






'''simple docstring'''


import torch

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







def 	__magic_name__( _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = [state.process_index]
						UpperCamelCase__         = gather_object(_A					)
						assert len(_A					) == state.num_processes, f"{gathered_obj}, {len(_A					)} != {state.num_processes}"
						assert gathered_obj == list(range(state.num_processes					)					), f"{gathered_obj} != {list(range(state.num_processes					)					)}"







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = create_tensor(_A					)
						UpperCamelCase__         = broadcast(_A					)
						assert broadcasted_tensor.shape == torch.Size([state.num_processes]					)
						assert broadcasted_tensor.tolist() == list(range(1						,      state.num_processes + 1					)					)







def 	__magic_name__( _A					):
						'''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(_A					)
						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 	__magic_name__( _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A					):
						'''simple docstring'''





						main()







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = PartialState()
						state.print(f"State: {state}"					)
						state.print("""testing gather"""					)
						test_gather(_A					)
						state.print("""testing gather_object"""					)
						test_gather_object(_A					)
						state.print("""testing broadcast"""					)
						test_broadcast(_A					)
						state.print("""testing pad_across_processes"""					)
						test_pad_across_processes(_A					)
						state.print("""testing reduce_sum"""					)
						test_reduce_sum(_A					)
						state.print("""testing reduce_mean"""					)
						test_reduce_mean(_A					)


if __name__ == "__main__":
					main()








'''simple docstring'''


# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						Optional[int] ,      lowercase							:						Optional[int] ,      lowercase							:						Optional[Any] ,      lowercase							:						bool = True ,      lowercase							:						bool = False			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = scheduler
									UpperCamelCase__         = optimizers if isinstance(lowercase ,      (list, tuple)			) else [optimizers]
									UpperCamelCase__         = split_batches
									UpperCamelCase__         = step_with_optimizer
									UpperCamelCase__         = GradientState()






			def 				A      (   self							:						int ,      *lowercase							:						int ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									if not self.step_with_optimizer:
															# No link between scheduler and optimizer -> just step
															self.scheduler.step(*lowercase ,      **lowercase			)
															return

									# Otherwise, first make sure the optimizer was stepped.
									if not self.gradient_state.sync_gradients:
															if self.gradient_state.adjust_scheduler:
																					self.scheduler._step_count += 1
															return

									for opt in self.optimizers:
															if opt.step_was_skipped:
																					return
									if self.split_batches:
															# Split batches -> the training dataloader batch size is not changed so one step per training step
															self.scheduler.step(*lowercase ,      **lowercase			)
									else:
															# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
															# num_processes steps per training step
															UpperCamelCase__         = AcceleratorState().num_processes
															for _ in range(lowercase			):
																					# Special case when using OneCycle and `drop_last` was not used
																					if hasattr(self.scheduler ,      """total_steps"""			):
																											if self.scheduler._step_count <= self.scheduler.total_steps:
																																	self.scheduler.step(*lowercase ,      **lowercase			)
																					else:
																											self.scheduler.step(*lowercase ,      **lowercase			)






			def 				A      (   self							:						List[str]			)							->    Optional[Any]:

									'''simple docstring'''







									return self.scheduler.get_last_lr()






			def 				A      (   self							:						Any			)							->    int:

									'''simple docstring'''







									return self.scheduler.state_dict()






			def 				A      (   self							:						Any ,      lowercase							:						int			)							->    Optional[int]:

									'''simple docstring'''







									self.scheduler.load_state_dict(lowercase			)






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									return self.scheduler.get_lr()







			def 				A      (   self							:						Union[str, Any] ,      *lowercase							:						List[Any] ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									return self.scheduler.print_lr(*lowercase ,      **lowercase			)
