






"""simple docstring"""







# 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							):







      """simple docstring"""






      return (torch.arange(state.num_processes							) + 1.0 + (state.num_processes * state.process_index)).to(state.device							)

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	create_tensor(_snake_case							)
      UpperCAmelCase          =	gather(_snake_case							)
      assert gathered_tensor.tolist() == list(range(1				,	state.num_processes**2 + 1							)							)

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[state.process_index]
      UpperCAmelCase          =	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							):







      """simple docstring"""






      UpperCAmelCase          =	create_tensor(_snake_case							)
      UpperCAmelCase          =	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							):







      """simple docstring"""






      if state.is_main_process:
            UpperCAmelCase          =	torch.arange(state.num_processes + 1							).to(state.device							)
      else:
            UpperCAmelCase          =	torch.arange(state.num_processes							).to(state.device							)
      UpperCAmelCase          =	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							):







      """simple docstring"""






      if state.num_processes != 2:
            return
      UpperCAmelCase          =	create_tensor(_snake_case							)
      UpperCAmelCase          =	reduce(_snake_case				,	"""sum"""							)
      UpperCAmelCase          =	torch.tensor([4.0, 6]							).to(state.device							)
      assert torch.allclose(_snake_case				,	_snake_case							), F'''{reduced_tensor} != {truth_tensor}'''

def _a			(		_snake_case							):







      """simple docstring"""






      if state.num_processes != 2:
            return
      UpperCAmelCase          =	create_tensor(_snake_case							)
      UpperCAmelCase          =	reduce(_snake_case				,	"""mean"""							)
      UpperCAmelCase          =	torch.tensor([2.0, 3]							).to(state.device							)
      assert torch.allclose(_snake_case				,	_snake_case							), F'''{reduced_tensor} != {truth_tensor}'''

def _a			(		_snake_case							):







      """simple docstring"""






      main()

def _a			(		):







      """simple docstring"""






      UpperCAmelCase          =	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()











"""simple docstring"""







def _a			(		_snake_case = 6008_5147_5143							):







      """simple docstring"""






      try:
            UpperCAmelCase          =	int(_snake_case							)
      except (TypeError, ValueError):
            raise TypeError("""Parameter n must be int or castable to int."""							)
      if n <= 0:
            raise ValueError("""Parameter n must be greater than or equal to one."""							)
      UpperCAmelCase          =	1
      UpperCAmelCase          =	2
      while i * i <= n:
            while n % i == 0:
                  UpperCAmelCase          =	i
                  n //= i
            i += 1
      if n > 1:
            UpperCAmelCase          =	n
      return int(_snake_case							)


if __name__ == "__main__":
  print(F"""{solution() = }""")



