"""simple docstring"""







import warnings
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	(    lowercase_						):
   lowercase							=						['image_processor', 'tokenizer']
   lowercase							=						'LayoutLMv2ImageProcessor'
   lowercase							=						('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')
   def __init__(    self  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,**__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'  ,__UpperCamelCase  ,)
             lowercase_			:       Tuple									=			kwargs.pop('feature_extractor'    )

        lowercase_			:       str									=			image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'    )
        if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'    )

        super().__init__(__UpperCamelCase  ,__UpperCamelCase    )
   def __call__(    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = True  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = 0  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = False  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   BatchEncoding:


        '''simple docstring'''


        if self.image_processor.apply_ocr and (boxes is not None):
             raise ValueError(
                 'You cannot provide bounding boxes '
                 'if you initialized the image processor with apply_ocr set to True.'    )

        if self.image_processor.apply_ocr and (word_labels is not None):
             raise ValueError(
                 'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'    )

        if return_overflowing_tokens is True and return_offsets_mapping is False:
             raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'    )

        # first, apply the image processor
        lowercase_			:       List[Any]									=			self.image_processor(images=__UpperCamelCase  ,return_tensors=__UpperCamelCase    )

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
             if isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
                  lowercase_			:       List[Any]									=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
             lowercase_			:       List[str]									=			features['words']

        lowercase_			:       str									=			self.tokenizer(
            text=text if text is not None else features['words']  ,text_pair=text_pair if text_pair is not None else None  ,boxes=boxes if boxes is not None else features['boxes']  ,word_labels=__UpperCamelCase  ,add_special_tokens=__UpperCamelCase  ,padding=__UpperCamelCase  ,truncation=__UpperCamelCase  ,max_length=__UpperCamelCase  ,stride=__UpperCamelCase  ,pad_to_multiple_of=__UpperCamelCase  ,return_token_type_ids=__UpperCamelCase  ,return_attention_mask=__UpperCamelCase  ,return_overflowing_tokens=__UpperCamelCase  ,return_special_tokens_mask=__UpperCamelCase  ,return_offsets_mapping=__UpperCamelCase  ,return_length=__UpperCamelCase  ,verbose=__UpperCamelCase  ,return_tensors=__UpperCamelCase  ,**__UpperCamelCase  ,)

        # add pixel values
        lowercase_			:       Optional[Any]									=			features.pop('pixel_values'    )
        if return_overflowing_tokens is True:
             lowercase_			:       Tuple									=			self.get_overflowing_images(__UpperCamelCase  ,encoded_inputs['overflow_to_sample_mapping']    )
        lowercase_			:       List[Any]									=			images

        return encoded_inputs
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Any									=			[]
        for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]    )

        if len(__UpperCamelCase    ) != len(__UpperCamelCase    ):
             raise ValueError(
                 'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                 f''' {len(__UpperCamelCase    )} and {len(__UpperCamelCase    )}'''    )

        return images_with_overflow
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return self.tokenizer.batch_decode(*__UpperCamelCase  ,**__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        return self.tokenizer.decode(*__UpperCamelCase  ,**__UpperCamelCase    )
   @property
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        return ["input_ids", "bbox", "attention_mask", "image"]
   @property
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,__UpperCamelCase  ,)
        return self.image_processor_class







   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,__UpperCamelCase  ,)
        return self.image_processor


"""simple docstring"""







__SCREAMING_SNAKE_CASE								="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"



def     lowercase__( __SCREAMING_SNAKE_CASE							:  bytes   ):
     # Make sure the supplied data is a bytes-like object
     if not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Dict									=			F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(__SCREAMING_SNAKE_CASE   )

     lowercase_			:       List[Any]									=			''.join(bin(__SCREAMING_SNAKE_CASE   )[2:].zfill(8   ) for byte in data   )

     lowercase_			:       List[Any]									=			len(__SCREAMING_SNAKE_CASE   ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          lowercase_			:       Optional[int]									=			b'=' * ((6 - len(__SCREAMING_SNAKE_CASE   ) % 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(__SCREAMING_SNAKE_CASE   ) % 6)
     else:
          lowercase_			:       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(__SCREAMING_SNAKE_CASE   )  ,    6   )   ).encode()
         + padding
     )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str   ):
     # Make sure encoded_data is either a string or a bytes-like object
     if not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ) and not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Tuple									=			(
              'argument should be a bytes-like object or ASCII string, '
              F'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(__SCREAMING_SNAKE_CASE   )

     # 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(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          try:
               lowercase_			:       List[str]									=			encoded_data.decode('utf-8'   )
          except UnicodeDecodeError:
               raise ValueError('base64 encoded data should only contain ASCII characters'   )

     lowercase_			:       List[str]									=			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(__SCREAMING_SNAKE_CASE   ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          lowercase_			:       Union[str, Any]									=			encoded_data[:-padding]

          lowercase_			:       List[Any]									=			''.join(
              bin(B64_CHARSET.index(__SCREAMING_SNAKE_CASE   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
     else:
          lowercase_			:       int									=			''.join(
              bin(B64_CHARSET.index(__SCREAMING_SNAKE_CASE   )   )[2:].zfill(6   ) for char in encoded_data   )

     lowercase_			:       str									=			[
         int(binary_stream[index : index + 8]  ,    2   )
         for index in range(0  ,    len(__SCREAMING_SNAKE_CASE   )  ,    8   )
     ]

     return bytes(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

