





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 					A     (__UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE       =				["image_processor", "tokenizer"]
      _SCREAMING_SNAKE_CASE       =				"BridgeTowerImageProcessor"
      _SCREAMING_SNAKE_CASE       =				("RobertaTokenizer", "RobertaTokenizerFast")



      def __init__(       self  ,	lowercase_  ,	lowercase_       )   -> List[str]:



             '''simple docstring'''

             super().__init__(snake_case__  ,	snake_case__       )



      def __call__(       self  ,	lowercase_  ,	lowercase_ = None  ,	lowercase_ = True  ,	lowercase_ = False  ,	lowercase_ = None  ,	lowercase_ = None  ,	lowercase_ = 0  ,	lowercase_ = None  ,	lowercase_ = None  ,	lowercase_ = None  ,	lowercase_ = False  ,	lowercase_ = False  ,	lowercase_ = False  ,	lowercase_ = False  ,	lowercase_ = True  ,	lowercase_ = None  ,	**lowercase_  ,	)   -> Optional[Any]:



             '''simple docstring'''

             _snake_case   :				Union[str, Any]  					=    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
             _snake_case   :				Union[str, Any]  					=    self.image_processor(
                 snake_case__  ,	return_tensors=snake_case__  ,	do_normalize=snake_case__  ,	do_center_crop=snake_case__  ,	**snake_case__       )
             encoding.update(snake_case__       )

             return encoding



      def  __a			(       self  ,	*lowercase_  ,	**lowercase_       )   -> Optional[Any]:



             '''simple docstring'''

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



      def  __a			(       self  ,	*lowercase_  ,	**lowercase_       )   -> int:



             '''simple docstring'''

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



      @property
      def  __a			(       self       )   -> Dict:



             '''simple docstring'''

             _snake_case   :				int  					=    self.tokenizer.model_input_names
             _snake_case   :				Optional[Any]  					=    self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )








'''simple docstring'''





import math







def snake_case_     (					lowercase__		):

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5   ,       int(math.sqrt(lowercase__		) + 1		)   ,       6		):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def snake_case_     (					lowercase__ = 0.1		):
  UpperCAmelCase__      :					Dict				       =							3
  UpperCAmelCase__      :					Tuple				       =							3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1   ,       (j + 2) * (j + 2)   ,       j + 1		):
      primes += is_prime(lowercase__		)
    j += 2
  return j


if __name__ == "__main__":
      import doctest

      doctest.testmod()



