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


__SCREAMING_SNAKE_CASE						:   List[Any]         =			logging.get_logger(__name__)

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



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'table-transformer'
							_lowerCamelCase						=		['past_key_values']
							_lowerCamelCase						=		{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}



							def __init__(						self				,    lowercase_=True				,    lowercase_=None				,    lowercase_=3				,    lowercase_=100				,    lowercase_=6				,    lowercase_=2_048				,    lowercase_=8				,    lowercase_=6				,    lowercase_=2_048				,    lowercase_=8				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=True				,    lowercase_="relu"				,    lowercase_=256				,    lowercase_=0.1				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=0.02				,    lowercase_=1.0				,    lowercase_=False				,    lowercase_="sine"				,    lowercase_="resnet50"				,    lowercase_=True				,    lowercase_=False				,    lowercase_=1				,    lowercase_=5				,    lowercase_=2				,    lowercase_=1				,    lowercase_=1				,    lowercase_=5				,    lowercase_=2				,    lowercase_=0.1				,    **lowercase_				,    ):
													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."    )
																									_snake_case						:					List[Any]    =   CONFIG_MAPPING["resnet"](out_features=["stage4"]    )
																			elif isinstance(lowercase_				,    lowercase_    ):
																									_snake_case						:					str    =   backbone_config.get("model_type"    )
																									_snake_case						:					Tuple    =   CONFIG_MAPPING[backbone_model_type]
																									_snake_case						:					List[str]    =   config_class.from_dict(lowercase_    )
																			# set timm attributes to None
																			_snake_case						:					Dict    =   None, None, None

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



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



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




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		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 __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass



@dataclass
class        lowercase_   :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42




class        lowercase_   :



							def __init__(						self				,    lowercase_    ):
													_snake_case						:					list[list[Edge]]    =   [[] for _ in range(lowercase_    )]
													_snake_case						:					Union[str, Any]    =   size



							def __getitem__(						self				,    lowercase_    ):
													return iter(self._graph[vertex]    )



							@property
							def 	UpperCamelCase	(						self    ):
													return self._size



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													if weight not in (0, 1):
																			raise ValueError("Edge weight must be either 0 or 1."    )

													if to_vertex < 0 or to_vertex >= self.size:
																			raise ValueError("Vertex indexes must be in [0; size)."    )

													self._graph[from_vertex].append(Edge(lowercase_				,    lowercase_    )    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Optional[int]    =   deque([start_vertex]    )
													_snake_case						:					list[int | None]    =   [None] * self.size
													_snake_case						:					Tuple    =   0

													while queue:
																			_snake_case						:					List[Any]    =   queue.popleft()
																			_snake_case						:					Tuple    =   distances[current_vertex]
																			if current_distance is None:
																									continue

																			for edge in self[current_vertex]:
																									_snake_case						:					Dict    =   current_distance + edge.weight
																									_snake_case						:					str    =   distances[edge.destination_vertex]
																									if (
																									    isinstance(lowercase_				,    lowercase_    )
																									    and new_distance >= dest_vertex_distance
																									):
																															continue
																									_snake_case						:					List[Any]    =   new_distance
																									if edge.weight == 0:
																															queue.appendleft(edge.destination_vertex    )
																									else:
																															queue.append(edge.destination_vertex    )

													if distances[finish_vertex] is None:
																			raise ValueError("No path from start_vertex to finish_vertex."    )

													return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()