




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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						Any	,					__UpperCamelCase      :						str				)  ->							Tuple:

    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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						int=True				)  ->							Any:
    model.train()
    UpperCAmelCase_										=					model(lowerCamelCase_				)
    UpperCAmelCase_										=					F.mse_loss(lowerCamelCase_	,					target.to(output.device				)				)
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(lowerCamelCase_				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						Tuple=False				)  ->							Optional[int]:
    set_seed(42				)
    UpperCAmelCase_										=					RegressionModel()
    UpperCAmelCase_										=					deepcopy(lowerCamelCase_				)
    UpperCAmelCase_										=					RegressionDataset(length=80				)
    UpperCAmelCase_										=					DataLoader(lowerCamelCase_	,					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(lowerCamelCase_	,					lr_lambda=lambda __UpperCamelCase				: epoch**0.65				)
        UpperCAmelCase_										=					LambdaLR(lowerCamelCase_	,					lr_lambda=lambda __UpperCamelCase				: epoch**0.65				)
    # Make a copy of `model`
    if sched:
        UpperCAmelCase_										=					accelerator.prepare(lowerCamelCase_	,					lowerCamelCase_	,					lowerCamelCase_	,					lowerCamelCase_				)
    else:
        UpperCAmelCase_										=					accelerator.prepare(lowerCamelCase_	,					lowerCamelCase_				)
    if sched:
        return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
    return model, ddp_model, dataloader




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple				)  ->							int:
    UpperCAmelCase_										=					get_training_setup(lowerCamelCase_				)
    # Use a single batch
    UpperCAmelCase_										=					next(iter(lowerCamelCase_				)				).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(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(1337 + iteration				)
        UpperCAmelCase_										=					ddp_input[torch.randperm(len(lowerCamelCase_				)				)]




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple				)  ->							int:
    UpperCAmelCase_										=					get_training_setup(lowerCamelCase_				)
    # Use a single batch
    UpperCAmelCase_										=					next(iter(lowerCamelCase_				)				).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(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(1337 + iteration				)
        UpperCAmelCase_										=					ddp_input[torch.randperm(len(lowerCamelCase_				)				)]




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict=False	,					__UpperCamelCase      :						Any=False				)  ->							Tuple:
    UpperCAmelCase_										=					Accelerator(
        split_batches=lowerCamelCase_	,					dispatch_batches=lowerCamelCase_	,					gradient_accumulation_steps=2				)
    # Test that context manager behaves properly
    UpperCAmelCase_										=					get_training_setup(lowerCamelCase_				)
    for iteration, batch in enumerate(lowerCamelCase_				):
        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(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(1337 + iteration				)
        UpperCAmelCase_										=					ddp_input[torch.randperm(len(lowerCamelCase_				)				)]
    GradientState._reset_state()




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int=False	,					__UpperCamelCase      :						Optional[int]=False				)  ->							Any:
    UpperCAmelCase_										=					Accelerator(
        split_batches=lowerCamelCase_	,					dispatch_batches=lowerCamelCase_	,					gradient_accumulation_steps=2				)
    # Test that context manager behaves properly
    UpperCAmelCase_										=					get_training_setup(lowerCamelCase_	,					lowerCamelCase_				)
    for iteration, batch in enumerate(lowerCamelCase_				):
        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(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_										=					(((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(1337 + iteration				)
    GradientState._reset_state()




def  SCREAMING_SNAKE_CASE     (	)  ->							List[str]:
    UpperCAmelCase_										=					Accelerator()

    UpperCAmelCase_										=					RegressionDataset(length=80				)
    UpperCAmelCase_										=					DataLoader(lowerCamelCase_	,					batch_size=16				)
    UpperCAmelCase_										=					RegressionDataset(length=96				)
    UpperCAmelCase_										=					DataLoader(lowerCamelCase_	,					batch_size=16				)
    UpperCAmelCase_										=					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  SCREAMING_SNAKE_CASE     (	)  ->							str:
    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(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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Any				)  ->							List[str]:
    main()


if __name__ == "__main__":
 main()











import numpy as np




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.array				)  ->							np.array:
    return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()





