
'''simple docstring'''







import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					['''image_processor''', '''tokenizer''']
					lowerCAmelCase_					       =					'''AutoImageProcessor'''
					lowerCAmelCase_					       =					'''AutoTokenizer'''






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	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_     :					Tuple													=	kwargs.pop('''feature_extractor'''		)

											lowercase_     :					List[Any]													=	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_     :					List[Any]													=	self.image_processor
											lowercase_     :					Any													=	False






					def __call__(       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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

											lowercase_     :					Union[str, Any]													=	kwargs.pop('''images'''    ,							lowerCAmelCase__		)
											lowercase_     :					Tuple													=	kwargs.pop('''text'''    ,							lowerCAmelCase__		)
											if len(lowerCAmelCase__		) > 0:
																	lowercase_     :					Dict													=	args[0]
																	lowercase_     :					str													=	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_     :					Optional[Any]													=	self.image_processor(lowerCAmelCase__    ,							*lowerCAmelCase__    ,							**lowerCAmelCase__		)
											if text is not None:
																	lowercase_     :					List[str]													=	self.tokenizer(lowerCAmelCase__    ,							**lowerCAmelCase__		)

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






					def      _snake_case   (       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def      _snake_case   (       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					@contextmanager
					def      _snake_case   (       self		):


											"""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_     :					Optional[Any]													=	True
											lowercase_     :					List[Any]													=	self.tokenizer
											yield
											lowercase_     :					Union[str, Any]													=	self.image_processor
											lowercase_     :					int													=	False






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





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

											lowercase_     :					int													=	{}

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

																							lowercase_     :					Tuple													=	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      _snake_case   (       self		):


											"""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      _snake_case   (       self		):


											"""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'''







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						return [
						    txt[:a] + txt[a].upper() + txt[a + 1 :]
						    for a in range(len(__SCREAMING_SNAKE_CASE							)							)
						    if txt[a].isalpha()
						]


if __name__ == "__main__":
							__import__("doctest").testmod()



