




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        __snake_case  (_a		):
				lowerCAmelCase__				=     ["image_processor", "tokenizer"]
				lowerCAmelCase__				=     "BlipImageProcessor"
				lowerCAmelCase__				=     "AutoTokenizer"



				def __init__(      self      :		Tuple		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		List[str]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      False
								super().__init__(_UpperCAmelCase		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      self.image_processor



				def __call__(      self      :		str		,	_UpperCAmelCase      :		ImageInput = None		,	_UpperCAmelCase      :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Union[bool, str, PaddingStrategy] = False		,	_UpperCAmelCase      :		Union[bool, str, TruncationStrategy] = None		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		int = 0		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Optional[Union[str, TensorType]] = None		,	**_UpperCAmelCase      :		Optional[Any]		,	)  ->    BatchEncoding:





								'''simple docstring'''




								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:
												_lowerCAmelCase    :    Tuple										=      self.tokenizer
												_lowerCAmelCase    :    Union[str, Any]										=      self.tokenizer(
												    text=_UpperCAmelCase		,	add_special_tokens=_UpperCAmelCase		,	padding=_UpperCAmelCase		,	truncation=_UpperCAmelCase		,	max_length=_UpperCAmelCase		,	stride=_UpperCAmelCase		,	pad_to_multiple_of=_UpperCAmelCase		,	return_attention_mask=_UpperCAmelCase		,	return_overflowing_tokens=_UpperCAmelCase		,	return_special_tokens_mask=_UpperCAmelCase		,	return_offsets_mapping=_UpperCAmelCase		,	return_token_type_ids=_UpperCAmelCase		,	return_length=_UpperCAmelCase		,	verbose=_UpperCAmelCase		,	return_tensors=_UpperCAmelCase		,	**_UpperCAmelCase		,	)
												return text_encoding

								# add pixel_values
								_lowerCAmelCase    :    List[Any]										=      self.image_processor(_UpperCAmelCase		,	return_tensors=_UpperCAmelCase      )

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

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

								return encoding_image_processor



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	*_UpperCAmelCase      :		Dict		,	**_UpperCAmelCase      :		Optional[int]      )  ->    int:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	*_UpperCAmelCase      :		Tuple		,	**_UpperCAmelCase      :		Any      )  ->    List[Any]:





								'''simple docstring'''




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



				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      self.tokenizer.model_input_names
								_lowerCAmelCase    :    Dict										=      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )







def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Dict										=      [0] * len(UpperCamelCase_       )

				for i in range(1      ,    len(UpperCamelCase_       )       ):
								# use last results for better performance - dynamic programming
								_lowerCAmelCase    :    Union[str, Any]										=      prefix_result[i - 1]
								while j > 0 and input_string[i] != input_string[j]:
												_lowerCAmelCase    :    str										=      prefix_result[j - 1]

								if input_string[i] == input_string[j]:
												j += 1
								_lowerCAmelCase    :    Optional[int]										=      j

				return prefix_result




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				return max(prefix_function(UpperCamelCase_       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

