


"""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_						:Tuple				,				a_						:Dict				,				a_						:str				,				a_						:int)     ->							Optional[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       lowerCamelCase					(a_						:Dict				,				a_						:str				,				a_						:Optional[int]				,				a_						:Optional[Any]				,				a_						:List[str]=True)     ->							Union[str, Any]:
       model.train()
       lowercase		:Optional[Any]       							=				model(a_)
       lowercase		:List[str]       							=				F.mse_loss(a_				,				target.to(output.device))
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(a_)






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






def       lowerCamelCase					(a_						:Union[str, Any])     ->							int:
       # Test when on a single CPU or GPU that the context manager does nothing
       lowercase							,  lowercase							,  lowercase		:int       							=				get_training_setup(a_)
       # Use a single batch
       lowercase							,  lowercase		:str       							=				next(iter(a_)).values()
       for iteration in range(3):
              # Gather the distributed inputs and targs for the base model
              lowercase							,  lowercase		:List[Any]       							=				accelerator.gather((ddp_input, ddp_target))
              lowercase							,  lowercase		:int       							=				input.to(accelerator.device), target.to(accelerator.device)
              # Perform our initial ground truth step in non "DDP"
              step_model(a_				,				a_				,				a_				,				a_)
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(a_):
                            step_model(a_				,				a_				,				a_				,				a_)
              else:
                     # Sync grads
                     step_model(a_				,				a_				,				a_				,				a_)

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






def       lowerCamelCase					(a_						:Optional[Any])     ->							str:
       # Test on distributed setup that context manager behaves properly
       lowercase							,  lowercase							,  lowercase		:Any       							=				get_training_setup(a_)
       # Use a single batch
       lowercase							,  lowercase		:Optional[int]       							=				next(iter(a_)).values()
       for iteration in range(3):
              # Gather the distributed inputs and targs for the base model
              lowercase							,  lowercase		:Union[str, Any]       							=				accelerator.gather((ddp_input, ddp_target))
              lowercase							,  lowercase		:Any       							=				input.to(accelerator.device), target.to(accelerator.device)
              # Perform our initial ground truth step in non "DDP"
              step_model(a_				,				a_				,				a_				,				a_)
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(a_):
                            step_model(a_				,				a_				,				a_				,				a_)
              else:
                     # Sync grads
                     step_model(a_				,				a_				,				a_				,				a_)

              # 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)
              lowercase		:Tuple       							=				ddp_input[torch.randperm(len(a_))]






def       lowerCamelCase					(a_						:Union[str, Any]=False				,				a_						:List[str]=False)     ->							List[Any]:
       lowercase		:List[Any]       							=				Accelerator(
           split_batches=a_				,				dispatch_batches=a_				,				gradient_accumulation_steps=2)
       # Test that context manager behaves properly
       lowercase							,  lowercase							,  lowercase		:Dict       							=				get_training_setup(a_)
       for iteration, batch in enumerate(a_):
              lowercase							,  lowercase		:Dict       							=				batch.values()
              # Gather the distributed inputs and targs for the base model
              lowercase							,  lowercase		:Any       							=				accelerator.gather((ddp_input, ddp_target))
              lowercase							,  lowercase		:Optional[int]       							=				input.to(accelerator.device), target.to(accelerator.device)
              # Perform our initial ground truth step in non "DDP"
              step_model(a_				,				a_				,				a_				,				a_				,				a_)
              # Do "gradient accumulation" (noop)
              with accelerator.accumulate(a_):
                     step_model(a_				,				a_				,				a_				,				a_)

              # 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(a_) - 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)
              lowercase		:List[str]       							=				ddp_input[torch.randperm(len(a_))]
       GradientState._reset_state()






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

              if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(a_)):
                     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(a_):
                     step_model(a_				,				a_				,				a_				,				a_)
                     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		:Optional[int]       							=				(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(a_))
              if accelerator.num_processes > 1:
                     check_model_parameters(a_				,				a_				,				a_				,				a_)
              # Shuffle ddp_input on each iteration
              torch.manual_seed(1337 + iteration)
       GradientState._reset_state()






def       lowerCamelCase					()     ->							List[str]:
       lowercase		:Union[str, Any]       							=				Accelerator()

       lowercase		:Any       							=				RegressionDataset(length=80)
       lowercase		:Optional[int]       							=				DataLoader(a_				,				batch_size=16)
       lowercase		:Optional[int]       							=				RegressionDataset(length=96)
       lowercase		:List[Any]       							=				DataLoader(a_				,				batch_size=16)
       lowercase							,  lowercase		:List[str]       							=				accelerator.prepare(a_				,				a_)
       assert accelerator.gradient_state.active_dataloader is None
       for iteration, _ in enumerate(a_):
              assert id(accelerator.gradient_state.active_dataloader) == id(a_)
              if iteration < len(a_) - 1:
                     assert not accelerator.gradient_state.end_of_dataloader
                     if iteration == 1:
                            for batch_num, _ in enumerate(a_):
                                   assert id(accelerator.gradient_state.active_dataloader) == id(a_)
                                   if batch_num < len(a_) - 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					()     ->							Union[str, Any]:
       lowercase		:Union[str, Any]       							=				Accelerator()
       lowercase		: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(a_)
       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(a_)
       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(a_				,				a_)

    # 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(a_				,				a_)






def       lowerCamelCase					(a_						:Dict)     ->							Optional[int]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
 main()





"""simple docstring"""



UpperCAmelCase      		=			{str(digit): digit**5 for digit in range(10)}






def       lowerCamelCase					(a_						:int)     ->							int:
       return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_))






def       lowerCamelCase					()     ->							int:
       return sum(
           number
           for number in range(1000				,				100_0000)
           if number == digits_fifth_powers_sum(a_))


if __name__ == "__main__":
 print(solution())

