



import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict


@pytest.mark.parametrize(
    """split_dict"""	,						[
        SplitDict(),
        SplitDict({"""train""": SplitInfo(name="""train"""	,						num_bytes=13_37	,						num_examples=42	,						dataset_name="""my_dataset"""			)}			),
        SplitDict({"""train""": SplitInfo(name="""train"""	,						num_bytes=13_37	,						num_examples=42			)}			),
        SplitDict({"""train""": SplitInfo()}			),
    ]	,						)
def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    split_dict._to_yaml_list()
					assert len(snake_case			) == len(snake_case			)
					_lowerCAmelCase						=    SplitDict._from_yaml_list(snake_case			)
					for split_name, split_info in split_dict.items():
										# dataset_name field is deprecated, and is therefore not part of the YAML dump
										_lowerCAmelCase						=    None
										# the split name of split_dict takes over the name of the split info object
										_lowerCAmelCase						=    split_name
					assert split_dict == reloaded


@pytest.mark.parametrize(
    """split_info"""	,						[SplitInfo(), SplitInfo(dataset_name=snake_case			), SplitInfo(dataset_name="""my_dataset"""			)]			)
def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    asdict(SplitDict({"""train""": split_info}			)			)
					assert "dataset_name" in split_dict_asdict["train"]
					assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name





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,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 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,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=True							):
 model.train()
 lowerCamelCase		:	Dict							=      model(lowerCamelCase							)
 lowerCamelCase		:	Any							=      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,	lowerCamelCase=False							):
 set_seed(42							)
 lowerCamelCase		:	Tuple							=      RegressionModel()
 lowerCamelCase		:	Any							=      deepcopy(lowerCamelCase							)
 lowerCamelCase		:	Any							=      RegressionDataset(length=80							)
 lowerCamelCase		:	Dict							=      DataLoader(lowerCamelCase,	batch_size=16							)
 model.to(accelerator.device							)
 if sched:
  lowerCamelCase		:	int							=      AdamW(params=model.parameters(),	lr=1e-3							)
  lowerCamelCase		:	Optional[Any]							=      AdamW(params=ddp_model.parameters(),	lr=1e-3							)
  lowerCamelCase		:	str							=      LambdaLR(lowerCamelCase,	lr_lambda=lambda lowerCamelCase							: epoch**0.6_5							)
  lowerCamelCase		:	Tuple							=      LambdaLR(lowerCamelCase,	lr_lambda=lambda lowerCamelCase							: epoch**0.6_5							)
 # Make a copy of `model`
 if sched:
  lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      accelerator.prepare(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)
 else:
  lowerCamelCase			,    lowerCamelCase		:	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							):
 # Test when on a single CPU or GPU that the context manager does nothing
 lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      get_training_setup(lowerCamelCase							)
 # Use a single batch
 lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      next(iter(lowerCamelCase							)							).values()
 for iteration in range(3							):
  # Gather the distributed inputs and targs for the base model
  lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      accelerator.gather((ddp_input, ddp_target)							)
  lowerCamelCase			,    lowerCamelCase		:	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							)
  lowerCamelCase		:	List[Any]							=      ddp_input[torch.randperm(len(lowerCamelCase							)							)]





def     _a					(						lowerCamelCase							):
 # Test on distributed setup that context manager behaves properly
 lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	int							=      get_training_setup(lowerCamelCase							)
 # Use a single batch
 lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      next(iter(lowerCamelCase							)							).values()
 for iteration in range(3							):
  # Gather the distributed inputs and targs for the base model
  lowerCamelCase			,    lowerCamelCase		:	Any							=      accelerator.gather((ddp_input, ddp_target)							)
  lowerCamelCase			,    lowerCamelCase		:	str							=      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							)
  lowerCamelCase		:	Optional[Any]							=      ddp_input[torch.randperm(len(lowerCamelCase							)							)]





def     _a					(						lowerCamelCase=False,	lowerCamelCase=False							):
 lowerCamelCase		:	Any							=      Accelerator(
     split_batches=lowerCamelCase,	dispatch_batches=lowerCamelCase,	gradient_accumulation_steps=2							)
 # Test that context manager behaves properly
 lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Any							=      get_training_setup(lowerCamelCase							)
 for iteration, batch in enumerate(lowerCamelCase							):
  lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      batch.values()
  # Gather the distributed inputs and targs for the base model
  lowerCamelCase			,    lowerCamelCase		:	str							=      accelerator.gather((ddp_input, ddp_target)							)
  lowerCamelCase			,    lowerCamelCase		:	Tuple							=      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							)
  lowerCamelCase		:	Any							=      ddp_input[torch.randperm(len(lowerCamelCase							)							)]
 GradientState._reset_state()





def     _a					(						lowerCamelCase=False,	lowerCamelCase=False							):
 lowerCamelCase		:	List[Any]							=      Accelerator(
     split_batches=lowerCamelCase,	dispatch_batches=lowerCamelCase,	gradient_accumulation_steps=2							)
 # Test that context manager behaves properly
 lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      get_training_setup(lowerCamelCase,	lowerCamelCase							)
 for iteration, batch in enumerate(lowerCamelCase							):
  lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      batch.values()
  # Gather the distributed inputs and targs for the base model
  lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      accelerator.gather((ddp_input, ddp_target)							)
  lowerCamelCase			,    lowerCamelCase		:	Tuple							=      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'''
  lowerCamelCase		:	Union[str, Any]							=      (((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					(						):
 lowerCamelCase		:	int							=      Accelerator()

 lowerCamelCase		:	Optional[Any]							=      RegressionDataset(length=80							)
 lowerCamelCase		:	List[str]							=      DataLoader(lowerCamelCase,	batch_size=16							)
 lowerCamelCase		:	int							=      RegressionDataset(length=96							)
 lowerCamelCase		:	Optional[int]							=      DataLoader(lowerCamelCase,	batch_size=16							)
 lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      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					(						):
 lowerCamelCase		:	List[Any]							=      Accelerator()
 lowerCamelCase		:	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(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							):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()




