


"""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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=True  )						->int:





  """simple docstring"""


  model.train()
  __UpperCAmelCase    :						Any       =						model(UpperCAmelCase_  )
  __UpperCAmelCase    :						Optional[int]       =						F.mse_loss(UpperCAmelCase_  ,			target.to(output.device  )  )
  if not do_backward:
    loss /= accelerator.gradient_accumulation_steps
    loss.backward()
  else:
    accelerator.backward(UpperCAmelCase_  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=False  )						->List[str]:





  """simple docstring"""


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


def       lowerCamelCase_       (   UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[Any]       =						get_training_setup(UpperCAmelCase_  )
  # Use a single batch
  __UpperCAmelCase       ,     __UpperCAmelCase    :						List[str]       =						next(iter(UpperCAmelCase_  )  ).values()
  for iteration in range(3  ):
    # Gather the distributed inputs and targs for the base model
    __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[Any]       =						accelerator.gather((ddp_input, ddp_target)  )
    __UpperCAmelCase       ,     __UpperCAmelCase    :						List[str]       =						input.to(accelerator.device  ), target.to(accelerator.device  )
    # Perform our initial ground truth step in non "DDP"
    step_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
    # Do "gradient accumulation" (noop)
    if iteration % 2 == 0:
      # Accumulate grads locally
      with accelerator.no_sync(UpperCAmelCase_  ):
        step_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
    else:
      # Sync grads
      step_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

    # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
    check_model_parameters(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
    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(13_37 + iteration  )
    __UpperCAmelCase    :						List[Any]       =						ddp_input[torch.randperm(len(UpperCAmelCase_  )  )]


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[int]:





  """simple docstring"""


  __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Dict       =						get_training_setup(UpperCAmelCase_  )
  # Use a single batch
  __UpperCAmelCase       ,     __UpperCAmelCase    :						Union[str, Any]       =						next(iter(UpperCAmelCase_  )  ).values()
  for iteration in range(3  ):
    # Gather the distributed inputs and targs for the base model
    __UpperCAmelCase       ,     __UpperCAmelCase    :						Union[str, Any]       =						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"
    step_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
    # Do "gradient accumulation" (noop)
    if iteration % 2 == 0:
      # Accumulate grads locally
      with accelerator.no_sync(UpperCAmelCase_  ):
        step_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
    else:
      # Sync grads
      step_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

    # 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(13_37 + iteration  )
    __UpperCAmelCase    :						Dict       =						ddp_input[torch.randperm(len(UpperCAmelCase_  )  )]


def       lowerCamelCase_       (   UpperCAmelCase_=False  ,			UpperCAmelCase_=False  )						->Any:





  """simple docstring"""


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

    # 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(UpperCAmelCase_  ) - 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(13_37 + iteration  )
    __UpperCAmelCase    :						Any       =						ddp_input[torch.randperm(len(UpperCAmelCase_  )  )]
  GradientState._reset_state()


def       lowerCamelCase_       (   UpperCAmelCase_=False  ,			UpperCAmelCase_=False  )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						Accelerator(
      split_batches=UpperCAmelCase_  ,			dispatch_batches=UpperCAmelCase_  ,			gradient_accumulation_steps=2  )
  # Test that context manager behaves properly
  __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						get_training_setup(UpperCAmelCase_  ,			UpperCAmelCase_  )
  for iteration, batch in enumerate(UpperCAmelCase_  ):
    __UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						batch.values()
    # Gather the distributed inputs and targs for the base model
    __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						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"
    model.train()
    ddp_model.train()
    step_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
    opt.step()

    if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCAmelCase_  )):
      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(UpperCAmelCase_  ):
      step_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
      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    :						List[str]       =						(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCAmelCase_  ))
    if accelerator.num_processes > 1:
      check_model_parameters(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
    # Shuffle ddp_input on each iteration
    torch.manual_seed(13_37 + iteration  )
  GradientState._reset_state()


def       lowerCamelCase_       (   )						->str:





  """simple docstring"""


  __UpperCAmelCase    :						Union[str, Any]       =						Accelerator()

  __UpperCAmelCase    :						Optional[Any]       =						RegressionDataset(length=80  )
  __UpperCAmelCase    :						int       =						DataLoader(UpperCAmelCase_  ,			batch_size=16  )
  __UpperCAmelCase    :						Union[str, Any]       =						RegressionDataset(length=96  )
  __UpperCAmelCase    :						Any       =						DataLoader(UpperCAmelCase_  ,			batch_size=16  )
  __UpperCAmelCase       ,     __UpperCAmelCase    :						List[Any]       =						accelerator.prepare(UpperCAmelCase_  ,			UpperCAmelCase_  )
  assert accelerator.gradient_state.active_dataloader is None
  for iteration, _ in enumerate(UpperCAmelCase_  ):
    assert id(accelerator.gradient_state.active_dataloader  ) == id(UpperCAmelCase_  )
    if iteration < len(UpperCAmelCase_  ) - 1:
      assert not accelerator.gradient_state.end_of_dataloader
      if iteration == 1:
        for batch_num, _ in enumerate(UpperCAmelCase_  ):
          assert id(accelerator.gradient_state.active_dataloader  ) == id(UpperCAmelCase_  )
          if batch_num < len(UpperCAmelCase_  ) - 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       lowerCamelCase_       (   )						->str:





  """simple docstring"""


  __UpperCAmelCase    :						Union[str, Any]       =						Accelerator()
  __UpperCAmelCase    :						Optional[int]       =						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(UpperCAmelCase_  )
  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(UpperCAmelCase_  )
  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(UpperCAmelCase_  ,			UpperCAmelCase_  )

    # 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(UpperCAmelCase_  ,			UpperCAmelCase_  )


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  main()


if __name__ == "__main__":
  main()






"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_ = 10_00  )						->int:





  """simple docstring"""


  return sum(2 * a * ((a - 1) // 2) for a in range(3  ,			n + 1  )  )


if __name__ == "__main__":
  print(solution())