




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 		SCREAMING_SNAKE_CASE__   (       lowercase		,lowercase		,lowercase		,lowercase	)    ->  Union[str, Any]:


   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 		SCREAMING_SNAKE_CASE__   (       lowercase		,lowercase		,lowercase		,lowercase		,lowercase=True	)    ->  List[Any]:
   model.train()
   snake_case :							List[str]           =			model(lowercase	)
   snake_case :							Optional[int]           =			F.mse_loss(lowercase		,target.to(output.device	)	)
   if not do_backward:
      loss /= accelerator.gradient_accumulation_steps
      loss.backward()
   else:
      accelerator.backward(lowercase	)




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




def 		SCREAMING_SNAKE_CASE__   (       lowercase	)    ->  Dict:
   snake_case      ,   snake_case      ,   snake_case :							Tuple           =			get_training_setup(lowercase	)
   # Use a single batch
   snake_case      ,   snake_case :							List[str]           =			next(iter(lowercase	)	).values()
   for iteration in range(3	):
      # Gather the distributed inputs and targs for the base model
      snake_case      ,   snake_case :							str           =			accelerator.gather((ddp_input, ddp_target)	)
      snake_case      ,   snake_case :							List[str]           =			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(1337 + iteration	)
      snake_case :							Dict           =			ddp_input[torch.randperm(len(lowercase	)	)]




def 		SCREAMING_SNAKE_CASE__   (       lowercase	)    ->  str:
   snake_case      ,   snake_case      ,   snake_case :							Any           =			get_training_setup(lowercase	)
   # Use a single batch
   snake_case      ,   snake_case :							List[str]           =			next(iter(lowercase	)	).values()
   for iteration in range(3	):
      # Gather the distributed inputs and targs for the base model
      snake_case      ,   snake_case :							List[str]           =			accelerator.gather((ddp_input, ddp_target)	)
      snake_case      ,   snake_case :							str           =			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(1337 + iteration	)
      snake_case :							Any           =			ddp_input[torch.randperm(len(lowercase	)	)]




def 		SCREAMING_SNAKE_CASE__   (       lowercase=False		,lowercase=False	)    ->  Union[str, Any]:
   snake_case :							Union[str, Any]           =			Accelerator(
       split_batches=lowercase		,dispatch_batches=lowercase		,gradient_accumulation_steps=2	)
   # Test that context manager behaves properly
   snake_case      ,   snake_case      ,   snake_case :							str           =			get_training_setup(lowercase	)
   for iteration, batch in enumerate(lowercase	):
      snake_case      ,   snake_case :							int           =			batch.values()
      # Gather the distributed inputs and targs for the base model
      snake_case      ,   snake_case :							int           =			accelerator.gather((ddp_input, ddp_target)	)
      snake_case      ,   snake_case :							int           =			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(1337 + iteration	)
      snake_case :							Union[str, Any]           =			ddp_input[torch.randperm(len(lowercase	)	)]
   GradientState._reset_state()




def 		SCREAMING_SNAKE_CASE__   (       lowercase=False		,lowercase=False	)    ->  int:
   snake_case :							int           =			Accelerator(
       split_batches=lowercase		,dispatch_batches=lowercase		,gradient_accumulation_steps=2	)
   # Test that context manager behaves properly
   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case :							Optional[Any]           =			get_training_setup(lowercase		,lowercase	)
   for iteration, batch in enumerate(lowercase	):
      snake_case      ,   snake_case :							int           =			batch.values()
      # Gather the distributed inputs and targs for the base model
      snake_case      ,   snake_case :							Optional[Any]           =			accelerator.gather((ddp_input, ddp_target)	)
      snake_case      ,   snake_case :							Union[str, 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(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"""
      snake_case :							Union[str, Any]           =			(((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(1337 + iteration	)
   GradientState._reset_state()




def 		SCREAMING_SNAKE_CASE__   (       )    ->  List[str]:
   snake_case :							Optional[Any]           =			Accelerator()

   snake_case :							Any           =			RegressionDataset(length=80	)
   snake_case :							Optional[int]           =			DataLoader(lowercase		,batch_size=16	)
   snake_case :							Dict           =			RegressionDataset(length=96	)
   snake_case :							Any           =			DataLoader(lowercase		,batch_size=16	)
   snake_case      ,   snake_case :							List[Any]           =			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 		SCREAMING_SNAKE_CASE__   (       )    ->  int:
   snake_case :							Any           =			Accelerator()
   snake_case :							Tuple           =			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 		SCREAMING_SNAKE_CASE__   (       lowercase	)    ->  Optional[Any]:
   main()


if __name__ == "__main__":
       main()








'''simple docstring'''



def       snake_case   (	snake_case      : list			) ->  list:


    """simple docstring"""


    if any(not isinstance(snake_case					,		snake_case			) or x < 0 for x in sequence			):
        raise TypeError('Sequence must be list of non-negative integers'			)
    for _ in range(len(snake_case			)			):
        for i, (rod_upper, rod_lower) in enumerate(zip(snake_case					,		sequence[1:]			)			):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





