
from __future__ import annotations


def 							a__					(							snake_case       ,			snake_case							):





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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

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






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce





def 				A__ (snake_case   :		List[Any]				)     ->						Optional[Any]:
					return (torch.arange(state.num_processes				) + 1.0 + (state.num_processes * state.process_index)).to(state.device				)





def 				A__ (snake_case   :		int				)     ->						Any:
					__UpperCamelCase					:	List[str]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	List[Any]             =      gather(snake_case				)
					assert gathered_tensor.tolist() == list(range(1		,	state.num_processes**2 + 1				)				)





def 				A__ (snake_case   :		Optional[Any]				)     ->						Dict:
					__UpperCamelCase					:	Any             =      [state.process_index]
					__UpperCamelCase					:	int             =      gather_object(snake_case				)
					assert len(snake_case				) == state.num_processes, F'''{gathered_obj}, {len(snake_case				)} != {state.num_processes}'''
					assert gathered_obj == list(range(state.num_processes				)				), F'''{gathered_obj} != {list(range(state.num_processes				)				)}'''





def 				A__ (snake_case   :		Any				)     ->						Optional[Any]:
					__UpperCamelCase					:	Optional[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	Any             =      broadcast(snake_case				)
					assert broadcasted_tensor.shape == torch.Size([state.num_processes]				)
					assert broadcasted_tensor.tolist() == list(range(1		,	state.num_processes + 1				)				)





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# We need to pad the tensor with one more element if we are the main process
					# to ensure that we can pad
					if state.is_main_process:
										__UpperCamelCase					:	List[Any]             =      torch.arange(state.num_processes + 1				).to(state.device				)
					else:
										__UpperCamelCase					:	List[str]             =      torch.arange(state.num_processes				).to(state.device				)
					__UpperCamelCase					:	List[str]             =      pad_across_processes(snake_case				)
					assert padded_tensor.shape == torch.Size([state.num_processes + 1]				)
					if not state.is_main_process:
										assert padded_tensor.tolist() == list(range(0		,	state.num_processes				)				) + [0]





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	Optional[int]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""sum"""				)
					__UpperCamelCase					:	Optional[int]             =      torch.tensor([4.0, 6]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Optional[Any]				)     ->						Optional[Any]:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	List[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""mean"""				)
					__UpperCamelCase					:	Union[str, Any]             =      torch.tensor([2.0, 3]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Tuple				)     ->						int:
					# For xla_spawn (TPUs)
					main()





def 				A__ ()     ->						List[Any]:
					__UpperCamelCase					:	List[Any]             =      PartialState()
					state.print(F'''State: {state}'''				)
					state.print("""testing gather"""				)
					test_gather(snake_case				)
					state.print("""testing gather_object"""				)
					test_gather_object(snake_case				)
					state.print("""testing broadcast"""				)
					test_broadcast(snake_case				)
					state.print("""testing pad_across_processes"""				)
					test_pad_across_processes(snake_case				)
					state.print("""testing reduce_sum"""				)
					test_reduce_sum(snake_case				)
					state.print("""testing reduce_mean"""				)
					test_reduce_mean(snake_case				)


if __name__ == "__main__":
	main()






