





"""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 						__lowercase	(  snake_case_    :    Union[str, Any]							,snake_case_    :    List[str]							,snake_case_    :    List[str]							,snake_case_    :    Union[str, Any]		)  ->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 						__lowercase	(  snake_case_    :    Optional[Any]							,snake_case_    :    List[str]							,snake_case_    :    List[Any]							,snake_case_    :    Union[str, Any]							,snake_case_    :    str=True		)  ->List[str]:



    '''simple docstring'''







    model.train()
    __A       :		List[Any]							=	model(snake_case_		)
    __A       :		List[str]							=	F.mse_loss(snake_case_							,target.to(output.device		)		)
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(snake_case_		)







def 						__lowercase	(  snake_case_    :    str							,snake_case_    :    Union[str, Any]=False		)  ->List[Any]:



    '''simple docstring'''







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







def 						__lowercase	(  snake_case_    :    List[Any]		)  ->Union[str, Any]:



    '''simple docstring'''







    __A					,			__A					,			__A       :		Dict							=	get_training_setup(snake_case_		)
    # Use a single batch
    __A					,			__A       :		Dict							=	next(iter(snake_case_		)		).values()
    for iteration in range(3		):
        # Gather the distributed inputs and targs for the base model
        __A					,			__A       :		Union[str, Any]							=	accelerator.gather((ddp_input, ddp_target)		)
        __A					,			__A       :		List[Any]							=	input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        step_model(snake_case_							,snake_case_							,snake_case_							,snake_case_		)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(snake_case_		):
                step_model(snake_case_							,snake_case_							,snake_case_							,snake_case_		)
        else:
            # Sync grads
            step_model(snake_case_							,snake_case_							,snake_case_							,snake_case_		)

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







def 						__lowercase	(  snake_case_    :    Optional[int]		)  ->List[str]:



    '''simple docstring'''







    __A					,			__A					,			__A       :		Optional[Any]							=	get_training_setup(snake_case_		)
    # Use a single batch
    __A					,			__A       :		Optional[Any]							=	next(iter(snake_case_		)		).values()
    for iteration in range(3		):
        # Gather the distributed inputs and targs for the base model
        __A					,			__A       :		Optional[int]							=	accelerator.gather((ddp_input, ddp_target)		)
        __A					,			__A       :		Dict							=	input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        step_model(snake_case_							,snake_case_							,snake_case_							,snake_case_		)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(snake_case_		):
                step_model(snake_case_							,snake_case_							,snake_case_							,snake_case_		)
        else:
            # Sync grads
            step_model(snake_case_							,snake_case_							,snake_case_							,snake_case_		)

        # 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		)
        __A       :		Dict							=	ddp_input[torch.randperm(len(snake_case_		)		)]







def 						__lowercase	(  snake_case_    :    Tuple=False							,snake_case_    :    Tuple=False		)  ->Any:



    '''simple docstring'''







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

        # 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(snake_case_		) - 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		)
        __A       :		Any							=	ddp_input[torch.randperm(len(snake_case_		)		)]
    GradientState._reset_state()







def 						__lowercase	(  snake_case_    :    Optional[int]=False							,snake_case_    :    Dict=False		)  ->Optional[Any]:



    '''simple docstring'''







    __A       :		str							=	Accelerator(
        split_batches=snake_case_							,dispatch_batches=snake_case_							,gradient_accumulation_steps=2		)
    # Test that context manager behaves properly
    __A					,			__A					,			__A					,			__A					,			__A					,			__A					,			__A       :		Dict							=	get_training_setup(snake_case_							,snake_case_		)
    for iteration, batch in enumerate(snake_case_		):
        __A					,			__A       :		Any							=	batch.values()
        # Gather the distributed inputs and targs for the base model
        __A					,			__A       :		Dict							=	accelerator.gather((ddp_input, ddp_target)		)
        __A					,			__A       :		Optional[int]							=	input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        model.train()
        ddp_model.train()
        step_model(snake_case_							,snake_case_							,snake_case_							,snake_case_							,snake_case_		)
        opt.step()

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







def 						__lowercase	(  )  ->int:



    '''simple docstring'''







    __A       :		Optional[Any]							=	Accelerator()

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



    '''simple docstring'''







    __A       :		Dict							=	Accelerator()
    __A       :		Tuple							=	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(snake_case_		)
    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(snake_case_		)
    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(snake_case_							,snake_case_		)

    # 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(snake_case_							,snake_case_		)







def 						__lowercase	(  snake_case_    :    Optional[Any]		)  ->Tuple:



    '''simple docstring'''







    main()


if __name__ == "__main__":
       main()











"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


a_					=     logging.get_logger(__name__)






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 def __init__(       self   ,		*__lowerCamelCase   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     warnings.warn(
         '''The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
         ''' Please use PoolFormerImageProcessor instead.'''   ,		__lowerCamelCase   ,		)
     super().__init__(*__lowerCamelCase   ,		**__lowerCamelCase    )




