


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(  _lowerCAmelCase   ):

					'''simple docstring'''





					snake_case_		:				List[str]										=	['image_processor', 'tokenizer']
					snake_case_		:				List[Any]										=	'BlipImageProcessor'
					snake_case_		:				Tuple										=	'AutoTokenizer'


					def __init__(						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		False
											super().__init__(_lowerCAmelCase     ,    _lowerCAmelCase						)
											__snake_case				=		self.image_processor


					def __call__(						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		ImageInput = None     ,    SCREAMING_SNAKE_CASE	:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,    SCREAMING_SNAKE_CASE	:		bool = True     ,    SCREAMING_SNAKE_CASE	:		Union[bool, str, PaddingStrategy] = False     ,    SCREAMING_SNAKE_CASE	:		Union[bool, str, TruncationStrategy] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = None     ,    SCREAMING_SNAKE_CASE	:		int = 0     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[bool] = None     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		bool = True     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, TensorType]] = None     ,    **SCREAMING_SNAKE_CASE	:		Tuple     ,    )			->	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:
																	__snake_case				=		self.tokenizer
																	__snake_case				=		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
											__snake_case				=		self.image_processor(_lowerCAmelCase     ,    return_tensors=_lowerCAmelCase						)

											if text is not None:
																	__snake_case				=		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:
																	__snake_case				=		None

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

											return encoding_image_processor


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    *SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    **SCREAMING_SNAKE_CASE	:		Any						)			->	str:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    *SCREAMING_SNAKE_CASE	:		Any     ,    **SCREAMING_SNAKE_CASE	:		Dict						)			->	Optional[Any]:




											'''simple docstring'''

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






					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		self.tokenizer.model_input_names
											__snake_case				=		self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)









import os
from typing import Dict, List, Tuple, TypeVar, Union


A							:					List[Any]     						=     TypeVar('T')

A							:					Dict     						=     Union[List[T], Tuple[T, ...]]
A							:					Any     						=     Union[T, List[T], Dict[str, T]]
A							:					Optional[int]     						=     Union[str, bytes, os.PathLike]





