

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_		(lowercase__						:       Any     ,					lowercase__						:       str     ,					lowercase__						:       List[Any]     ,					lowercase__						:       Tuple       )		->    Dict:


				'''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 	lowerCAmelCase_		(lowercase__						:       Any     ,					lowercase__						:       Union[str, Any]     ,					lowercase__						:       str     ,					lowercase__						:       Union[str, Any]     ,					lowercase__						:       Tuple=True       )		->    Optional[int]:


				'''simple docstring'''




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





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]     ,					lowercase__						:       Optional[Any]=False       )		->    int:


				'''simple docstring'''




				set_seed(42       )
				lowerCAmelCase__      			=						RegressionModel()
				lowerCAmelCase__      			=						deepcopy(lowercase__       )
				lowerCAmelCase__      			=						RegressionDataset(length=80       )
				lowerCAmelCase__      			=						DataLoader(lowercase__     ,					batch_size=16       )
				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(lowercase__     ,					lr_lambda=lambda lowercase__       : epoch**0.65       )
								lowerCAmelCase__      			=						LambdaLR(lowercase__     ,					lr_lambda=lambda lowercase__       : epoch**0.65       )
				# Make a copy of `model`
				if sched:
								lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.prepare(lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       )
				else:
								lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.prepare(lowercase__     ,					lowercase__       )
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader





def 	lowerCAmelCase_		(lowercase__						:       str       )		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						get_training_setup(lowercase__       )
				# Use a single batch
				lowerCAmelCase__			,		lowerCAmelCase__      			=						next(iter(lowercase__       )       ).values()
				for iteration in range(3       ):
								# Gather the distributed inputs and targs for the base model
								lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather((ddp_input, ddp_target)       )
								lowerCAmelCase__			,		lowerCAmelCase__      			=						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(13_37 + iteration       )
								lowerCAmelCase__      			=						ddp_input[torch.randperm(len(lowercase__       )       )]





def 	lowerCAmelCase_		(lowercase__						:       Optional[Any]       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						get_training_setup(lowercase__       )
				# Use a single batch
				lowerCAmelCase__			,		lowerCAmelCase__      			=						next(iter(lowercase__       )       ).values()
				for iteration in range(3       ):
								# Gather the distributed inputs and targs for the base model
								lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather((ddp_input, ddp_target)       )
								lowerCAmelCase__			,		lowerCAmelCase__      			=						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(13_37 + iteration       )
								lowerCAmelCase__      			=						ddp_input[torch.randperm(len(lowercase__       )       )]





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]=False     ,					lowercase__						:       List[str]=False       )		->    List[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						Accelerator(
				    split_batches=lowercase__     ,					dispatch_batches=lowercase__     ,					gradient_accumulation_steps=2       )
				# Test that context manager behaves properly
				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						get_training_setup(lowercase__       )
				for iteration, batch in enumerate(lowercase__       ):
								lowerCAmelCase__			,		lowerCAmelCase__      			=						batch.values()
								# Gather the distributed inputs and targs for the base model
								lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather((ddp_input, ddp_target)       )
								lowerCAmelCase__			,		lowerCAmelCase__      			=						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(13_37 + iteration       )
								lowerCAmelCase__      			=						ddp_input[torch.randperm(len(lowercase__       )       )]
				GradientState._reset_state()





def 	lowerCAmelCase_		(lowercase__						:       List[Any]=False     ,					lowercase__						:       Optional[int]=False       )		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						Accelerator(
				    split_batches=lowercase__     ,					dispatch_batches=lowercase__     ,					gradient_accumulation_steps=2       )
				# Test that context manager behaves properly
				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						get_training_setup(lowercase__     ,					lowercase__       )
				for iteration, batch in enumerate(lowercase__       ):
								lowerCAmelCase__			,		lowerCAmelCase__      			=						batch.values()
								# Gather the distributed inputs and targs for the base model
								lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather((ddp_input, ddp_target)       )
								lowerCAmelCase__			,		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(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'
								lowerCAmelCase__      			=						(((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(13_37 + iteration       )
				GradientState._reset_state()





def 	lowerCAmelCase_		()		->    Any:


				'''simple docstring'''




				lowerCAmelCase__      			=						Accelerator()

				lowerCAmelCase__      			=						RegressionDataset(length=80       )
				lowerCAmelCase__      			=						DataLoader(lowercase__     ,					batch_size=16       )
				lowerCAmelCase__      			=						RegressionDataset(length=96       )
				lowerCAmelCase__      			=						DataLoader(lowercase__     ,					batch_size=16       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						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 	lowerCAmelCase_		()		->    Tuple:


				'''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(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 	lowerCAmelCase_		(lowercase__						:       Tuple       )		->    Any:


				'''simple docstring'''




				main()


if __name__ == "__main__":
			main()













from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





