






"""simple docstring"""

lowercase    			=       {
    '''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:int ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     set()
						# keep track of all the paths to be checked
						__magic_name__            =     [[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
												__magic_name__            =     queue.pop(0   )
												# get the last node from the path
												__magic_name__            =     path[-1]
												if node not in explored:
																		__magic_name__            =     graph[node]
																		# go through all neighbour nodes, construct a new path and
																		# push it into the queue
																		for neighbour in neighbours:
																								__magic_name__            =     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:Union[str, Any] ,						__lowerCamelCase:Tuple   ):

						'''simple docstring'''






						if not graph or start not in graph or target not in graph:
												return -1
						if start == target:
												return 0
						__magic_name__            =     [start]
						__magic_name__            =     set(__lowerCamelCase   )
						# Keep tab on distances from `start` node.
						__magic_name__            =     {start: 0, target: -1}
						while queue:
												__magic_name__            =     queue.pop(0   )
												if node == target:
																		__magic_name__            =     (
																		    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   )
																								__magic_name__            =     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











"""simple docstring"""

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 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				['''image_processor''', '''tokenizer''']
				UpperCAmelCase__        =				'''BlipImageProcessor'''
				UpperCAmelCase__        =				'''AutoTokenizer'''







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						int	)				->					str:
										__magic_name__            =     False
										super().__init__(__lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     self.image_processor







				def __call__(							self							:						List[Any]						,    __lowerCamelCase							:						ImageInput = None						,    __lowerCamelCase							:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Union[bool, str, PaddingStrategy] = False						,    __lowerCamelCase							:						Union[bool, str, TruncationStrategy] = None						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						int = 0						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[Union[str, TensorType]] = None						,    **__lowerCamelCase							:						Optional[int]						,    )				->					BatchEncoding:
										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:
																__magic_name__            =     self.tokenizer
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
																return text_encoding

										# add pixel_values
										__magic_name__            =     self.image_processor(__lowerCamelCase						,    return_tensors=__lowerCamelCase	)

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

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

										return encoding_image_processor







				def     _snake_case					(							self							:						Union[str, Any]						,    *__lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						Dict	)				->					Dict:
										return self.tokenizer.batch_decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    *__lowerCamelCase							:						Optional[Any]						,    **__lowerCamelCase							:						Tuple	)				->					Optional[Any]:
										return self.tokenizer.decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										__magic_name__            =     self.tokenizer.model_input_names
										__magic_name__            =     self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



