





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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      ["image_processor", "tokenizer"]
 lowercase_		=      "LayoutLMv2ImageProcessor"
 lowercase_		=      ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")

 def __init__(self    :       Any	,			UpperCAmelCase_    :       List[str]=None	,			UpperCAmelCase_    :       List[Any]=None	,			**UpperCAmelCase_    :       Optional[int])	->str:



   '''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_	,			)
     lowerCamelCase__:				List[str]        =kwargs.pop("feature_extractor")

   lowerCamelCase__:				Optional[int]        =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    :       List[str]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,			UpperCAmelCase_    :       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,			UpperCAmelCase_    :       Union[List[List[int]], List[List[List[int]]]] = None	,			UpperCAmelCase_    :       Optional[Union[List[int], List[List[int]]]] = None	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       Union[bool, str, PaddingStrategy] = False	,			UpperCAmelCase_    :       Union[bool, str, TruncationStrategy] = None	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       int = 0	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       Optional[bool] = None	,			UpperCAmelCase_    :       Optional[bool] = None	,			UpperCAmelCase_    :       bool = False	,			UpperCAmelCase_    :       bool = False	,			UpperCAmelCase_    :       bool = False	,			UpperCAmelCase_    :       bool = False	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       Optional[Union[str, TensorType]] = None	,			**UpperCAmelCase_    :       int	,			)	->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
   lowerCamelCase__:				List[str]        =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_):
       lowerCamelCase__:				int        =[text]  # add batch dimension (as the image processor always adds a batch dimension)
     lowerCamelCase__:				Union[str, Any]        =features["words"]

   lowerCamelCase__:				int        =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
   lowerCamelCase__:				Dict        =features.pop("pixel_values")
   if return_overflowing_tokens is True:
     lowerCamelCase__:				Optional[int]        =self.get_overflowing_images(UpperCAmelCase_	,			encoded_inputs["overflow_to_sample_mapping"])
   lowerCamelCase__:				Tuple        =images

   return encoded_inputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       Any)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =[]
   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 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			*UpperCAmelCase_    :       List[Any]	,			**UpperCAmelCase_    :       Dict)	->Tuple:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			*UpperCAmelCase_    :       List[str]	,			**UpperCAmelCase_    :       int)	->List[str]:



   '''simple docstring'''



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

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Any:



   '''simple docstring'''



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

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Optional[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 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Optional[int]:



   '''simple docstring'''



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







def 					lowerCAmelCase_			(	__a		)					->		str:







  """simple docstring"""





  if isinstance(__a     ,   __a		):
    raise TypeError("'float' object cannot be interpreted as an integer"		)
  if isinstance(__a     ,   __a		):
    raise TypeError("'str' object cannot be interpreted as an integer"		)

  if num == 0:
    return "0b0"

  lowerCamelCase__:				Optional[int]        =False

  if num < 0:
    lowerCamelCase__:				Optional[Any]        =True
    lowerCamelCase__:				List[Any]        =-num

  lowerCamelCase__:				list[int]        =[]
  while num > 0:
    binary.insert(0     ,   num % 2		)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(__a		) for e in binary		)

  return "0b" + "".join(str(__a		) for e in binary		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

