



from queue import PriorityQueue
from typing import Any

import numpy as np



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	dict				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	set				,       SCREAMING_SNAKE_CASE__       :	set				,       SCREAMING_SNAKE_CASE__       :	dict				,       SCREAMING_SNAKE_CASE__       :	dict				,       SCREAMING_SNAKE_CASE__       :	PriorityQueue				,       SCREAMING_SNAKE_CASE__       :	dict				,       SCREAMING_SNAKE_CASE__       :	float | int				,       )  ->     float | int:
 for nxt, d in graph[v]:
  if nxt in visited_forward:
   continue
  _snake_case    :  Union[str, Any]					=					cst_fwd.get(SCREAMING_SNAKE_CASE__				,       np.inf				)
  _snake_case    :  int					=					cst_fwd[v] + d
  if new_cost_f < old_cost_f:
   queue.put((new_cost_f, nxt)				)
   _snake_case    :  int					=					new_cost_f
   _snake_case    :  List[Any]					=					v
  if nxt in visited_backward:
   if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
    _snake_case    :  str					=					cst_fwd[v] + d + cst_bwd[nxt]
 return shortest_distance



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	dict				,       SCREAMING_SNAKE_CASE__       :	dict				)  ->     int:
 _snake_case    :  int					=					-1

 _snake_case    :  List[Any]					=					set()
 _snake_case    :  Optional[Any]					=					set()
 _snake_case    :  Any					=					{source: 0}
 _snake_case    :  Optional[int]					=					{destination: 0}
 _snake_case    :  str					=					{source: None}
 _snake_case    :  str					=					{destination: None}
 _snake_case    :  PriorityQueue[Any]					=					PriorityQueue()
 _snake_case    :  PriorityQueue[Any]					=					PriorityQueue()

 _snake_case    :  List[Any]					=					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():
  _snake_case	,    _snake_case    :  Dict					=					queue_forward.get()
  visited_forward.add(SCREAMING_SNAKE_CASE__				)

  _snake_case	,    _snake_case    :  Tuple					=					queue_backward.get()
  visited_backward.add(SCREAMING_SNAKE_CASE__				)

  _snake_case    :  Optional[int]					=					pass_and_relaxation(
      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__				,       )

  _snake_case    :  Optional[int]					=					pass_and_relaxation(
      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__				,       )

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

 if shortest_distance != np.inf:
  _snake_case    :  List[Any]					=					shortest_distance
 return shortest_path_distance


a__    =	{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
a__    =	{
    """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()





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		["""image_processor""", """tokenizer"""]
   snake_case_					: str          =		"""ChineseCLIPImageProcessor"""
   snake_case_					: Tuple          =		("""BertTokenizer""", """BertTokenizerFast""")

   def __init__(					self							:			Any				,		lowerCAmelCase							:			Dict=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			str)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""				,		lowerCAmelCase				,		)
     _snake_case    :  Tuple					=					kwargs.pop("""feature_extractor""")

    _snake_case    :  Dict					=					image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`.""")
    if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`.""")

    super().__init__(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[int]					=					self.image_processor

   def __call__(					self							:			List[Any]				,		lowerCAmelCase							:			str=None				,		lowerCAmelCase							:			Optional[int]=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			Optional[int])				->				Optional[Any]:

    """simple docstring"""







    if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none.""")

    if text is not None:
     _snake_case    :  Dict					=					self.tokenizer(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if images is not None:
     _snake_case    :  Any					=					self.image_processor(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if text is not None and images is not None:
     _snake_case    :  Optional[Any]					=					image_features.pixel_values
     return encoding
    elif text is not None:
     return encoding
    else:
     return BatchEncoding(data=dict(**lowerCAmelCase)				,		tensor_type=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Dict				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			Any)				->				Optional[Any]:

    """simple docstring"""







    return self.tokenizer.batch_decode(*lowerCAmelCase				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Tuple				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			int)				->				str:

    """simple docstring"""







    return self.tokenizer.decode(*lowerCAmelCase				,		**lowerCAmelCase)

   @property
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Dict					=					self.tokenizer.model_input_names
    _snake_case    :  int					=					self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


   @property
   def      UpperCamelCase_  (					self							:			str)				->				Tuple:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,		lowerCAmelCase				,		)
    return self.image_processor_class
