





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			(    A				,							A				,							A				,							A     ):




   '''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			(    A				,							A				,							A				,							A				,							A=True     ):




   '''simple docstring'''

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






def 				_UpperCAmelCase			(    A				,							A=False     ):




   '''simple docstring'''

   set_seed(42     )
   UpperCAmelCase__						=RegressionModel()
   UpperCAmelCase__						=deepcopy(__snake_case     )
   UpperCAmelCase__						=RegressionDataset(length=80     )
   UpperCAmelCase__						=DataLoader(__snake_case				,							batch_size=16     )
   model.to(accelerator.device     )
   if sched:
      UpperCAmelCase__						=AdamW(params=model.parameters()				,							lr=1e-3     )
      UpperCAmelCase__						=AdamW(params=ddp_model.parameters()				,							lr=1e-3     )
      UpperCAmelCase__						=LambdaLR(__snake_case				,							lr_lambda=lambda A     : epoch**0.65     )
      UpperCAmelCase__						=LambdaLR(__snake_case				,							lr_lambda=lambda A     : epoch**0.65     )
   # Make a copy of `model`
   if sched:
      UpperCAmelCase__						=accelerator.prepare(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )
   else:
      UpperCAmelCase__						=accelerator.prepare(__snake_case				,							__snake_case     )
   if sched:
      return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
   return model, ddp_model, dataloader






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_training_setup(__snake_case     )
   # Use a single batch
   UpperCAmelCase__						=next(iter(__snake_case     )     ).values()
   for iteration in range(3     ):
      # Gather the distributed inputs and targs for the base model
      UpperCAmelCase__						=accelerator.gather((ddp_input, ddp_target)     )
      UpperCAmelCase__						=input.to(accelerator.device     ), target.to(accelerator.device     )
      # Perform our initial ground truth step in non "DDP"
      step_model(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )
      # Do "gradient accumulation" (noop)
      if iteration % 2 == 0:
         # Accumulate grads locally
         with accelerator.no_sync(__snake_case     ):
            step_model(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )
      else:
         # Sync grads
         step_model(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )

      # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
      check_model_parameters(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )
      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     )
      UpperCAmelCase__						=ddp_input[torch.randperm(len(__snake_case     )     )]






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_training_setup(__snake_case     )
   # Use a single batch
   UpperCAmelCase__						=next(iter(__snake_case     )     ).values()
   for iteration in range(3     ):
      # Gather the distributed inputs and targs for the base model
      UpperCAmelCase__						=accelerator.gather((ddp_input, ddp_target)     )
      UpperCAmelCase__						=input.to(accelerator.device     ), target.to(accelerator.device     )
      # Perform our initial ground truth step in non "DDP"
      step_model(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )
      # Do "gradient accumulation" (noop)
      if iteration % 2 == 0:
         # Accumulate grads locally
         with accelerator.no_sync(__snake_case     ):
            step_model(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )
      else:
         # Sync grads
         step_model(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )

      # 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     )
      UpperCAmelCase__						=ddp_input[torch.randperm(len(__snake_case     )     )]






def 				_UpperCAmelCase			(    A=False				,							A=False     ):




   '''simple docstring'''

   UpperCAmelCase__						=Accelerator(
       split_batches=__snake_case				,							dispatch_batches=__snake_case				,							gradient_accumulation_steps=2     )
   # Test that context manager behaves properly
   UpperCAmelCase__						=get_training_setup(__snake_case     )
   for iteration, batch in enumerate(__snake_case     ):
      UpperCAmelCase__						=batch.values()
      # Gather the distributed inputs and targs for the base model
      UpperCAmelCase__						=accelerator.gather((ddp_input, ddp_target)     )
      UpperCAmelCase__						=input.to(accelerator.device     ), target.to(accelerator.device     )
      # Perform our initial ground truth step in non "DDP"
      step_model(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )
      # Do "gradient accumulation" (noop)
      with accelerator.accumulate(__snake_case     ):
         step_model(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )

      # 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(__snake_case     ) - 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     )
      UpperCAmelCase__						=ddp_input[torch.randperm(len(__snake_case     )     )]
   GradientState._reset_state()






def 				_UpperCAmelCase			(    A=False				,							A=False     ):




   '''simple docstring'''

   UpperCAmelCase__						=Accelerator(
       split_batches=__snake_case				,							dispatch_batches=__snake_case				,							gradient_accumulation_steps=2     )
   # Test that context manager behaves properly
   UpperCAmelCase__						=get_training_setup(__snake_case				,							__snake_case     )
   for iteration, batch in enumerate(__snake_case     ):
      UpperCAmelCase__						=batch.values()
      # Gather the distributed inputs and targs for the base model
      UpperCAmelCase__						=accelerator.gather((ddp_input, ddp_target)     )
      UpperCAmelCase__						=input.to(accelerator.device     ), target.to(accelerator.device     )
      # Perform our initial ground truth step in non "DDP"
      model.train()
      ddp_model.train()
      step_model(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )
      opt.step()

      if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__snake_case     )):
         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(__snake_case     ):
         step_model(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )
         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"""
      UpperCAmelCase__						=(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__snake_case     ))
      if accelerator.num_processes > 1:
         check_model_parameters(__snake_case				,							__snake_case				,							__snake_case				,							__snake_case     )
      # Shuffle ddp_input on each iteration
      torch.manual_seed(1337 + iteration     )
   GradientState._reset_state()






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=Accelerator()

   UpperCAmelCase__						=RegressionDataset(length=80     )
   UpperCAmelCase__						=DataLoader(__snake_case				,							batch_size=16     )
   UpperCAmelCase__						=RegressionDataset(length=96     )
   UpperCAmelCase__						=DataLoader(__snake_case				,							batch_size=16     )
   UpperCAmelCase__						=accelerator.prepare(__snake_case				,							__snake_case     )
   assert accelerator.gradient_state.active_dataloader is None
   for iteration, _ in enumerate(__snake_case     ):
      assert id(accelerator.gradient_state.active_dataloader     ) == id(__snake_case     )
      if iteration < len(__snake_case     ) - 1:
         assert not accelerator.gradient_state.end_of_dataloader
         if iteration == 1:
            for batch_num, _ in enumerate(__snake_case     ):
               assert id(accelerator.gradient_state.active_dataloader     ) == id(__snake_case     )
               if batch_num < len(__snake_case     ) - 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			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=Accelerator()
   UpperCAmelCase__						=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(__snake_case     )
   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(__snake_case     )
   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(__snake_case				,							__snake_case     )

    # 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(__snake_case				,							__snake_case     )






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   main()


if __name__ == "__main__":
    main()










'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					int					,				__snake_case    :					int     )	->	float:
     __A		:					Dict						=			(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total






def 			_lowerCAmelCase     (    )	->	Union[str, Any]:
     print(sum_of_series(1					,				1					,				10     )     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()