
"""simple docstring"""







_SCREAMING_SNAKE_CASE													=   {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}
def 	__UpperCamelCase (       SCREAMING_SNAKE_CASE				,     SCREAMING_SNAKE_CASE				,     SCREAMING_SNAKE_CASE			)	->  list[str]:







							"""simple docstring"""


							__snake_case        =							set()
							# keep track of all the paths to be checked
							__snake_case        =							[[start]]

							# return path if start is goal
							if start == goal:
														return [start]

							# keeps looping until all possible paths have been checked
							while queue:
														# pop the first path from the queue
														__snake_case        =							queue.pop(0			)
														# get the last node from the path
														__snake_case        =							path[-1]
														if node not in explored:
																					__snake_case        =							graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												__snake_case        =							list(__UpperCamelCase			)
																												new_path.append(__UpperCamelCase			)
																												queue.append(__UpperCamelCase			)
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(__UpperCamelCase			)

    # in case there's no path between the 2 nodes
							return []
def 	__UpperCamelCase (       SCREAMING_SNAKE_CASE				,     SCREAMING_SNAKE_CASE				,     SCREAMING_SNAKE_CASE			)	->  int:







							"""simple docstring"""


							if not graph or start not in graph or target not in graph:
														return -1
							if start == target:
														return 0
							__snake_case        =							[start]
							__snake_case        =							set(__UpperCamelCase			)
							# Keep tab on distances from `start` node.
							__snake_case        =							{start: 0, target: -1}
							while queue:
														__snake_case        =							queue.pop(0			)
														if node == target:
																					__snake_case        =							(
																					    dist[node] if dist[target] == -1 else min(dist[target]				,     dist[node]			)
																					)
														for adjacent in graph[node]:
																					if adjacent not in visited:
																												visited.add(__UpperCamelCase			)
																												queue.append(__UpperCamelCase			)
																												__snake_case        =							dist[node] + 1
							return dist[target]


if __name__ == "__main__":
		print(bfs_shortest_path(demo_graph, """G""", """D"""))  # returns ['G', 'C', 'A', 'B', 'D']
		print(bfs_shortest_path_distance(demo_graph, """G""", """D"""))  # returns 4





# 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__   			=   {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__   			=   [
							    '''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__   			=   _LazyModule(__name__, globals()['''__file__'''], _import_structure)




