

'''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	(       __snake_case					:		Any,		__snake_case					:		Optional[Any],		__snake_case					:		Optional[int],		__snake_case					:		Optional[Any]	)	->							str:






						"""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 						__lowerCamelCase	(       __snake_case					:		Tuple,		__snake_case					:		Optional[Any],		__snake_case					:		Tuple,		__snake_case					:		List[Any],		__snake_case					:		Optional[Any]=True	)	->							List[str]:






						"""simple docstring"""





						model.train()
						A__					:       str     =model(__lowerCamelCase	)
						A__					:       List[str]     =F.mse_loss(__lowerCamelCase,		target.to(output.device	)	)
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(__lowerCamelCase	)
def 						__lowerCamelCase	(       __snake_case					:		str,		__snake_case					:		Dict=False	)	->							List[Any]:






						"""simple docstring"""





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






						"""simple docstring"""





						A__					:       Optional[int]     =get_training_setup(__lowerCamelCase	)
						# Use a single batch
						A__					:       List[Any]     =next(iter(__lowerCamelCase	)	).values()
						for iteration in range(3	):
												# Gather the distributed inputs and targs for the base model
												A__					:       str     =accelerator.gather((ddp_input, ddp_target)	)
												A__					:       Optional[Any]     =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(1_337 + iteration	)
												A__					:       int     =ddp_input[torch.randperm(len(__lowerCamelCase	)	)]
def 						__lowerCamelCase	(       __snake_case					:		List[str]	)	->							List[Any]:






						"""simple docstring"""





						A__					:       str     =get_training_setup(__lowerCamelCase	)
						# Use a single batch
						A__					:       Optional[Any]     =next(iter(__lowerCamelCase	)	).values()
						for iteration in range(3	):
												# Gather the distributed inputs and targs for the base model
												A__					:       int     =accelerator.gather((ddp_input, ddp_target)	)
												A__					:       Dict     =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(1_337 + iteration	)
												A__					:       Tuple     =ddp_input[torch.randperm(len(__lowerCamelCase	)	)]
def 						__lowerCamelCase	(       __snake_case					:		Optional[Any]=False,		__snake_case					:		int=False	)	->							int:






						"""simple docstring"""





						A__					:       int     =Accelerator(
						    split_batches=__lowerCamelCase,		dispatch_batches=__lowerCamelCase,		gradient_accumulation_steps=2	)
						# Test that context manager behaves properly
						A__					:       Optional[int]     =get_training_setup(__lowerCamelCase	)
						for iteration, batch in enumerate(__lowerCamelCase	):
												A__					:       Dict     =batch.values()
												# Gather the distributed inputs and targs for the base model
												A__					:       List[str]     =accelerator.gather((ddp_input, ddp_target)	)
												A__					:       int     =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(1_337 + iteration	)
												A__					:       Optional[int]     =ddp_input[torch.randperm(len(__lowerCamelCase	)	)]
						GradientState._reset_state()
def 						__lowerCamelCase	(       __snake_case					:		Any=False,		__snake_case					:		int=False	)	->							int:






						"""simple docstring"""





						A__					:       Optional[Any]     =Accelerator(
						    split_batches=__lowerCamelCase,		dispatch_batches=__lowerCamelCase,		gradient_accumulation_steps=2	)
						# Test that context manager behaves properly
						A__					:       Tuple     =get_training_setup(__lowerCamelCase,		__lowerCamelCase	)
						for iteration, batch in enumerate(__lowerCamelCase	):
												A__					:       Any     =batch.values()
												# Gather the distributed inputs and targs for the base model
												A__					:       Dict     =accelerator.gather((ddp_input, ddp_target)	)
												A__					:       str     =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__					:       Dict     =(((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(1_337 + iteration	)
						GradientState._reset_state()
def 						__lowerCamelCase	(       )	->							Optional[int]:






						"""simple docstring"""





						A__					:       int     =Accelerator()

						A__					:       Optional[Any]     =RegressionDataset(length=80	)
						A__					:       Tuple     =DataLoader(__lowerCamelCase,		batch_size=16	)
						A__					:       int     =RegressionDataset(length=96	)
						A__					:       int     =DataLoader(__lowerCamelCase,		batch_size=16	)
						A__					:       Any     =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 						__lowerCamelCase	(       )	->							Optional[Any]:






						"""simple docstring"""





						A__					:       Union[str, Any]     =Accelerator()
						A__					:       Any     =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 						__lowerCamelCase	(       __snake_case					:		Dict	)	->							Optional[int]:






						"""simple docstring"""





						main()


if __name__ == "__main__":
		main()




'''simple docstring'''



import math

__snake_case			:     List[Any]										=      10
__snake_case			:     Dict										=      7
__snake_case			:     str										=      BALLS_PER_COLOUR * NUM_COLOURS
def 						__lowerCamelCase	(       __snake_case					:		int = 20	)	->							str:






						"""simple docstring"""





						A__					:       Union[str, Any]     =math.comb(__snake_case,		__snake_case	)
						A__					:       str     =math.comb(NUM_BALLS - BALLS_PER_COLOUR,		__snake_case	)

						A__					:       Optional[int]     =NUM_COLOURS * (1 - missing_colour / total)

						return f"{result:.9f}"


if __name__ == "__main__":
		print(solution(20))

