'''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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=True     ):
 model.train()
 lowercase__				:    Union[str, Any]  =					model(UpperCAmelCase     )
 lowercase__				:    int  =					F.mse_loss(UpperCAmelCase		,     target.to(output.device     )     )
 if not do_backward:
  loss /= accelerator.gradient_accumulation_steps
  loss.backward()
 else:
  accelerator.backward(UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase=False     ):
 set_seed(42     )
 lowercase__				:    Dict  =					RegressionModel()
 lowercase__				:    str  =					deepcopy(UpperCAmelCase     )
 lowercase__				:    Dict  =					RegressionDataset(length=80     )
 lowercase__				:    int  =					DataLoader(UpperCAmelCase		,     batch_size=16     )
 model.to(accelerator.device     )
 if sched:
  lowercase__				:    str  =					AdamW(params=model.parameters()		,     lr=1E-3     )
  lowercase__				:    Optional[int]  =					AdamW(params=ddp_model.parameters()		,     lr=1E-3     )
  lowercase__				:    str  =					LambdaLR(UpperCAmelCase		,     lr_lambda=lambda UpperCAmelCase     : epoch**0.6_5     )
  lowercase__				:    Dict  =					LambdaLR(UpperCAmelCase		,     lr_lambda=lambda UpperCAmelCase     : epoch**0.6_5     )
 # Make a copy of `model`
 if sched:
  lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    Dict  =					accelerator.prepare(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
 else:
  lowercase__ ,							lowercase__				:    Dict  =					accelerator.prepare(UpperCAmelCase		,     UpperCAmelCase     )
 if sched:
  return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
 return model, ddp_model, dataloader
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 # Test when on a single CPU or GPU that the context manager does nothing
 lowercase__ ,							lowercase__ ,							lowercase__				:    str  =					get_training_setup(UpperCAmelCase     )
 # Use a single batch
 lowercase__ ,							lowercase__				:    Optional[Any]  =					next(iter(UpperCAmelCase     )     ).values()
 for iteration in range(3     ):
  # Gather the distributed inputs and targs for the base model
  lowercase__ ,							lowercase__				:    List[str]  =					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(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(1337 + iteration     )
  lowercase__				:    int  =					ddp_input[torch.randperm(len(UpperCAmelCase     )     )]
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 # Test on distributed setup that context manager behaves properly
 lowercase__ ,							lowercase__ ,							lowercase__				:    Union[str, Any]  =					get_training_setup(UpperCAmelCase     )
 # Use a single batch
 lowercase__ ,							lowercase__				:    Dict  =					next(iter(UpperCAmelCase     )     ).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(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(1337 + iteration     )
  lowercase__				:    Any  =					ddp_input[torch.randperm(len(UpperCAmelCase     )     )]
def 			__UpperCamelCase       (   UpperCAmelCase=False		,     UpperCAmelCase=False     ):
 lowercase__				:    List[Any]  =					Accelerator(
     split_batches=UpperCAmelCase		,     dispatch_batches=UpperCAmelCase		,     gradient_accumulation_steps=2     )
 # Test that context manager behaves properly
 lowercase__ ,							lowercase__ ,							lowercase__				:    Dict  =					get_training_setup(UpperCAmelCase     )
 for iteration, batch in enumerate(UpperCAmelCase     ):
  lowercase__ ,							lowercase__				:    Dict  =					batch.values()
  # Gather the distributed inputs and targs for the base model
  lowercase__ ,							lowercase__				:    Dict  =					accelerator.gather((ddp_input, ddp_target)     )
  lowercase__ ,							lowercase__				:    int  =					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(1337 + iteration     )
  lowercase__				:    Tuple  =					ddp_input[torch.randperm(len(UpperCAmelCase     )     )]
 GradientState._reset_state()
def 			__UpperCamelCase       (   UpperCAmelCase=False		,     UpperCAmelCase=False     ):
 lowercase__				:    Union[str, Any]  =					Accelerator(
     split_batches=UpperCAmelCase		,     dispatch_batches=UpperCAmelCase		,     gradient_accumulation_steps=2     )
 # Test that context manager behaves properly
 lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    str  =					get_training_setup(UpperCAmelCase		,     UpperCAmelCase     )
 for iteration, batch in enumerate(UpperCAmelCase     ):
  lowercase__ ,							lowercase__				:    Any  =					batch.values()
  # Gather the distributed inputs and targs for the base model
  lowercase__ ,							lowercase__				:    Any  =					accelerator.gather((ddp_input, ddp_target)     )
  lowercase__ ,							lowercase__				:    Any  =					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"""
  lowercase__				:    int  =					(((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(1337 + iteration     )
 GradientState._reset_state()
def 			__UpperCamelCase       (   ):
 lowercase__				:    Any  =					Accelerator()

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


if __name__ == "__main__":
  main()


'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    List[str]  =					0
 while num > 0:
  digit_sum += num % 10
  num //= 10
 return digit_sum
def 			__UpperCamelCase       (   UpperCAmelCase = 100     ):
 lowercase__				:    Dict  =					1
 lowercase__				:    Optional[int]  =					2

 for i in range(2		,     max_n + 1     ):
  lowercase__				:    List[str]  =					pre_numerator
  lowercase__				:    Optional[Any]  =					2 * i // 3 if i % 3 == 0 else 1
  lowercase__				:    Union[str, Any]  =					cur_numerator
  lowercase__				:    int  =					e_cont * pre_numerator + temp

 return sum_digits(UpperCAmelCase     )


if __name__ == "__main__":
  print(F'{solution() = }')

