


"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


__SCREAMING_SNAKE_CASE							:      Dict						=      logging.get_logger(__name__)





class        __A       (snake_case__):





							'''simple docstring'''

							def __init__(			self							: List[Any]						,    *UpperCAmelCase_							: List[str]						,    **UpperCAmelCase_							: str    )   ->None:



														"""simple docstring"""



														warnings.warn(
														    """The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
														    """ Please use PerceiverImageProcessor instead."""						,    UpperCAmelCase_						,    )
														super().__init__(*UpperCAmelCase_						,    **UpperCAmelCase_    )







"""simple docstring"""
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						Any							 =      ["""image_processor""", """tokenizer"""]
							__lowercase:						Dict							 =      """AutoImageProcessor"""
							__lowercase:						List[Any]							 =      """AutoTokenizer"""



							def __init__(			self							: Tuple						,    UpperCAmelCase_							: int=None						,    UpperCAmelCase_							: Tuple=None						,    **UpperCAmelCase_							: str    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																					    """ instead."""						,    UpperCAmelCase_						,    )
																					snake_case_          =						kwargs.pop("""feature_extractor"""    )

														snake_case_          =						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__(UpperCAmelCase_						,    UpperCAmelCase_    )
														snake_case_          =						self.image_processor
														snake_case_          =						False



							def __call__(			self							: Any						,    *UpperCAmelCase_							: Optional[int]						,    **UpperCAmelCase_							: str    )   ->List[str]:



														"""simple docstring"""



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

														snake_case_          =						kwargs.pop("""images"""						,    UpperCAmelCase_    )
														snake_case_          =						kwargs.pop("""text"""						,    UpperCAmelCase_    )
														if len(UpperCAmelCase_    ) > 0:
																					snake_case_          =						args[0]
																					snake_case_          =						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_          =						self.image_processor(UpperCAmelCase_						,    *UpperCAmelCase_						,    **UpperCAmelCase_    )
														if text is not None:
																					snake_case_          =						self.tokenizer(UpperCAmelCase_						,    **UpperCAmelCase_    )

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



							def 						lowerCAmelCase    (			self							: Optional[Any]						,    *UpperCAmelCase_							: Optional[Any]						,    **UpperCAmelCase_							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Optional[int]						,    *UpperCAmelCase_							: Union[str, Any]						,    **UpperCAmelCase_							: List[Any]    )   ->str:



														"""simple docstring"""



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



							@contextmanager
							def 						lowerCAmelCase    (			self							: List[str]    )   ->Dict:



														"""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."""    )
														snake_case_          =						True
														snake_case_          =						self.tokenizer
														yield
														snake_case_          =						self.image_processor
														snake_case_          =						False



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: str=False						,    UpperCAmelCase_							: str=None    )   ->Tuple:



														"""simple docstring"""



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

														snake_case_          =						{}

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

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

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



							@property
							def 						lowerCAmelCase    (			self							: List[Any]    )   ->Optional[int]:



														"""simple docstring"""



														warnings.warn(
														    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,    UpperCAmelCase_						,    )
														return self.image_processor_class



							@property
							def 						lowerCAmelCase    (			self							: List[str]    )   ->str:



														"""simple docstring"""



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



