

from typing import List, Optional, Union

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






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  Optional[Any]						      =      ['''image_processor''', '''tokenizer''']
							_UpperCAmelCase    :  Union[str, Any]						      =      '''Pix2StructImageProcessor'''
							_UpperCAmelCase    :  Any						      =      ('''T5Tokenizer''', '''T5TokenizerFast''')
							def __init__(		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			int):
								__lowerCamelCase				:				List[Any]					       = False
								super().__init__(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
							def __call__(		self		:			str   ,SCREAMING_SNAKE_CASE__		:			Any=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__		:			Optional[int] = 2_0_4_8   ,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__		:			Dict   ,):
								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 and not self.image_processor.is_vqa:
									__lowerCamelCase				:				Tuple					       = self.tokenizer
									__lowerCamelCase				:				Dict					       = self.tokenizer(
									    text=SCREAMING_SNAKE_CASE__   ,add_special_tokens=SCREAMING_SNAKE_CASE__   ,padding=SCREAMING_SNAKE_CASE__   ,truncation=SCREAMING_SNAKE_CASE__   ,max_length=SCREAMING_SNAKE_CASE__   ,stride=SCREAMING_SNAKE_CASE__   ,pad_to_multiple_of=SCREAMING_SNAKE_CASE__   ,return_attention_mask=SCREAMING_SNAKE_CASE__   ,return_overflowing_tokens=SCREAMING_SNAKE_CASE__   ,return_special_tokens_mask=SCREAMING_SNAKE_CASE__   ,return_offsets_mapping=SCREAMING_SNAKE_CASE__   ,return_token_type_ids=SCREAMING_SNAKE_CASE__   ,return_length=SCREAMING_SNAKE_CASE__   ,verbose=SCREAMING_SNAKE_CASE__   ,return_tensors=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__   ,)
									return text_encoding

								if not self.image_processor.is_vqa:
									# add pixel_values
									__lowerCamelCase				:				List[Any]					       = self.image_processor(
									    SCREAMING_SNAKE_CASE__   ,return_tensors=SCREAMING_SNAKE_CASE__   ,max_patches=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
								else:
									# add pixel_values and bbox
									__lowerCamelCase				:				List[Any]					       = self.image_processor(
									    SCREAMING_SNAKE_CASE__   ,return_tensors=SCREAMING_SNAKE_CASE__   ,max_patches=SCREAMING_SNAKE_CASE__   ,header_text=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)

								if text is not None and not self.image_processor.is_vqa:
									__lowerCamelCase				:				List[Any]					       = self.tokenizer(
									    text=SCREAMING_SNAKE_CASE__   ,add_special_tokens=SCREAMING_SNAKE_CASE__   ,padding=SCREAMING_SNAKE_CASE__   ,truncation=SCREAMING_SNAKE_CASE__   ,max_length=SCREAMING_SNAKE_CASE__   ,stride=SCREAMING_SNAKE_CASE__   ,pad_to_multiple_of=SCREAMING_SNAKE_CASE__   ,return_attention_mask=SCREAMING_SNAKE_CASE__   ,return_overflowing_tokens=SCREAMING_SNAKE_CASE__   ,return_special_tokens_mask=SCREAMING_SNAKE_CASE__   ,return_offsets_mapping=SCREAMING_SNAKE_CASE__   ,return_token_type_ids=SCREAMING_SNAKE_CASE__   ,return_length=SCREAMING_SNAKE_CASE__   ,verbose=SCREAMING_SNAKE_CASE__   ,return_tensors=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__   ,)

									if "attention_mask" in text_encoding:
										__lowerCamelCase				:				List[Any]					       = text_encoding.pop('attention_mask')
									if "input_ids" in text_encoding:
										__lowerCamelCase				:				Dict					       = text_encoding.pop('input_ids')
								else:
									__lowerCamelCase				:				Optional[int]					       = None

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

								return encoding_image_processor
							def     lowerCAmelCase     (		self		:			Dict   ,*SCREAMING_SNAKE_CASE__		:			str   ,**SCREAMING_SNAKE_CASE__		:			int):
								return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			List[str]   ,*SCREAMING_SNAKE_CASE__		:			int   ,**SCREAMING_SNAKE_CASE__		:			Dict):
								return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)


							@property
							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase				:				Dict					       = self.tokenizer.model_input_names
								__lowerCamelCase				:				int					       = self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))











"""simple docstring"""





def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->bool:





  """simple docstring"""


  return numa ^ numa < 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()
