




'''simple docstring'''





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 				_snake_case	(			lowercase_							):
			lowerCAmelCase_  :   Dict		 =					["image_processor", "tokenizer"]
			lowerCAmelCase_  :   Optional[Any]		 =					"AutoImageProcessor"
			lowerCAmelCase_  :   List[str]		 =					"AutoTokenizer"




			def __init__(      self       ,							a__=None       ,							a__=None       ,							**a__							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   None
										if "feature_extractor" in kwargs:
																	warnings.warn(
																	    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																	    " instead."       ,							a__       ,							)
																	snake_case_			      =   kwargs.pop("feature_extractor"							)

										snake_case_			      =   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__(a__       ,							a__							)
										snake_case_			      =   self.image_processor
										snake_case_			      =   False




			def __call__(      self       ,							*a__       ,							**a__							)       ->       str:

										'''simple docstring'''




										if self._in_target_context_manager:
																	return self.current_processor(*a__       ,							**a__							)

										snake_case_			      =   kwargs.pop("images"       ,							a__							)
										snake_case_			      =   kwargs.pop("text"       ,							a__							)
										if len(a__							) > 0:
																	snake_case_			      =   args[0]
																	snake_case_			      =   args[1:]

										if images is None and text is None:
																	raise ValueError("You need to specify either an `images` or `text` input to process."							)

										if images is not None:
																	snake_case_			      =   self.image_processor(a__       ,							*a__       ,							**a__							)
										if text is not None:
																	snake_case_			      =   self.tokenizer(a__       ,							**a__							)

										if text is None:
																	return inputs
										elif images is None:
																	return encodings
										else:
																	snake_case_			      =   encodings["input_ids"]
																	return inputs




			def      lowerCAmelCase__       (      self       ,							*a__       ,							**a__							)       ->       List[Any]:

										'''simple docstring'''




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




			def      lowerCAmelCase__       (      self       ,							*a__       ,							**a__							)       ->       Tuple:

										'''simple docstring'''




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




			@contextmanager
			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										warnings.warn(
										    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
										    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
										    "your images inputs, or in a separate call."							)
										snake_case_			      =   True
										snake_case_			      =   self.tokenizer
										yield
										snake_case_			      =   self.image_processor
										snake_case_			      =   False




			@property
			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




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




			@property
			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,							a__       ,							)
										return self.image_processor










'''simple docstring'''





import heapq


def   UpperCamelCase_(			snake_case							: List[Any]							,       snake_case							: List[str]							,       snake_case							: Dict  ):

							'''simple docstring'''






							snake_case_			      =   [(0, start)]  # cost from start node,end node
							snake_case_			      =   set()
							while heap:
														((snake_case_)      ,   (snake_case_))			      =   heapq.heappop(snake_case  )
														if u in visited:
																					continue
														visited.add(snake_case  )
														if u == end:
																					return cost
														for v, c in graph[u]:
																					if v in visited:
																												continue
																					snake_case_			      =   cost + c
																					heapq.heappush(snake_case							,       (next_item, v)  )
							return -1


_SCREAMING_SNAKE_CASE							:     Dict   	=				{
    "A": [["B", 2], ["C", 5]],
    "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
    "C": [["A", 5], ["F", 3]],
    "D": [["B", 3]],
    "E": [["B", 4], ["F", 3]],
    "F": [["C", 3], ["E", 3]],
}
_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["F", 3]],
    "F": [],
}
_SCREAMING_SNAKE_CASE							:     Any   	=				{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}

_SCREAMING_SNAKE_CASE							:     Tuple   	=				dijkstra(G, "E", "C")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

_SCREAMING_SNAKE_CASE							:     str   	=				dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 3 --> F == 3

_SCREAMING_SNAKE_CASE							:     List[Any]   	=				dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
		import doctest

		doctest.testmod()




