
"""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 						_snake_case       (	lowercase__		:  Optional[Any]      ,     lowercase__		:  Any      ,     lowercase__		:  List[str]      ,     lowercase__		:  List[Any]		)  ->				Optional[Any]:


			'''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 						_snake_case       (	lowercase__		:  Tuple      ,     lowercase__		:  Any      ,     lowercase__		:  Union[str, Any]      ,     lowercase__		:  str      ,     lowercase__		:  List[Any]=True		)  ->				Any:


			'''simple docstring'''





			model.train()
			lowerCAmelCase_    :Optional[int]			     =  model(lowercase__		)
			lowerCAmelCase_    :Union[str, Any]			     =  F.mse_loss(lowercase__      ,     target.to(output.device		)		)
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(lowercase__		)






def 						_snake_case       (	lowercase__		:  Tuple      ,     lowercase__		:  int=False		)  ->				List[Any]:


			'''simple docstring'''





			set_seed(4_2		)
			lowerCAmelCase_    :Any			     =  RegressionModel()
			lowerCAmelCase_    :str			     =  deepcopy(lowercase__		)
			lowerCAmelCase_    :Any			     =  RegressionDataset(length=8_0		)
			lowerCAmelCase_    :List[Any]			     =  DataLoader(lowercase__      ,     batch_size=1_6		)
			model.to(accelerator.device		)
			if sched:
						lowerCAmelCase_    :str			     =  AdamW(params=model.parameters()      ,     lr=1E-3		)
						lowerCAmelCase_    :List[Any]			     =  AdamW(params=ddp_model.parameters()      ,     lr=1E-3		)
						lowerCAmelCase_    :Optional[int]			     =  LambdaLR(lowercase__      ,     lr_lambda=lambda lowercase__		: epoch**0.65		)
						lowerCAmelCase_    :str			     =  LambdaLR(lowercase__      ,     lr_lambda=lambda lowercase__		: epoch**0.65		)
			# Make a copy of `model`
			if sched:
						lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_    :str			     =  accelerator.prepare(lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__		)
			else:
						lowerCAmelCase_     ,       lowerCAmelCase_    :Tuple			     =  accelerator.prepare(lowercase__      ,     lowercase__		)
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader






def 						_snake_case       (	lowercase__		:  int		)  ->				Tuple:


			'''simple docstring'''





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

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__		)
						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_3_3_7 + iteration		)
						lowerCAmelCase_    :Tuple			     =  ddp_input[torch.randperm(len(lowercase__		)		)]






def 						_snake_case       (	lowercase__		:  Tuple		)  ->				str:


			'''simple docstring'''





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

						# 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_3_3_7 + iteration		)
						lowerCAmelCase_    :List[Any]			     =  ddp_input[torch.randperm(len(lowercase__		)		)]






def 						_snake_case       (	lowercase__		:  List[Any]=False      ,     lowercase__		:  List[Any]=False		)  ->				Tuple:


			'''simple docstring'''





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

						# 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(lowercase__		) - 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_3_3_7 + iteration		)
						lowerCAmelCase_    :List[str]			     =  ddp_input[torch.randperm(len(lowercase__		)		)]
			GradientState._reset_state()






def 						_snake_case       (	lowercase__		:  List[Any]=False      ,     lowercase__		:  List[str]=False		)  ->				Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :List[Any]			     =  Accelerator(
			    split_batches=lowercase__      ,     dispatch_batches=lowercase__      ,     gradient_accumulation_steps=2		)
			# Test that context manager behaves properly
			lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_    :int			     =  get_training_setup(lowercase__      ,     lowercase__		)
			for iteration, batch in enumerate(lowercase__		):
						lowerCAmelCase_     ,       lowerCAmelCase_    :Optional[Any]			     =  batch.values()
						# Gather the distributed inputs and targs for the base model
						lowerCAmelCase_     ,       lowerCAmelCase_    :List[str]			     =  accelerator.gather((ddp_input, ddp_target)		)
						lowerCAmelCase_     ,       lowerCAmelCase_    :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(lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__		)
						opt.step()

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowercase__		)):
									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(lowercase__		):
									step_model(lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__		)
									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"""
						lowerCAmelCase_    :str			     =  (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowercase__		))
						if accelerator.num_processes > 1:
									check_model_parameters(lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__		)
						# Shuffle ddp_input on each iteration
						torch.manual_seed(1_3_3_7 + iteration		)
			GradientState._reset_state()






def 						_snake_case       (	)  ->				List[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :Union[str, Any]			     =  Accelerator()

			lowerCAmelCase_    :Any			     =  RegressionDataset(length=8_0		)
			lowerCAmelCase_    :str			     =  DataLoader(lowercase__      ,     batch_size=1_6		)
			lowerCAmelCase_    :str			     =  RegressionDataset(length=9_6		)
			lowerCAmelCase_    :Tuple			     =  DataLoader(lowercase__      ,     batch_size=1_6		)
			lowerCAmelCase_     ,       lowerCAmelCase_    :int			     =  accelerator.prepare(lowercase__      ,     lowercase__		)
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(lowercase__		):
						assert id(accelerator.gradient_state.active_dataloader		) == id(lowercase__		)
						if iteration < len(lowercase__		) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(lowercase__		):
															assert id(accelerator.gradient_state.active_dataloader		) == id(lowercase__		)
															if batch_num < len(lowercase__		) - 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 						_snake_case       (	)  ->				Any:


			'''simple docstring'''





			lowerCAmelCase_    :int			     =  Accelerator()
			lowerCAmelCase_    :List[str]			     =  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(lowercase__		)
			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(lowercase__		)
			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(lowercase__      ,     lowercase__		)

    # 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(lowercase__      ,     lowercase__		)






def 						_snake_case       (	lowercase__		:  int		)  ->				List[Any]:


			'''simple docstring'''





			main()


if __name__ == "__main__":
				main()










from importlib import import_module

from .logging import get_logger


__lowerCAmelCase	: str  				=get_logger(__name__)
class        _lowercase      :






				'''simple docstring'''



				def __init__(       self			:List[Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:str=None	)		->			int:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		attrs or []
										if module is not None:
																for key in module.__dict__:
																						if key in attrs or not key.startswith('''__'''	):
																												setattr(self			,			lowerCAmelCase__			,			getattr(lowerCAmelCase__			,			lowerCAmelCase__	)	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		module._original_module if isinstance(lowerCAmelCase__			,			_PatchedModuleObj	) else module




class        _lowercase      :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		[]


				def __init__(       self			:Tuple			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Any			,			lowerCAmelCase__			:Dict=None	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		obj
										__SCREAMING_SNAKE_CASE						:     str					=		target
										__SCREAMING_SNAKE_CASE						:     Dict					=		new
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		target.split('''.'''	)[0]
										__SCREAMING_SNAKE_CASE						:     List[str]					=		{}
										__SCREAMING_SNAKE_CASE						:     Tuple					=		attrs or []


				def __enter__(       self			:int	)		->			Dict:
										*__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.target.split('''.'''	)

										# Patch modules:
										# it's used to patch attributes of submodules like "os.path.join";
										# in this case we need to patch "os" and "os.path"

										for i in range(len(lowerCAmelCase__	)	):
																try:
																						__SCREAMING_SNAKE_CASE						:     Any					=		import_module('''.'''.join(submodules[: i + 1]	)	)
																except ModuleNotFoundError:
																						continue
																# We iterate over all the globals in self.obj in case we find "os" or "os.path"
																for attr in self.obj.__dir__():
																						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		getattr(self.obj			,			lowerCAmelCase__	)
																						# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
																						# This allows to patch renamed modules like "from os import path as ospath".
																						if obj_attr is submodule or (
																						    (isinstance(lowerCAmelCase__			,			_PatchedModuleObj	) and obj_attr._original_module is submodule)
																						):
																												__SCREAMING_SNAKE_CASE						:     int					=		obj_attr
																												# patch at top level
																												setattr(self.obj			,			lowerCAmelCase__			,			_PatchedModuleObj(lowerCAmelCase__			,			attrs=self.attrs	)	)
																												__SCREAMING_SNAKE_CASE						:     List[str]					=		getattr(self.obj			,			lowerCAmelCase__	)
																												# construct lower levels patches
																												for key in submodules[i + 1 :]:
																																		setattr(lowerCAmelCase__			,			lowerCAmelCase__			,			_PatchedModuleObj(getattr(lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__	)			,			attrs=self.attrs	)	)
																																		__SCREAMING_SNAKE_CASE						:     Tuple					=		getattr(lowerCAmelCase__			,			lowerCAmelCase__	)
																												# finally set the target attribute
																												setattr(lowerCAmelCase__			,			lowerCAmelCase__			,			self.new	)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

										if submodules:  # if it's an attribute of a submodule like "os.path.join"
																try:
																						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		getattr(import_module('''.'''.join(lowerCAmelCase__	)	)			,			lowerCAmelCase__	)
																except (AttributeError, ModuleNotFoundError):
																						return
																# We iterate over all the globals in self.obj in case we find "os.path.join"
																for attr in self.obj.__dir__():
																						# We don't check for the name of the global, but rather if its value *is* "os.path.join".
																						# This allows to patch renamed attributes like "from os.path import join as pjoin".
																						if getattr(self.obj			,			lowerCAmelCase__	) is attr_value:
																												__SCREAMING_SNAKE_CASE						:     Any					=		getattr(self.obj			,			lowerCAmelCase__	)
																												setattr(self.obj			,			lowerCAmelCase__			,			self.new	)
										elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		globals()['''__builtins__'''][target_attr]
																setattr(self.obj			,			lowerCAmelCase__			,			self.new	)
										else:
																raise RuntimeError(f'''Tried to patch attribute {target_attr} instead of a submodule.'''	)


				def __exit__(       self			:str			,			*lowerCAmelCase__			:Union[str, Any]	)		->			Optional[int]:
										for attr in list(self.original	):
																setattr(self.obj			,			lowerCAmelCase__			,			self.original.pop(lowerCAmelCase__	)	)


				def 		__magic_name__(       self			:List[Any]	)		->			List[Any]:
										self.__enter__()
										self._active_patches.append(self	)



				def 		__magic_name__(       self			:Optional[int]	)		->			int:
										try:
																self._active_patches.remove(self	)
										except ValueError:
																# If the patch hasn't been started this will fail
																return None

										return self.__exit__()
