



from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					['''image_processor''', '''tokenizer''']
			UpperCamelCase        =					'''BlipImageProcessor'''
			UpperCamelCase        =					'''AutoTokenizer'''



			def __init__(						self       :  str			,		A       :  Tuple			,		A       :  Optional[Any])       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       False
						super().__init__(__a			,		__a)
						_UpperCAmelCase			=       self.image_processor



			def __call__(						self       :  Optional[Any]			,		A       :  int = None			,		A       :  List[Any] = None			,		A       :  Tuple = True			,		A       :  str = False			,		A       :  List[str] = None			,		A       :  Union[str, Any] = None			,		A       :  Dict = 0			,		A       :  int = None			,		A       :  int = None			,		A       :  Optional[int] = False			,		A       :  Dict = False			,		A       :  Dict = False			,		A       :  Union[str, Any] = False			,		A       :  List[str] = False			,		A       :  Any = True			,		A       :  Dict = None			,		**A       :  Any			,		)       ->   BatchEncoding:



						"""simple docstring"""






						if images is None and text is None:
									raise ValueError('You have to specify either images or text.')

						# Get only text
						if images is None:
									_UpperCAmelCase			=       self.tokenizer
									_UpperCAmelCase			=       self.tokenizer(
									    text=__a			,		add_special_tokens=__a			,		padding=__a			,		truncation=__a			,		max_length=__a			,		stride=__a			,		pad_to_multiple_of=__a			,		return_attention_mask=__a			,		return_overflowing_tokens=__a			,		return_special_tokens_mask=__a			,		return_offsets_mapping=__a			,		return_token_type_ids=__a			,		return_length=__a			,		verbose=__a			,		return_tensors=__a			,		**__a			,		)
									return text_encoding

						# add pixel_values
						_UpperCAmelCase			=       self.image_processor(__a			,		return_tensors=__a)

						if text is not None:
									_UpperCAmelCase			=       self.tokenizer(
									    text=__a			,		add_special_tokens=__a			,		padding=__a			,		truncation=__a			,		max_length=__a			,		stride=__a			,		pad_to_multiple_of=__a			,		return_attention_mask=__a			,		return_overflowing_tokens=__a			,		return_special_tokens_mask=__a			,		return_offsets_mapping=__a			,		return_token_type_ids=__a			,		return_length=__a			,		verbose=__a			,		return_tensors=__a			,		**__a			,		)
						else:
									_UpperCAmelCase			=       None

						if text_encoding is not None:
									encoding_image_processor.update(__a)

						return encoding_image_processor



			def    _lowerCamelCase	(						self       :  Tuple			,		*A       :  List[Any]			,		**A       :  int)       ->   Optional[Any]:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Dict			,		*A       :  Any			,		**A       :  Any)       ->   List[Any]:



						"""simple docstring"""






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



			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def    _lowerCamelCase	(						self       :  int)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       self.tokenizer.model_input_names
						_UpperCAmelCase			=       self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





UpperCAmelCase__       =	{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[Any]   )    ->			list[str]:







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

            # mark node as explored
									explored.add(_UpperCAmelCase   )

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





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]   )    ->			int:







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