


'''simple docstring'''



from __future__ import annotations

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






class  UpperCamelCase__:




				def __init__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				dict[str, list[str]]   ,					lowerCAmelCase    :				str   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			graph
										# mapping node to its parent in resulting breadth first tree
										UpperCAmelCase         =			{}
										UpperCAmelCase         =			source_vertex




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

										"""simple docstring"""




										UpperCAmelCase         =			{self.source_vertex}
										UpperCAmelCase         =			None
										UpperCAmelCase         =			[self.source_vertex]  # first in first out queue

										while queue:
																UpperCAmelCase         =			queue.pop(0   )
																for adjacent_vertex in self.graph[vertex]:
																						if adjacent_vertex not in visited:
																												visited.add(lowerCAmelCase   )
																												UpperCAmelCase         =			vertex
																												queue.append(lowerCAmelCase   )





				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				str   )->  str:

										"""simple docstring"""




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

										UpperCAmelCase         =			self.parent.get(lowerCAmelCase   )
										if target_vertex_parent is None:
																UpperCAmelCase         =			(
																    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__":
	_lowercase    :    Tuple			     =  Graph(graph, """G""")
	g.breath_first_search()
	print(g.shortest_path("""D"""))
	print(g.shortest_path("""G"""))
	print(g.shortest_path("""Foo"""))









'''simple docstring'''



# Copyright 2022 The HuggingFace Team and The OpenBMB 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


_lowercase    :    Any			     =  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    int			     =  [
	    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """CpmAntForCausalLM""",
	    """CpmAntModel""",
	    """CpmAntPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
	from .tokenization_cpmant import CpmAntTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_cpmant import (
		    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CpmAntForCausalLM,
		    CpmAntModel,
		    CpmAntPreTrainedModel,
		)


else:
	import sys

	_lowercase    :    Dict			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





