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_			(					__A,  __A,  __A,  __A							)					->    str:







      '''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_			(					__A,  __A,  __A,  __A,  __A=True							)					->    Optional[Any]:







      '''simple docstring'''


      model.train()
      UpperCAmelCase__								=  model(lowerCAmelCase_							)
      UpperCAmelCase__								=  F.mse_loss(lowerCAmelCase_,  target.to(output.device							)							)
      if not do_backward:
            loss /= accelerator.gradient_accumulation_steps
            loss.backward()
      else:
            accelerator.backward(lowerCAmelCase_							)




def        lowerCAmelCase_			(					__A,  __A=False							)					->    Union[str, Any]:







      '''simple docstring'''


      set_seed(42							)
      UpperCAmelCase__								=  RegressionModel()
      UpperCAmelCase__								=  deepcopy(lowerCAmelCase_							)
      UpperCAmelCase__								=  RegressionDataset(length=80							)
      UpperCAmelCase__								=  DataLoader(lowerCAmelCase_,  batch_size=16							)
      model.to(accelerator.device							)
      if sched:
            UpperCAmelCase__								=  AdamW(params=model.parameters(),  lr=1e-3							)
            UpperCAmelCase__								=  AdamW(params=ddp_model.parameters(),  lr=1e-3							)
            UpperCAmelCase__								=  LambdaLR(lowerCAmelCase_,  lr_lambda=lambda __A							: epoch**0.65							)
            UpperCAmelCase__								=  LambdaLR(lowerCAmelCase_,  lr_lambda=lambda __A							: epoch**0.65							)
      # Make a copy of `model`
      if sched:
            UpperCAmelCase__								=  accelerator.prepare(lowerCAmelCase_,  lowerCAmelCase_,  lowerCAmelCase_,  lowerCAmelCase_							)
      else:
            UpperCAmelCase__								=  accelerator.prepare(lowerCAmelCase_,  lowerCAmelCase_							)
      if sched:
            return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
      return model, ddp_model, dataloader




def        lowerCAmelCase_			(					__A							)					->    Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  get_training_setup(lowerCAmelCase_							)
      # Use a single batch
      UpperCAmelCase__								=  next(iter(lowerCAmelCase_							)							).values()
      for iteration in range(3							):
            # Gather the distributed inputs and targs for the base model
            UpperCAmelCase__								=  accelerator.gather((ddp_input, ddp_target)							)
            UpperCAmelCase__								=  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(1_337 + iteration							)
            UpperCAmelCase__								=  ddp_input[torch.randperm(len(lowerCAmelCase_							)							)]




def        lowerCAmelCase_			(					__A							)					->    str:







      '''simple docstring'''


      UpperCAmelCase__								=  get_training_setup(lowerCAmelCase_							)
      # Use a single batch
      UpperCAmelCase__								=  next(iter(lowerCAmelCase_							)							).values()
      for iteration in range(3							):
            # Gather the distributed inputs and targs for the base model
            UpperCAmelCase__								=  accelerator.gather((ddp_input, ddp_target)							)
            UpperCAmelCase__								=  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(1_337 + iteration							)
            UpperCAmelCase__								=  ddp_input[torch.randperm(len(lowerCAmelCase_							)							)]




def        lowerCAmelCase_			(					__A=False,  __A=False							)					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  Accelerator(
          split_batches=lowerCAmelCase_,  dispatch_batches=lowerCAmelCase_,  gradient_accumulation_steps=2							)
      # Test that context manager behaves properly
      UpperCAmelCase__								=  get_training_setup(lowerCAmelCase_							)
      for iteration, batch in enumerate(lowerCAmelCase_							):
            UpperCAmelCase__								=  batch.values()
            # Gather the distributed inputs and targs for the base model
            UpperCAmelCase__								=  accelerator.gather((ddp_input, ddp_target)							)
            UpperCAmelCase__								=  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(1_337 + iteration							)
            UpperCAmelCase__								=  ddp_input[torch.randperm(len(lowerCAmelCase_							)							)]
      GradientState._reset_state()




def        lowerCAmelCase_			(					__A=False,  __A=False							)					->    Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  Accelerator(
          split_batches=lowerCAmelCase_,  dispatch_batches=lowerCAmelCase_,  gradient_accumulation_steps=2							)
      # Test that context manager behaves properly
      UpperCAmelCase__								=  get_training_setup(lowerCAmelCase_,  lowerCAmelCase_							)
      for iteration, batch in enumerate(lowerCAmelCase_							):
            UpperCAmelCase__								=  batch.values()
            # Gather the distributed inputs and targs for the base model
            UpperCAmelCase__								=  accelerator.gather((ddp_input, ddp_target)							)
            UpperCAmelCase__								=  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__								=  (((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(1_337 + iteration							)
      GradientState._reset_state()




def        lowerCAmelCase_			(					)					->    Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  Accelerator()

      UpperCAmelCase__								=  RegressionDataset(length=80							)
      UpperCAmelCase__								=  DataLoader(lowerCAmelCase_,  batch_size=16							)
      UpperCAmelCase__								=  RegressionDataset(length=96							)
      UpperCAmelCase__								=  DataLoader(lowerCAmelCase_,  batch_size=16							)
      UpperCAmelCase__								=  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        lowerCAmelCase_			(					)					->    Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  Accelerator()
      UpperCAmelCase__								=  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        lowerCAmelCase_			(					__A							)					->    Any:







      '''simple docstring'''


      main()


if __name__ == "__main__":
       main()




def        lowerCAmelCase_			(					__A							)					->    str:







      '''simple docstring'''


      return " ".join(input_str.split()[::-1]							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



