



"""simple docstring"""





import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''detr'''
       UpperCamelCase		=  ['''past_key_values''']
       UpperCamelCase		=  {
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
       }



       def __init__(							self    :						Optional[Any]	,       _UpperCAmelCase    :						int=True	,       _UpperCAmelCase    :						Tuple=None	,       _UpperCAmelCase    :						Optional[int]=3	,       _UpperCAmelCase    :						List[str]=100	,       _UpperCAmelCase    :						Union[str, Any]=6	,       _UpperCAmelCase    :						List[str]=2048	,       _UpperCAmelCase    :						Optional[int]=8	,       _UpperCAmelCase    :						Union[str, Any]=6	,       _UpperCAmelCase    :						List[Any]=2048	,       _UpperCAmelCase    :						List[Any]=8	,       _UpperCAmelCase    :						Optional[Any]=0.0	,       _UpperCAmelCase    :						str=0.0	,       _UpperCAmelCase    :						Tuple=True	,       _UpperCAmelCase    :						str="relu"	,       _UpperCAmelCase    :						Union[str, Any]=256	,       _UpperCAmelCase    :						Dict=0.1	,       _UpperCAmelCase    :						Any=0.0	,       _UpperCAmelCase    :						Union[str, Any]=0.0	,       _UpperCAmelCase    :						List[str]=0.02	,       _UpperCAmelCase    :						Optional[int]=1.0	,       _UpperCAmelCase    :						Dict=False	,       _UpperCAmelCase    :						Any="sine"	,       _UpperCAmelCase    :						Union[str, Any]="resnet50"	,       _UpperCAmelCase    :						List[str]=True	,       _UpperCAmelCase    :						List[Any]=False	,       _UpperCAmelCase    :						int=1	,       _UpperCAmelCase    :						str=5	,       _UpperCAmelCase    :						str=2	,       _UpperCAmelCase    :						List[Any]=1	,       _UpperCAmelCase    :						Tuple=1	,       _UpperCAmelCase    :						Optional[Any]=5	,       _UpperCAmelCase    :						int=2	,       _UpperCAmelCase    :						Dict=0.1	,       **_UpperCAmelCase    :						int	,       )				->		Any:


             '''simple docstring'''




             if backbone_config is not None and use_timm_backbone:
                   raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."					)

             if not use_timm_backbone:
                   if backbone_config is None:
                         logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."					)
                         UpperCAmelCase_    				=							CONFIG_MAPPING["resnet"](out_features=["stage4"]					)
                   elif isinstance(_UpperCAmelCase	,       _UpperCAmelCase					):
                         UpperCAmelCase_    				=							backbone_config.get("model_type"					)
                         UpperCAmelCase_    				=							CONFIG_MAPPING[backbone_model_type]
                         UpperCAmelCase_    				=							config_class.from_dict(_UpperCAmelCase					)
                   # set timm attributes to None
                   UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							None, None, None

             UpperCAmelCase_    				=							use_timm_backbone
             UpperCAmelCase_    				=							backbone_config
             UpperCAmelCase_    				=							num_channels
             UpperCAmelCase_    				=							num_queries
             UpperCAmelCase_    				=							d_model
             UpperCAmelCase_    				=							encoder_ffn_dim
             UpperCAmelCase_    				=							encoder_layers
             UpperCAmelCase_    				=							encoder_attention_heads
             UpperCAmelCase_    				=							decoder_ffn_dim
             UpperCAmelCase_    				=							decoder_layers
             UpperCAmelCase_    				=							decoder_attention_heads
             UpperCAmelCase_    				=							dropout
             UpperCAmelCase_    				=							attention_dropout
             UpperCAmelCase_    				=							activation_dropout
             UpperCAmelCase_    				=							activation_function
             UpperCAmelCase_    				=							init_std
             UpperCAmelCase_    				=							init_xavier_std
             UpperCAmelCase_    				=							encoder_layerdrop
             UpperCAmelCase_    				=							decoder_layerdrop
             UpperCAmelCase_    				=							encoder_layers
             UpperCAmelCase_    				=							auxiliary_loss
             UpperCAmelCase_    				=							position_embedding_type
             UpperCAmelCase_    				=							backbone
             UpperCAmelCase_    				=							use_pretrained_backbone
             UpperCAmelCase_    				=							dilation
             # Hungarian matcher
             UpperCAmelCase_    				=							class_cost
             UpperCAmelCase_    				=							bbox_cost
             UpperCAmelCase_    				=							giou_cost
             # Loss coefficients
             UpperCAmelCase_    				=							mask_loss_coefficient
             UpperCAmelCase_    				=							dice_loss_coefficient
             UpperCAmelCase_    				=							bbox_loss_coefficient
             UpperCAmelCase_    				=							giou_loss_coefficient
             UpperCAmelCase_    				=							eos_coefficient
             super().__init__(is_encoder_decoder=_UpperCAmelCase	,       **_UpperCAmelCase					)



       @property
       def   lowercase__       (							self    :						Optional[int]					)				->		int:


             '''simple docstring'''




             return self.encoder_attention_heads



       @property
       def   lowercase__       (							self    :						Dict					)				->		int:


             '''simple docstring'''




             return self.d_model



       @classmethod
       def   lowercase__       (							cls    :						Optional[int]	,       _UpperCAmelCase    :						PretrainedConfig	,       **_UpperCAmelCase    :						Optional[Any]					)				->		str:


             '''simple docstring'''




             return cls(backbone_config=_UpperCAmelCase	,       **_UpperCAmelCase					)







       def   lowercase__       (							self    :						str					)				->		Dict[str, any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							copy.deepcopy(self.__dict__					)
             if output["backbone_config"] is not None:
                   UpperCAmelCase_    				=							self.backbone_config.to_dict()
             UpperCAmelCase_    				=							self.__class__.model_type
             return output



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  version.parse('''1.11'''   )



       @property
       def   lowercase__       (							self    :						Any					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             return OrderedDict(
                 [
                     ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                     ("pixel_mask", {0: "batch"}),
                 ]					)



       @property
       def   lowercase__       (							self    :						Union[str, Any]					)				->		float:


             '''simple docstring'''




             return 1e-5







       @property
       def   lowercase__       (							self    :						Tuple					)				->		int:


             '''simple docstring'''




             return 12









"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(lowerCAmelCase__   ) == 2 and len(matrix[0]   ) == 2 and len(matrix[1]   ) == 2:
            # Calculate the determinant of the matrix
            UpperCAmelCase_    				=							float(
                d(matrix[0][0]   ) * d(matrix[1][1]   ) - d(matrix[1][0]   ) * d(matrix[0][1]   )   )
            if determinant == 0:
                  raise ValueError("This matrix has no inverse."   )

            # Creates a copy of the matrix with swapped positions of the elements
            UpperCAmelCase_    				=							[[0.0, 0.0], [0.0, 0.0]]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							matrix[1][1], matrix[0][0]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							-matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(lowerCAmelCase__   )   ) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(lowerCAmelCase__   ) == 3
          and len(matrix[0]   ) == 3
          and len(matrix[1]   ) == 3
          and len(matrix[2]   ) == 3
      ):
            # Calculate the determinant of the matrix using Sarrus rule
            UpperCAmelCase_    				=							float(
                (
                    (d(matrix[0][0]   ) * d(matrix[1][1]   ) * d(matrix[2][2]   ))
                    + (d(matrix[0][1]   ) * d(matrix[1][2]   ) * d(matrix[2][0]   ))
                    + (d(matrix[0][2]   ) * d(matrix[1][0]   ) * d(matrix[2][1]   ))
                )
                - (
                    (d(matrix[0][2]   ) * d(matrix[1][1]   ) * d(matrix[2][0]   ))
                    + (d(matrix[0][1]   ) * d(matrix[1][0]   ) * d(matrix[2][2]   ))
                    + (d(matrix[0][0]   ) * d(matrix[1][2]   ) * d(matrix[2][1]   ))
                )   )
            if determinant == 0:
                  raise ValueError("This matrix has no inverse."   )

            # Creating cofactor matrix
            UpperCAmelCase_    				=							[
                [d(0.0   ), d(0.0   ), d(0.0   )],
                [d(0.0   ), d(0.0   ), d(0.0   )],
                [d(0.0   ), d(0.0   ), d(0.0   )],
            ]
            UpperCAmelCase_    				=							(d(matrix[1][1]   ) * d(matrix[2][2]   )) - (
                d(matrix[1][2]   ) * d(matrix[2][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[1][0]   ) * d(matrix[2][2]   )) - (d(matrix[1][2]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[1][0]   ) * d(matrix[2][1]   )) - (
                d(matrix[1][1]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][1]   ) * d(matrix[2][2]   )) - (d(matrix[0][2]   ) * d(matrix[2][1]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[2][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[2][1]   )) - (d(matrix[0][1]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][1]   ) * d(matrix[1][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[1][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[1][2]   )) - (d(matrix[0][2]   ) * d(matrix[1][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[1][1]   )) - (
                d(matrix[0][1]   ) * d(matrix[1][0]   )
            )

            # Transpose the cofactor matrix (Adjoint matrix)
            UpperCAmelCase_    				=							array(lowerCAmelCase__   )
            for i in range(3   ):
                  for j in range(3   ):
                        UpperCAmelCase_    				=							cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
            UpperCAmelCase_    				=							array(lowerCAmelCase__   )
            for i in range(3   ):
                  for j in range(3   ):
                        inverse_matrix[i][j] /= d(lowerCAmelCase__   )

        # Calculate the inverse of the matrix
            return [[float(d(lowerCAmelCase__   )   ) or 0.0 for n in row] for row in inverse_matrix]
      raise ValueError("Please provide a matrix of size 2x2 or 3x3."   )




