'''simple docstring'''



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Dict     =    ["image_processor", "tokenizer"]
	a      :			List[str]     =    "AutoImageProcessor"
	a      :			Tuple     =    "AutoTokenizer"


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







				'''simple docstring'''





				__lowercase			  =							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       ,)
							__lowercase			  =							kwargs.pop('''feature_extractor'''	)

				__lowercase			  =							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	)
				__lowercase			  =							self.image_processor
				__lowercase			  =							False


	def __call__(self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							Tuple:







				'''simple docstring'''





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

				__lowercase			  =							kwargs.pop('''images'''       ,_lowerCamelCase	)
				__lowercase			  =							kwargs.pop('''text'''       ,_lowerCamelCase	)
				if len(_lowerCamelCase	) > 0:
							__lowercase			  =							args[0]
							__lowercase			  =							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:
							__lowercase			  =							self.image_processor(_lowerCamelCase       ,*_lowerCamelCase       ,**_lowerCamelCase	)
				if text is not None:
							__lowercase			  =							self.tokenizer(_lowerCamelCase       ,**_lowerCamelCase	)

				if text is None:
							return inputs
				elif images is None:
							return encodings
				else:
							__lowercase			  =							encodings['''input_ids''']
							return inputs


	def   _UpperCAmelCase       (self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							str:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							Dict:







				'''simple docstring'''





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


	@contextmanager
	def   _UpperCAmelCase       (self	)							->							Optional[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.'''	)
				__lowercase			  =							True
				__lowercase			  =							self.tokenizer
				yield
				__lowercase			  =							self.image_processor
				__lowercase			  =							False


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=False       ,_lowerCamelCase=None	)							->							List[str]:







				'''simple docstring'''





				if added_vocab is None:
							__lowercase			  =							self.tokenizer.get_added_vocab()

				__lowercase			  =							{}

				while tokens:
							__lowercase			  =							re.search(R'''<s_(.*?)>'''       ,_lowerCamelCase       ,re.IGNORECASE	)
							if start_token is None:
										break
							__lowercase			  =							start_token.group(1	)
							__lowercase			  =							re.search(Rf"</s_{key}>"       ,_lowerCamelCase       ,re.IGNORECASE	)
							__lowercase			  =							start_token.group()
							if end_token is None:
										__lowercase			  =							tokens.replace(_lowerCamelCase       ,''''''	)
							else:
										__lowercase			  =							end_token.group()
										__lowercase			  =							re.escape(_lowerCamelCase	)
										__lowercase			  =							re.escape(_lowerCamelCase	)
										__lowercase			  =							re.search(f"{start_token_escaped}(.*?){end_token_escaped}"       ,_lowerCamelCase       ,re.IGNORECASE	)
										if content is not None:
													__lowercase			  =							content.group(1	).strip()
													if r"<s_" in content and r"</s_" in content:  # non-leaf node
																__lowercase			  =							self.tokenajson(_lowerCamelCase       ,is_inner_value=_lowerCamelCase       ,added_vocab=_lowerCamelCase	)
																if value:
																			if len(_lowerCamelCase	) == 1:
																						__lowercase			  =							value[0]
																			__lowercase			  =							value
													else:  # leaf nodes
																__lowercase			  =							[]
																for leaf in content.split(R'''<sep/>'''	):
																			__lowercase			  =							leaf.strip()
																			if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																						__lowercase			  =							leaf[1:-2]  # for categorical special tokens
																			output[key].append(_lowerCamelCase	)
																if len(output[key]	) == 1:
																			__lowercase			  =							output[key][0]

										__lowercase			  =							tokens[tokens.find(_lowerCamelCase	) + len(_lowerCamelCase	) :].strip()
										if tokens[:6] == r"<sep/>":  # non-leaf nodes
													return [output] + self.tokenajson(tokens[6:]       ,is_inner_value=_lowerCamelCase       ,added_vocab=_lowerCamelCase	)

				if len(_lowerCamelCase	):
							return [output] if is_inner_value else output
				else:
							return [] if is_inner_value else {"text_sequence": tokens}


	@property
	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''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


	@property
	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





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












'''simple docstring'''



_SCREAMING_SNAKE_CASE   =      {
    '''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_     :					Optional[Any]						,    lowerCamelCase_     :					List[str]			):
			__lowercase			  =							set()
			# keep track of all the paths to be checked
			__lowercase			  =							[[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
						__lowercase			  =							queue.pop(0			)
						# get the last node from the path
						__lowercase			  =							path[-1]
						if node not in explored:
									__lowercase			  =							graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												__lowercase			  =							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_     :					str						,    lowerCamelCase_     :					str			):
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			__lowercase			  =							[start]
			__lowercase			  =							set(lowerCamelCase_			)
			# Keep tab on distances from `start` node.
			__lowercase			  =							{start: 0, target: -1}
			while queue:
						__lowercase			  =							queue.pop(0			)
						if node == target:
									__lowercase			  =							(
									    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_			)
												__lowercase			  =							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





