




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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase=True     ):
							model.train()
							SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    F.mse_loss(_lowerCAmelCase  ,    target.to(output.device     )     )
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(_lowerCAmelCase     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=False     ):
							set_seed(4_2     )
							SCREAMING_SNAKE_CASE_   	=    RegressionModel()
							SCREAMING_SNAKE_CASE_   	=    deepcopy(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    RegressionDataset(length=8_0     )
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    batch_size=1_6     )
							model.to(accelerator.device     )
							if sched:
														SCREAMING_SNAKE_CASE_   	=    AdamW(params=model.parameters()  ,    lr=1e-3     )
														SCREAMING_SNAKE_CASE_   	=    AdamW(params=ddp_model.parameters()  ,    lr=1e-3     )
														SCREAMING_SNAKE_CASE_   	=    LambdaLR(_lowerCAmelCase  ,    lr_lambda=lambda _lowerCAmelCase     : epoch**0.65     )
														SCREAMING_SNAKE_CASE_   	=    LambdaLR(_lowerCAmelCase  ,    lr_lambda=lambda _lowerCAmelCase     : epoch**0.65     )
							# Make a copy of `model`
							if sched:
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							else:
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(_lowerCAmelCase  ,    _lowerCAmelCase     )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader

def 			a   (_lowerCAmelCase     ):
							# Test when on a single CPU or GPU that the context manager does nothing
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_training_setup(_lowerCAmelCase     )
							# Use a single batch
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    next(iter(_lowerCAmelCase     )     ).values()
							for iteration in range(3     ):
														# Gather the distributed inputs and targs for the base model
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather((ddp_input, ddp_target)     )
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    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_3_3_7 + iteration     )
														SCREAMING_SNAKE_CASE_   	=    ddp_input[torch.randperm(len(_lowerCAmelCase     )     )]

def 			a   (_lowerCAmelCase     ):
							# Test on distributed setup that context manager behaves properly
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_training_setup(_lowerCAmelCase     )
							# Use a single batch
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    next(iter(_lowerCAmelCase     )     ).values()
							for iteration in range(3     ):
														# Gather the distributed inputs and targs for the base model
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather((ddp_input, ddp_target)     )
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    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_3_3_7 + iteration     )
														SCREAMING_SNAKE_CASE_   	=    ddp_input[torch.randperm(len(_lowerCAmelCase     )     )]

def 			a   (_lowerCAmelCase=False  ,    _lowerCAmelCase=False     ):
							SCREAMING_SNAKE_CASE_   	=    Accelerator(
							    split_batches=_lowerCAmelCase  ,    dispatch_batches=_lowerCAmelCase  ,    gradient_accumulation_steps=2     )
							# Test that context manager behaves properly
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_training_setup(_lowerCAmelCase     )
							for iteration, batch in enumerate(_lowerCAmelCase     ):
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    batch.values()
														# Gather the distributed inputs and targs for the base model
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather((ddp_input, ddp_target)     )
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    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_3_3_7 + iteration     )
														SCREAMING_SNAKE_CASE_   	=    ddp_input[torch.randperm(len(_lowerCAmelCase     )     )]
							GradientState._reset_state()

def 			a   (_lowerCAmelCase=False  ,    _lowerCAmelCase=False     ):
							SCREAMING_SNAKE_CASE_   	=    Accelerator(
							    split_batches=_lowerCAmelCase  ,    dispatch_batches=_lowerCAmelCase  ,    gradient_accumulation_steps=2     )
							# Test that context manager behaves properly
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_training_setup(_lowerCAmelCase  ,    _lowerCAmelCase     )
							for iteration, batch in enumerate(_lowerCAmelCase     ):
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    batch.values()
														# Gather the distributed inputs and targs for the base model
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather((ddp_input, ddp_target)     )
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    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"
														SCREAMING_SNAKE_CASE_   	=    (((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_3_3_7 + iteration     )
							GradientState._reset_state()

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    Accelerator()

							SCREAMING_SNAKE_CASE_   	=    RegressionDataset(length=8_0     )
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    batch_size=1_6     )
							SCREAMING_SNAKE_CASE_   	=    RegressionDataset(length=9_6     )
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    batch_size=1_6     )
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    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 			a   ():
							SCREAMING_SNAKE_CASE_   	=    Accelerator()
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							# 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 						=1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

__SCREAMING_SNAKE_CASE 						=3e8  # unit of c : m * s^-1

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):

							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:
														SCREAMING_SNAKE_CASE_   	=    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
														    2_4_0 * (distance) ** 4
														)
														return {"force": force}
							elif area == 0:
														SCREAMING_SNAKE_CASE_   	=    (2_4_0 * force * (distance) ** 4) / (
														    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
														)
														return {"area": area}
							elif distance == 0:
														SCREAMING_SNAKE_CASE_   	=    (
														    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * 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()


