





from __future__ import annotations


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Dict   ,    _lowercase     : str			)   ->							list[str]:




		'''simple docstring'''
		if nth_term == "":
				return [""]
		lowercase__					:      Optional[Any]      =		int(SCREAMING_SNAKE_CASE_			)
		lowercase__					:      Any      =		int(SCREAMING_SNAKE_CASE_			)
		lowercase__					:      Optional[Any]      =		[]
		for temp in range(int(SCREAMING_SNAKE_CASE_			)			):
				series.append(f"""1 / {pow(temp + 1   ,    int(SCREAMING_SNAKE_CASE_			)			)}""" if series else '1'			)
		return series


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	Tuple = int(input("""Enter the last number (nth term) of the P-Series"""))
			__UpperCamelCase:	Optional[Any] = 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))



'''simple docstring'''


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						  =		{"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case						  =		[
           """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """FocalNetForImageClassification""",
           """FocalNetForMaskedImageModeling""",
           """FocalNetBackbone""",
           """FocalNetModel""",
           """FocalNetPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_focalnet import (
                  FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FocalNetBackbone,
                  FocalNetForImageClassification,
                  FocalNetForMaskedImageModeling,
                  FocalNetModel,
                  FocalNetPreTrainedModel,
              )

else:
       import sys

       __snake_case						  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

