from __future__ import annotations

__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class        lowercase_   :



							def __init__(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					str    =   graph
													# mapping node to its parent in resulting breadth first tree
													_snake_case						:					dict[str, str | None]    =   {}
													_snake_case						:					str    =   source_vertex



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   {self.source_vertex}
													_snake_case						:					Optional[int]    =   None
													_snake_case						:					Optional[Any]    =   [self.source_vertex]  # first in first out queue

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



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													if target_vertex == self.source_vertex:
																			return self.source_vertex

													_snake_case						:					int    =   self.parent.get(lowercase_    )
													if target_vertex_parent is None:
																			_snake_case						:					List[str]    =   (
																			    f"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
																			)
																			raise ValueError(lowercase_    )

													return self.shortest_path(lowercase_    ) + f"""->{target_vertex}"""


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			Graph(graph, 'G')
				g.breath_first_search()
				print(g.shortest_path('D'))
				print(g.shortest_path('G'))
				print(g.shortest_path('Foo'))






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						:   Any         =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MraForMaskedLM',
				    'MraForMultipleChoice',
				    'MraForQuestionAnswering',
				    'MraForSequenceClassification',
				    'MraForTokenClassification',
				    'MraLayer',
				    'MraModel',
				    'MraPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mra import (
								    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MraForMaskedLM,
								    MraForMultipleChoice,
								    MraForQuestionAnswering,
								    MraForSequenceClassification,
								    MraForTokenClassification,
								    MraLayer,
								    MraModel,
								    MraPreTrainedModel,
								)
else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure)