



'''simple docstring'''
from __future__ import annotations

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






@dataclass
class 							lowercase__	:
	lowercase__  				=			42
	lowercase__  				=			42






class 							lowercase__	:





	def __init__( self					:				List[str] ,lowerCamelCase__					:				int ):







							'''simple docstring'''
							_UpperCamelCase					:		list[list[Edge]] 				=   [[] for _ in range(lowerCamelCase__ )]
							_UpperCamelCase					:		Dict 				=   size





	def __getitem__( self					:				Tuple ,lowerCamelCase__					:				int ):







							'''simple docstring'''
							return iter(self._graph[vertex] )





	@property
	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							return self._size





	def 	UpperCamelCase_  ( self					:				Tuple ,lowerCamelCase__					:				int ,lowerCamelCase__					:				int ,lowerCamelCase__					:				int ):







							'''simple docstring'''
							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(lowerCamelCase__ ,lowerCamelCase__ ) )





	def 	UpperCamelCase_  ( self					:				Dict ,lowerCamelCase__					:				int ,lowerCamelCase__					:				int ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   deque([start_vertex] )
							_UpperCamelCase					:		list[int | None] 				=   [None] * self.size
							_UpperCamelCase					:		Union[str, Any] 				=   0

							while queue:
													_UpperCamelCase					:		str 				=   queue.popleft()
													_UpperCamelCase					:		int 				=   distances[current_vertex]
													if current_distance is None:
																			continue

													for edge in self[current_vertex]:
																			_UpperCamelCase					:		Optional[int] 				=   current_distance + edge.weight
																			_UpperCamelCase					:		Any 				=   distances[edge.destination_vertex]
																			if (
																			    isinstance(lowerCamelCase__ ,lowerCamelCase__ )
																			    and new_distance >= dest_vertex_distance
																			):
																									continue
																			_UpperCamelCase					:		Optional[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()









'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_					:    int      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Tuple      	=			{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='big_bird'




				def __init__(  self     :					Optional[int]					,				__a     :					Dict=5_03_58					,				__a     :					str=7_68					,				__a     :					List[Any]=12					,				__a     :					List[str]=12					,				__a     :					Union[str, Any]=30_72					,				__a     :					str="gelu_new"					,				__a     :					Dict=0.1					,				__a     :					Union[str, Any]=0.1					,				__a     :					Any=40_96					,				__a     :					int=2					,				__a     :					Tuple=0.02					,				__a     :					List[Any]=1e-1_2					,				__a     :					int=True					,				__a     :					List[str]=0					,				__a     :					Tuple=1					,				__a     :					Optional[Any]=2					,				__a     :					Tuple=66					,				__a     :					str="block_sparse"					,				__a     :					Tuple=True					,				__a     :					Optional[int]=False					,				__a     :					str=64					,				__a     :					Tuple=3					,				__a     :					Any=None					,				**__a     :					Dict					,				):
								super().__init__(
								    pad_token_id=__a					,				bos_token_id=__a					,				eos_token_id=__a					,				sep_token_id=__a					,				**__a					,				)

								_a								= vocab_size
								_a								= max_position_embeddings
								_a								= hidden_size
								_a								= num_hidden_layers
								_a								= num_attention_heads
								_a								= intermediate_size
								_a								= hidden_act
								_a								= hidden_dropout_prob
								_a								= attention_probs_dropout_prob
								_a								= initializer_range
								_a								= type_vocab_size
								_a								= layer_norm_eps
								_a								= use_cache

								_a								= rescale_embeddings
								_a								= attention_type
								_a								= use_bias
								_a								= block_size
								_a								= num_random_blocks
								_a								= classifier_dropout


class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				@property
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								if self.task == "multiple-choice":
												_a								= {0: "batch", 1: "choice", 2: "sequence"}
								else:
												_a								= {0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								    ]					)
