

'''simple docstring'''


class      a      :






						"""simple docstring"""




						def __init__(				self       :   Tuple      ,      snake_case_       :   int	):



								'''simple docstring'''



								snake_case__    :							str							    =	n
								snake_case__    :							List[Any]							    =	[None] * self.n
								snake_case__    :							Any							    =	0  # index of the first element
								snake_case__    :							List[str]							    =	0
								snake_case__    :							Tuple							    =	0







						def __len__(				self       :   List[str]	):



								'''simple docstring'''



								return self.size







						def    __magic_name__				(				self       :   Tuple	):



								'''simple docstring'''



								return self.size == 0







						def    __magic_name__				(				self       :   Optional[int]	):



								'''simple docstring'''



								return False if self.is_empty() else self.array[self.front]







						def    __magic_name__				(				self       :   str      ,      snake_case_       :   Dict	):



								'''simple docstring'''



								if self.size >= self.n:
										raise Exception('''QUEUE IS FULL'''	)

								snake_case__    :							Dict							    =	data
								snake_case__    :							Optional[int]							    =	(self.rear + 1) % self.n
								self.size += 1
								return self







						def    __magic_name__				(				self       :   Optional[int]	):



								'''simple docstring'''



								if self.size == 0:
										raise Exception('''UNDERFLOW'''	)

								snake_case__    :							Any							    =	self.array[self.front]
								snake_case__    :							Optional[int]							    =	None
								snake_case__    :							int							    =	(self.front + 1) % self.n
								self.size -= 1
								return temp










# 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    (       _lowerCAmelCase			:			Optional[int]						)			->    Dict:
   return (torch.arange(state.num_processes						) + 1.0 + (state.num_processes * state.process_index)).to(state.device						)

def   __snake_case    (       _lowerCAmelCase			:			Tuple						)			->    Optional[Any]:
   A_       :					Optional[Any]        =      create_tensor(_lowerCAmelCase						)
   A_       :					Dict        =      gather(_lowerCAmelCase						)
   assert gathered_tensor.tolist() == list(range(1					,					state.num_processes**2 + 1						)						)

def   __snake_case    (       _lowerCAmelCase			:			List[Any]						)			->    Any:
   A_       :					int        =      [state.process_index]
   A_       :					Union[str, Any]        =      gather_object(_lowerCAmelCase						)
   assert len(_lowerCAmelCase						) == state.num_processes, f"{gathered_obj}, {len(_lowerCAmelCase						)} != {state.num_processes}"
   assert gathered_obj == list(range(state.num_processes						)						), f"{gathered_obj} != {list(range(state.num_processes						)						)}"

def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]						)			->    Optional[Any]:
   A_       :					List[str]        =      create_tensor(_lowerCAmelCase						)
   A_       :					Optional[Any]        =      broadcast(_lowerCAmelCase						)
   assert broadcasted_tensor.shape == torch.Size([state.num_processes]						)
   assert broadcasted_tensor.tolist() == list(range(1					,					state.num_processes + 1						)						)

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

def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]						)			->    Dict:
   # For now runs on only two processes
   if state.num_processes != 2:
      return
   A_       :					List[str]        =      create_tensor(_lowerCAmelCase						)
   A_       :					Tuple        =      reduce(_lowerCAmelCase					,					"mean"						)
   A_       :					List[str]        =      torch.tensor([2.0, 3]						).to(state.device						)
   assert torch.allclose(_lowerCAmelCase					,					_lowerCAmelCase						), f"{reduced_tensor} != {truth_tensor}"

def   __snake_case    (       _lowerCAmelCase			:			List[str]						)			->    Dict:
   # For xla_spawn (TPUs)
   main()

def   __snake_case    (       )			->    List[str]:
   A_       :					Tuple        =      PartialState()
   state.print(f"State: {state}"						)
   state.print("testing gather"						)
   test_gather(_lowerCAmelCase						)
   state.print("testing gather_object"						)
   test_gather_object(_lowerCAmelCase						)
   state.print("testing broadcast"						)
   test_broadcast(_lowerCAmelCase						)
   state.print("testing pad_across_processes"						)
   test_pad_across_processes(_lowerCAmelCase						)
   state.print("testing reduce_sum"						)
   test_reduce_sum(_lowerCAmelCase						)
   state.print("testing reduce_mean"						)
   test_reduce_mean(_lowerCAmelCase						)


if __name__ == "__main__":
 main()
