





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





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple       ):
    # Test when on a single CPU or GPU that the context manager does nothing
    A__,	A__,	A__				=			get_training_setup(_lowerCamelCase       )
    # Use a single batch
    A__,	A__				=			next(iter(_lowerCamelCase       )       ).values()
    for iteration in range(3       ):
        # Gather the distributed inputs and targs for the base model
        A__,	A__				=			accelerator.gather((ddp_input, ddp_target)       )
        A__,	A__				=			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(13_37 + iteration       )
        A__				=			ddp_input[torch.randperm(len(_lowerCamelCase       )       )]





def 						UpperCamelCase				(							_lowerCamelCase					:					List[str]       ):
    # Test on distributed setup that context manager behaves properly
    A__,	A__,	A__				=			get_training_setup(_lowerCamelCase       )
    # Use a single batch
    A__,	A__				=			next(iter(_lowerCamelCase       )       ).values()
    for iteration in range(3       ):
        # Gather the distributed inputs and targs for the base model
        A__,	A__				=			accelerator.gather((ddp_input, ddp_target)       )
        A__,	A__				=			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(13_37 + iteration       )
        A__				=			ddp_input[torch.randperm(len(_lowerCamelCase       )       )]





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]=False						,       _lowerCamelCase					:					Optional[int]=False       ):
    A__				=			Accelerator(
        split_batches=_lowerCamelCase						,       dispatch_batches=_lowerCamelCase						,       gradient_accumulation_steps=2       )
    # Test that context manager behaves properly
    A__,	A__,	A__				=			get_training_setup(_lowerCamelCase       )
    for iteration, batch in enumerate(_lowerCamelCase       ):
        A__,	A__				=			batch.values()
        # Gather the distributed inputs and targs for the base model
        A__,	A__				=			accelerator.gather((ddp_input, ddp_target)       )
        A__,	A__				=			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(13_37 + iteration       )
        A__				=			ddp_input[torch.randperm(len(_lowerCamelCase       )       )]
    GradientState._reset_state()





def 						UpperCamelCase				(							_lowerCamelCase					:					int=False						,       _lowerCamelCase					:					str=False       ):
    A__				=			Accelerator(
        split_batches=_lowerCamelCase						,       dispatch_batches=_lowerCamelCase						,       gradient_accumulation_steps=2       )
    # Test that context manager behaves properly
    A__,	A__,	A__,	A__,	A__,	A__,	A__				=			get_training_setup(_lowerCamelCase						,       _lowerCamelCase       )
    for iteration, batch in enumerate(_lowerCamelCase       ):
        A__,	A__				=			batch.values()
        # Gather the distributed inputs and targs for the base model
        A__,	A__				=			accelerator.gather((ddp_input, ddp_target)       )
        A__,	A__				=			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"
        A__				=			(((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(13_37 + iteration       )
    GradientState._reset_state()





def 						UpperCamelCase				(							):
    A__				=			Accelerator()

    A__				=			RegressionDataset(length=80       )
    A__				=			DataLoader(_lowerCamelCase						,       batch_size=16       )
    A__				=			RegressionDataset(length=96       )
    A__				=			DataLoader(_lowerCamelCase						,       batch_size=16       )
    A__,	A__				=			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				(							):
    A__				=			Accelerator()
    A__				=			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				(							_lowerCamelCase					:					int       ):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()










'''simple docstring'''

from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__lowerCAmelCase							:	Optional[Any]			=logging.get_logger(__name__)






class    UpperCAmelCase	:
 __lowercase   		=				42
 __lowercase   		=				None
 @staticmethod
 def      UpperCAmelCase_							(				)->							Dict:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :str						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							str:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :int							)->							Any:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Optional[Any]:
     if not self.is_available():
         raise RuntimeError(
             F"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."							)



 @classmethod
 def      UpperCAmelCase_							(				cls      :int							)->							Any:
     return F"`pip install {cls.pip_package or cls.name}`"






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""optuna"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							int:
     return is_optuna_available()
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :str						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :List[Any]							)->							Tuple:
     return run_hp_search_optuna(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :Optional[int]							)->							Optional[Any]:
     return default_hp_space_optuna(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""ray"""
 __lowercase   		=				"""'ray[tune]'"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							str:
     return is_ray_available()
 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :Dict						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :List[str]							)->							int:
     return run_hp_search_ray(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Dict							)->							int:
     return default_hp_space_ray(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""sigopt"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							Union[str, Any]:
     return is_sigopt_available()
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :Union[str, Any]						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							Dict:
     return run_hp_search_sigopt(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Optional[int]							)->							List[str]:
     return default_hp_space_sigopt(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""wandb"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							List[str]:
     return is_wandb_available()
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							List[str]:
     return run_hp_search_wandb(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :str							)->							Dict:
     return default_hp_space_wandb(lowercase_							)

__lowerCAmelCase							:	int			={
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}





def 						UpperCamelCase				(							):
    A__				=			[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
    if len(_lowerCamelCase       ) > 0:
        A__				=			available_backends[0].name
        if len(_lowerCamelCase       ) > 1:
            logger.info(
                F"{len(_lowerCamelCase       )} hyperparameter search backends available. Using {name} as the default."       )
        return name
    raise RuntimeError(
        "No hyperparameter search backend available.\n"
        + "\n".join(
            F" - To install {backend.name} run {backend.pip_install()}"
            for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()       )       )



