





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      ):
      _UpperCAmelCase     :List[Any]						   =       ['image_processor', 'tokenizer']
      _UpperCAmelCase     :List[str]						   =       'Pix2StructImageProcessor'
      _UpperCAmelCase     :Optional[int]						   =       ('T5Tokenizer', 'T5TokenizerFast')





      def __init__(			self	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       False
       super().__init__(A_	,							A_       )





      def __call__(			self	,							A_=None	,							A_ = None	,							A_ = True	,							A_ = False	,							A_ = None	,							A_ = None	,							A_ = 2048	,							A_ = 0	,							A_ = None	,							A_ = None	,							A_ = False	,							A_ = False	,							A_ = False	,							A_ = False	,							A_ = False	,							A_ = True	,							A_ = None	,							**A_	,							):
       '''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:
        UpperCamelCase     : Dict	     =       self.tokenizer
        UpperCamelCase     : 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_	,							)
        return text_encoding

       if not self.image_processor.is_vqa:
        # add pixel_values
        UpperCamelCase     : Dict	     =       self.image_processor(
            A_	,							return_tensors=A_	,							max_patches=A_	,							**A_       )
       else:
        # add pixel_values and bbox
        UpperCamelCase     : str	     =       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:
        UpperCamelCase     : int	     =       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:
         UpperCamelCase     : Any	     =       text_encoding.pop("attention_mask"       )
        if "input_ids" in text_encoding:
         UpperCamelCase     : Any	     =       text_encoding.pop("input_ids"       )
       else:
        UpperCamelCase     : Optional[int]	     =       None

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

       return encoding_image_processor





      def 	__UpperCamelCase(			self	,							*A_	,							**A_       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							*A_	,							**A_       ):
       '''simple docstring'''




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





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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







__lowerCamelCase	:					Tuple			  =					"""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure the supplied data is a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : str	     =       F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
  raise TypeError(_lowerCAmelCase					)

 UpperCamelCase     : Optional[int]	     =       "".join(bin(_lowerCAmelCase					)[2:].zfill(8					) for byte in data					)

 UpperCamelCase     : str	     =       len(_lowerCAmelCase					) % 6 != 0

 if padding_needed:
  # The padding that will be added later
  UpperCamelCase     : Optional[Any]	     =       b"=" * ((6 - len(_lowerCAmelCase					) % 6) // 2)

  # Append binary_stream with arbitrary binary digits (0's by default) to make its
  # length a multiple of 6.
  binary_stream += "0" * (6 - len(_lowerCAmelCase					) % 6)
 else:
  UpperCamelCase     : List[Any]	     =       b""

 # Encode every 6 binary digits to their corresponding Base64 character
 return (
     "".join(
         B64_CHARSET[int(binary_stream[index : index + 6]							,       2					)]
         for index in range(0							,       len(_lowerCAmelCase					)							,       6					)					).encode()
     + padding
 )



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure encoded_data is either a string or a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					) and not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : List[Any]	     =       (
      "argument should be a bytes-like object or ASCII string, "
      F"""not '{encoded_data.__class__.__name__}'"""
  )
  raise TypeError(_lowerCAmelCase					)

 # In case encoded_data is a bytes-like object, make sure it contains only
 # ASCII characters so we convert it to a string object
 if isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  try:
   UpperCamelCase     : Any	     =       encoded_data.decode("utf-8"					)
  except UnicodeDecodeError:
   raise ValueError("base64 encoded data should only contain ASCII characters"					)

 UpperCamelCase     : Union[str, Any]	     =       encoded_data.count("="					)

 # Check if the encoded string contains non base64 characters
 if padding:
  assert all(
      char in B64_CHARSET for char in encoded_data[:-padding]					), "Invalid base64 character(s) found."
 else:
  assert all(
      char in B64_CHARSET for char in encoded_data					), "Invalid base64 character(s) found."

 # Check the padding
 assert len(_lowerCAmelCase					) % 4 == 0 and padding < 3, "Incorrect padding"

 if padding:
  # Remove padding if there is one
  UpperCamelCase     : List[str]	     =       encoded_data[:-padding]

  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)[: -padding * 2]
 else:
  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)

 UpperCamelCase     : Any	     =       [
     int(binary_stream[index : index + 8]							,       2					)
     for index in range(0							,       len(_lowerCAmelCase					)							,       8					)
 ]

 return bytes(_lowerCAmelCase					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
