





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(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			['image_processor', 'tokenizer']
     __magic_name__   =			'ViltImageProcessor'
     __magic_name__   =			('BertTokenizer', 'BertTokenizerFast')




     def __init__(    self				,     snake_case_=None				,     snake_case_=None				,     **snake_case_							):
         _A     =							None
         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     =							kwargs.pop('feature_extractor'							)

         _A     =							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_							)
         _A     =							self.image_processor




     def __call__(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = True				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = 0				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = True				,     snake_case_ = None				,     **snake_case_				,     ):
         _A     =							self.tokenizer(
             text=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 + pixel_mask
         _A     =							self.image_processor(snake_case_				,     return_tensors=snake_case_							)
         encoding.update(snake_case_							)

         return encoding




     def        lowerCAmelCase__		(    self				,     *snake_case_				,     **snake_case_							):
         return self.tokenizer.batch_decode(*snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     *snake_case_				,     **snake_case_							):
         return self.tokenizer.decode(*snake_case_				,     **snake_case_							)




     @property
     def        lowerCAmelCase__		(    self							):
         _A     =							self.tokenizer.model_input_names
         _A     =							self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




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












def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> bool:




    """simple docstring"""




    if not isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        raise ValueError('check_bouncy() accepts only integer arguments'				)
    _A     =							str(_SCREAMING_SNAKE_CASE				)
    _A     =							''.join(sorted(_SCREAMING_SNAKE_CASE				)				)
    return sorted_str_n != str_n and sorted_str_n[::-1] != str_n





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 99				)							-> int:




    """simple docstring"""




    if not 0 < percent < 100:
        raise ValueError('solution() only accepts values from 0 to 100'				)
    _A     =							0
    _A     =							1

    while True:
        if check_bouncy(_SCREAMING_SNAKE_CASE				):
            bouncy_num += 1
        if (bouncy_num / num) * 100 >= percent:
            return num
        num += 1


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 print(f"{solution(99)}")





