






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 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Union[str, 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 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=True) ->			Tuple:
      """simple docstring"""

      model.train()
      UpperCamelCase					       =  model(_UpperCamelCase)
      UpperCamelCase					       =  F.mse_loss(_UpperCamelCase       ,							target.to(output.device))
      if not do_backward:
            loss /= accelerator.gradient_accumulation_steps
            loss.backward()
      else:
            accelerator.backward(_UpperCamelCase)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase=False) ->			str:
      """simple docstring"""

      set_seed(42)
      UpperCamelCase					       =  RegressionModel()
      UpperCamelCase					       =  deepcopy(_UpperCamelCase)
      UpperCamelCase					       =  RegressionDataset(length=80)
      UpperCamelCase					       =  DataLoader(_UpperCamelCase       ,							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(_UpperCamelCase       ,							lr_lambda=lambda _UpperCamelCase: epoch**0.6_5)
            UpperCamelCase					       =  LambdaLR(_UpperCamelCase       ,							lr_lambda=lambda _UpperCamelCase: epoch**0.6_5)
      # Make a copy of `model`
      if sched:
            UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  accelerator.prepare(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)
      else:
            UpperCamelCase       ,  UpperCamelCase					       =  accelerator.prepare(_UpperCamelCase       ,							_UpperCamelCase)
      if sched:
            return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
      return model, ddp_model, dataloader
def 		lowercase__  (				_UpperCamelCase) ->			Tuple:
      """simple docstring"""

      UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  get_training_setup(_UpperCamelCase)
      # Use a single batch
      UpperCamelCase       ,  UpperCamelCase					       =  next(iter(_UpperCamelCase)).values()
      for iteration in range(3):
            # Gather the distributed inputs and targs for the base model
            UpperCamelCase       ,  UpperCamelCase					       =  accelerator.gather((ddp_input, ddp_target))
            UpperCamelCase       ,  UpperCamelCase					       =  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					       =  ddp_input[torch.randperm(len(_UpperCamelCase))]
def 		lowercase__  (				_UpperCamelCase) ->			List[str]:
      """simple docstring"""

      UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  get_training_setup(_UpperCamelCase)
      # Use a single batch
      UpperCamelCase       ,  UpperCamelCase					       =  next(iter(_UpperCamelCase)).values()
      for iteration in range(3):
            # Gather the distributed inputs and targs for the base model
            UpperCamelCase       ,  UpperCamelCase					       =  accelerator.gather((ddp_input, ddp_target))
            UpperCamelCase       ,  UpperCamelCase					       =  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					       =  ddp_input[torch.randperm(len(_UpperCamelCase))]
def 		lowercase__  (				_UpperCamelCase=False       ,							_UpperCamelCase=False) ->			List[Any]:
      """simple docstring"""

      UpperCamelCase					       =  Accelerator(
          split_batches=_UpperCamelCase       ,							dispatch_batches=_UpperCamelCase       ,							gradient_accumulation_steps=2)
      # Test that context manager behaves properly
      UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  get_training_setup(_UpperCamelCase)
      for iteration, batch in enumerate(_UpperCamelCase):
            UpperCamelCase       ,  UpperCamelCase					       =  batch.values()
            # Gather the distributed inputs and targs for the base model
            UpperCamelCase       ,  UpperCamelCase					       =  accelerator.gather((ddp_input, ddp_target))
            UpperCamelCase       ,  UpperCamelCase					       =  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					       =  ddp_input[torch.randperm(len(_UpperCamelCase))]
      GradientState._reset_state()
def 		lowercase__  (				_UpperCamelCase=False       ,							_UpperCamelCase=False) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  Accelerator(
          split_batches=_UpperCamelCase       ,							dispatch_batches=_UpperCamelCase       ,							gradient_accumulation_steps=2)
      # Test that context manager behaves properly
      UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  get_training_setup(_UpperCamelCase       ,							_UpperCamelCase)
      for iteration, batch in enumerate(_UpperCamelCase):
            UpperCamelCase       ,  UpperCamelCase					       =  batch.values()
            # Gather the distributed inputs and targs for the base model
            UpperCamelCase       ,  UpperCamelCase					       =  accelerator.gather((ddp_input, ddp_target))
            UpperCamelCase       ,  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(_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					       =  (((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 		lowercase__  (				) ->			Union[str, Any]:
      """simple docstring"""

      UpperCamelCase					       =  Accelerator()

      UpperCamelCase					       =  RegressionDataset(length=80)
      UpperCamelCase					       =  DataLoader(_UpperCamelCase       ,							batch_size=16)
      UpperCamelCase					       =  RegressionDataset(length=96)
      UpperCamelCase					       =  DataLoader(_UpperCamelCase       ,							batch_size=16)
      UpperCamelCase       ,  UpperCamelCase					       =  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 		lowercase__  (				) ->			Any:
      """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(_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 		lowercase__  (				_UpperCamelCase) ->			Optional[Any]:
      """simple docstring"""

      main()


if __name__ == "__main__":
       main()











from pathlib import Path

import fire
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  Path(_UpperCamelCase)
      UpperCamelCase					       =  Path(_UpperCamelCase)
      dest_dir.mkdir(exist_ok=_UpperCamelCase)
      for path in src_dir.iterdir():
            UpperCamelCase					       =  [x.rstrip() for x in list(path.open().readlines())][:n]
            UpperCamelCase					       =  dest_dir.joinpath(path.name)
            print(_UpperCamelCase)
            dest_path.open('w').write('\n'.join(_UpperCamelCase))


if __name__ == "__main__":
       fire.Fire(minify)



