





import copy
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


UpperCAmelCase						:   str			  =     logging.get_logger(__name__)

UpperCAmelCase						:   str			  =     {
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Any					    =	'''conditional_detr'''
				UpperCamelCase    :  int					    =	['''past_key_values''']
				UpperCamelCase    :  str					    =	{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}



				def __init__(			self     ,					_A=True     ,					_A=None     ,					_A=3     ,					_A=300     ,					_A=6     ,					_A=2048     ,					_A=8     ,					_A=6     ,					_A=2048     ,					_A=8     ,					_A=0.0     ,					_A=0.0     ,					_A=True     ,					_A="relu"     ,					_A=256     ,					_A=0.1     ,					_A=0.0     ,					_A=0.0     ,					_A=0.0_2     ,					_A=1.0     ,					_A=False     ,					_A="sine"     ,					_A="resnet50"     ,					_A=True     ,					_A=False     ,					_A=2     ,					_A=5     ,					_A=2     ,					_A=1     ,					_A=1     ,					_A=2     ,					_A=5     ,					_A=2     ,					_A=0.2_5     ,					**_A     ,					):
									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.'     )
																			__A	:						Union[str, Any]       =				CONFIG_MAPPING['resnet'](out_features=['stage4']     )
														elif isinstance(_A     ,					_A     ):
																			__A	:						List[str]       =				backbone_config.get('model_type'     )
																			__A	:						Tuple       =				CONFIG_MAPPING[backbone_model_type]
																			__A	:						Dict       =				config_class.from_dict(_A     )

									__A	:						Optional[Any]       =				use_timm_backbone
									__A	:						Optional[int]       =				backbone_config
									__A	:						Optional[Any]       =				num_channels
									__A	:						Optional[int]       =				num_queries
									__A	:						str       =				d_model
									__A	:						Optional[Any]       =				encoder_ffn_dim
									__A	:						str       =				encoder_layers
									__A	:						List[Any]       =				encoder_attention_heads
									__A	:						int       =				decoder_ffn_dim
									__A	:						Optional[int]       =				decoder_layers
									__A	:						Optional[int]       =				decoder_attention_heads
									__A	:						Optional[int]       =				dropout
									__A	:						Dict       =				attention_dropout
									__A	:						Optional[int]       =				activation_dropout
									__A	:						List[str]       =				activation_function
									__A	:						Dict       =				init_std
									__A	:						List[Any]       =				init_xavier_std
									__A	:						Dict       =				encoder_layerdrop
									__A	:						List[str]       =				decoder_layerdrop
									__A	:						Union[str, Any]       =				encoder_layers
									__A	:						Any       =				auxiliary_loss
									__A	:						List[Any]       =				position_embedding_type
									__A	:						Union[str, Any]       =				backbone
									__A	:						int       =				use_pretrained_backbone
									__A	:						Optional[Any]       =				dilation
									# Hungarian matcher
									__A	:						List[Any]       =				class_cost
									__A	:						str       =				bbox_cost
									__A	:						List[Any]       =				giou_cost
									# Loss coefficients
									__A	:						Any       =				mask_loss_coefficient
									__A	:						List[str]       =				dice_loss_coefficient
									__A	:						List[str]       =				cls_loss_coefficient
									__A	:						Optional[int]       =				bbox_loss_coefficient
									__A	:						Optional[int]       =				giou_loss_coefficient
									__A	:						List[str]       =				focal_alpha
									super().__init__(is_encoder_decoder=_A     ,					**_A     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									return self.encoder_attention_heads



				@property
				def 					UpperCAmelCase_		(			self     ):
									return self.d_model





				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				copy.deepcopy(self.__dict__     )
									if self.backbone_config is not None:
														__A	:						Union[str, Any]       =				self.backbone_config.to_dict()
									__A	:						List[Any]       =				self.__class__.model_type
									return output





class 						_A(					snake_case__   ):


				"""simple docstring"""


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



				@property
				def 					UpperCAmelCase_		(			self     ):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									        ('pixel_mask', {0: 'batch'}),
									    ]     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									return 1e-5





				@property
				def 					UpperCAmelCase_		(			self     ):
									return 12











from heapq import heappop, heappush

import numpy as np







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a					,						)      ->      tuple[float | int, list[tuple[int, int]]]:
					__A    ,				__A	:						int       =				grid.shape
					__A	:						Any       =				[-1, 1, 0, 0]
					__A	:						Optional[Any]       =				[0, 0, -1, 1]
					if allow_diagonal:
										dx += [-1, -1, 1, 1]
										dy += [-1, 1, -1, 1]

					__A    ,				__A	:						Optional[int]       =				[(0, source)], set()
					__A	:						Any       =				np.full((rows, cols)					,						np.inf  )
					__A	:						Any       =				0
					__A	:						Any       =				np.empty((rows, cols)					,						dtype=a  )
					__A	:						Optional[Any]       =				None

					while queue:
										((__A)    ,				(__A))	:						List[str]       =				heappop(a  )
										if (x, y) in visited:
															continue
										visited.add((x, y)  )

										if (x, y) == destination:
															__A	:						int       =				[]
															while (x, y) != source:
																				path.append((x, y)  )
																				__A    ,				__A	:						Optional[int]       =				predecessors[x, y]
															path.append(a  )  # add the source manually
															path.reverse()
															return matrix[destination], path

										for i in range(len(a  )  ):
															__A    ,				__A	:						Union[str, Any]       =				x + dx[i], y + dy[i]
															if 0 <= nx < rows and 0 <= ny < cols:
																				__A	:						Optional[int]       =				grid[nx][ny]
																				if next_node == 1 and matrix[nx, ny] > dist + 1:
																									heappush(a					,						(dist + 1, (nx, ny))  )
																									__A	:						List[Any]       =				dist + 1
																									__A	:						Union[str, Any]       =				(x, y)

					return np.inf, []


if __name__ == "__main__":
				import doctest

				doctest.testmod()




