






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					["image_processor", "tokenizer"]
							A_						  =					"AutoImageProcessor"
							A_						  =					"AutoTokenizer"

							def __init__(  self:   Tuple		,     __A:   List[Any]=None		,     __A:   Union[str, Any]=None		,     **__A:   Union[str, Any]   )    ->				List[Any]:
												_A								= None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''		,     __A		,     )
																	_A								= kwargs.pop('''feature_extractor'''   )

												_A								= 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__(__A		,     __A   )
												_A								= self.image_processor
												_A								= False

							def __call__(  self:   Union[str, Any]		,     *__A:   Optional[int]		,     **__A:   Tuple   )    ->				Any:
												# For backward compatibility
												if self._in_target_context_manager:
																	return self.current_processor(*__A		,     **__A   )

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

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

							def       __A (  self:   int		,     *__A:   Union[str, Any]		,     **__A:   Optional[Any]   )    ->				int:
												return self.tokenizer.batch_decode(*__A		,     **__A   )

							def       __A (  self:   Optional[int]		,     *__A:   Optional[Any]		,     **__A:   Union[str, Any]   )    ->				Optional[Any]:
												return self.tokenizer.decode(*__A		,     **__A   )

							@contextmanager
							def       __A (  self:   Tuple   )    ->				Optional[int]:
												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.'''   )
												_A								= True
												_A								= self.tokenizer
												yield
												_A								= self.image_processor
												_A								= False

							def       __A (  self:   Dict		,     __A:   str		,     __A:   List[Any]=False		,     __A:   Any=None   )    ->				Tuple:
												if added_vocab is None:
																	_A								= self.tokenizer.get_added_vocab()

												_A								= {}

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

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

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

							@property
							def       __A (  self:   List[str]   )    ->				str:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,     __A		,     )
												return self.image_processor_class



							@property
							def       __A (  self:   Optional[Any]   )    ->				int:
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		,     __A		,     )
												return self.image_processor











from __future__ import annotations




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):  # noqa: E741

					'''simple docstring'''







					while r - l > 1:
										_A								= (l + r) // 2
										if v[m] >= key:
															_A								= m
										else:
															_A								= m  # noqa: E741
					return r




def      __A					(     _lowercase ):

					'''simple docstring'''







					if len(_lowercase ) == 0:
										return 0

					_A								= [0] * len(_lowercase )
					_A								= 1

					_A								= v[0]

					for i in range(1					,			len(_lowercase ) ):
										if v[i] < tail[0]:
															_A								= v[i]
										elif v[i] > tail[length - 1]:
															_A								= v[i]
															length += 1
										else:
															_A								= v[i]

					return length


if __name__ == "__main__":
				import doctest

				doctest.testmod()



