

'''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 							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()
						_lowerCAmelCase										=model(__lowerCAmelCase      )
						_lowerCAmelCase										=F.mse_loss(__lowerCAmelCase			,      target.to(output.device      )      )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(__lowerCAmelCase      )



def 							UpperCamelCase__			(			a__			,      a__=False      ):


						'''simple docstring'''






						set_seed(4_2      )
						_lowerCAmelCase										=RegressionModel()
						_lowerCAmelCase										=deepcopy(__lowerCAmelCase      )
						_lowerCAmelCase										=RegressionDataset(length=8_0      )
						_lowerCAmelCase										=DataLoader(__lowerCAmelCase			,      batch_size=1_6      )
						model.to(accelerator.device      )
						if sched:
												_lowerCAmelCase										=AdamW(params=model.parameters()			,      lr=1E-3      )
												_lowerCAmelCase										=AdamW(params=ddp_model.parameters()			,      lr=1E-3      )
												_lowerCAmelCase										=LambdaLR(__lowerCAmelCase			,      lr_lambda=lambda a__      : epoch**0.65      )
												_lowerCAmelCase										=LambdaLR(__lowerCAmelCase			,      lr_lambda=lambda a__      : epoch**0.65      )
						# Make a copy of `model`
						if sched:
												_lowerCAmelCase										=accelerator.prepare(__lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase      )
						else:
												_lowerCAmelCase										=accelerator.prepare(__lowerCAmelCase			,      __lowerCAmelCase      )
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=get_training_setup(__lowerCAmelCase      )
						# Use a single batch
						_lowerCAmelCase										=next(iter(__lowerCAmelCase      )      ).values()
						for iteration in range(3      ):
												# Gather the distributed inputs and targs for the base model
												_lowerCAmelCase										=accelerator.gather((ddp_input, ddp_target)      )
												_lowerCAmelCase										=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(1_3_3_7 + iteration      )
												_lowerCAmelCase										=ddp_input[torch.randperm(len(__lowerCAmelCase      )      )]



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=get_training_setup(__lowerCAmelCase      )
						# Use a single batch
						_lowerCAmelCase										=next(iter(__lowerCAmelCase      )      ).values()
						for iteration in range(3      ):
												# Gather the distributed inputs and targs for the base model
												_lowerCAmelCase										=accelerator.gather((ddp_input, ddp_target)      )
												_lowerCAmelCase										=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(1_3_3_7 + iteration      )
												_lowerCAmelCase										=ddp_input[torch.randperm(len(__lowerCAmelCase      )      )]



def 							UpperCamelCase__			(			a__=False			,      a__=False      ):


						'''simple docstring'''






						_lowerCAmelCase										=Accelerator(
						    split_batches=__lowerCAmelCase			,      dispatch_batches=__lowerCAmelCase			,      gradient_accumulation_steps=2      )
						# Test that context manager behaves properly
						_lowerCAmelCase										=get_training_setup(__lowerCAmelCase      )
						for iteration, batch in enumerate(__lowerCAmelCase      ):
												_lowerCAmelCase										=batch.values()
												# Gather the distributed inputs and targs for the base model
												_lowerCAmelCase										=accelerator.gather((ddp_input, ddp_target)      )
												_lowerCAmelCase										=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(1_3_3_7 + iteration      )
												_lowerCAmelCase										=ddp_input[torch.randperm(len(__lowerCAmelCase      )      )]
						GradientState._reset_state()



def 							UpperCamelCase__			(			a__=False			,      a__=False      ):


						'''simple docstring'''






						_lowerCAmelCase										=Accelerator(
						    split_batches=__lowerCAmelCase			,      dispatch_batches=__lowerCAmelCase			,      gradient_accumulation_steps=2      )
						# Test that context manager behaves properly
						_lowerCAmelCase										=get_training_setup(__lowerCAmelCase			,      __lowerCAmelCase      )
						for iteration, batch in enumerate(__lowerCAmelCase      ):
												_lowerCAmelCase										=batch.values()
												# Gather the distributed inputs and targs for the base model
												_lowerCAmelCase										=accelerator.gather((ddp_input, ddp_target)      )
												_lowerCAmelCase										=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'''
												_lowerCAmelCase										=(((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(1_3_3_7 + iteration      )
						GradientState._reset_state()



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=Accelerator()

						_lowerCAmelCase										=RegressionDataset(length=8_0      )
						_lowerCAmelCase										=DataLoader(__lowerCAmelCase			,      batch_size=1_6      )
						_lowerCAmelCase										=RegressionDataset(length=9_6      )
						_lowerCAmelCase										=DataLoader(__lowerCAmelCase			,      batch_size=1_6      )
						_lowerCAmelCase										=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 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=Accelerator()
						_lowerCAmelCase										=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 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						main()


if __name__ == "__main__":
							main()






'''simple docstring'''

lowercase_     		= '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowercase_     		= [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_     		= {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





