


from typing import TYPE_CHECKING

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


_snake_case  :	Dict			      =       {"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OPTConfig"]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_snake_case  :	List[Any]			      =       [
			    "OPT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "OPTForCausalLM",
			    "OPTModel",
			    "OPTPreTrainedModel",
			    "OPTForSequenceClassification",
			    "OPTForQuestionAnswering",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_snake_case  :	int			      =       ["TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel"]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_snake_case  :	Tuple			      =       [
			    "FlaxOPTForCausalLM",
			    "FlaxOPTModel",
			    "FlaxOPTPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_opt import (
						    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    OPTForCausalLM,
						    OPTForQuestionAnswering,
						    OPTForSequenceClassification,
						    OPTModel,
						    OPTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
			import sys

			_snake_case  :	Tuple			      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





from __future__ import annotations

_snake_case  :	Union[str, Any]			      =       {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}

class        a	:




							"""simple docstring"""


							def __init__(							self :						Tuple					,			lowerCamelCase :						dict[str, list[str]]					,			lowerCamelCase :						str       )			->					None:
														__snake_case	:    Tuple											=     graph
														# mapping node to its parent in resulting breadth first tree
														__snake_case	:    dict[str, str | None]											=     {}
														__snake_case	:    Dict											=     source_vertex

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

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






							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str       )			->					str:
														if target_vertex == self.source_vertex:
																					return self.source_vertex

														__snake_case	:    Optional[Any]											=     self.parent.get(lowerCamelCase       )
														if target_vertex_parent is None:
																					__snake_case	:    Optional[Any]											=     (
																					    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__":
			_snake_case  :	Optional[Any]			      =       Graph(graph, "G")
			g.breath_first_search()
			print(g.shortest_path("D"))
			print(g.shortest_path("G"))
			print(g.shortest_path("Foo"))

