



'''simple docstring'''






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				SCREAMING_SNAKE_CASE(							snake_case_			):







	"""simple docstring"""






	lowerCamelCase__			 =       ["""image_processor""", """tokenizer"""]
	lowerCamelCase__			 =       """BlipImageProcessor"""
	lowerCamelCase__			 =       ("""BertTokenizer""", """BertTokenizerFast""")







	def __init__(						self			:     Tuple		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     int		)			->		Tuple:
								UpperCAmelCase :       Optional[int] 							=		False
								super().__init__(__snake_case		,			__snake_case		)
								UpperCAmelCase :       List[str] 							=		self.image_processor







	def __call__(						self			:     Dict		,			__snake_case			:     Dict = None		,			__snake_case			:     Optional[Any] = None		,			__snake_case			:     Optional[Any] = True		,			__snake_case			:     Any = False		,			__snake_case			:     str = None		,			__snake_case			:     int = None		,			__snake_case			:     int = 0		,			__snake_case			:     str = None		,			__snake_case			:     Union[str, Any] = None		,			__snake_case			:     Optional[Any] = False		,			__snake_case			:     List[Any] = False		,			__snake_case			:     Optional[int] = False		,			__snake_case			:     List[Any] = False		,			__snake_case			:     Any = False		,			__snake_case			:     Union[str, Any] = True		,			__snake_case			:     Dict = None		,			**__snake_case			:     Optional[Any]		,			)			->		BatchEncoding:
								if images is None and text is None:
															raise ValueError('''You have to specify either images or text.'''		)

								# Get only text
								if images is None:
															UpperCAmelCase :       Tuple 							=		self.tokenizer
															UpperCAmelCase :       int 							=		self.tokenizer(
															    text=__snake_case		,			add_special_tokens=__snake_case		,			padding=__snake_case		,			truncation=__snake_case		,			max_length=__snake_case		,			stride=__snake_case		,			pad_to_multiple_of=__snake_case		,			return_attention_mask=__snake_case		,			return_overflowing_tokens=__snake_case		,			return_special_tokens_mask=__snake_case		,			return_offsets_mapping=__snake_case		,			return_token_type_ids=__snake_case		,			return_length=__snake_case		,			verbose=__snake_case		,			return_tensors=__snake_case		,			**__snake_case		,			)
															return text_encoding

								# add pixel_values
								UpperCAmelCase :       Optional[int] 							=		self.image_processor(__snake_case		,			return_tensors=__snake_case		)

								if text is not None:
															UpperCAmelCase :       Optional[int] 							=		self.tokenizer(
															    text=__snake_case		,			add_special_tokens=__snake_case		,			padding=__snake_case		,			truncation=__snake_case		,			max_length=__snake_case		,			stride=__snake_case		,			pad_to_multiple_of=__snake_case		,			return_attention_mask=__snake_case		,			return_overflowing_tokens=__snake_case		,			return_special_tokens_mask=__snake_case		,			return_offsets_mapping=__snake_case		,			return_token_type_ids=__snake_case		,			return_length=__snake_case		,			verbose=__snake_case		,			return_tensors=__snake_case		,			**__snake_case		,			)
								else:
															UpperCAmelCase :       Union[str, Any] 							=		None

								if text_encoding is not None:
															encoding_image_processor.update(__snake_case		)

								return encoding_image_processor







	def      A	(						self			:     List[Any]		,			*__snake_case			:     List[Any]		,			**__snake_case			:     Union[str, Any]		)			->		int:
								return self.tokenizer.batch_decode(*__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[str]		,			*__snake_case			:     List[str]		,			**__snake_case			:     Tuple		)			->		Any:
								return self.tokenizer.decode(*__snake_case		,			**__snake_case		)







	@property
	def      A	(						self			:     List[Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[int] 							=		self.tokenizer.model_input_names
								UpperCAmelCase :       Optional[int] 							=		self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)













import warnings

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


lowercase  							=					logging.get_logger(__name__)




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			*a       ,			**a					)       ->    None:
								warnings.warn(
								    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
								    ' Please use PerceiverImageProcessor instead.'       ,			a       ,			)
								super().__init__(*a       ,			**a					)





