





"""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  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      int         =["""image_processor""", """tokenizer"""]
							lowerCamelCase     :      str         ="""BlipImageProcessor"""
							lowerCamelCase     :      int         ="""AutoTokenizer"""






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






							def __call__(     self   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = 0   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				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:
									a   :	int			=       self.tokenizer
									a   :	List[str]			=       self.tokenizer(
									    text=lowerCAmelCase__   ,      add_special_tokens=lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      stride=lowerCAmelCase__   ,      pad_to_multiple_of=lowerCAmelCase__   ,      return_attention_mask=lowerCAmelCase__   ,      return_overflowing_tokens=lowerCAmelCase__   ,      return_special_tokens_mask=lowerCAmelCase__   ,      return_offsets_mapping=lowerCAmelCase__   ,      return_token_type_ids=lowerCAmelCase__   ,      return_length=lowerCAmelCase__   ,      verbose=lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )
									return text_encoding

								# add pixel_values
								a   :	str			=       self.image_processor(lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   )

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

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

								return encoding_image_processor






							def 					__a     (     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Union[str, Any]:
								return self.tokenizer.batch_decode(*lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				List[Any]:
								return self.tokenizer.decode(*lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def 					__a     (     self   )			->				Any:
								a   :	List[Any]			=       self.tokenizer.model_input_names
								a   :	List[str]			=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )









"""simple docstring"""




import baseaa






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->bytes:






	'''simple docstring'''






	return baseaa.aaaencode(string.encode("utf-8"      )      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					bytes      )				->str:






	'''simple docstring'''






	return baseaa.aaadecode(_lowercase      ).decode("utf-8"      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


