


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






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase=False   ):
		set_seed(42   )
		_UpperCAmelCase		:       Any								=      RegressionModel()
		_UpperCAmelCase		:       Union[str, Any]								=      deepcopy(__lowerCAmelCase   )
		_UpperCAmelCase		:       int								=      RegressionDataset(length=80   )
		_UpperCAmelCase		:       Optional[Any]								=      DataLoader(__lowerCAmelCase      , batch_size=16   )
		model.to(accelerator.device   )
		if sched:
				_UpperCAmelCase		:       int								=      AdamW(params=model.parameters()      , lr=1e-3   )
				_UpperCAmelCase		:       Optional[Any]								=      AdamW(params=ddp_model.parameters()      , lr=1e-3   )
				_UpperCAmelCase		:       List[Any]								=      LambdaLR(__lowerCAmelCase      , lr_lambda=lambda __lowerCAmelCase   : epoch**0.6_5   )
				_UpperCAmelCase		:       List[str]								=      LambdaLR(__lowerCAmelCase      , lr_lambda=lambda __lowerCAmelCase   : epoch**0.6_5   )
		# Make a copy of `model`
		if sched:
				_UpperCAmelCase		:       List[str]								=      accelerator.prepare(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )
		else:
				_UpperCAmelCase		:       Tuple								=      accelerator.prepare(__lowerCAmelCase      , __lowerCAmelCase   )
		if sched:
				return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
		return model, ddp_model, dataloader






def  __lowerCAmelCase (__lowerCAmelCase   ):
		# Test when on a single CPU or GPU that the context manager does nothing
		_UpperCAmelCase		:       List[str]								=      get_training_setup(__lowerCAmelCase   )
		# Use a single batch
		_UpperCAmelCase		:       List[Any]								=      next(iter(__lowerCAmelCase   )   ).values()
		for iteration in range(3   ):
				# Gather the distributed inputs and targs for the base model
				_UpperCAmelCase		:       List[str]								=      accelerator.gather((ddp_input, ddp_target)   )
				_UpperCAmelCase		:       List[str]								=      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_337 + iteration   )
				_UpperCAmelCase		:       Any								=      ddp_input[torch.randperm(len(__lowerCAmelCase   )   )]






def  __lowerCAmelCase (__lowerCAmelCase   ):
		# Test on distributed setup that context manager behaves properly
		_UpperCAmelCase		:       Optional[int]								=      get_training_setup(__lowerCAmelCase   )
		# Use a single batch
		_UpperCAmelCase		:       Tuple								=      next(iter(__lowerCAmelCase   )   ).values()
		for iteration in range(3   ):
				# Gather the distributed inputs and targs for the base model
				_UpperCAmelCase		:       int								=      accelerator.gather((ddp_input, ddp_target)   )
				_UpperCAmelCase		:       List[Any]								=      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_337 + iteration   )
				_UpperCAmelCase		:       Union[str, Any]								=      ddp_input[torch.randperm(len(__lowerCAmelCase   )   )]






def  __lowerCAmelCase (__lowerCAmelCase=False      , __lowerCAmelCase=False   ):
		_UpperCAmelCase		:       str								=      Accelerator(
		    split_batches=__lowerCAmelCase      , dispatch_batches=__lowerCAmelCase      , gradient_accumulation_steps=2   )
		# Test that context manager behaves properly
		_UpperCAmelCase		:       Optional[Any]								=      get_training_setup(__lowerCAmelCase   )
		for iteration, batch in enumerate(__lowerCAmelCase   ):
				_UpperCAmelCase		:       Any								=      batch.values()
				# Gather the distributed inputs and targs for the base model
				_UpperCAmelCase		:       List[str]								=      accelerator.gather((ddp_input, ddp_target)   )
				_UpperCAmelCase		:       List[Any]								=      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_337 + iteration   )
				_UpperCAmelCase		:       Optional[Any]								=      ddp_input[torch.randperm(len(__lowerCAmelCase   )   )]
		GradientState._reset_state()






def  __lowerCAmelCase (__lowerCAmelCase=False      , __lowerCAmelCase=False   ):
		_UpperCAmelCase		:       List[str]								=      Accelerator(
		    split_batches=__lowerCAmelCase      , dispatch_batches=__lowerCAmelCase      , gradient_accumulation_steps=2   )
		# Test that context manager behaves properly
		_UpperCAmelCase		:       Any								=      get_training_setup(__lowerCAmelCase      , __lowerCAmelCase   )
		for iteration, batch in enumerate(__lowerCAmelCase   ):
				_UpperCAmelCase		:       Optional[Any]								=      batch.values()
				# Gather the distributed inputs and targs for the base model
				_UpperCAmelCase		:       int								=      accelerator.gather((ddp_input, ddp_target)   )
				_UpperCAmelCase		:       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(__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"""
				_UpperCAmelCase		:       str								=      (((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_337 + iteration   )
		GradientState._reset_state()






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       Optional[Any]								=      Accelerator()

		_UpperCAmelCase		:       int								=      RegressionDataset(length=80   )
		_UpperCAmelCase		:       List[Any]								=      DataLoader(__lowerCAmelCase      , batch_size=16   )
		_UpperCAmelCase		:       Dict								=      RegressionDataset(length=96   )
		_UpperCAmelCase		:       List[Any]								=      DataLoader(__lowerCAmelCase      , batch_size=16   )
		_UpperCAmelCase		:       Union[str, Any]								=      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  __lowerCAmelCase ():
		_UpperCAmelCase		:       int								=      Accelerator()
		_UpperCAmelCase		:       Union[str, Any]								=      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  __lowerCAmelCase (__lowerCAmelCase   ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
						main()







'''simple docstring'''


from __future__ import annotations

import numpy as np






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return np.maximum(0      , __lowerCAmelCase   )


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



