


"""simple docstring"""


from __future__ import annotations


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			if nth_term == "":
						return [""]
			__lowerCAmelCase							       =     int(_UpperCamelCase )
			__lowerCAmelCase							       =     int(_UpperCamelCase )
			__lowerCAmelCase							       =     []
			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	:  Any            =     int(input("Enter the last number (nth term) of the P-Series"))
	A	:  int            =     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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase 							=  {
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """UperNetForSemanticSegmentation""",
          """UperNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_upernet import UperNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
      import sys

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

