





'''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    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							str         =					["image_processor", "tokenizer"]
 __lowerCamelCase						:							Optional[int]         =					"LayoutLMv3ImageProcessor"
 __lowerCamelCase						:							int         =					("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")




 def __init__(	self			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Any:
     _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."			,     _lowerCAmelCase			,     )
         _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__(_lowerCAmelCase			,     _lowerCAmelCase		)




 def __call__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = True			,     _lowerCAmelCase = False			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = 0			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = False			,     _lowerCAmelCase = False			,     _lowerCAmelCase = False			,     _lowerCAmelCase = False			,     _lowerCAmelCase = True			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							BatchEncoding:
     # 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=_lowerCAmelCase			,     return_tensors=_lowerCAmelCase		)

     # second, apply the tokenizer
     if text is not None and self.image_processor.apply_ocr and text_pair is None:
         if isinstance(_lowerCAmelCase			,     _lowerCAmelCase		):
             _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=_lowerCAmelCase			,     add_special_tokens=_lowerCAmelCase			,     padding=_lowerCAmelCase			,     truncation=_lowerCAmelCase			,     max_length=_lowerCAmelCase			,     stride=_lowerCAmelCase			,     pad_to_multiple_of=_lowerCAmelCase			,     return_token_type_ids=_lowerCAmelCase			,     return_attention_mask=_lowerCAmelCase			,     return_overflowing_tokens=_lowerCAmelCase			,     return_special_tokens_mask=_lowerCAmelCase			,     return_offsets_mapping=_lowerCAmelCase			,     return_length=_lowerCAmelCase			,     verbose=_lowerCAmelCase			,     return_tensors=_lowerCAmelCase			,     **_lowerCAmelCase			,     )

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

     return encoded_inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							List[str]:
     # 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(_lowerCAmelCase		) != len(_lowerCAmelCase		):
         raise ValueError(
             "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
             f''' {len(_lowerCAmelCase		)} and {len(_lowerCAmelCase		)}'''		)

     return images_with_overflow




 def 							_snake_case   (	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							Optional[Any]:
     return self.tokenizer.batch_decode(*_lowerCAmelCase			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[str]:
     return self.tokenizer.decode(*_lowerCAmelCase			,     **_lowerCAmelCase		)




 @property
 def 							_snake_case   (	self		)    ->							Optional[Any]:
     return ["input_ids", "bbox", "attention_mask", "pixel_values"]




 @property
 def 							_snake_case   (	self		)    ->							Tuple:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,     _lowerCAmelCase			,     )
     return self.image_processor_class





 @property
 def 							_snake_case   (	self		)    ->							Optional[int]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,     _lowerCAmelCase			,     )
     return self.image_processor











'''simple docstring'''


import math


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 100    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(i * i for i in range(1		,       n + 1    )    )
    _lowerCAmelCase				=      int(math.pow(sum(range(1		,       n + 1    )    )		,       2    )    )
    return square_of_sum - sum_of_squares


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


