





'''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  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      Optional[int]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Dict						)	->	str:
				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  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      int    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[int]    ,							lowercase						:      int=True						)	->	Any:
				model.train()
				_a								= model(lowercase						)
				_a								= F.mse_loss(lowercase    ,							target.to(output.device						)						)
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(lowercase						)






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






def  _lowerCamelCase				( lowercase						:      Optional[Any]						)	->	Optional[int]:
				# Test when on a single CPU or GPU that the context manager does nothing
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				# Use a single batch
				_a	,					_a								= next(iter(lowercase						)						).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(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(1337 + iteration						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      Tuple						)	->	Tuple:
				# Test on distributed setup that context manager behaves properly
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				# Use a single batch
				_a	,					_a								= next(iter(lowercase						)						).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(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(1337 + iteration						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      List[Any]=False    ,							lowercase						:      Optional[int]=False						)	->	Any:
				_a								= Accelerator(
				    split_batches=lowercase    ,							dispatch_batches=lowercase    ,							gradient_accumulation_steps=2						)
				# Test that context manager behaves properly
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				for iteration, batch in enumerate(lowercase						):
								_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(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(1337 + iteration						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]
				GradientState._reset_state()






def  _lowerCamelCase				( lowercase						:      int=False    ,							lowercase						:      int=False						)	->	Dict:
				_a								= Accelerator(
				    split_batches=lowercase    ,							dispatch_batches=lowercase    ,							gradient_accumulation_steps=2						)
				# Test that context manager behaves properly
				_a	,					_a	,					_a	,					_a	,					_a	,					_a	,					_a								= get_training_setup(lowercase    ,							lowercase						)
				for iteration, batch in enumerate(lowercase						):
								_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(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'
								_a								= (((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(1337 + iteration						)
				GradientState._reset_state()






def  _lowerCamelCase				( )	->	Any:
				_a								= Accelerator()

				_a								= RegressionDataset(length=80						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				_a								= RegressionDataset(length=96						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				_a	,					_a								= 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  _lowerCamelCase				( )	->	Optional[Any]:
				_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(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  _lowerCamelCase				( lowercase						:      Any						)	->	Tuple:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()

'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase = 1		,     UpperCAmelCase = 1000     ):
 lowercase__				:    Dict  =					1
 lowercase__				:    Dict  =					0

 for divide_by_number in range(UpperCAmelCase		,     digit + 1     ):
  lowercase__				:    list[int]  =					[]
  lowercase__				:    Union[str, Any]  =					numerator
  for _ in range(1		,     digit + 1     ):
   if now_divide in has_been_divided:
    if longest_list_length < len(UpperCAmelCase     ):
     lowercase__				:    Dict  =					len(UpperCAmelCase     )
     lowercase__				:    Optional[Any]  =					divide_by_number
   else:
    has_been_divided.append(UpperCAmelCase     )
    lowercase__				:    int  =					now_divide * 10 % divide_by_number

 return the_digit


# Tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()

