

from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase							:  str            =						logging.get_logger(__name__)

lowerCAmelCase							:  Optional[Any]            =						{
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							str      							=					'''xlm-roberta-xl'''



			def __init__(			self  :					Dict       ,						lowerCAmelCase__  :					Dict=25_0880       ,						lowerCAmelCase__  :					int=2560       ,						lowerCAmelCase__  :					Tuple=36       ,						lowerCAmelCase__  :					str=32       ,						lowerCAmelCase__  :					Optional[int]=1_0240       ,						lowerCAmelCase__  :					int="gelu"       ,						lowerCAmelCase__  :					Optional[Any]=0.1       ,						lowerCAmelCase__  :					List[str]=0.1       ,						lowerCAmelCase__  :					Optional[Any]=514       ,						lowerCAmelCase__  :					List[str]=1       ,						lowerCAmelCase__  :					Any=0.02       ,						lowerCAmelCase__  :					Optional[int]=1E-05       ,						lowerCAmelCase__  :					int=1       ,						lowerCAmelCase__  :					Union[str, Any]=0       ,						lowerCAmelCase__  :					Union[str, Any]=2       ,						lowerCAmelCase__  :					Optional[int]="absolute"       ,						lowerCAmelCase__  :					List[Any]=True       ,						lowerCAmelCase__  :					Dict=None       ,						**lowerCAmelCase__  :					Optional[Any]       ,						):
									super().__init__(pad_token_id=lowerCAmelCase__       ,						bos_token_id=lowerCAmelCase__       ,						eos_token_id=lowerCAmelCase__       ,						**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	vocab_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			str  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	num_attention_heads
									SCREAMING_SNAKE_CASE_:			int  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			int  		=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max_position_embeddings
									SCREAMING_SNAKE_CASE_:			str  		=	type_vocab_size
									SCREAMING_SNAKE_CASE_:			Any  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	layer_norm_eps
									SCREAMING_SNAKE_CASE_:			int  		=	position_embedding_type
									SCREAMING_SNAKE_CASE_:			Any  		=	use_cache
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	classifier_dropout







class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									if self.task == "multiple-choice":
															SCREAMING_SNAKE_CASE_:			str  		=	{0: "batch", 1: "choice", 2: "sequence"}
									else:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{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   __lowercase    :




			"""simple docstring"""







			_UpperCAmelCase       :							int
			_UpperCAmelCase       :							int







class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					Any       ,						lowerCAmelCase__  :					int):
									SCREAMING_SNAKE_CASE_:			list[list[Edge]]  		=	[[] for _ in range(lowerCAmelCase__)]
									SCREAMING_SNAKE_CASE_:			Dict  		=	size



			def __getitem__(			self  :					Dict       ,						lowerCAmelCase__  :					int):
									return iter(self._graph[vertex])



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									return self._size



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int):
									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 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	deque([start_vertex])
									SCREAMING_SNAKE_CASE_:			list[int | None]  		=	[None] * self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	0

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

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






