


'''simple docstring'''






import warnings

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






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







				"""simple docstring"""



				lowercase       			= ['image_processor', 'tokenizer']
				lowercase       			= 'CLIPImageProcessor'
				lowercase       			= ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')

				def __init__(       self   :  Union[str, Any]   ,						lowerCamelCase   :  List[str]=None   ,						lowerCamelCase   :  Union[str, Any]=None   ,						**lowerCamelCase   :  List[Any]		)       ->  Optional[Any]:
						lowerCAmelCase_      :				List[str]      =       None
						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_      :				Optional[int]      =       kwargs.pop("""feature_extractor"""		)

						lowerCAmelCase_      :				List[str]      =       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   :  Optional[Any]   ,						lowerCamelCase   :  List[Any]=None   ,						lowerCamelCase   :  Tuple=None   ,						lowerCamelCase   :  int=None   ,						**lowerCamelCase   :  int		)       ->  Optional[int]:

						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:
								lowerCAmelCase_      :				int      =       self.tokenizer(lowerCamelCase   ,						return_tensors=lowerCamelCase   ,						**lowerCamelCase		)

						if images is not None:
								lowerCAmelCase_      :				int      =       self.image_processor(lowerCamelCase   ,						return_tensors=lowerCamelCase   ,						**lowerCamelCase		)

						if text is not None and images is not None:
								lowerCAmelCase_      :				Optional[int]      =       image_features.pixel_values
								return encoding
						elif text is not None:
								return encoding
						else:
								return BatchEncoding(data=dict(**lowerCamelCase		)   ,						tensor_type=lowerCamelCase		)

				def __lowercase       (       self   :  int   ,						*lowerCamelCase   :  Optional[Any]   ,						**lowerCamelCase   :  Union[str, Any]		)       ->  List[str]:
						return self.tokenizer.batch_decode(*lowerCamelCase   ,						**lowerCamelCase		)

				def __lowercase       (       self   :  Optional[int]   ,						*lowerCamelCase   :  str   ,						**lowerCamelCase   :  Union[str, Any]		)       ->  List[str]:
						return self.tokenizer.decode(*lowerCamelCase   ,						**lowerCamelCase		)





				@property
				def __lowercase       (       self   :  Optional[Any]		)       ->  int:
						lowerCAmelCase_      :				List[str]      =       self.tokenizer.model_input_names
						lowerCAmelCase_      :				List[str]      =       self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)







'''simple docstring'''






from __future__ import annotations







def     UpperCamelCase_      (   A__ :       int | float | str							,   A__ :       int | float | str ):
  '''simple docstring'''





  if nth_term == "":
    return [""]
  lowerCAmelCase_      :				str      =       int(A__ )
  lowerCAmelCase_      :				Tuple      =       int(A__ )
  lowerCAmelCase_      :				list[str]      =       []
  for temp in range(int(A__ ) ):
    series.append(f'1 / {pow(temp + 1							,   int(A__ ) )}' if series else """1""" )
  return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __A :  str    							=				int(input("Enter the last number (nth term) of the P-Series"))
  __A :  Tuple    							=				int(input("Enter the power for  P-Series"))
  print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
  print(p_series(nth_term, power))



