





'''simple docstring'''



from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''CLIPTokenizerFast''']

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''CLIPFeatureExtractor''']
	_SCREAMING_SNAKE_CASE   =      ['''CLIPImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''CLIPModel''',
	    '''CLIPPreTrainedModel''',
	    '''CLIPTextModel''',
	    '''CLIPTextModelWithProjection''',
	    '''CLIPVisionModel''',
	    '''CLIPVisionModelWithProjection''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFCLIPModel''',
	    '''TFCLIPPreTrainedModel''',
	    '''TFCLIPTextModel''',
	    '''TFCLIPVisionModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FlaxCLIPModel''',
	    '''FlaxCLIPPreTrainedModel''',
	    '''FlaxCLIPTextModel''',
	    '''FlaxCLIPTextPreTrainedModel''',
	    '''FlaxCLIPVisionModel''',
	    '''FlaxCLIPVisionPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_clip import (
	    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPConfig,
	    CLIPOnnxConfig,
	    CLIPTextConfig,
	    CLIPVisionConfig,
	)
	from .processing_clip import CLIPProcessor
	from .tokenization_clip import CLIPTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_clip_fast import CLIPTokenizerFast

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_clip import CLIPFeatureExtractor
		from .image_processing_clip import CLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clip import (
		    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPModel,
		    CLIPPreTrainedModel,
		    CLIPTextModel,
		    CLIPTextModelWithProjection,
		    CLIPVisionModel,
		    CLIPVisionModelWithProjection,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_clip import (
		    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCLIPModel,
		    TFCLIPPreTrainedModel,
		    TFCLIPTextModel,
		    TFCLIPVisionModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_clip import (
		    FlaxCLIPModel,
		    FlaxCLIPPreTrainedModel,
		    FlaxCLIPTextModel,
		    FlaxCLIPTextPreTrainedModel,
		    FlaxCLIPVisionModel,
		    FlaxCLIPVisionPreTrainedModel,
		)


else:
	import sys

	_SCREAMING_SNAKE_CASE   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












'''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 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					List[str]			):
			__lowercase			  =							set()
			# keep track of all the paths to be checked
			__lowercase			  =							[[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
						__lowercase			  =							queue.pop(0			)
						# get the last node from the path
						__lowercase			  =							path[-1]
						if node not in explored:
									__lowercase			  =							graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												__lowercase			  =							list(lowerCamelCase_			)
												new_path.append(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(lowerCamelCase_			)

    # in case there's no path between the 2 nodes
			return []




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





