





from collections import OrderedDict
from typing import Mapping

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


snake_case								=							logging.get_logger(__name__)

snake_case								=							{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Optional[Any]  			=	'''camembert'''







       def __init__(     self						:						Optional[int]    ,__A						:						Optional[int]=3_0522    ,__A						:						List[Any]=768    ,__A						:						Optional[int]=12    ,__A						:						int=12    ,__A						:						Dict=3072    ,__A						:						Union[str, Any]="gelu"    ,__A						:						Union[str, Any]=0.1    ,__A						:						Any=0.1    ,__A						:						int=512    ,__A						:						Union[str, Any]=2    ,__A						:						Union[str, Any]=0.02    ,__A						:						Union[str, Any]=1e-12    ,__A						:						Dict=1    ,__A						:						List[str]=0    ,__A						:						int=2    ,__A						:						Tuple="absolute"    ,__A						:						Union[str, Any]=True    ,__A						:						Optional[Any]=None    ,**__A						:						Optional[Any]    ,)    ->       Optional[int]:
         super().__init__(pad_token_id=__A    ,bos_token_id=__A    ,eos_token_id=__A    ,**__A	)

         _lowercase						    =			vocab_size
         _lowercase						    =			hidden_size
         _lowercase						    =			num_hidden_layers
         _lowercase						    =			num_attention_heads
         _lowercase						    =			hidden_act
         _lowercase						    =			intermediate_size
         _lowercase						    =			hidden_dropout_prob
         _lowercase						    =			attention_probs_dropout_prob
         _lowercase						    =			max_position_embeddings
         _lowercase						    =			type_vocab_size
         _lowercase						    =			initializer_range
         _lowercase						    =			layer_norm_eps
         _lowercase						    =			position_embedding_type
         _lowercase						    =			use_cache
         _lowercase						    =			classifier_dropout





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       @property
       def  __UpperCAmelCase			(     self						:						Any	)    ->       Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
           _lowercase						    =			{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           _lowercase						    =			{0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ]	)

from __future__ import annotations

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





@dataclass
class     lowerCamelCase      :
	UpperCamelCase_  :					int
	UpperCamelCase_  :					int





class     lowerCamelCase      :







	def __init__(     self      :Dict   ,     lowercase      :int			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [[] for _ in range(lowercase			)]
				SCREAMING_SNAKE_CASE     						= size







	def __getitem__(     self      :Union[str, Any]   ,     lowercase      :int			)		->      Iterator[Edge]:







				"""simple docstring"""






				return iter(self._graph[vertex]			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      str:







				"""simple docstring"""






				return self._size







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :int   ,     lowercase      :int   ,     lowercase      :int			)		->      List[Any]:







				"""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(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int | None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= deque([start_vertex]			)
				SCREAMING_SNAKE_CASE     						= [None] * self.size
				SCREAMING_SNAKE_CASE     						= 0

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

							for edge in self[current_vertex]:
										SCREAMING_SNAKE_CASE     						= current_distance + edge.weight
										SCREAMING_SNAKE_CASE     						= distances[edge.destination_vertex]
										if (
										    isinstance(lowercase   ,     lowercase			)
										    and new_distance >= dest_vertex_distance
										):
													continue
										SCREAMING_SNAKE_CASE     						= 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()