


"""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    _snake_case  (  _snake_case :	int	):
      return (torch.arange(state.num_processes	) + 1.0 + (state.num_processes * state.process_index)).to(state.device	)


def    _snake_case  (  _snake_case :	Optional[int]	):
      lowerCAmelCase    :		Optional[int]				=      create_tensor(_snake_case	)
      lowerCAmelCase    :		Dict				=      gather(_snake_case	)
      assert gathered_tensor.tolist() == list(range(1       ,      state.num_processes**2 + 1	)	)


def    _snake_case  (  _snake_case :	Dict	):
      lowerCAmelCase    :		Optional[Any]				=      [state.process_index]
      lowerCAmelCase    :		Tuple				=      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    _snake_case  (  _snake_case :	Dict	):
      lowerCAmelCase    :		Dict				=      create_tensor(_snake_case	)
      lowerCAmelCase    :		Optional[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    _snake_case  (  _snake_case :	int	):
      # 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:
            lowerCAmelCase    :		str				=      torch.arange(state.num_processes + 1	).to(state.device	)
      else:
            lowerCAmelCase    :		Union[str, Any]				=      torch.arange(state.num_processes	).to(state.device	)
      lowerCAmelCase    :		List[Any]				=      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    _snake_case  (  _snake_case :	Any	):
      # For now runs on only two processes
      if state.num_processes != 2:
            return
      lowerCAmelCase    :		Optional[Any]				=      create_tensor(_snake_case	)
      lowerCAmelCase    :		Any				=      reduce(_snake_case       ,      '''sum'''	)
      lowerCAmelCase    :		Optional[int]				=      torch.tensor([4.0, 6]	).to(state.device	)
      assert torch.allclose(_snake_case       ,      _snake_case	), f'''{reduced_tensor} != {truth_tensor}'''


def    _snake_case  (  _snake_case :	List[str]	):
      # For now runs on only two processes
      if state.num_processes != 2:
            return
      lowerCAmelCase    :		Optional[int]				=      create_tensor(_snake_case	)
      lowerCAmelCase    :		Optional[Any]				=      reduce(_snake_case       ,      '''mean'''	)
      lowerCAmelCase    :		Dict				=      torch.tensor([2.0, 3]	).to(state.device	)
      assert torch.allclose(_snake_case       ,      _snake_case	), f'''{reduced_tensor} != {truth_tensor}'''


def    _snake_case  (  _snake_case :	int	):
      # For xla_spawn (TPUs)
      main()


def    _snake_case  (  ):
      lowerCAmelCase    :		Dict				=      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   __magic_name__	(      __snake_case				:       int     , __snake_case				:       int     , __snake_case				:       int  ) ->				float:
       lowercase    :				List[Any]       		=							(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
       # formula for sum of series
       return total


def   __magic_name__	(      ) ->				int:
       print(sum_of_series(1     , 1     , 10  )  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


