




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      a	(  _A     ):

     '''simple docstring'''





     lowerCAmelCase							:			List[Any]  						=    ['image_processor', 'tokenizer']
     lowerCAmelCase							:			str  						=    'LayoutLMv2ImageProcessor'
     lowerCAmelCase							:			Optional[Any]  						=    ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')
     def __init__(      self  :     int   ,       __snake_case  :     List[Any]=None   ,       __snake_case  :     List[str]=None   ,       **__snake_case  :     Union[str, Any]							):
         if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''   ,       _lowerCAmelCase   ,       )
             UpperCAmelCase_									=	kwargs.pop('''feature_extractor'''							)

         UpperCAmelCase_									=	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__(_lowerCAmelCase   ,       _lowerCAmelCase							)
     def __call__(      self  :     Optional[int]   ,       __snake_case  :     Union[str, Any]   ,       __snake_case  :     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,       __snake_case  :     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,       __snake_case  :     Union[List[List[int]], List[List[List[int]]]] = None   ,       __snake_case  :     Optional[Union[List[int], List[List[int]]]] = None   ,       __snake_case  :     bool = True   ,       __snake_case  :     Union[bool, str, PaddingStrategy] = False   ,       __snake_case  :     Union[bool, str, TruncationStrategy] = None   ,       __snake_case  :     Optional[int] = None   ,       __snake_case  :     int = 0   ,       __snake_case  :     Optional[int] = None   ,       __snake_case  :     Optional[bool] = None   ,       __snake_case  :     Optional[bool] = None   ,       __snake_case  :     bool = False   ,       __snake_case  :     bool = False   ,       __snake_case  :     bool = False   ,       __snake_case  :     bool = False   ,       __snake_case  :     bool = True   ,       __snake_case  :     Optional[Union[str, TensorType]] = None   ,       **__snake_case  :     Any   ,       ):
         # verify input
         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
         UpperCAmelCase_									=	self.image_processor(images=_lowerCAmelCase   ,       return_tensors=_lowerCAmelCase							)

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

         UpperCAmelCase_									=	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=_lowerCAmelCase   ,       add_special_tokens=_lowerCAmelCase   ,       padding=_lowerCAmelCase   ,       truncation=_lowerCAmelCase   ,       max_length=_lowerCAmelCase   ,       stride=_lowerCAmelCase   ,       pad_to_multiple_of=_lowerCAmelCase   ,       return_token_type_ids=_lowerCAmelCase   ,       return_attention_mask=_lowerCAmelCase   ,       return_overflowing_tokens=_lowerCAmelCase   ,       return_special_tokens_mask=_lowerCAmelCase   ,       return_offsets_mapping=_lowerCAmelCase   ,       return_length=_lowerCAmelCase   ,       verbose=_lowerCAmelCase   ,       return_tensors=_lowerCAmelCase   ,       **_lowerCAmelCase   ,       )

         # add pixel values
         UpperCAmelCase_									=	features.pop('''pixel_values'''							)
         if return_overflowing_tokens is True:
             UpperCAmelCase_									=	self.get_overflowing_images(_lowerCAmelCase   ,       encoded_inputs['''overflow_to_sample_mapping''']							)
         UpperCAmelCase_									=	images

         return encoded_inputs
     def     lowerCamelCase_  (      self  :     Tuple   ,       __snake_case  :     Dict   ,       __snake_case  :     List[Any]							):
         UpperCAmelCase_									=	[]
         for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]							)

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

         return images_with_overflow
     def     lowerCamelCase_  (      self  :     Optional[Any]   ,       *__snake_case  :     str   ,       **__snake_case  :     Any							):
         return self.tokenizer.batch_decode(*_lowerCAmelCase   ,       **_lowerCAmelCase							)
     def     lowerCamelCase_  (      self  :     Union[str, Any]   ,       *__snake_case  :     Dict   ,       **__snake_case  :     Union[str, Any]							):
         return self.tokenizer.decode(*_lowerCAmelCase   ,       **_lowerCAmelCase							)
     @property
     def     lowerCamelCase_  (      self  :     Optional[int]							):
         return ["input_ids", "bbox", "attention_mask", "image"]
     @property
     def     lowerCamelCase_  (      self  :     Optional[int]							):
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,       _lowerCAmelCase   ,       )
         return self.image_processor_class




     @property
     def     lowerCamelCase_  (      self  :     Optional[int]							):
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,       _lowerCAmelCase   ,       )
         return self.image_processor









def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:int				)->Optional[int]:
   '''simple docstring'''






   snake_case_            =       0
   while b > 0:
      if b & 1:
         res += a

      a += a
      b >>= 1

   return res



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Dict				)->Dict:
   '''simple docstring'''






   snake_case_            =       0
   while b > 0:
      if b & 1:
         snake_case_            =       ((res % c) + (a % c)) % c

      a += a
      b >>= 1

   return res




