

from __future__ import annotations






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int | float | str							,       UpperCAmelCase_							:     int | float | str	)						->				list[str]:




 '''simple docstring'''




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


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       lowerCAmelCase__								=		int(input("""Enter the last number (nth term) of the P-Series"""))
       lowerCAmelCase__								=		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))







from ...processing_utils import ProcessorMixin







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  ["image_processor", "feature_extractor"]
		a__					       =  "TvltImageProcessor"
		a__					       =  "TvltFeatureExtractor"


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__(image_processor=__snake_case	,       feature_extractor=__snake_case)

			_UpperCamelCase   :		List[str]			=						image_processor
			_UpperCamelCase   :		Dict			=						feature_extractor


		def __call__(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=False	,       __snake_case=False	,       *__snake_case	,       **__snake_case	,       ):

			if images is None and audio is None:
				raise ValueError('You need to specify either an `images` or `audio` input to process.')

			_UpperCamelCase   :		Union[str, Any]			=						None
			if images is not None:
				_UpperCamelCase   :		Tuple			=						self.image_processor(__snake_case	,       mask_pixel=__snake_case	,       *__snake_case	,       **__snake_case)
			if images_mixed is not None:
				_UpperCamelCase   :		Union[str, Any]			=						self.image_processor(__snake_case	,       is_mixed=__snake_case	,       *__snake_case	,       **__snake_case)
			if audio is not None:
				_UpperCamelCase   :		Tuple			=						self.feature_extractor(
				    __snake_case	,       *__snake_case	,       sampling_rate=__snake_case	,       mask_audio=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		Tuple			=						{}
			if audio is not None:
				output_dict.update(__snake_case)
			if images is not None:
				output_dict.update(__snake_case)
			if images_mixed_dict is not None:
				output_dict.update(__snake_case)
			return output_dict






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.image_processor.model_input_names
			_UpperCamelCase   :		List[Any]			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))




