



'''simple docstring'''

import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		Tuple        =				["""image_processor""", """tokenizer"""]
							UpperCAmelCase			:		Optional[Any]        =				"""AutoImageProcessor"""
							UpperCAmelCase			:		Union[str, Any]        =				"""AutoTokenizer"""


							def __init__(				self   :						Tuple      ,						a_   :						Dict=None      ,						a_   :						Optional[Any]=None      ,						**a_   :						int			)			->					Dict:
										snake_case:    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.'      ,						a_      ,						)
													snake_case:    Optional[Any]      							=kwargs.pop('feature_extractor'			)

										snake_case:    Optional[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__(a_      ,						a_			)
										snake_case:    List[Any]      							=self.image_processor
										snake_case:    List[str]      							=False


							def __call__(				self   :						str      ,						*a_   :						Dict      ,						**a_   :						Optional[int]			)			->					List[str]:
										# For backward compatibility
										if self._in_target_context_manager:
													return self.current_processor(*a_      ,						**a_			)

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

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


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						*a_   :						List[Any]      ,						**a_   :						Union[str, Any]			)			->					str:
										return self.tokenizer.batch_decode(*a_      ,						**a_			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						*a_   :						List[Any]      ,						**a_   :						str			)			->					int:
										return self.tokenizer.decode(*a_      ,						**a_			)


							@contextmanager
							def    UpperCamelCase     (				self   :						Any			)			->					Any:
										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.'			)
										snake_case:    List[Any]      							=True
										snake_case:    Optional[Any]      							=self.tokenizer
										yield
										snake_case:    List[str]      							=self.image_processor
										snake_case:    List[str]      							=False


							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						int      ,						a_   :						Optional[Any]=False      ,						a_   :						Union[str, Any]=None			)			->					str:
										if added_vocab is None:
													snake_case:    Optional[Any]      							=self.tokenizer.get_added_vocab()

										snake_case:    Dict      							={}

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

																snake_case:    int      							=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    UpperCamelCase     (				self   :						Tuple			)			->					int:
										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    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										warnings.warn(
										    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,						a_      ,						)
										return self.image_processor









'''simple docstring'''

from diffusers.utils.testing_utils import require_onnxruntime






@require_onnxruntime
class 						a_		:
							pass




