





'''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_		(		__lowerCAmelCase		):
 __lowerCamelCase						:							Optional[Any]         =					["image_processor", "tokenizer"]
 __lowerCamelCase						:							Optional[Any]         =					"LayoutLMv2ImageProcessor"
 __lowerCamelCase						:							List[str]         =					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")




 def __init__(	self			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Optional[Any]:






     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:






     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				=      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		)    ->							str:
     _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		)    ->							str:
     return self.tokenizer.batch_decode(*lowerCamelCase__			,     **lowerCamelCase__		)




 def 							_snake_case   (	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							Tuple:
     return self.tokenizer.decode(*lowerCamelCase__			,     **lowerCamelCase__		)




 @property
 def 							_snake_case   (	self		)    ->							str:
     return ["input_ids", "bbox", "attention_mask", "image"]




 @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'''


from math import isqrt, loga


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      [True] * max_number
    for i in range(2		,       isqrt(max_number - 1    ) + 1    ):
        if is_prime[i]:
            for j in range(i**2		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
                _lowerCAmelCase				=      False

    return [i for i in range(2		,       SCREAMING_SNAKE_CASE_    ) if is_prime[i]]


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 800800		,       SCREAMING_SNAKE_CASE_  :					int = 800800    ):



    '''simple docstring'''





    _lowerCAmelCase				=      degree * loga(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      int(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      calculate_prime_numbers(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      len(SCREAMING_SNAKE_CASE_    ) - 1
    while left < right:
        while (
            prime_numbers[right] * loga(prime_numbers[left]    )
            + prime_numbers[left] * loga(prime_numbers[right]    )
            > upper_bound
        ):
            right -= 1
        hybrid_integers_count += right - left
        left += 1

    return hybrid_integers_count


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


