


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		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> 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		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase=True )					-> Any:


     """simple docstring"""


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






def 		_A		(		_lowercase					,  _lowercase=False )					-> List[Any]:


     """simple docstring"""


     set_seed(42 )
     __UpperCamelCase =			RegressionModel()
     __UpperCamelCase =			deepcopy(_lowercase )
     __UpperCamelCase =			RegressionDataset(length=80 )
     __UpperCamelCase =			DataLoader(_lowercase					,  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(_lowercase					,  lr_lambda=lambda _lowercase : epoch**0.65 )
          __UpperCamelCase =			LambdaLR(_lowercase					,  lr_lambda=lambda _lowercase : epoch**0.65 )
     # Make a copy of `model`
     if sched:
          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			accelerator.prepare(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
     else:
          __UpperCamelCase,    __UpperCamelCase =			accelerator.prepare(_lowercase					,  _lowercase )
     if sched:
          return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
     return model, ddp_model, dataloader






def 		_A		(		_lowercase )					-> Tuple:


     """simple docstring"""


     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			get_training_setup(_lowercase )
     # Use a single batch
     __UpperCamelCase,    __UpperCamelCase =			next(iter(_lowercase ) ).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(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
          # Do "gradient accumulation" (noop)
          if iteration % 2 == 0:
               # Accumulate grads locally
               with accelerator.no_sync(_lowercase ):
                    step_model(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
          else:
               # Sync grads
               step_model(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )

          # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
          check_model_parameters(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
          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(_lowercase ) )]






def 		_A		(		_lowercase )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			get_training_setup(_lowercase )
     # Use a single batch
     __UpperCamelCase,    __UpperCamelCase =			next(iter(_lowercase ) ).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(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
          # Do "gradient accumulation" (noop)
          if iteration % 2 == 0:
               # Accumulate grads locally
               with accelerator.no_sync(_lowercase ):
                    step_model(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
          else:
               # Sync grads
               step_model(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )

          # 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(_lowercase ) )]






def 		_A		(		_lowercase=False					,  _lowercase=False )					-> str:


     """simple docstring"""


     __UpperCamelCase =			Accelerator(
         split_batches=_lowercase					,  dispatch_batches=_lowercase					,  gradient_accumulation_steps=2 )
     # Test that context manager behaves properly
     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			get_training_setup(_lowercase )
     for iteration, batch in enumerate(_lowercase ):
          __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(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
          # Do "gradient accumulation" (noop)
          with accelerator.accumulate(_lowercase ):
               step_model(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )

          # 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(_lowercase ) - 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(_lowercase ) )]
     GradientState._reset_state()






def 		_A		(		_lowercase=False					,  _lowercase=False )					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			Accelerator(
         split_batches=_lowercase					,  dispatch_batches=_lowercase					,  gradient_accumulation_steps=2 )
     # Test that context manager behaves properly
     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			get_training_setup(_lowercase					,  _lowercase )
     for iteration, batch in enumerate(_lowercase ):
          __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(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
          opt.step()

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






def 		_A		(		)					-> Dict:


     """simple docstring"""


     __UpperCamelCase =			Accelerator()

     __UpperCamelCase =			RegressionDataset(length=80 )
     __UpperCamelCase =			DataLoader(_lowercase					,  batch_size=16 )
     __UpperCamelCase =			RegressionDataset(length=96 )
     __UpperCamelCase =			DataLoader(_lowercase					,  batch_size=16 )
     __UpperCamelCase,    __UpperCamelCase =			accelerator.prepare(_lowercase					,  _lowercase )
     assert accelerator.gradient_state.active_dataloader is None
     for iteration, _ in enumerate(_lowercase ):
          assert id(accelerator.gradient_state.active_dataloader ) == id(_lowercase )
          if iteration < len(_lowercase ) - 1:
               assert not accelerator.gradient_state.end_of_dataloader
               if iteration == 1:
                    for batch_num, _ in enumerate(_lowercase ):
                         assert id(accelerator.gradient_state.active_dataloader ) == id(_lowercase )
                         if batch_num < len(_lowercase ) - 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		(		)					-> 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(_lowercase )
     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(_lowercase )
     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(_lowercase					,  _lowercase )

    # 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(_lowercase					,  _lowercase )






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     main()


if __name__ == "__main__":
    main()






import pytest

import datasets


# Import fixture modules as plugins
__snake_case   							=						['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']






def 		_A		(		_lowercase					,  _lowercase )					-> Tuple:


     """simple docstring"""


     for item in items:
          if any(marker in item.keywords for marker in ['integration', 'unit'] ):
               continue
          item.add_marker(pytest.mark.unit )






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     config.addinivalue_line('markers'					,  'torchaudio_latest: mark test to run with torchaudio>=0.12' )






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			tmp_path_factory.getbasetemp() / 'cache'
     __UpperCamelCase =			test_hf_cache_home / 'datasets'
     __UpperCamelCase =			test_hf_cache_home / 'metrics'
     __UpperCamelCase =			test_hf_cache_home / 'modules'
     monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads'
     monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads' / 'extracted'
     monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'					,  str(_lowercase ) )






@pytest.fixture(autouse=_lowercase					,  scope='session' )
def 		_A		(		)					-> Dict:


     """simple docstring"""


     datasets.disable_progress_bar()






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase )					-> Tuple:


     """simple docstring"""


     monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'					,  _lowercase )






@pytest.fixture
def 		_A		(		_lowercase )					-> Any:


     """simple docstring"""


     monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'					,  _lowercase )


