





'''simple docstring'''






from __future__ import annotations

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







class 			lowerCamelCase					:


					def __init__(					self	:  Optional[int]       ,     __snake_case	:  dict[str, list[str]]       ,     __snake_case	:  str       ):





											'''simple docstring'''





											_snake_case:   str  					=		graph
											# mapping node to its parent in resulting breadth first tree
											_snake_case:   dict[str, str | None]  					=		{}
											_snake_case:   List[str]  					=		source_vertex


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





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

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




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  str       ):





											'''simple docstring'''





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

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












'''simple docstring'''






from typing import TYPE_CHECKING

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


A   : Optional[Any]             =			{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : str             =			['DeiTFeatureExtractor']
		A   : Optional[int]             =			['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Optional[Any]             =			[
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			[
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		A   : Union[str, Any]             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





