


'''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      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  ["image_processor", "tokenizer"]
  _lowerCamelCase		    =  "LayoutLMv3ImageProcessor"
  _lowerCamelCase		    =  ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






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







   """simple docstring"""
   lowerCamelCase_      =  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			,  )
    lowerCamelCase_      =  kwargs.pop("feature_extractor"     )

   lowerCamelCase_      =  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			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = True			,  UpperCamelCase = False			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = 0			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = False			,  UpperCamelCase = False			,  UpperCamelCase = False			,  UpperCamelCase = False			,  UpperCamelCase = True			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   # 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."     )

   # first, apply the image processor
   lowerCamelCase_      =  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_      =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
    lowerCamelCase_      =  features["words"]

   lowerCamelCase_      =  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_      =  features.pop("pixel_values"     )
   if return_overflowing_tokens is True:
    lowerCamelCase_      =  self.get_overflowing_images(UpperCamelCase			,  encoded_inputs["overflow_to_sample_mapping"]     )
   lowerCamelCase_      =  images

   return encoded_inputs






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   lowerCamelCase_      =  []
   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   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







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






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







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






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   return ["input_ids", "bbox", "attention_mask", "pixel_values"]






  @property
  def   snake_case		(    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   snake_case		(    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'''

import math





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  math.loga(math.sqrt(4 * positive_integer + 1		) / 2 + 1 / 2		)

 return exponent == int(UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						float = 1 / 12345		):
 lowerCamelCase_      =  0
 lowerCamelCase_      =  0

 lowerCamelCase_      =  3
 while True:
  lowerCamelCase_      =  (integer**2 - 1) / 4
  # if candidate is an integer, then there is a partition for k
  if partition_candidate == int(UpperCAmelCase_		):
   lowerCamelCase_      =  int(UpperCAmelCase_		)
   total_partitions += 1
   if check_partition_perfect(UpperCAmelCase_		):
    perfect_partitions += 1
  if perfect_partitions > 0:
   if perfect_partitions / total_partitions < max_proportion:
    return int(UpperCAmelCase_		)
  integer += 1


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



