

'''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   (       lowercase_						:    List[Any]     ,					lowercase_						:    Tuple     ,					lowercase_						:    int     ,					lowercase_						:    Optional[Any]	)		->	List[str]:




  '''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 	UpperCamelCase   (       lowercase_						:    int     ,					lowercase_						:    Any     ,					lowercase_						:    Union[str, Any]     ,					lowercase_						:    Optional[int]     ,					lowercase_						:    Union[str, Any]=True	)		->	Optional[Any]:




  '''simple docstring'''



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




def 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    Dict=False	)		->	int:




  '''simple docstring'''



  set_seed(4_2	)
  lowercase									=RegressionModel()
  lowercase									=deepcopy(lowercase_	)
  lowercase									=RegressionDataset(length=8_0	)
  lowercase									=DataLoader(lowercase_     ,					batch_size=1_6	)
  model.to(accelerator.device	)
  if sched:
    lowercase									=AdamW(params=model.parameters()     ,					lr=1E-3	)
    lowercase									=AdamW(params=ddp_model.parameters()     ,					lr=1E-3	)
    lowercase									=LambdaLR(lowercase_     ,					lr_lambda=lambda lowercase_	: epoch**0.6_5	)
    lowercase									=LambdaLR(lowercase_     ,					lr_lambda=lambda lowercase_	: epoch**0.6_5	)
  # Make a copy of `model`
  if sched:
    lowercase							, lowercase							, lowercase							, lowercase									=accelerator.prepare(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)
  else:
    lowercase							, lowercase									=accelerator.prepare(lowercase_     ,					lowercase_	)
  if sched:
    return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
  return model, ddp_model, dataloader




def 	UpperCamelCase   (       lowercase_						:    int	)		->	List[str]:




  '''simple docstring'''



  lowercase							, lowercase							, lowercase									=get_training_setup(lowercase_	)
  # Use a single batch
  lowercase							, lowercase									=next(iter(lowercase_	)	).values()
  for iteration in range(3	):
    # Gather the distributed inputs and targs for the base model
    lowercase							, lowercase									=accelerator.gather((ddp_input, ddp_target)	)
    lowercase							, lowercase									=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(1_3_3_7 + iteration	)
    lowercase									=ddp_input[torch.randperm(len(lowercase_	)	)]




def 	UpperCamelCase   (       lowercase_						:    Any	)		->	Optional[Any]:




  '''simple docstring'''



  lowercase							, lowercase							, lowercase									=get_training_setup(lowercase_	)
  # Use a single batch
  lowercase							, lowercase									=next(iter(lowercase_	)	).values()
  for iteration in range(3	):
    # Gather the distributed inputs and targs for the base model
    lowercase							, lowercase									=accelerator.gather((ddp_input, ddp_target)	)
    lowercase							, lowercase									=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(1_3_3_7 + iteration	)
    lowercase									=ddp_input[torch.randperm(len(lowercase_	)	)]




def 	UpperCamelCase   (       lowercase_						:    Dict=False     ,					lowercase_						:    Tuple=False	)		->	str:




  '''simple docstring'''



  lowercase									=Accelerator(
      split_batches=lowercase_     ,					dispatch_batches=lowercase_     ,					gradient_accumulation_steps=2	)
  # Test that context manager behaves properly
  lowercase							, lowercase							, lowercase									=get_training_setup(lowercase_	)
  for iteration, batch in enumerate(lowercase_	):
    lowercase							, lowercase									=batch.values()
    # Gather the distributed inputs and targs for the base model
    lowercase							, lowercase									=accelerator.gather((ddp_input, ddp_target)	)
    lowercase							, lowercase									=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(1_3_3_7 + iteration	)
    lowercase									=ddp_input[torch.randperm(len(lowercase_	)	)]
  GradientState._reset_state()




def 	UpperCamelCase   (       lowercase_						:    List[str]=False     ,					lowercase_						:    str=False	)		->	Union[str, Any]:




  '''simple docstring'''



  lowercase									=Accelerator(
      split_batches=lowercase_     ,					dispatch_batches=lowercase_     ,					gradient_accumulation_steps=2	)
  # Test that context manager behaves properly
  lowercase							, lowercase							, lowercase							, lowercase							, lowercase							, lowercase							, lowercase									=get_training_setup(lowercase_     ,					lowercase_	)
  for iteration, batch in enumerate(lowercase_	):
    lowercase							, lowercase									=batch.values()
    # Gather the distributed inputs and targs for the base model
    lowercase							, lowercase									=accelerator.gather((ddp_input, ddp_target)	)
    lowercase							, lowercase									=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'
    lowercase									=(((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(1_3_3_7 + iteration	)
  GradientState._reset_state()




def 	UpperCamelCase   (       )		->	Dict:




  '''simple docstring'''



  lowercase									=Accelerator()

  lowercase									=RegressionDataset(length=8_0	)
  lowercase									=DataLoader(lowercase_     ,					batch_size=1_6	)
  lowercase									=RegressionDataset(length=9_6	)
  lowercase									=DataLoader(lowercase_     ,					batch_size=1_6	)
  lowercase							, lowercase									=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 	UpperCamelCase   (       )		->	Optional[Any]:




  '''simple docstring'''



  lowercase									=Accelerator()
  lowercase									=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 	UpperCamelCase   (       lowercase_						:    Dict	)		->	Optional[int]:




  '''simple docstring'''



  main()


if __name__ == "__main__":
     main()



'''simple docstring'''




from __future__ import annotations


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int | float | str							, SCREAMING_SNAKE_CASE   : int | float | str    ):
  if nth_term == "":
    return [""]
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						[]
  for temp in range(int(SCREAMING_SNAKE_CASE    )    ):
    series.append(f'''1 / {pow(temp + 1							, int(SCREAMING_SNAKE_CASE    )    )}''' if series else '1'    )
  return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  _a					:			List[Any]							=    int(input('Enter the last number (nth term) of the P-Series'))
  _a					:			Tuple							=    int(input('Enter the power for  P-Series'))
  print('Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p')
  print(p_series(nth_term, power))


