

"""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     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      List[str]         =  ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  """BlipImageProcessor"""
				SCREAMING_SNAKE_CASE_   :      Dict         =  """AutoTokenizer"""

				def __init__(	self							:				int     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				Optional[int])->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			False
						super().__init__(UpperCamelCase__     ,  UpperCamelCase__)
						__lowerCAmelCase:       Any          =			self.image_processor

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

						# add pixel_values
						__lowerCAmelCase:       Optional[int]          =			self.image_processor(UpperCamelCase__     ,  return_tensors=UpperCamelCase__)

						if text is not None:
								__lowerCAmelCase:       Tuple          =			self.tokenizer(
								    text=UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__     ,  padding=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  stride=UpperCamelCase__     ,  pad_to_multiple_of=UpperCamelCase__     ,  return_attention_mask=UpperCamelCase__     ,  return_overflowing_tokens=UpperCamelCase__     ,  return_special_tokens_mask=UpperCamelCase__     ,  return_offsets_mapping=UpperCamelCase__     ,  return_token_type_ids=UpperCamelCase__     ,  return_length=UpperCamelCase__     ,  verbose=UpperCamelCase__     ,  return_tensors=UpperCamelCase__     ,  **UpperCamelCase__     ,  )
						else:
								__lowerCAmelCase:       Optional[Any]          =			None

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

						return encoding_image_processor

				def 							lowercase_     (	self							:				Any     ,  *UpperCamelCase__							:				Dict     ,  **UpperCamelCase__							:				Tuple)->   List[Any]:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				str     ,  *UpperCamelCase__							:				Dict     ,  **UpperCamelCase__							:				Optional[int])->   int:



						'''simple docstring'''


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




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



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			self.tokenizer.model_input_names
						__lowerCAmelCase:       Union[str, Any]          =			self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





"""simple docstring"""


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






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	list[str]:
		__lowerCAmelCase:       Tuple          =			set()
		# keep track of all the paths to be checked
		__lowerCAmelCase:       Optional[Any]          =			[[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
				__lowerCAmelCase:       str          =			queue.pop(0 )
				# get the last node from the path
				__lowerCAmelCase:       List[Any]          =			path[-1]
				if node not in explored:
						__lowerCAmelCase:       str          =			graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								__lowerCAmelCase:       Optional[int]          =			list(__SCREAMING_SNAKE_CASE )
								new_path.append(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(__SCREAMING_SNAKE_CASE )

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






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		__lowerCAmelCase:       Dict          =			[start]
		__lowerCAmelCase:       Any          =			set(__SCREAMING_SNAKE_CASE )
		# Keep tab on distances from `start` node.
		__lowerCAmelCase:       int          =			{start: 0, target: -1}
		while queue:
				__lowerCAmelCase:       Optional[Any]          =			queue.pop(0 )
				if node == target:
						__lowerCAmelCase:       Dict          =			(
						    dist[node] if dist[target] == -1 else min(dist[target]					, dist[node] )
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								__lowerCAmelCase:       List[str]          =			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


