


'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Optional[int]		=     '''table-transformer'''
     lowerCamelCase				:	List[str]		=     ['''past_key_values''']
     lowerCamelCase				:	List[Any]		=     {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      str=True    ,      UpperCAmelCase__       :      List[Any]=None    ,      UpperCAmelCase__       :      Union[str, Any]=3    ,      UpperCAmelCase__       :      Tuple=1_0_0    ,      UpperCAmelCase__       :      Any=6    ,      UpperCAmelCase__       :      int=2_0_4_8    ,      UpperCAmelCase__       :      List[str]=8    ,      UpperCAmelCase__       :      Union[str, Any]=6    ,      UpperCAmelCase__       :      Tuple=2_0_4_8    ,      UpperCAmelCase__       :      int=8    ,      UpperCAmelCase__       :      str=0.0    ,      UpperCAmelCase__       :      str=0.0    ,      UpperCAmelCase__       :      Optional[int]=True    ,      UpperCAmelCase__       :      List[Any]="relu"    ,      UpperCAmelCase__       :      Dict=2_5_6    ,      UpperCAmelCase__       :      Any=0.1    ,      UpperCAmelCase__       :      Union[str, Any]=0.0    ,      UpperCAmelCase__       :      str=0.0    ,      UpperCAmelCase__       :      Optional[Any]=0.02    ,      UpperCAmelCase__       :      Union[str, Any]=1.0    ,      UpperCAmelCase__       :      Any=False    ,      UpperCAmelCase__       :      List[Any]="sine"    ,      UpperCAmelCase__       :      Optional[int]="resnet50"    ,      UpperCAmelCase__       :      Union[str, Any]=True    ,      UpperCAmelCase__       :      Any=False    ,      UpperCAmelCase__       :      int=1    ,      UpperCAmelCase__       :      Dict=5    ,      UpperCAmelCase__       :      Tuple=2    ,      UpperCAmelCase__       :      List[Any]=1    ,      UpperCAmelCase__       :      Optional[int]=1    ,      UpperCAmelCase__       :      Dict=5    ,      UpperCAmelCase__       :      List[Any]=2    ,      UpperCAmelCase__       :      List[Any]=0.1    ,      **UpperCAmelCase__       :      Union[str, Any]    ,      )				->  Optional[int]:
        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.'    )
              lowerCAmelCase     =			CONFIG_MAPPING['resnet'](out_features=['stage4']    )
           elif isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			backbone_config.get('model_type'    )
              lowerCAmelCase     =			CONFIG_MAPPING[backbone_model_type]
              lowerCAmelCase     =			config_class.from_dict(UpperCAmelCase__    )
           # set timm attributes to None
           lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			None, None, None

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





     @property
     def 							__UpperCAmelCase      ( self       :      int    )				->  int:
        return self.encoder_attention_heads





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  int:
        return self.d_model




class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Dict		=     version.parse('''1.11'''		)





     @property
     def 							__UpperCAmelCase      ( self       :      Any    )				->  Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ('pixel_mask', {0: 'batch'}),
            ]    )





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  float:
        return 1E-5





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  int:
        return 1_2







from __future__ import annotations

from typing import Any




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Tuple			,  lowercase	:    int			,  lowercase	:    int			,  lowercase	:    float = 0	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			row, column
				_snake_case		=			[[default_value for c in range(lowercase	)] for r in range(lowercase	)]



	def __str__(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

				# Make string identifier
				_snake_case		=			0
				for row_vector in self.array:
							for obj in row_vector:
										_snake_case		=			max(lowercase			,  len(str(lowercase	)	)	)
				_snake_case		=			f'''%{max_element_length}s'''

				# Make string and return
				def single_line(lowercase	:    list[float]	) -> str:
							nonlocal string_format_identifier
							_snake_case		=			'['
							line += ", ".join(string_format_identifier % (obj,) for obj in row_vector	)
							line += "]"
							return line

				s += "\n".join(single_line(lowercase	) for row_vector in self.array	)
				return s



	def __repr__(  self	:    Dict	):


				'''simple docstring'''


				return str(self	)



	def  A							(  self	:    str			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				if not (isinstance(lowercase			,  (list, tuple)	) and len(lowercase	) == 2):
							return False
				elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
							return False
				else:
							return True



	def __getitem__(  self	:    Dict			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				return self.array[loc[0]][loc[1]]



	def __setitem__(  self	:    str			,  lowercase	:    tuple[int, int]			,  lowercase	:    float	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				_snake_case		=			value



	def __add__(  self	:    str			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	)
				assert self.row == another.row and self.column == another.column

				# Add
				_snake_case		=			Matrix(self.row			,  self.column	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			self[r, c] + another[r, c]
				return result



	def __neg__(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.row			,  self.column	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			-self[r, c]
				return result



	def __sub__(  self	:    List[str]			,  lowercase	:    Matrix	):


				'''simple docstring'''


				return self + (-another)



	def __mul__(  self	:    Dict			,  lowercase	:    int | float | Matrix	):


				'''simple docstring'''


				if isinstance(lowercase			,  (int, float)	):  # Scalar multiplication
							_snake_case		=			Matrix(self.row			,  self.column	)
							for r in range(self.row	):
										for c in range(self.column	):
													_snake_case		=			self[r, c] * another
							return result
				elif isinstance(lowercase			,  lowercase	):  # Matrix multiplication
							assert self.column == another.row
							_snake_case		=			Matrix(self.row			,  another.column	)
							for r in range(self.row	):
										for c in range(another.column	):
													for i in range(self.column	):
																result[r, c] += self[r, i] * another[i, c]
							return result
				else:
							_snake_case		=			f'''Unsupported type given for another ({type(lowercase	)})'''
							raise TypeError(lowercase	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.column			,  self.row	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			self[r, c]
				return result





	def  A							(  self	:    List[Any]			,  lowercase	:    Matrix			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	) and isinstance(lowercase			,  lowercase	)
				assert self.row == self.column == u.row == v.row  # u, v should be column vector
				assert u.column == v.column == 1  # u, v should be column vector

				# Calculate
				_snake_case		=			v.transpose()
				_snake_case		=			(v_t * self * u)[0, 0] + 1
				if numerator_factor == 0:
							return None  # It's not invertable
				return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":






			def        a_		(       ) ->  None:
						# a^(-1)
						_snake_case		=			Matrix(3       ,					3       ,					0 )
						for i in range(3 ):
									_snake_case		=			1
						print(f'''a^(-1) is {ainv}''' )
						# u, v
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			1, 2, -3
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			4, -2, 5
						print(f'''u is {u}''' )
						print(f'''v is {v}''' )
						print(f'''uv^T is {u * v.transpose()}''' )
						# Sherman Morrison
						print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowercase       ,					__lowercase )}''' )






			def        a_		(       ) ->  None:
						import doctest

						doctest.testmod()

			testa()