


'''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  :      Optional[Any]							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Tuple):
   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  :      Any							,						lowerCamelCase  :      int							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      str							,						lowerCamelCase  :      Tuple=True):
   model.train()
   A_						:  int          =				model(__lowerCAmelCase)
   A_						:  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      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Any=False):
   set_seed(42)
   A_						:  List[Any]          =				RegressionModel()
   A_						:  str          =				deepcopy(__lowerCAmelCase)
   A_						:  Dict          =				RegressionDataset(length=80)
   A_						:  str          =				DataLoader(__lowerCAmelCase							,						batch_size=16)
   model.to(accelerator.device)
   if sched:
      A_						:  Any          =				AdamW(params=model.parameters()							,						lr=1E-3)
      A_						:  str          =				AdamW(params=ddp_model.parameters()							,						lr=1E-3)
      A_						:  List[Any]          =				LambdaLR(__lowerCAmelCase							,						lr_lambda=lambda lowerCamelCase: epoch**0.65)
      A_						:  Optional[Any]          =				LambdaLR(__lowerCAmelCase							,						lr_lambda=lambda lowerCamelCase: epoch**0.65)
   # Make a copy of `model`
   if sched:
      A_   ,					A_   ,					A_   ,					A_						:  int          =				accelerator.prepare(__lowerCAmelCase							,						__lowerCAmelCase							,						__lowerCAmelCase							,						__lowerCAmelCase)
   else:
      A_   ,					A_						:  List[str]          =				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  :      Any):
   # Test when on a single CPU or GPU that the context manager does nothing
   A_   ,					A_   ,					A_						:  List[Any]          =				get_training_setup(__lowerCAmelCase)
   # Use a single batch
   A_   ,					A_						:  Union[str, Any]          =				next(iter(__lowerCAmelCase)).values()
   for iteration in range(3):
      # Gather the distributed inputs and targs for the base model
      A_   ,					A_						:  List[Any]          =				accelerator.gather((ddp_input, ddp_target))
      A_   ,					A_						:  Any          =				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)
      A_						:  List[str]          =				ddp_input[torch.randperm(len(__lowerCAmelCase))]




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




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




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

   A_						:  Dict          =				RegressionDataset(length=80)
   A_						:  Any          =				DataLoader(__lowerCAmelCase							,						batch_size=16)
   A_						:  int          =				RegressionDataset(length=96)
   A_						:  Any          =				DataLoader(__lowerCAmelCase							,						batch_size=16)
   A_   ,					A_						:  Union[str, 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					(      ):
   A_						:  int          =				Accelerator()
   A_						:  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  :      Any):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
     main()




'''simple docstring'''


from __future__ import annotations




def      lowerCamelCase					(      lowerCamelCase  :      dict							,						lowerCamelCase  :      str):
      A_   ,					A_						:  List[Any]          =				set(lowerCamelCase), [start]

      while stack:
            A_						:  Optional[Any]          =				stack.pop()
            explored.add(lowerCamelCase)
            # 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(lowerCamelCase)
      return explored


__magic_name__			=      {
    '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'))
