





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      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			['image_processor', 'tokenizer']
     __magic_name__   =			'Pix2StructImageProcessor'
     __magic_name__   =			('T5Tokenizer', 'T5TokenizerFast')




     def __init__(    self				,     snake_case_				,     snake_case_							):
         _A     =							False
         super().__init__(snake_case_				,     snake_case_							)




     def __call__(    self				,     snake_case_=None				,     snake_case_ = None				,     snake_case_ = True				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = 2048				,     snake_case_ = 0				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = True				,     snake_case_ = None				,     **snake_case_				,     ):
         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     =							self.tokenizer
             _A     =							self.tokenizer(
                 text=snake_case_				,     add_special_tokens=snake_case_				,     padding=snake_case_				,     truncation=snake_case_				,     max_length=snake_case_				,     stride=snake_case_				,     pad_to_multiple_of=snake_case_				,     return_attention_mask=snake_case_				,     return_overflowing_tokens=snake_case_				,     return_special_tokens_mask=snake_case_				,     return_offsets_mapping=snake_case_				,     return_token_type_ids=snake_case_				,     return_length=snake_case_				,     verbose=snake_case_				,     return_tensors=snake_case_				,     **snake_case_				,     )
             return text_encoding

         if not self.image_processor.is_vqa:
             # add pixel_values
             _A     =							self.image_processor(
                 snake_case_				,     return_tensors=snake_case_				,     max_patches=snake_case_				,     **snake_case_							)
         else:
             # add pixel_values and bbox
             _A     =							self.image_processor(
                 snake_case_				,     return_tensors=snake_case_				,     max_patches=snake_case_				,     header_text=snake_case_				,     **snake_case_							)

         if text is not None and not self.image_processor.is_vqa:
             _A     =							self.tokenizer(
                 text=snake_case_				,     add_special_tokens=snake_case_				,     padding=snake_case_				,     truncation=snake_case_				,     max_length=snake_case_				,     stride=snake_case_				,     pad_to_multiple_of=snake_case_				,     return_attention_mask=snake_case_				,     return_overflowing_tokens=snake_case_				,     return_special_tokens_mask=snake_case_				,     return_offsets_mapping=snake_case_				,     return_token_type_ids=snake_case_				,     return_length=snake_case_				,     verbose=snake_case_				,     return_tensors=snake_case_				,     **snake_case_				,     )

             if "attention_mask" in text_encoding:
                 _A     =							text_encoding.pop('attention_mask'							)
             if "input_ids" in text_encoding:
                 _A     =							text_encoding.pop('input_ids'							)
         else:
             _A     =							None

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

         return encoding_image_processor




     def        lowerCAmelCase__		(    self				,     *snake_case_				,     **snake_case_							):
         return self.tokenizer.batch_decode(*snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     *snake_case_				,     **snake_case_							):
         return self.tokenizer.decode(*snake_case_				,     **snake_case_							)




     @property
     def        lowerCAmelCase__		(    self							):
         _A     =							self.tokenizer.model_input_names
         _A     =							self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)













def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : int	):




     '''simple docstring'''






     if digit_amount > 0:
          return round(number - int(snake_case__	)						,snake_case__	)
     return number - int(snake_case__	)


if __name__ == "__main__":
     print(decimal_isolate(1.5_3, 0))
     print(decimal_isolate(3_5.3_4_5, 1))
     print(decimal_isolate(3_5.3_4_5, 2))
     print(decimal_isolate(3_5.3_4_5, 3))
     print(decimal_isolate(-1_4.7_8_9, 3))
     print(decimal_isolate(0, 2))
     print(decimal_isolate(-1_4.1_2_3, 1))
     print(decimal_isolate(-1_4.1_2_3, 2))
     print(decimal_isolate(-1_4.1_2_3, 3))


