



'''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__       (						_A ,			_A ,			_A ,			_A				):





  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__       (						_A ,			_A ,			_A ,			_A ,			_A=True				):
  model.train()
  a						:					str        =     model(UpperCAmelCase_				)
  a						:					List[str]        =     F.mse_loss(UpperCAmelCase_ ,			target.to(output.device				)				)
  if not do_backward:
    loss /= accelerator.gradient_accumulation_steps
    loss.backward()
  else:
    accelerator.backward(UpperCAmelCase_				)
def       lowerCamelCase__       (						_A ,			_A=False				):
  set_seed(42				)
  a						:					List[Any]        =     RegressionModel()
  a						:					Any        =     deepcopy(UpperCAmelCase_				)
  a						:					Tuple        =     RegressionDataset(length=80				)
  a						:					Tuple        =     DataLoader(UpperCAmelCase_ ,			batch_size=16				)
  model.to(accelerator.device				)
  if sched:
    a						:					str        =     AdamW(params=model.parameters() ,			lr=1E-3				)
    a						:					str        =     AdamW(params=ddp_model.parameters() ,			lr=1E-3				)
    a						:					List[str]        =     LambdaLR(UpperCAmelCase_ ,			lr_lambda=lambda _A				: epoch**0.65				)
    a						:					List[str]        =     LambdaLR(UpperCAmelCase_ ,			lr_lambda=lambda _A				: epoch**0.65				)
  # Make a copy of `model`
  if sched:
    a						:					List[Any]        =     accelerator.prepare(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				)
  else:
    a						:					str        =     accelerator.prepare(UpperCAmelCase_ ,			UpperCAmelCase_				)
  if sched:
    return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
  return model, ddp_model, dataloader
def       lowerCamelCase__       (						_A				):
  a						:					str        =     get_training_setup(UpperCAmelCase_				)
  # Use a single batch
  a						:					Dict        =     next(iter(UpperCAmelCase_				)				).values()
  for iteration in range(3				):
    # Gather the distributed inputs and targs for the base model
    a						:					int        =     accelerator.gather((ddp_input, ddp_target)				)
    a						:					Union[str, Any]        =     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				)
    a						:					Union[str, Any]        =     ddp_input[torch.randperm(len(UpperCAmelCase_				)				)]
def       lowerCamelCase__       (						_A				):
  a						:					List[str]        =     get_training_setup(UpperCAmelCase_				)
  # Use a single batch
  a						:					List[str]        =     next(iter(UpperCAmelCase_				)				).values()
  for iteration in range(3				):
    # Gather the distributed inputs and targs for the base model
    a						:					List[Any]        =     accelerator.gather((ddp_input, ddp_target)				)
    a						:					Union[str, Any]        =     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				)
    a						:					Any        =     ddp_input[torch.randperm(len(UpperCAmelCase_				)				)]
def       lowerCamelCase__       (						_A=False ,			_A=False				):
  a						:					Optional[int]        =     Accelerator(
      split_batches=UpperCAmelCase_ ,			dispatch_batches=UpperCAmelCase_ ,			gradient_accumulation_steps=2				)
  # Test that context manager behaves properly
  a						:					List[str]        =     get_training_setup(UpperCAmelCase_				)
  for iteration, batch in enumerate(UpperCAmelCase_				):
    a						:					List[Any]        =     batch.values()
    # Gather the distributed inputs and targs for the base model
    a						:					List[str]        =     accelerator.gather((ddp_input, ddp_target)				)
    a						:					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_ ,			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				)
    a						:					List[str]        =     ddp_input[torch.randperm(len(UpperCAmelCase_				)				)]
  GradientState._reset_state()
def       lowerCamelCase__       (						_A=False ,			_A=False				):
  a						:					Optional[Any]        =     Accelerator(
      split_batches=UpperCAmelCase_ ,			dispatch_batches=UpperCAmelCase_ ,			gradient_accumulation_steps=2				)
  # Test that context manager behaves properly
  a						:					Optional[Any]        =     get_training_setup(UpperCAmelCase_ ,			UpperCAmelCase_				)
  for iteration, batch in enumerate(UpperCAmelCase_				):
    a						:					int        =     batch.values()
    # Gather the distributed inputs and targs for the base model
    a						:					List[str]        =     accelerator.gather((ddp_input, ddp_target)				)
    a						:					str        =     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"""
    a						:					Tuple        =     (((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       lowerCamelCase__       (						):
  a						:					Optional[Any]        =     Accelerator()

  a						:					int        =     RegressionDataset(length=80				)
  a						:					List[str]        =     DataLoader(UpperCAmelCase_ ,			batch_size=16				)
  a						:					List[Any]        =     RegressionDataset(length=96				)
  a						:					Any        =     DataLoader(UpperCAmelCase_ ,			batch_size=16				)
  a						:					Optional[int]        =     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       lowerCamelCase__       (						):
  a						:					Optional[int]        =     Accelerator()
  a						:					Optional[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(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       lowerCamelCase__       (						_A				):
  main()


if __name__ == "__main__":
     main()



from __future__ import annotations
def 				__lowerCamelCase						( UpperCAmelCase_						:		dict ,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      a							,				a						:Optional[Any] 							=       set(UpperCAmelCase_						), [start]

      while stack:
            a						:Optional[int] 							=       stack.pop()
            explored.add(UpperCAmelCase_						)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]						):
                  if adj not in explored:
                        stack.append(UpperCAmelCase_						)
      return explored


snake_case				:    Optional[int]       				=     {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, '''A'''))





