

'''simple docstring'''
import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 							A__    (       A__  ):
  A__		 =				['image_processor', 'tokenizer']
  A__		 =				'FlavaImageProcessor'
  A__		 =				('BertTokenizer', 'BertTokenizerFast')





  def __init__(		self      :	Optional[Any]							,					_a      :	Tuple=None							,					_a      :	Optional[Any]=None							,					**_a      :	List[str] )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =None
       if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.'							,					_a							,					)
            _SCREAMING_SNAKE_CASE							    =kwargs.pop('feature_extractor' )

       _SCREAMING_SNAKE_CASE							    =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__(_a							,					_a )
       _SCREAMING_SNAKE_CASE							    =self.image_processor





  def __call__(		self      :	List[str]							,					_a      :	Optional[ImageInput] = None							,					_a      :	Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None							,					_a      :	bool = True							,					_a      :	Union[bool, str, PaddingStrategy] = False							,					_a      :	Union[bool, str, TruncationStrategy] = False							,					_a      :	Optional[int] = None							,					_a      :	int = 0							,					_a      :	Optional[int] = None							,					_a      :	Optional[bool] = None							,					_a      :	Optional[bool] = None							,					_a      :	Optional[bool] = None							,					_a      :	Optional[bool] = None							,					_a      :	bool = False							,					_a      :	bool = False							,					_a      :	bool = False							,					_a      :	bool = False							,					_a      :	bool = True							,					_a      :	Optional[Union[str, TensorType]] = None							,					**_a      :	str							,					)	->    Tuple:





       '''simple docstring'''






       if text is None and images is None:
            raise ValueError('You have to specify either text or images. Both cannot be none.' )

       if text is not None:
            _SCREAMING_SNAKE_CASE							    =self.tokenizer(
                text=_a							,					add_special_tokens=_a							,					padding=_a							,					truncation=_a							,					max_length=_a							,					stride=_a							,					pad_to_multiple_of=_a							,					return_token_type_ids=_a							,					return_attention_mask=_a							,					return_overflowing_tokens=_a							,					return_special_tokens_mask=_a							,					return_offsets_mapping=_a							,					return_length=_a							,					verbose=_a							,					return_tensors=_a							,					**_a							,					)
       if images is not None:
            _SCREAMING_SNAKE_CASE							    =self.image_processor(
                _a							,					return_image_mask=_a							,					return_codebook_pixels=_a							,					return_tensors=_a							,					**_a							,					)

       if text is not None and images is not None:
            encoding.update(_a )
            return encoding
       elif text is not None:
            return encoding
       else:
            return BatchEncoding(data=dict(**_a )							,					tensor_type=_a )





  def 						A  (		self      :	Dict							,					*_a      :	Optional[Any]							,					**_a      :	Tuple )	->    List[Any]:





       '''simple docstring'''






       return self.tokenizer.batch_decode(*_a							,					**_a )





  def 						A  (		self      :	List[str]							,					*_a      :	Optional[Any]							,					**_a      :	List[Any] )	->    List[Any]:





       '''simple docstring'''






       return self.tokenizer.decode(*_a							,					**_a )





  @property
  def 						A  (		self      :	Any )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.tokenizer.model_input_names
       _SCREAMING_SNAKE_CASE							    =self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )





  @property
  def 						A  (		self      :	Optional[int] )	->    Dict:





       '''simple docstring'''






       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'							,					_a							,					)
       return self.image_processor_class





  @property
  def 						A  (		self      :	Optional[Any] )	->    Dict:





       '''simple docstring'''






       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,					_a							,					)
       return self.image_processor







def 		lowerCAmelCase_    (  __UpperCAmelCase:			int = 100_0000      )      ->	int:
    UpperCamelCase__							:				str          =      limit + 1
    UpperCamelCase__							:				List[str]          =      [0] * limit
    for first_term in range(1		,   __UpperCAmelCase      ):
        for n in range(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      ):
            UpperCamelCase__							:				str          =      first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    UpperCamelCase__							:				Any          =      sum(1 for x in frequency[1:limit] if x == 10      )

    return count


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

