


'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__    	=     {
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     ['XLMRobertaTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     ['XLMRobertaTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'XLMRobertaForCausalLM',
						    'XLMRobertaForMaskedLM',
						    'XLMRobertaForMultipleChoice',
						    'XLMRobertaForQuestionAnswering',
						    'XLMRobertaForSequenceClassification',
						    'XLMRobertaForTokenClassification',
						    'XLMRobertaModel',
						    'XLMRobertaPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFXLMRobertaForCausalLM',
						    'TFXLMRobertaForMaskedLM',
						    'TFXLMRobertaForMultipleChoice',
						    'TFXLMRobertaForQuestionAnswering',
						    'TFXLMRobertaForSequenceClassification',
						    'TFXLMRobertaForTokenClassification',
						    'TFXLMRobertaModel',
						    'TFXLMRobertaPreTrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'FlaxXLMRobertaForMaskedLM',
						    'FlaxXLMRobertaForCausalLM',
						    'FlaxXLMRobertaForMultipleChoice',
						    'FlaxXLMRobertaForQuestionAnswering',
						    'FlaxXLMRobertaForSequenceClassification',
						    'FlaxXLMRobertaForTokenClassification',
						    'FlaxXLMRobertaModel',
						    'FlaxXLMRobertaPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    XLMRobertaConfig,
						    XLMRobertaOnnxConfig,
						)

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_roberta import XLMRobertaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_roberta import (
												    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMRobertaForCausalLM,
												    XLMRobertaForMaskedLM,
												    XLMRobertaForMultipleChoice,
												    XLMRobertaForQuestionAnswering,
												    XLMRobertaForSequenceClassification,
												    XLMRobertaForTokenClassification,
												    XLMRobertaModel,
												    XLMRobertaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlm_roberta import (
												    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLMRobertaForCausalLM,
												    TFXLMRobertaForMaskedLM,
												    TFXLMRobertaForMultipleChoice,
												    TFXLMRobertaForQuestionAnswering,
												    TFXLMRobertaForSequenceClassification,
												    TFXLMRobertaForTokenClassification,
												    TFXLMRobertaModel,
												    TFXLMRobertaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_xlm_roberta import (
												    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxXLMRobertaForCausalLM,
												    FlaxXLMRobertaForMaskedLM,
												    FlaxXLMRobertaForMultipleChoice,
												    FlaxXLMRobertaForQuestionAnswering,
												    FlaxXLMRobertaForSequenceClassification,
												    FlaxXLMRobertaForTokenClassification,
												    FlaxXLMRobertaModel,
												    FlaxXLMRobertaPreTrainedModel,
												)

else:
						import sys

						lowerCamelCase__    	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''


from __future__ import annotations

lowerCamelCase__    	=     {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}






class 	lowerCAmelCase__			:




			def __init__(		self  :     int	,  lowerCamelCase__  :     dict[str, list[str]]	,  lowerCamelCase__  :     str )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      graph
					# mapping node to its parent in resulting breadth first tree
					_UpperCAmelCase		:       dict[str, str | None]								=      {}
					_UpperCAmelCase		:       List[Any]								=      source_vertex




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      {self.source_vertex}
					_UpperCAmelCase		:       List[Any]								=      None
					_UpperCAmelCase		:       List[str]								=      [self.source_vertex]  # first in first out queue

					while queue:
							_UpperCAmelCase		:       int								=      queue.pop(0 )
							for adjacent_vertex in self.graph[vertex]:
									if adjacent_vertex not in visited:
											visited.add(lowerCamelCase__ )
											_UpperCAmelCase		:       List[Any]								=      vertex
											queue.append(lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     str )   ->str:



					'''simple docstring'''







					if target_vertex == self.source_vertex:
							return self.source_vertex

					_UpperCAmelCase		:       int								=      self.parent.get(lowerCamelCase__ )
					if target_vertex_parent is None:
							_UpperCAmelCase		:       Tuple								=      (
							    F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
							)
							raise ValueError(lowerCamelCase__ )

					return self.shortest_path(lowerCamelCase__ ) + F"""->{target_vertex}"""


if __name__ == "__main__":
						lowerCamelCase__    	=     Graph(graph, 'G')
						g.breath_first_search()
						print(g.shortest_path('D'))
						print(g.shortest_path('G'))
						print(g.shortest_path('Foo'))



