






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A     :      Union[str, Any]   					=     {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     :      str   					=     [
			    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Data2VecAudioForAudioFrameClassification''',
			    '''Data2VecAudioForCTC''',
			    '''Data2VecAudioForSequenceClassification''',
			    '''Data2VecAudioForXVector''',
			    '''Data2VecAudioModel''',
			    '''Data2VecAudioPreTrainedModel''',
			]
			_A     :      int   					=     [
			    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Data2VecTextForCausalLM''',
			    '''Data2VecTextForMaskedLM''',
			    '''Data2VecTextForMultipleChoice''',
			    '''Data2VecTextForQuestionAnswering''',
			    '''Data2VecTextForSequenceClassification''',
			    '''Data2VecTextForTokenClassification''',
			    '''Data2VecTextModel''',
			    '''Data2VecTextPreTrainedModel''',
			]
			_A     :      List[str]   					=     [
			    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Data2VecVisionForImageClassification''',
			    '''Data2VecVisionForMaskedImageModeling''',
			    '''Data2VecVisionForSemanticSegmentation''',
			    '''Data2VecVisionModel''',
			    '''Data2VecVisionPreTrainedModel''',
			]

if is_tf_available():
			_A     :      Any   					=     [
			    '''TFData2VecVisionForImageClassification''',
			    '''TFData2VecVisionForSemanticSegmentation''',
			    '''TFData2VecVisionModel''',
			    '''TFData2VecVisionPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
			from .configuration_dataavec_text import (
			    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DataaVecTextConfig,
			    DataaVecTextOnnxConfig,
			)
			from .configuration_dataavec_vision import (
			    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DataaVecVisionConfig,
			    DataaVecVisionOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dataavec_audio import (
						    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecAudioForAudioFrameClassification,
						    DataaVecAudioForCTC,
						    DataaVecAudioForSequenceClassification,
						    DataaVecAudioForXVector,
						    DataaVecAudioModel,
						    DataaVecAudioPreTrainedModel,
						)
						from .modeling_dataavec_text import (
						    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecTextForCausalLM,
						    DataaVecTextForMaskedLM,
						    DataaVecTextForMultipleChoice,
						    DataaVecTextForQuestionAnswering,
						    DataaVecTextForSequenceClassification,
						    DataaVecTextForTokenClassification,
						    DataaVecTextModel,
						    DataaVecTextPreTrainedModel,
						)
						from .modeling_dataavec_vision import (
						    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecVisionForImageClassification,
						    DataaVecVisionForMaskedImageModeling,
						    DataaVecVisionForSemanticSegmentation,
						    DataaVecVisionModel,
						    DataaVecVisionPreTrainedModel,
						)
			if is_tf_available():
						from .modeling_tf_dataavec_vision import (
						    TFDataaVecVisionForImageClassification,
						    TFDataaVecVisionForSemanticSegmentation,
						    TFDataaVecVisionModel,
						    TFDataaVecVisionPreTrainedModel,
						)

else:
			import sys

			_A     :      Dict   					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''simple docstring'''







from __future__ import annotations





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	list[str]:
							if nth_term == "":
														return [""]
							lowerCamelCase__ :     str	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     Union[str, Any]	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     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()

			_A     :      Optional[Any]   					=int(input('''Enter the last number (nth term) of the P-Series'''))
			_A     :      List[str]   					=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))

