




from __future__ import annotations

__lowerCamelCase   :						List[Any]				=			{
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}


class    lowerCamelCase    :






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   dict[str, list[str]]       ,						lowerCamelCase_			:   str  )						->   Dict:
								__magic_name__							:	List[Any]    							=							graph
								# mapping node to its parent in resulting breadth first tree
								__magic_name__							:	List[Any]    							=							{}
								__magic_name__							:	Dict    							=							source_vertex

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   List[str]:
								__magic_name__							:	Dict    							=							{self.source_vertex}
								__magic_name__							:	Optional[int]    							=							None
								__magic_name__							:	Any    							=							[self.source_vertex]  # first in first out queue

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


			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   str  )						->   Dict:


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

								__magic_name__							:	Union[str, Any]    							=							self.parent.get(_snake_case  )
								if target_vertex_parent is None:
													__magic_name__							:	Tuple    							=							(
													    F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
													)
													raise ValueError(_snake_case  )

								return self.shortest_path(_snake_case  ) + F'''->{target_vertex}'''


if __name__ == "__main__":
							__lowerCamelCase   :						Optional[int]				=			Graph(graph, '''G''')
							g.breath_first_search()
							print(g.shortest_path('''D'''))
							print(g.shortest_path('''G'''))
							print(g.shortest_path('''Foo'''))









from typing import TYPE_CHECKING

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


__lowerCamelCase   :						List[str]				=			{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Dict				=			[
	    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RobertaPreLayerNormForCausalLM''',
	    '''RobertaPreLayerNormForMaskedLM''',
	    '''RobertaPreLayerNormForMultipleChoice''',
	    '''RobertaPreLayerNormForQuestionAnswering''',
	    '''RobertaPreLayerNormForSequenceClassification''',
	    '''RobertaPreLayerNormForTokenClassification''',
	    '''RobertaPreLayerNormModel''',
	    '''RobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRobertaPreLayerNormForCausalLM''',
	    '''TFRobertaPreLayerNormForMaskedLM''',
	    '''TFRobertaPreLayerNormForMultipleChoice''',
	    '''TFRobertaPreLayerNormForQuestionAnswering''',
	    '''TFRobertaPreLayerNormForSequenceClassification''',
	    '''TFRobertaPreLayerNormForTokenClassification''',
	    '''TFRobertaPreLayerNormMainLayer''',
	    '''TFRobertaPreLayerNormModel''',
	    '''TFRobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''FlaxRobertaPreLayerNormForCausalLM''',
	    '''FlaxRobertaPreLayerNormForMaskedLM''',
	    '''FlaxRobertaPreLayerNormForMultipleChoice''',
	    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
	    '''FlaxRobertaPreLayerNormForSequenceClassification''',
	    '''FlaxRobertaPreLayerNormForTokenClassification''',
	    '''FlaxRobertaPreLayerNormModel''',
	    '''FlaxRobertaPreLayerNormPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Dict				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



