


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


def    _lowerCamelCase   (     lowerCamelCase__	:							List[str]  ):
 # Test when on a single CPU or GPU that the context manager does nothing
 lowercase__	,       lowercase__	,       lowercase__       :       List[Any]				=						get_training_setup(lowerCamelCase__  )
 # Use a single batch
 lowercase__	,       lowercase__       :       Any				=						next(iter(lowerCamelCase__  )  ).values()
 for iteration in range(3  ):
  # Gather the distributed inputs and targs for the base model
  lowercase__	,       lowercase__       :       Any				=						accelerator.gather((ddp_input, ddp_target)  )
  lowercase__	,       lowercase__       :       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__  )

  # 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(13_37 + iteration  )
  lowercase__       :       List[Any]				=						ddp_input[torch.randperm(len(lowerCamelCase__  )  )]


def    _lowerCamelCase   (     lowerCamelCase__	:							int  ):
 # Test on distributed setup that context manager behaves properly
 lowercase__	,       lowercase__	,       lowercase__       :       Tuple				=						get_training_setup(lowerCamelCase__  )
 # Use a single batch
 lowercase__	,       lowercase__       :       str				=						next(iter(lowerCamelCase__  )  ).values()
 for iteration in range(3  ):
  # Gather the distributed inputs and targs for the base model
  lowercase__	,       lowercase__       :       Optional[int]				=						accelerator.gather((ddp_input, ddp_target)  )
  lowercase__	,       lowercase__       :       List[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(13_37 + iteration  )
  lowercase__       :       Dict				=						ddp_input[torch.randperm(len(lowerCamelCase__  )  )]


def    _lowerCamelCase   (     lowerCamelCase__	:							Dict=False       ,		lowerCamelCase__	:							Tuple=False  ):
 lowercase__       :       List[Any]				=						Accelerator(
     split_batches=lowerCamelCase__       ,		dispatch_batches=lowerCamelCase__       ,		gradient_accumulation_steps=2  )
 # Test that context manager behaves properly
 lowercase__	,       lowercase__	,       lowercase__       :       str				=						get_training_setup(lowerCamelCase__  )
 for iteration, batch in enumerate(lowerCamelCase__  ):
  lowercase__	,       lowercase__       :       int				=						batch.values()
  # Gather the distributed inputs and targs for the base model
  lowercase__	,       lowercase__       :       Optional[int]				=						accelerator.gather((ddp_input, ddp_target)  )
  lowercase__	,       lowercase__       :       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(13_37 + iteration  )
  lowercase__       :       Optional[int]				=						ddp_input[torch.randperm(len(lowerCamelCase__  )  )]
 GradientState._reset_state()


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[Any]=False       ,		lowerCamelCase__	:							Any=False  ):
 lowercase__       :       Optional[int]				=						Accelerator(
     split_batches=lowerCamelCase__       ,		dispatch_batches=lowerCamelCase__       ,		gradient_accumulation_steps=2  )
 # Test that context manager behaves properly
 lowercase__	,       lowercase__	,       lowercase__	,       lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       str				=						get_training_setup(lowerCamelCase__       ,		lowerCamelCase__  )
 for iteration, batch in enumerate(lowerCamelCase__  ):
  lowercase__	,       lowercase__       :       Tuple				=						batch.values()
  # Gather the distributed inputs and targs for the base model
  lowercase__	,       lowercase__       :       List[Any]				=						accelerator.gather((ddp_input, ddp_target)  )
  lowercase__	,       lowercase__       :       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(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'''
  lowercase__       :       Dict				=						(((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(13_37 + iteration  )
 GradientState._reset_state()


def    _lowerCamelCase   (     ):
 lowercase__       :       Tuple				=						Accelerator()

 lowercase__       :       Union[str, Any]				=						RegressionDataset(length=80  )
 lowercase__       :       Any				=						DataLoader(lowerCamelCase__       ,		batch_size=16  )
 lowercase__       :       Dict				=						RegressionDataset(length=96  )
 lowercase__       :       Dict				=						DataLoader(lowerCamelCase__       ,		batch_size=16  )
 lowercase__	,       lowercase__       :       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    _lowerCamelCase   (     ):
 lowercase__       :       str				=						Accelerator()
 lowercase__       :       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    _lowerCamelCase   (     lowerCamelCase__	:							str  ):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
   main()



"""simple docstring"""


import random


def    _lowerCamelCase   (     lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							str  ):
 lowercase__       :       List[Any]				=						a[left_index]
 lowercase__       :       List[Any]				=						left_index + 1
 for j in range(left_index + 1       ,		lowerCamelCase__  ):
  if a[j] < pivot:
   lowercase__	,       lowercase__       :       Dict				=						a[i], a[j]
   i += 1
 lowercase__	,       lowercase__       :       Any				=						a[i - 1], a[left_index]
 return i - 1


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Optional[int]  ):
 if left < right:
  lowercase__       :       str				=						random.randint(lowerCamelCase__       ,		right - 1  )
  lowercase__	,       lowercase__       :       Any				=						(
      a[left],
      a[pivot],
  )  # switches the pivot with the left most bound
  lowercase__       :       str				=						partition(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )
  quick_sort_random(
      lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )  # recursive quicksort to the left of the pivot point
  quick_sort_random(
      lowerCamelCase__       ,		pivot_index + 1       ,		lowerCamelCase__  )  # recursive quicksort to the right of the pivot point


def    _lowerCamelCase   (     ):
 lowercase__       :       List[Any]				=						input("""Enter numbers separated by a comma:\n"""  ).strip()
 lowercase__       :       int				=						[int(lowerCamelCase__  ) for item in user_input.split(""","""  )]

 quick_sort_random(lowerCamelCase__       ,		0       ,		len(lowerCamelCase__  )  )

 print(lowerCamelCase__  )


if __name__ == "__main__":
   main()