


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__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		['image_processor', 'tokenizer']
    __magic_name__        =		'Pix2StructImageProcessor'
    __magic_name__        =		('T5Tokenizer', 'T5TokenizerFast')
    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    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 = 2_0_4_8      , __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:
              snake_case					=    self.tokenizer
              snake_case					=    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
              snake_case					=    self.image_processor(
                  __snake_case      , return_tensors=__snake_case      , max_patches=__snake_case      , **__snake_case   )
         else:
              # add pixel_values and bbox
              snake_case					=    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:
              snake_case					=    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:
                   snake_case					=    text_encoding.pop('''attention_mask'''   )
              if "input_ids" in text_encoding:
                   snake_case					=    text_encoding.pop('''input_ids'''   )
         else:
              snake_case					=    None

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

         return encoding_image_processor
    def 		a_      (	self      , *__snake_case      , **__snake_case   ):
         return self.tokenizer.batch_decode(*__snake_case      , **__snake_case   )
    def 		a_      (	self      , *__snake_case      , **__snake_case   ):
         return self.tokenizer.decode(*__snake_case      , **__snake_case   )




    @property
    def 		a_      (	self   ):
         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   )   )








def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    len(UpperCamelCase_    )
     snake_case					=    [[0] * n for i in range(UpperCamelCase_    )]
     for i in range(UpperCamelCase_    ):
          snake_case					=    y_points[i]

     for i in range(2							,UpperCamelCase_    ):
          for j in range(UpperCamelCase_							,UpperCamelCase_    ):
               snake_case					=    (
                   (xa - x_points[j - i + 1]) * q[j][i - 1]
                   - (xa - x_points[j]) * q[j - 1][i - 1]
               ) / (x_points[j] - x_points[j - i + 1])

     return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




