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 __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["image_processor", "tokenizer"]
   lowerCAmelCase_														=							"LayoutLMv2ImageProcessor"
   lowerCAmelCase_														=							("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")




   def __init__(    self			:				Union[str, Any]       ,	_lowercase			:				str=None       ,	_lowercase			:				Optional[int]=None       ,	**_lowercase			:				List[Any]      ):



     """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."""       ,	_lowercase       ,	)
       SCREAMING_SNAKE_CASE__										=      kwargs.pop("""feature_extractor"""      )

     SCREAMING_SNAKE_CASE__										=      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__(_lowercase       ,	_lowercase      )




   def __call__(    self			:				Union[str, Any]       ,	_lowercase			:				Tuple       ,	_lowercase			:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,	_lowercase			:				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,	_lowercase			:				Union[List[List[int]], List[List[List[int]]]] = None       ,	_lowercase			:				Optional[Union[List[int], List[List[int]]]] = None       ,	_lowercase			:				bool = True       ,	_lowercase			:				Union[bool, str, PaddingStrategy] = False       ,	_lowercase			:				Union[bool, str, TruncationStrategy] = None       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				int = 0       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = True       ,	_lowercase			:				Optional[Union[str, TensorType]] = None       ,	**_lowercase			:				List[Any]       ,	):



     """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
     SCREAMING_SNAKE_CASE__										=      self.image_processor(images=_lowercase       ,	return_tensors=_lowercase      )

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

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

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

     return encoded_inputs




   def 	__a						(    self			:				str       ,	_lowercase			:				str       ,	_lowercase			:				int      ):



     """simple docstring"""






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

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

     return images_with_overflow




   def 	__a						(    self			:				List[Any]       ,	*_lowercase			:				Union[str, Any]       ,	**_lowercase			:				int      ):



     """simple docstring"""






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




   def 	__a						(    self			:				str       ,	*_lowercase			:				Union[str, Any]       ,	**_lowercase			:				Optional[Any]      ):



     """simple docstring"""






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




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     return ["input_ids", "bbox", "attention_mask", "image"]




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






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




   @property
   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






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

__lowerCamelCase			: List[Any]							   =		6_5521



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      1
  SCREAMING_SNAKE_CASE__										=      0
  for plain_chr in plain_text:
    SCREAMING_SNAKE_CASE__										=      (a + ord(__UpperCamelCase	)) % MOD_ADLER
    SCREAMING_SNAKE_CASE__										=      (b + a) % MOD_ADLER
  return (b << 16) | a
