



from queue import PriorityQueue
from typing import Any

import numpy as np



def  snake_case_					(     _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						):






	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		__lowercase									=      cst_fwd.get(__a	,						np.inf )
		__lowercase									=      cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt) )
			__lowercase									=      new_cost_f
			__lowercase									=      v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				__lowercase									=      cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance



def  snake_case_					(     _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE ):
	__lowercase									=      -1

	__lowercase									=      set()
	__lowercase									=      set()
	__lowercase									=      {source: 0}
	__lowercase									=      {destination: 0}
	__lowercase									=      {source: None}
	__lowercase									=      {destination: None}
	__lowercase									=      PriorityQueue()
	__lowercase									=      PriorityQueue()

	__lowercase									=      np.inf

	queue_forward.put((0, source) )
	queue_backward.put((0, destination) )

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		__lowercase									=      queue_forward.get()
		visited_forward.add(__a )

		__lowercase									=      queue_backward.get()
		visited_backward.add(__a )

		__lowercase									=      pass_and_relaxation(
		    __a	,						__a	,						__a	,						__a	,						__a	,						__a	,						__a	,						__a	,						__a	,						)

		__lowercase									=      pass_and_relaxation(
		    __a	,						__a	,						__a	,						__a	,						__a	,						__a	,						__a	,						__a	,						__a	,						)

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		__lowercase									=      shortest_distance
	return shortest_path_distance


snake_case__       :  Optional[int]     		= {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
snake_case__       :  int     		= {
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
	import doctest

	doctest.testmod()







'''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,
)


__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:
		__snake_case				 =	['''CLIPTokenizerFast''']

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

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

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__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

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