

"""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     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_		,	A_					):
	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     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_		,	A_		,	A_=True					):
	model.train()
	lowerCAmelCase__      :		Dict    							=      model(A_					)
	lowerCAmelCase__      :		Optional[Any]    							=      F.mse_loss(A_		,	target.to(output.device					)					)
	if not do_backward:
		loss /= accelerator.gradient_accumulation_steps
		loss.backward()
	else:
		accelerator.backward(A_					)

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

def     __SCREAMING_SNAKE_CASE    ( A_					):
	# Test when on a single CPU or GPU that the context manager does nothing
	lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      get_training_setup(A_					)
	# Use a single batch
	lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      next(iter(A_					)					).values()
	for iteration in range(3					):
		# Gather the distributed inputs and targs for the base model
		lowerCAmelCase__							,lowerCAmelCase__      :		Optional[int]    							=      accelerator.gather((ddp_input, ddp_target)					)
		lowerCAmelCase__							,lowerCAmelCase__      :		Tuple    							=      input.to(accelerator.device					), target.to(accelerator.device					)
		# Perform our initial ground truth step in non "DDP"
		step_model(A_		,	A_		,	A_		,	A_					)
		# Do "gradient accumulation" (noop)
		if iteration % 2 == 0:
			# Accumulate grads locally
			with accelerator.no_sync(A_					):
				step_model(A_		,	A_		,	A_		,	A_					)
		else:
			# Sync grads
			step_model(A_		,	A_		,	A_		,	A_					)

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

def     __SCREAMING_SNAKE_CASE    ( A_					):
	# Test on distributed setup that context manager behaves properly
	lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		int    							=      get_training_setup(A_					)
	# Use a single batch
	lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      next(iter(A_					)					).values()
	for iteration in range(3					):
		# Gather the distributed inputs and targs for the base model
		lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      accelerator.gather((ddp_input, ddp_target)					)
		lowerCAmelCase__							,lowerCAmelCase__      :		int    							=      input.to(accelerator.device					), target.to(accelerator.device					)
		# Perform our initial ground truth step in non "DDP"
		step_model(A_		,	A_		,	A_		,	A_					)
		# Do "gradient accumulation" (noop)
		if iteration % 2 == 0:
			# Accumulate grads locally
			with accelerator.no_sync(A_					):
				step_model(A_		,	A_		,	A_		,	A_					)
		else:
			# Sync grads
			step_model(A_		,	A_		,	A_		,	A_					)

		# 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					)
		lowerCAmelCase__      :		List[Any]    							=      ddp_input[torch.randperm(len(A_					)					)]

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

		# 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(A_					) - 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					)
		lowerCAmelCase__      :		Dict    							=      ddp_input[torch.randperm(len(A_					)					)]
	GradientState._reset_state()

def     __SCREAMING_SNAKE_CASE    ( A_=False		,	A_=False					):
	lowerCAmelCase__      :		Union[str, Any]    							=      Accelerator(
	    split_batches=A_		,	dispatch_batches=A_		,	gradient_accumulation_steps=2					)
	# Test that context manager behaves properly
	lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		Optional[int]    							=      get_training_setup(A_		,	A_					)
	for iteration, batch in enumerate(A_					):
		lowerCAmelCase__							,lowerCAmelCase__      :		int    							=      batch.values()
		# Gather the distributed inputs and targs for the base model
		lowerCAmelCase__							,lowerCAmelCase__      :		int    							=      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"
		model.train()
		ddp_model.train()
		step_model(A_		,	A_		,	A_		,	A_		,	A_					)
		opt.step()

		if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(A_					)):
			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(A_					):
			step_model(A_		,	A_		,	A_		,	A_					)
			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__      :		List[Any]    							=      (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(A_					))
		if accelerator.num_processes > 1:
			check_model_parameters(A_		,	A_		,	A_		,	A_					)
		# Shuffle ddp_input on each iteration
		torch.manual_seed(13_37 + iteration					)
	GradientState._reset_state()

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		int    							=      Accelerator()

	lowerCAmelCase__      :		Tuple    							=      RegressionDataset(length=80					)
	lowerCAmelCase__      :		int    							=      DataLoader(A_		,	batch_size=16					)
	lowerCAmelCase__      :		str    							=      RegressionDataset(length=96					)
	lowerCAmelCase__      :		List[Any]    							=      DataLoader(A_		,	batch_size=16					)
	lowerCAmelCase__							,lowerCAmelCase__      :		int    							=      accelerator.prepare(A_		,	A_					)
	assert accelerator.gradient_state.active_dataloader is None
	for iteration, _ in enumerate(A_					):
		assert id(accelerator.gradient_state.active_dataloader					) == id(A_					)
		if iteration < len(A_					) - 1:
			assert not accelerator.gradient_state.end_of_dataloader
			if iteration == 1:
				for batch_num, _ in enumerate(A_					):
					assert id(accelerator.gradient_state.active_dataloader					) == id(A_					)
					if batch_num < len(A_					) - 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     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		Tuple    							=      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(A_					)
	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(A_					)
	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(A_		,	A_					)

    # 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(A_		,	A_					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()







"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Any    							=      int(A_					)
	if decimal in (0, 1):  # Exit cases for the recursion
		return str(A_					)
	lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      divmod(A_		,	2					)
	return binary_recursive(A_					) + str(A_					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Union[str, Any]    							=      str(A_					).strip()
	if not number:
		raise ValueError('''No input value was provided'''					)
	lowerCAmelCase__      :		Any    							=      '''-''' if number.startswith('''-'''					) else ''''''
	lowerCAmelCase__      :		Dict    							=      number.lstrip('''-'''					)
	if not number.isnumeric():
		raise ValueError('''Input value is not an integer'''					)
	return f'{negative}0b{binary_recursive(int(A_					)					)}'


if __name__ == "__main__":
							from doctest import testmod

							testmod()




