
"""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_			(__SCREAMING_SNAKE_CASE   ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__    :		Dict						  =	["""image_processor""", """tokenizer"""]
  SCREAMING_SNAKE_CASE__    :		List[str]						  =	"""LayoutLMv2ImageProcessor"""
  SCREAMING_SNAKE_CASE__    :		str						  =	("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







  def __init__(	self       ,     lowercase_=None       ,     lowercase_=None       ,     **lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      Dict      =					None
     if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."       ,     UpperCamelCase__       ,     )
        UpperCAmelCase_      :      Dict      =					kwargs.pop("feature_extractor"						)

     UpperCAmelCase_      :      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       ,     lowercase_       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = True       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = None       ,     lowercase_ = 0       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = True       ,     lowercase_ = None       ,     **lowercase_       ,     ):

     """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
     UpperCAmelCase_      :      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__						):
           UpperCAmelCase_      :      Optional[Any]      =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
        UpperCAmelCase_      :      Any      =					features['''words''']

     UpperCAmelCase_      :      List[Any]      =					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
     UpperCAmelCase_      :      Optional[Any]      =					features.pop("pixel_values"						)
     if return_overflowing_tokens is True:
        UpperCAmelCase_      :      int      =					self.get_overflowing_images(UpperCamelCase__       ,     encoded_inputs["overflow_to_sample_mapping"]						)
     UpperCAmelCase_      :      List[str]      =					images

     return encoded_inputs







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      List[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       ,     *lowercase_       ,     **lowercase_						):

     """simple docstring"""
     return self.tokenizer.batch_decode(*UpperCamelCase__       ,     **UpperCamelCase__						)







  def  UpperCamelCase__      (	self       ,     *lowercase_       ,     **lowercase_						):

     """simple docstring"""
     return self.tokenizer.decode(*UpperCamelCase__       ,     **UpperCamelCase__						)







  @property
  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







  @property
  def  UpperCamelCase__      (	self						):

     """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						):

     """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"""





def 			__a  (	__lowerCamelCase = 1000						):
   UpperCAmelCase_      :      List[Any]      =					0
   UpperCAmelCase_      :      List[str]      =					(n - 1) // 3
   total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
   UpperCAmelCase_      :      Any      =					(n - 1) // 5
   total += ((terms) * (10 + (terms - 1) * 5)) // 2
   UpperCAmelCase_      :      Optional[int]      =					(n - 1) // 15
   total -= ((terms) * (30 + (terms - 1) * 15)) // 2
   return total


if __name__ == "__main__":
    print(f"""{solution() = }""")


