



'''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 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""image_processor""", """tokenizer"""]
    lowercase__			=   """LayoutLMv2ImageProcessor"""
    lowercase__			=   ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")







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

      a						: Union[str, Any]					       =					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__(__snake_case						,      __snake_case     )







    def __call__(				self			:  Tuple						,      __snake_case			:  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			:  int						,      ):
      # 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
      a						: List[Any]					       =					self.image_processor(images=__snake_case						,      return_tensors=__snake_case     )

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

      a						: Tuple					       =					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=__snake_case						,      add_special_tokens=__snake_case						,      padding=__snake_case						,      truncation=__snake_case						,      max_length=__snake_case						,      stride=__snake_case						,      pad_to_multiple_of=__snake_case						,      return_token_type_ids=__snake_case						,      return_attention_mask=__snake_case						,      return_overflowing_tokens=__snake_case						,      return_special_tokens_mask=__snake_case						,      return_offsets_mapping=__snake_case						,      return_length=__snake_case						,      verbose=__snake_case						,      return_tensors=__snake_case						,      **__snake_case						,      )

      # add pixel values
      a						: str					       =					features.pop('pixel_values'     )
      if return_overflowing_tokens is True:
        a						: Optional[int]					       =					self.get_overflowing_images(__snake_case						,      encoded_inputs['overflow_to_sample_mapping']     )
      a						: List[str]					       =					images

      return encoded_inputs







    def       lowercase_	(				self			:  Tuple						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Optional[Any]     ):
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      a						: str					       =					[]
      for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx]     )

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

      return images_with_overflow







    def       lowercase_	(				self			:  List[Any]						,      *__snake_case			:  List[Any]						,      **__snake_case			:  Optional[Any]     ):
      return self.tokenizer.batch_decode(*__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      *__snake_case			:  List[str]						,      **__snake_case			:  List[str]     ):
      return self.tokenizer.decode(*__snake_case						,      **__snake_case     )







    @property
    def       lowercase_	(				self			:  Optional[int]     ):
      return ["input_ids", "bbox", "attention_mask", "image"]







    @property
    def       lowercase_	(				self			:  int     ):
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,      __snake_case						,      )
      return self.image_processor_class







    @property
    def       lowercase_	(				self			:  int     ):
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,      __snake_case						,      )
      return self.image_processor




'''simple docstring'''






from math import factorial, pi
def 				lowerCamelCase__	(						_A			,      _A = 30    ):

  if not isinstance(_A			,      (int, float)    ):
    raise ValueError('maclaurin_sin() requires either an int or float for theta'    )

  if not isinstance(_A			,      _A    ) or accuracy <= 0:
    raise ValueError('maclaurin_sin() requires a positive int for accuracy'    )

  a						: Dict					       =					float(_A    )
  a						: List[Any]					       =					theta // (2 * pi)
  theta -= 2 * div * pi
  return sum(
      (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1    ) for r in range(_A    )    )
def 				lowerCamelCase__	(						_A			,      _A = 30    ):

  if not isinstance(_A			,      (int, float)    ):
    raise ValueError('maclaurin_cos() requires either an int or float for theta'    )

  if not isinstance(_A			,      _A    ) or accuracy <= 0:
    raise ValueError('maclaurin_cos() requires a positive int for accuracy'    )

  a						: int					       =					float(_A    )
  a						: str					       =					theta // (2 * pi)
  theta -= 2 * div * pi
  return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r    ) for r in range(_A    )    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(maclaurin_sin(1_0))
     print(maclaurin_sin(-1_0))
     print(maclaurin_sin(1_0, 1_5))
     print(maclaurin_sin(-1_0, 1_5))

     print(maclaurin_cos(5))
     print(maclaurin_cos(-5))
     print(maclaurin_cos(1_0, 1_5))
     print(maclaurin_cos(-1_0, 1_5))