





"""simple docstring"""






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 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	int				     =				['image_processor', 'tokenizer']
				__lowerCamelCase				:	List[str]				     =				'Pix2StructImageProcessor'
				__lowerCamelCase				:	Optional[Any]				     =				('T5Tokenizer', 'T5TokenizerFast')
				def __init__(		self    :			List[Any]	, a_    :			Dict	, a_    :			Dict    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	int		 =			False
							super().__init__(a_	, a_    )
				def __call__(		self    :			List[str]	, a_    :			Tuple=None	, a_    :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	, a_    :			bool = True	, a_    :			Union[bool, str, PaddingStrategy] = False	, a_    :			Union[bool, str, TruncationStrategy] = None	, a_    :			Optional[int] = None	, a_    :			Optional[int] = 20_48	, a_    :			int = 0	, a_    :			Optional[int] = None	, a_    :			Optional[bool] = None	, a_    :			bool = False	, a_    :			bool = False	, a_    :			bool = False	, a_    :			bool = False	, a_    :			bool = False	, a_    :			bool = True	, a_    :			Optional[Union[str, TensorType]] = None	, **a_    :			Any	, )	->	Optional[int]:



							'''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 and not self.image_processor.is_vqa:
										a__  :	Tuple		 =			self.tokenizer
										a__  :	str		 =			self.tokenizer(
										    text=a_	, add_special_tokens=a_	, padding=a_	, truncation=a_	, max_length=a_	, stride=a_	, pad_to_multiple_of=a_	, return_attention_mask=a_	, return_overflowing_tokens=a_	, return_special_tokens_mask=a_	, return_offsets_mapping=a_	, return_token_type_ids=a_	, return_length=a_	, verbose=a_	, return_tensors=a_	, **a_	, )
										return text_encoding

							if not self.image_processor.is_vqa:
										# add pixel_values
										a__  :	Tuple		 =			self.image_processor(
										    a_	, return_tensors=a_	, max_patches=a_	, **a_    )
							else:
										# add pixel_values and bbox
										a__  :	Optional[Any]		 =			self.image_processor(
										    a_	, return_tensors=a_	, max_patches=a_	, header_text=a_	, **a_    )

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

										if "attention_mask" in text_encoding:
													a__  :	Tuple		 =			text_encoding.pop("attention_mask"    )
										if "input_ids" in text_encoding:
													a__  :	Tuple		 =			text_encoding.pop("input_ids"    )
							else:
										a__  :	Optional[Any]		 =			None

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

							return encoding_image_processor
				def       UpperCAmelCase      (		self    :			Optional[int]	, *a_    :			str	, **a_    :			Any    )	->	Optional[int]:



							'''simple docstring'''



							return self.tokenizer.batch_decode(*a_	, **a_    )
				def       UpperCAmelCase      (		self    :			Any	, *a_    :			Optional[int]	, **a_    :			str    )	->	str:



							'''simple docstring'''



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






				@property
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Tuple:



							'''simple docstring'''



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







'''simple docstring'''
from __future__ import annotations




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  2
   lowercase_						:     Tuple							     =  []
   while i * i <= n:
      if n % i:
         i += 1
      else:
         n //= i
         factors.append(_UpperCamelCase				)
   if n > 1:
      factors.append(_UpperCamelCase				)
   return factors


if __name__ == "__main__":
 import doctest

 doctest.testmod()




