
"""simple docstring"""




from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed



def 							a__ (				lowerCAmelCase	:       int       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       int		):






     '''simple docstring'''
     for param, grad_param in zip(model_a.parameters()       ,    model_b.parameters()		):
          if not param.requires_grad:
               continue
          if not did_step:
               # Grads should not be in sync
               assert (
                   torch.allclose(param.grad       ,    grad_param.grad		) is False
               ), F"Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"
          else:
               # Grads should be in sync
               assert (
                   torch.allclose(param.grad       ,    grad_param.grad		) is True
               ), F"Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"



def 							a__ (				lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Tuple=True		):






     '''simple docstring'''
     model.train()
     UpperCAmelCase__							: int 							=						model(_lowerCamelCase		)
     UpperCAmelCase__							: Dict 							=						F.mse_loss(_lowerCamelCase       ,    target.to(output.device		)		)
     if not do_backward:
          loss /= accelerator.gradient_accumulation_steps
          loss.backward()
     else:
          accelerator.backward(_lowerCamelCase		)



def 							a__ (				lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Optional[int]=False		):






     '''simple docstring'''
     set_seed(42		)
     UpperCAmelCase__							: str 							=						RegressionModel()
     UpperCAmelCase__							: Dict 							=						deepcopy(_lowerCamelCase		)
     UpperCAmelCase__							: List[Any] 							=						RegressionDataset(length=80		)
     UpperCAmelCase__							: Tuple 							=						DataLoader(_lowerCamelCase       ,    batch_size=16		)
     model.to(accelerator.device		)
     if sched:
          UpperCAmelCase__							: Any 							=						AdamW(params=model.parameters()       ,    lr=1E-3		)
          UpperCAmelCase__							: Tuple 							=						AdamW(params=ddp_model.parameters()       ,    lr=1E-3		)
          UpperCAmelCase__							: Any 							=						LambdaLR(_lowerCamelCase       ,    lr_lambda=lambda lowerCAmelCase		: epoch**0.65		)
          UpperCAmelCase__							: Optional[Any] 							=						LambdaLR(_lowerCamelCase       ,    lr_lambda=lambda lowerCAmelCase		: epoch**0.65		)
     # Make a copy of `model`
     if sched:
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						accelerator.prepare(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)
     else:
          UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						accelerator.prepare(_lowerCamelCase       ,    _lowerCamelCase		)
     if sched:
          return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
     return model, ddp_model, dataloader



def 							a__ (				lowerCAmelCase	:       Any		):






     '''simple docstring'''
     # Test when on a single CPU or GPU that the context manager does nothing
     UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						get_training_setup(_lowerCamelCase		)
     # Use a single batch
     UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						next(iter(_lowerCamelCase		)		).values()
     for iteration in range(3		):
          # Gather the distributed inputs and targs for the base model
          UpperCAmelCase__							,	UpperCAmelCase__							: str 							=						accelerator.gather((ddp_input, ddp_target)		)
          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						input.to(accelerator.device		), target.to(accelerator.device		)
          # Perform our initial ground truth step in non "DDP"
          step_model(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)
          # Do "gradient accumulation" (noop)
          if iteration % 2 == 0:
               # Accumulate grads locally
               with accelerator.no_sync(_lowerCamelCase		):
                    step_model(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)
          else:
               # Sync grads
               step_model(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)

          # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
          check_model_parameters(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)
          for param, ddp_param in zip(model.parameters()       ,    ddp_model.parameters()		):
               if not param.requires_grad:
                    continue
               assert torch.allclose(
                   param.grad       ,    ddp_param.grad		), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

          # Shuffle ddp_input on each iteration
          torch.manual_seed(1337 + iteration		)
          UpperCAmelCase__							: Dict 							=						ddp_input[torch.randperm(len(_lowerCamelCase		)		)]



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     # Test on distributed setup that context manager behaves properly
     UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						get_training_setup(_lowerCamelCase		)
     # Use a single batch
     UpperCAmelCase__							,	UpperCAmelCase__							: Any 							=						next(iter(_lowerCamelCase		)		).values()
     for iteration in range(3		):
          # Gather the distributed inputs and targs for the base model
          UpperCAmelCase__							,	UpperCAmelCase__							: Tuple 							=						accelerator.gather((ddp_input, ddp_target)		)
          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						input.to(accelerator.device		), target.to(accelerator.device		)
          # Perform our initial ground truth step in non "DDP"
          step_model(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)
          # Do "gradient accumulation" (noop)
          if iteration % 2 == 0:
               # Accumulate grads locally
               with accelerator.no_sync(_lowerCamelCase		):
                    step_model(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)
          else:
               # Sync grads
               step_model(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)

          # DDP model and model should only be in sync when not (iteration % 2 == 0)
          for param, ddp_param in zip(model.parameters()       ,    ddp_model.parameters()		):
               if not param.requires_grad:
                    continue
               if iteration % 2 == 0:
                    # Grads should not be in sync
                    assert (
                        torch.allclose(param.grad       ,    ddp_param.grad		) is False
                    ), F"Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"
               else:
                    # Grads should be in sync
                    assert (
                        torch.allclose(param.grad       ,    ddp_param.grad		) is True
                    ), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
          torch.manual_seed(1337 + iteration		)
          UpperCAmelCase__							: Union[str, Any] 							=						ddp_input[torch.randperm(len(_lowerCamelCase		)		)]



def 							a__ (				lowerCAmelCase	:       int=False       ,    lowerCAmelCase	:       Optional[int]=False		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						Accelerator(
         split_batches=_lowerCamelCase       ,    dispatch_batches=_lowerCamelCase       ,    gradient_accumulation_steps=2		)
     # Test that context manager behaves properly
     UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						get_training_setup(_lowerCamelCase		)
     for iteration, batch in enumerate(_lowerCamelCase		):
          UpperCAmelCase__							,	UpperCAmelCase__							: int 							=						batch.values()
          # Gather the distributed inputs and targs for the base model
          UpperCAmelCase__							,	UpperCAmelCase__							: Tuple 							=						accelerator.gather((ddp_input, ddp_target)		)
          UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						input.to(accelerator.device		), target.to(accelerator.device		)
          # Perform our initial ground truth step in non "DDP"
          step_model(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)
          # Do "gradient accumulation" (noop)
          with accelerator.accumulate(_lowerCamelCase		):
               step_model(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)

          # DDP model and model should only be in sync when not (iteration % 2 == 0)
          for param, ddp_param in zip(model.parameters()       ,    ddp_model.parameters()		):
               if not param.requires_grad:
                    continue
               if ((iteration + 1) % 2 == 0) or (iteration == len(_lowerCamelCase		) - 1):
                    # Grads should be in sync
                    assert (
                        torch.allclose(param.grad       ,    ddp_param.grad		) is True
                    ), F"Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
               else:
                    # Grads should not be in sync
                    assert (
                        torch.allclose(param.grad       ,    ddp_param.grad		) is False
                    ), F"Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
          torch.manual_seed(1337 + iteration		)
          UpperCAmelCase__							: Dict 							=						ddp_input[torch.randperm(len(_lowerCamelCase		)		)]
     GradientState._reset_state()



def 							a__ (				lowerCAmelCase	:       Tuple=False       ,    lowerCAmelCase	:       Any=False		):






     '''simple docstring'''
     UpperCAmelCase__							: Any 							=						Accelerator(
         split_batches=_lowerCamelCase       ,    dispatch_batches=_lowerCamelCase       ,    gradient_accumulation_steps=2		)
     # Test that context manager behaves properly
     UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						get_training_setup(_lowerCamelCase       ,    _lowerCamelCase		)
     for iteration, batch in enumerate(_lowerCamelCase		):
          UpperCAmelCase__							,	UpperCAmelCase__							: Dict 							=						batch.values()
          # Gather the distributed inputs and targs for the base model
          UpperCAmelCase__							,	UpperCAmelCase__							: Dict 							=						accelerator.gather((ddp_input, ddp_target)		)
          UpperCAmelCase__							,	UpperCAmelCase__							: int 							=						input.to(accelerator.device		), target.to(accelerator.device		)
          # Perform our initial ground truth step in non "DDP"
          model.train()
          ddp_model.train()
          step_model(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)
          opt.step()

          if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_lowerCamelCase		)):
               if split_batches:
                    sched.step()
               else:
                    for _ in range(accelerator.num_processes		):
                         sched.step()
          opt.zero_grad()
          # Perform gradient accumulation under wrapper
          with accelerator.accumulate(_lowerCamelCase		):
               step_model(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)
               ddp_opt.step()
               ddp_sched.step()
               ddp_opt.zero_grad()

          # Learning rates should be the same
          assert (
              opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
          ), F"Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"
          UpperCAmelCase__							: str 							=						(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_lowerCamelCase		))
          if accelerator.num_processes > 1:
               check_model_parameters(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase		)
          # Shuffle ddp_input on each iteration
          torch.manual_seed(1337 + iteration		)
     GradientState._reset_state()



def 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[int] 							=						Accelerator()

     UpperCAmelCase__							: Optional[int] 							=						RegressionDataset(length=80		)
     UpperCAmelCase__							: int 							=						DataLoader(_lowerCamelCase       ,    batch_size=16		)
     UpperCAmelCase__							: str 							=						RegressionDataset(length=96		)
     UpperCAmelCase__							: str 							=						DataLoader(_lowerCamelCase       ,    batch_size=16		)
     UpperCAmelCase__							,	UpperCAmelCase__							: Dict 							=						accelerator.prepare(_lowerCamelCase       ,    _lowerCamelCase		)
     assert accelerator.gradient_state.active_dataloader is None
     for iteration, _ in enumerate(_lowerCamelCase		):
          assert id(accelerator.gradient_state.active_dataloader		) == id(_lowerCamelCase		)
          if iteration < len(_lowerCamelCase		) - 1:
               assert not accelerator.gradient_state.end_of_dataloader
               if iteration == 1:
                    for batch_num, _ in enumerate(_lowerCamelCase		):
                         assert id(accelerator.gradient_state.active_dataloader		) == id(_lowerCamelCase		)
                         if batch_num < len(_lowerCamelCase		) - 1:
                              assert not accelerator.gradient_state.end_of_dataloader
                         else:
                              assert accelerator.gradient_state.end_of_dataloader
          else:
               assert accelerator.gradient_state.end_of_dataloader
     assert accelerator.gradient_state.active_dataloader is None



def 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: List[Any] 							=						Accelerator()
     UpperCAmelCase__							: Union[str, Any] 							=						accelerator.state
     if state.local_process_index == 0:
          print("**Test `accumulate` gradient accumulation with dataloader break**"		)
     test_dataloader_break()
     if state.distributed_type == DistributedType.NO:
          if state.local_process_index == 0:
               print("**Test NOOP `no_sync` context manager**"		)
          test_noop_sync(_lowerCamelCase		)
     if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
          if state.local_process_index == 0:
               print("**Test Distributed `no_sync` context manager**"		)
          test_distributed_sync(_lowerCamelCase		)
     if state.distributed_type == DistributedType.MULTI_GPU:
          for split_batch in [True, False]:
               for dispatch_batches in [True, False]:
                    if state.local_process_index == 0:
                         print(
                             "**Test `accumulate` gradient accumulation, "       ,    F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"       ,    )
                    test_gradient_accumulation(_lowerCamelCase       ,    _lowerCamelCase		)

    # Currently will break on torch 2.0 +, need to investigate why
     if is_torch_version("<"       ,    "2.0"		) or state.distributed_type == DistributedType.NO:
          if state.local_process_index == 0:
               print(
                   "**Test `accumulate` gradient accumulation with optimizer and scheduler, "       ,    "`split_batches=False`, `dispatch_batches=False`**"       ,    )
          test_gradient_accumulation_with_opt_and_scheduler()
          if state.distributed_type == DistributedType.MULTI_GPU:
               for split_batch in [True, False]:
                    for dispatch_batches in [True, False]:
                         if not split_batch and not dispatch_batches:
                              continue
                         if state.local_process_index == 0:
                              print(
                                  "**Test `accumulate` gradient accumulation with optimizer and scheduler, "       ,    F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"       ,    )
                         test_gradient_accumulation_with_opt_and_scheduler(_lowerCamelCase       ,    _lowerCamelCase		)



def 							a__ (				lowerCAmelCase	:       str		):






     '''simple docstring'''
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
    main()


"""simple docstring"""




from timeit import timeit



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if number < 0:
          raise ValueError("the value of input must not be negative"		)
     UpperCAmelCase__							: Tuple 							=						0
     while number:
          number &= number - 1
          result += 1
     return result



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if number < 0:
          raise ValueError("the value of input must not be negative"		)
     UpperCAmelCase__							: Union[str, Any] 							=						0
     while number:
          if number % 2 == 1:
               result += 1
          number >>= 1
     return result



def 							a__ (				):






     '''simple docstring'''

     def do_benchmark(lowerCAmelCase	:       int		) -> None:
          UpperCAmelCase__							: Dict 							=						"import __main__ as z"
          print(F"Benchmark when {number = }:"		)
          print(F"{get_set_bits_count_using_modulo_operator(lowerCAmelCase		) = }"		)
          UpperCAmelCase__							: Tuple 							=						timeit("z.get_set_bits_count_using_modulo_operator(25)"       ,    setup=lowerCAmelCase		)
          print(F"timeit() runs in {timing} seconds"		)
          print(F"{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase		) = }"		)
          UpperCAmelCase__							: Any 							=						timeit(
              "z.get_set_bits_count_using_brian_kernighans_algorithm(25)"       ,    setup=lowerCAmelCase       ,    )
          print(F"timeit() runs in {timing} seconds"		)

     for number in (25, 37, 58, 0):
          do_benchmark(lowerCAmelCase		)
          print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()



