



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

def  lowerCamelCase_(lowerCamelCase_     )			->							Optional[int]:
						# Test when on a single CPU or GPU that the context manager does nothing
						UpperCAmelCase								=  get_training_setup(lowerCamelCase_     )
						# Use a single batch
						UpperCAmelCase								=  next(iter(lowerCamelCase_     )     ).values()
						for iteration in range(3     ):
												# Gather the distributed inputs and targs for the base model
												UpperCAmelCase								=  accelerator.gather((ddp_input, ddp_target)     )
												UpperCAmelCase								=  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     )
												UpperCAmelCase								=  ddp_input[torch.randperm(len(lowerCamelCase_     )     )]

def  lowerCamelCase_(lowerCamelCase_     )			->							str:
						# Test on distributed setup that context manager behaves properly
						UpperCAmelCase								=  get_training_setup(lowerCamelCase_     )
						# Use a single batch
						UpperCAmelCase								=  next(iter(lowerCamelCase_     )     ).values()
						for iteration in range(3     ):
												# Gather the distributed inputs and targs for the base model
												UpperCAmelCase								=  accelerator.gather((ddp_input, ddp_target)     )
												UpperCAmelCase								=  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     )
												UpperCAmelCase								=  ddp_input[torch.randperm(len(lowerCamelCase_     )     )]

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

def  lowerCamelCase_(lowerCamelCase_=False     , lowerCamelCase_=False     )			->							Dict:
						UpperCAmelCase								=  Accelerator(
						    split_batches=lowerCamelCase_     , dispatch_batches=lowerCamelCase_     , gradient_accumulation_steps=2     )
						# Test that context manager behaves properly
						UpperCAmelCase								=  get_training_setup(lowerCamelCase_     , lowerCamelCase_     )
						for iteration, batch in enumerate(lowerCamelCase_     ):
												UpperCAmelCase								=  batch.values()
												# Gather the distributed inputs and targs for the base model
												UpperCAmelCase								=  accelerator.gather((ddp_input, ddp_target)     )
												UpperCAmelCase								=  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'
												UpperCAmelCase								=  (((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_()			->							Dict:
						UpperCAmelCase								=  Accelerator()

						UpperCAmelCase								=  RegressionDataset(length=80     )
						UpperCAmelCase								=  DataLoader(lowerCamelCase_     , batch_size=16     )
						UpperCAmelCase								=  RegressionDataset(length=96     )
						UpperCAmelCase								=  DataLoader(lowerCamelCase_     , batch_size=16     )
						UpperCAmelCase								=  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_()			->							Tuple:
						UpperCAmelCase								=  Accelerator()
						UpperCAmelCase								=  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_(lowerCamelCase_     )			->							Any:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
		main()







from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					1.0_5457_1817E-34  # unit of ℏ : J * s

SCREAMING_SNAKE_CASE			: int				   =					3E8  # unit of c : m * s^-1



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		dict[str, float]:

    if (force, area, distance).count(0					) != 1:
        raise ValueError('One and only one argument must be 0'					)
    if force < 0:
        raise ValueError('Magnitude of force can not be negative'					)
    if distance < 0:
        raise ValueError('Distance can not be negative'					)
    if area < 0:
        raise ValueError('Area can not be negative'					)
    if force == 0:
        _lowercase			:				int 						=						(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
            240 * (distance) ** 4
        )
        return {"force": force}
    elif area == 0:
        _lowercase			:				List[Any] 						=						(240 * force * (distance) ** 4) / (
            REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
        )
        return {"area": area}
    elif distance == 0:
        _lowercase			:				List[Any] 						=						(
            (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
        ) ** (1 / 4)
        return {"distance": distance}
    raise ValueError('One and only one argument must be 0'					)


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

 doctest.testmod()


