





import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
 import transformers

def    __snake_case  (    _lowerCAmelCase       :       Optional[int]		)       ->  Optional[int]:
   random.seed(UpperCamelCase__		)
   np.random.seed(UpperCamelCase__		)
   torch.manual_seed(UpperCamelCase__		)
   torch.cuda.manual_seed_all(UpperCamelCase__		)
   # ^^ safe to call this function even if cuda is not available





class     __magic_name__     :





     """simple docstring"""



     def __init__(							self				:Dict	,					snake_case				:Optional[int]	,					snake_case				:Union[str, Any] = 0.9999	,					snake_case				:str = 0.0	,					snake_case				:List[str] = 0	,					snake_case				:Optional[int] = False	,					snake_case				:Tuple = 1.0	,					snake_case				:Optional[Any] = 2 / 3	,					snake_case				:Optional[int] = None	,					snake_case				:Optional[Any] = None	,					**snake_case				:Dict	,					):





        '''simple docstring'''




        if isinstance(__UpperCAmelCase	,					torch.nn.Module     ):
           A_    : Tuple								=	(
               "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
               "Please pass the parameters of the module instead."
           )
           deprecate(
               "passing a `torch.nn.Module` to `ExponentialMovingAverage`"	,					"1.0.0"	,					__UpperCAmelCase	,					standard_warn=__UpperCAmelCase	,					)
           A_    : str								=	parameters.parameters()

           # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
           A_    : List[Any]								=	True

        if kwargs.get("max_value"	,					__UpperCAmelCase     ) is not None:
           A_    : int								=	"The `max_value` argument is deprecated. Please use `decay` instead."
           deprecate("max_value"	,					"1.0.0"	,					__UpperCAmelCase	,					standard_warn=__UpperCAmelCase     )
           A_    : List[Any]								=	kwargs["max_value"]

        if kwargs.get("min_value"	,					__UpperCAmelCase     ) is not None:
           A_    : str								=	"The `min_value` argument is deprecated. Please use `min_decay` instead."
           deprecate("min_value"	,					"1.0.0"	,					__UpperCAmelCase	,					standard_warn=__UpperCAmelCase     )
           A_    : Union[str, Any]								=	kwargs["min_value"]

        A_    : List[str]								=	list(__UpperCAmelCase     )
        A_    : List[Any]								=	[p.clone().detach() for p in parameters]

        if kwargs.get("device"	,					__UpperCAmelCase     ) is not None:
           A_    : Any								=	"The `device` argument is deprecated. Please use `to` instead."
           deprecate("device"	,					"1.0.0"	,					__UpperCAmelCase	,					standard_warn=__UpperCAmelCase     )
           self.to(device=kwargs["device"]     )

        A_    : str								=	None

        A_    : Dict								=	decay
        A_    : Dict								=	min_decay
        A_    : Optional[int]								=	update_after_step
        A_    : Tuple								=	use_ema_warmup
        A_    : str								=	inv_gamma
        A_    : List[Any]								=	power
        A_    : int								=	0
        A_    : int								=	None  # set in `step()`

        A_    : Dict								=	model_cls
        A_    : Any								=	model_config



     @classmethod
     def   SCREAMING_SNAKE_CASE						(							cls				:Optional[Any]	,					snake_case				:Any	,					snake_case				:Union[str, Any]     ):





        '''simple docstring'''




        A_					,  A_    : List[Any]								=	model_cls.load_config(__UpperCAmelCase	,					return_unused_kwargs=__UpperCAmelCase     )
        A_    : Any								=	model_cls.from_pretrained(__UpperCAmelCase     )

        A_    : Union[str, Any]								=	cls(model.parameters()	,					model_cls=__UpperCAmelCase	,					model_config=model.config     )

        ema_model.load_state_dict(__UpperCAmelCase     )
        return ema_model



     def   SCREAMING_SNAKE_CASE						(							self				:Any	,					snake_case				:List[str]     ):





        '''simple docstring'''




        if self.model_cls is None:
           raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."     )

        if self.model_config is None:
           raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."     )

        A_    : Dict								=	self.model_cls.from_config(self.model_config     )
        A_    : int								=	self.state_dict()
        state_dict.pop("shadow_params"	,					__UpperCAmelCase     )

        model.register_to_config(**__UpperCAmelCase     )
        self.copy_to(model.parameters()     )
        model.save_pretrained(__UpperCAmelCase     )



     def   SCREAMING_SNAKE_CASE						(							self				:List[Any]	,					snake_case				:Dict     ):





        '''simple docstring'''




        A_    : Union[str, Any]								=	max(0	,					optimization_step - self.update_after_step - 1     )

        if step <= 0:
           return 0.0

        if self.use_ema_warmup:
           A_    : List[str]								=	1 - (1 + step / self.inv_gamma) ** -self.power
        else:
           A_    : int								=	(1 + step) / (10 + step)

        A_    : Optional[Any]								=	min(__UpperCAmelCase	,					self.decay     )
        # make sure decay is not smaller than min_decay
        A_    : Union[str, Any]								=	max(__UpperCAmelCase	,					self.min_decay     )
        return cur_decay_value



     @torch.no_grad()
     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:List[Any]     ):





        '''simple docstring'''




        if isinstance(__UpperCAmelCase	,					torch.nn.Module     ):
           A_    : Tuple								=	(
               "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
               "Please pass the parameters of the module instead."
           )
           deprecate(
               "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"	,					"1.0.0"	,					__UpperCAmelCase	,					standard_warn=__UpperCAmelCase	,					)
           A_    : int								=	parameters.parameters()

        A_    : int								=	list(__UpperCAmelCase     )

        self.optimization_step += 1

        # Compute the decay factor for the exponential moving average.
        A_    : Any								=	self.get_decay(self.optimization_step     )
        A_    : Any								=	decay
        A_    : Dict								=	1 - decay

        A_    : Tuple								=	contextlib.nullcontext
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
           import deepspeed

        for s_param, param in zip(self.shadow_params	,					__UpperCAmelCase     ):
           if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
              A_    : Optional[int]								=	deepspeed.zero.GatheredParameters(__UpperCAmelCase	,					modifier_rank=__UpperCAmelCase     )

           with context_manager():
              if param.requires_grad:
                 s_param.sub_(one_minus_decay * (s_param - param)     )
              else:
                 s_param.copy_(__UpperCAmelCase     )



     def   SCREAMING_SNAKE_CASE						(							self				:Any	,					snake_case				:Tuple     ):





        '''simple docstring'''




        A_    : Tuple								=	list(__UpperCAmelCase     )
        for s_param, param in zip(self.shadow_params	,					__UpperCAmelCase     ):
           param.data.copy_(s_param.to(param.device     ).data     )



     def   SCREAMING_SNAKE_CASE						(							self				:str	,					snake_case				:Optional[int]=None	,					snake_case				:Optional[int]=None     ):





        '''simple docstring'''




        A_    : str								=	[
            p.to(device=__UpperCAmelCase	,					dtype=__UpperCAmelCase     ) if p.is_floating_point() else p.to(device=__UpperCAmelCase     )
            for p in self.shadow_params
        ]



     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]     ):





        '''simple docstring'''




        return {
            "decay": self.decay,
            "min_decay": self.min_decay,
            "optimization_step": self.optimization_step,
            "update_after_step": self.update_after_step,
            "use_ema_warmup": self.use_ema_warmup,
            "inv_gamma": self.inv_gamma,
            "power": self.power,
            "shadow_params": self.shadow_params,
        }



     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]	,					snake_case				:str     ):





        '''simple docstring'''




        A_    : Optional[Any]								=	[param.detach().cpu().clone() for param in parameters]



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]	,					snake_case				:List[Any]     ):





        '''simple docstring'''




        if self.temp_stored_params is None:
           raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"     )
        for c_param, param in zip(self.temp_stored_params	,					__UpperCAmelCase     ):
           param.data.copy_(c_param.data     )

        # Better memory-wise.
        A_    : Optional[int]								=	None





     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:Optional[Any]     ):





        '''simple docstring'''




        A_    : Tuple								=	copy.deepcopy(__UpperCAmelCase     )

        A_    : List[str]								=	state_dict.get("decay"	,					self.decay     )
        if self.decay < 0.0 or self.decay > 1.0:
           raise ValueError("Decay must be between 0 and 1"     )

        A_    : int								=	state_dict.get("min_decay"	,					self.min_decay     )
        if not isinstance(self.min_decay	,					__UpperCAmelCase     ):
           raise ValueError("Invalid min_decay"     )

        A_    : Any								=	state_dict.get("optimization_step"	,					self.optimization_step     )
        if not isinstance(self.optimization_step	,					__UpperCAmelCase     ):
           raise ValueError("Invalid optimization_step"     )

        A_    : str								=	state_dict.get("update_after_step"	,					self.update_after_step     )
        if not isinstance(self.update_after_step	,					__UpperCAmelCase     ):
           raise ValueError("Invalid update_after_step"     )

        A_    : int								=	state_dict.get("use_ema_warmup"	,					self.use_ema_warmup     )
        if not isinstance(self.use_ema_warmup	,					__UpperCAmelCase     ):
           raise ValueError("Invalid use_ema_warmup"     )

        A_    : Union[str, Any]								=	state_dict.get("inv_gamma"	,					self.inv_gamma     )
        if not isinstance(self.inv_gamma	,					(float, int)     ):
           raise ValueError("Invalid inv_gamma"     )

        A_    : Dict								=	state_dict.get("power"	,					self.power     )
        if not isinstance(self.power	,					(float, int)     ):
           raise ValueError("Invalid power"     )

        A_    : str								=	state_dict.get("shadow_params"	,					__UpperCAmelCase     )
        if shadow_params is not None:
           A_    : Tuple								=	shadow_params
           if not isinstance(self.shadow_params	,					__UpperCAmelCase     ):
              raise ValueError("shadow_params must be a list"     )
           if not all(isinstance(__UpperCAmelCase	,					torch.Tensor     ) for p in self.shadow_params     ):
              raise ValueError("shadow_params must all be Tensors"     )

"""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    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__=True ):






							"""simple docstring"""





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






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=False ):






							"""simple docstring"""





							set_seed(42 )
							A__		   =							RegressionModel()
							A__		   =							deepcopy(UpperCamelCase__ )
							A__		   =							RegressionDataset(length=80 )
							A__		   =							DataLoader(UpperCamelCase__				,			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(UpperCamelCase__				,			lr_lambda=lambda UpperCamelCase__ : epoch**0.6_5 )
														A__		   =							LambdaLR(UpperCamelCase__				,			lr_lambda=lambda UpperCamelCase__ : epoch**0.6_5 )
							# Make a copy of `model`
							if sched:
														A__   ,     A__   ,     A__   ,     A__		   =							accelerator.prepare(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
							else:
														A__   ,     A__		   =							accelerator.prepare(UpperCamelCase__				,			UpperCamelCase__ )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__ )
							# Use a single batch
							A__   ,     A__		   =							next(iter(UpperCamelCase__ ) ).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(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCamelCase__ ):
																												step_model(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
														else:
																					# Sync grads
																					step_model(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )

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






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__ )
							# Use a single batch
							A__   ,     A__		   =							next(iter(UpperCamelCase__ ) ).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(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCamelCase__ ):
																												step_model(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
														else:
																					# Sync grads
																					step_model(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )

														# 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 )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]






def      UpperCAmelCase    (		UpperCamelCase__=False				,			UpperCamelCase__=False ):






							"""simple docstring"""





							A__		   =							Accelerator(
							    split_batches=UpperCamelCase__				,			dispatch_batches=UpperCamelCase__				,			gradient_accumulation_steps=2 )
							# Test that context manager behaves properly
							A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__ )
							for iteration, batch in enumerate(UpperCamelCase__ ):
														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(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
														# Do "gradient accumulation" (noop)
														with accelerator.accumulate(UpperCamelCase__ ):
																					step_model(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )

														# 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(UpperCamelCase__ ) - 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 )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]
							GradientState._reset_state()






def      UpperCAmelCase    (		UpperCamelCase__=False				,			UpperCamelCase__=False ):






							"""simple docstring"""





							A__		   =							Accelerator(
							    split_batches=UpperCamelCase__				,			dispatch_batches=UpperCamelCase__				,			gradient_accumulation_steps=2 )
							# Test that context manager behaves properly
							A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__				,			UpperCamelCase__ )
							for iteration, batch in enumerate(UpperCamelCase__ ):
														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(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
														opt.step()

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__ )):
																					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(UpperCamelCase__ ):
																					step_model(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
																					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(UpperCamelCase__ ))
														if accelerator.num_processes > 1:
																					check_model_parameters(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_337 + iteration )
							GradientState._reset_state()






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							Accelerator()

							A__		   =							RegressionDataset(length=80 )
							A__		   =							DataLoader(UpperCamelCase__				,			batch_size=16 )
							A__		   =							RegressionDataset(length=96 )
							A__		   =							DataLoader(UpperCamelCase__				,			batch_size=16 )
							A__   ,     A__		   =							accelerator.prepare(UpperCamelCase__				,			UpperCamelCase__ )
							assert accelerator.gradient_state.active_dataloader is None
							for iteration, _ in enumerate(UpperCamelCase__ ):
														assert id(accelerator.gradient_state.active_dataloader ) == id(UpperCamelCase__ )
														if iteration < len(UpperCamelCase__ ) - 1:
																					assert not accelerator.gradient_state.end_of_dataloader
																					if iteration == 1:
																												for batch_num, _ in enumerate(UpperCamelCase__ ):
																																			assert id(accelerator.gradient_state.active_dataloader ) == id(UpperCamelCase__ )
																																			if batch_num < len(UpperCamelCase__ ) - 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    (		):






							"""simple docstring"""





							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(UpperCamelCase__ )
							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(UpperCamelCase__ )
							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(UpperCamelCase__				,			UpperCamelCase__ )

    # 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(UpperCamelCase__				,			UpperCamelCase__ )






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							main()


if __name__ == "__main__":
				main()





