'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					["""image_processor""", """tokenizer"""]
  __lowerCAmelCase				      =					"""ViTImageProcessor"""
  __lowerCAmelCase				      =					("""CLIPTokenizer""", """CLIPTokenizerFast""")







  def __init__(							self   ,    snake_case_=None   ,    snake_case_=None   ,    **snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= None
    if "feature_extractor" in kwargs:
      warnings.warn(
          """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
          """ instead."""   ,    _SCREAMING_SNAKE_CASE   ,    )
      __UpperCAmelCase:					int		= kwargs.pop("""feature_extractor"""					)

    __UpperCAmelCase:					Dict		= 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__(_SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE					)







  def __call__(							self   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_=None   ,    **snake_case_					):

    '''simple docstring'''







    if text is None and visual_prompt is None and images is None:
      raise ValueError("""You have to specify either text, visual prompt or images."""					)

    if text is not None and visual_prompt is not None:
      raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""					)

    if text is not None:
      __UpperCAmelCase:					Optional[Any]		= self.tokenizer(_SCREAMING_SNAKE_CASE   ,    return_tensors=_SCREAMING_SNAKE_CASE   ,    **_SCREAMING_SNAKE_CASE					)

    if visual_prompt is not None:
      __UpperCAmelCase:					Optional[int]		= self.image_processor(_SCREAMING_SNAKE_CASE   ,    return_tensors=_SCREAMING_SNAKE_CASE   ,    **_SCREAMING_SNAKE_CASE					)

    if images is not None:
      __UpperCAmelCase:					str		= self.image_processor(_SCREAMING_SNAKE_CASE   ,    return_tensors=_SCREAMING_SNAKE_CASE   ,    **_SCREAMING_SNAKE_CASE					)

    if visual_prompt is not None and images is not None:
      __UpperCAmelCase:					Tuple		= {
          """pixel_values""": image_features.pixel_values,
          """conditional_pixel_values""": prompt_features.pixel_values,
      }
      return encoding
    elif text is not None and images is not None:
      __UpperCAmelCase:					Optional[int]		= image_features.pixel_values
      return encoding
    elif text is not None:
      return encoding
    elif visual_prompt is not None:
      __UpperCAmelCase:					List[str]		= {
          """conditional_pixel_values""": prompt_features.pixel_values,
      }
      return encoding
    else:
      return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE					)   ,    tensor_type=_SCREAMING_SNAKE_CASE					)







  def    lowercase_  (							self   ,    *snake_case_   ,    **snake_case_					):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    *snake_case_   ,    **snake_case_					):

    '''simple docstring'''







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







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







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







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







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






import itertools
import math






def  UpperCamelCase__					(					_lowercase    :				int     )							->	bool:

  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  UpperCamelCase__					(					)							->	Optional[int]:
  __UpperCAmelCase:					Union[str, Any]		= 2
  while True:
    if is_prime(_lowercase     ):
      yield num
    num += 1






def  UpperCamelCase__					(					_lowercase    :				int = 1_0_0_0_1     )							->	int:
  return next(itertools.islice(prime_generator()   ,    nth - 1   ,    _lowercase     )     )


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