelCase__       ,							UpperCamelCase__       ,							parent.dim       ,							self.dtype		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						int = 0		)     ->   Tuple:
         _A   : List[Any]       					=				index.to(layer.weight.device		)
         _A   : Tuple       					=				layer.weight.index_select(UpperCamelCase__       ,							UpperCamelCase__		).clone().detach()
         if layer.bias is not None:
                if dim == 1:
                       _A   : int       					=				layer.bias.clone().detach()
                else:
                       _A   : int       					=				layer.bias[index].clone().detach()
         _A   : Any       					=				list(layer.weight.size()		)
         _A   : Union[str, Any]       					=				len(UpperCamelCase__		)
         _A   : int       					=				nn.Linear(new_size[1]       ,							new_size[0]       ,							bias=layer.bias is not None		).to(layer.weight.device		)
         _A   : Union[str, Any]       					=				False
         new_layer.weight.copy_(W.contiguous()		)
         _A   : Dict       					=				True
         if layer.bias is not None:
                _A   : str       					=				False
                new_layer.bias.copy_(b.contiguous()		)
                _A   : Union[str, Any]       					=				True
         return new_layer
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       nn.Module):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       int:
              super().__init__()
              _A   : List[Any]       					=				nf
              _A   : Any       					=				nn.Parameter(torch.empty(UpperCamelCase__				,		UpperCamelCase__))
              _A   : List[Any]       					=				nn.Parameter(torch.zeros(UpperCamelCase__))
              nn.init.normal_(self.weight				,		std=0.0_2)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Optional[int]:
              _A   : str       					=				x.size()[:-1] + (self.nf,)
              _A   : Union[str, Any]       					=				torch.addmm(self.bias				,		x.view(-1				,		x.size(-1))				,		self.weight)
              _A   : Union[str, Any]       					=				x.view(UpperCamelCase__)
              return x
  
  
  
  
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Tuple       ,							UpperCamelCase__  :						str = 1		)     ->   str:
         _A   : str       					=				index.to(layer.weight.device		)
         _A   : Union[str, Any]       					=				layer.weight.index_select(UpperCamelCase__       ,							UpperCamelCase__		).clone().detach()
         if dim == 0:
                _A   : Tuple       					=				layer.bias.clone().detach()
         else:
                _A   : Optional[Any]       					=				layer.bias[index].clone().detach()
         _A   : int       					=				list(layer.weight.size()		)
         _A   : int       					=				len(UpperCamelCase__		)
         _A   : List[str]       					=				ConvaD(new_size[1]       ,							new_size[0]		).to(layer.weight.device		)
         _A   : Dict       					=				False
         new_layer.weight.copy_(W.contiguous()		)
         _A   : Optional[int]       					=				True
         _A   : Optional[Any]       					=				False
         new_layer.bias.copy_(b.contiguous()		)
         _A   : Any       					=				True
         return new_layer
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Optional[int] = None		)     ->   List[Any]:
  
  
  
  
  
  
  
         if isinstance(UpperCamelCase__       ,							nn.Linear		):
                return prune_linear_layer(UpperCamelCase__       ,							UpperCamelCase__       ,							dim=0 if dim is None else dim		)
         elif isinstance(UpperCamelCase__       ,							UpperCamelCase__		):
                return prune_convad_layer(UpperCamelCase__       ,							UpperCamelCase__       ,							dim=1 if dim is None else dim		)
         else:
                raise ValueError(f"Can\'t prune layer of class {layer.__class__}"		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						Tuple       ,							*UpperCamelCase__  :						Tuple		)     ->   List[Any]:
         assert len(UpperCamelCase__		) > 0, f"{input_tensors} has to be a tuple/list of tensors"
  
         # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
         _A   : Dict       					=				len(inspect.signature(UpperCamelCase__		).parameters		)
         if num_args_in_forward_chunk_fn != len(UpperCamelCase__		):
                raise ValueError(
                    f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCamelCase__		)} input "
                    "tensors are given"		)
  
         if chunk_size > 0:
                _A   : Optional[Any]       					=				input_tensors[0].shape[chunk_dim]
                for input_tensor in input_tensors:
                       if input_tensor.shape[chunk_dim] != tensor_shape:
                              raise ValueError(
                                  f"All input tenors have to be of the same shape: {tensor_shape}, "
                                  f"found shape {input_tensor.shape[chunk_dim]}"		)
  
                if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
                       raise ValueError(
                           f"The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk "
                           f"size {chunk_size}"		)
  
                _A   : Optional[Any]       					=				input_tensors[0].shape[chunk_dim] // chunk_size
  
                # chunk input tensor into tuples
                _A   : Optional[Any]       					=				tuple(input_tensor.chunk(UpperCamelCase__       ,							dim=UpperCamelCase__		) for input_tensor in input_tensors		)
                # apply forward fn to every tuple
                _A   : Optional[Any]       					=				tuple(forward_fn(*UpperCamelCase__		) for input_tensors_chunk in zip(*UpperCamelCase__		)		)
                # concatenate output at same dimension
                return torch.cat(UpperCamelCase__       ,							dim=UpperCamelCase__		)
  
         return forward_fn(*UpperCamelCase__		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						str		)     ->   str:
         _A   : List[Any]       					=				torch.ones(UpperCamelCase__       ,							UpperCamelCase__		)
         _A   : Dict       					=				set(UpperCamelCase__		) - already_pruned_heads  # Convert to set and remove already pruned heads
         for head in heads:
                # Compute how many pruned heads are before the head and move the index accordingly
                _A   : int       					=				head - sum(1 if h < head else 0 for h in already_pruned_heads		)
                _A   : str       					=				0
         _A   : str       					=				mask.view(-1		).contiguous().eq(1		)
         _A   : Union[str, Any]       					=				torch.arange(len(UpperCamelCase__		)		)[mask].long()
         return heads, index
  def 							_UpperCAmelCase       (*UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						Union[str, Any] = None		)     ->   Tuple:
         return torch.meshgrid(*UpperCamelCase__       ,							indexing=UpperCamelCase__		)
  
  
  
  
  
  
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[str]		)     ->   Tuple:
         return tensor.device, storage_ptr(UpperCamelCase__		), storage_size(UpperCamelCase__		)
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase__   	=				{'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['DetrFeatureExtractor']
      lowerCAmelCase__   	=				['DetrImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
          'DetrForObjectDetection',
          'DetrForSegmentation',
          'DetrModel',
          'DetrPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_detr import DetrFeatureExtractor
          from .image_processing_detr import DetrImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_detr import (
              DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetrForObjectDetection,
              DetrForSegmentation,
              DetrModel,
              DetrPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  