


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__		)




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__)
