"""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   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()
				UpperCAmelCase__             =							model(SCREAMING_SNAKE_CASE__       )
				UpperCAmelCase__             =							F.mse_loss(SCREAMING_SNAKE_CASE__					,			target.to(output.device       )       )
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(SCREAMING_SNAKE_CASE__       )



def   a_						(					lowerCamelCase					,			lowerCamelCase=False       ):
				set_seed(4_2       )
				UpperCAmelCase__             =							RegressionModel()
				UpperCAmelCase__             =							deepcopy(SCREAMING_SNAKE_CASE__       )
				UpperCAmelCase__             =							RegressionDataset(length=8_0       )
				UpperCAmelCase__             =							DataLoader(SCREAMING_SNAKE_CASE__					,			batch_size=1_6       )
				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(SCREAMING_SNAKE_CASE__					,			lr_lambda=lambda lowerCamelCase       : epoch**0.65       )
								UpperCAmelCase__             =							LambdaLR(SCREAMING_SNAKE_CASE__					,			lr_lambda=lambda lowerCamelCase       : epoch**0.65       )
				# Make a copy of `model`
				if sched:
								UpperCAmelCase__             =							accelerator.prepare(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
				else:
								UpperCAmelCase__             =							accelerator.prepare(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							get_training_setup(SCREAMING_SNAKE_CASE__       )
				# Use a single batch
				UpperCAmelCase__             =							next(iter(SCREAMING_SNAKE_CASE__       )       ).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(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(SCREAMING_SNAKE_CASE__       ):
																step_model(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
								else:
												# Sync grads
												step_model(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )

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



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							get_training_setup(SCREAMING_SNAKE_CASE__       )
				# Use a single batch
				UpperCAmelCase__             =							next(iter(SCREAMING_SNAKE_CASE__       )       ).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(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(SCREAMING_SNAKE_CASE__       ):
																step_model(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
								else:
												# Sync grads
												step_model(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )

								# 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       )
								UpperCAmelCase__             =							ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE__       )       )]



def   a_						(					lowerCamelCase=False					,			lowerCamelCase=False       ):
				UpperCAmelCase__             =							Accelerator(
				    split_batches=SCREAMING_SNAKE_CASE__					,			dispatch_batches=SCREAMING_SNAKE_CASE__					,			gradient_accumulation_steps=2       )
				# Test that context manager behaves properly
				UpperCAmelCase__             =							get_training_setup(SCREAMING_SNAKE_CASE__       )
				for iteration, batch in enumerate(SCREAMING_SNAKE_CASE__       ):
								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(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
								# Do "gradient accumulation" (noop)
								with accelerator.accumulate(SCREAMING_SNAKE_CASE__       ):
												step_model(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )

								# 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(SCREAMING_SNAKE_CASE__       ) - 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       )
								UpperCAmelCase__             =							ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE__       )       )]
				GradientState._reset_state()



def   a_						(					lowerCamelCase=False					,			lowerCamelCase=False       ):
				UpperCAmelCase__             =							Accelerator(
				    split_batches=SCREAMING_SNAKE_CASE__					,			dispatch_batches=SCREAMING_SNAKE_CASE__					,			gradient_accumulation_steps=2       )
				# Test that context manager behaves properly
				UpperCAmelCase__             =							get_training_setup(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
				for iteration, batch in enumerate(SCREAMING_SNAKE_CASE__       ):
								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(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
								opt.step()

								if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE__       )):
												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(SCREAMING_SNAKE_CASE__       ):
												step_model(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
												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(SCREAMING_SNAKE_CASE__       ))
								if accelerator.num_processes > 1:
												check_model_parameters(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
								# Shuffle ddp_input on each iteration
								torch.manual_seed(1_3_3_7 + iteration       )
				GradientState._reset_state()



def   a_						(					):
				UpperCAmelCase__             =							Accelerator()

				UpperCAmelCase__             =							RegressionDataset(length=8_0       )
				UpperCAmelCase__             =							DataLoader(SCREAMING_SNAKE_CASE__					,			batch_size=1_6       )
				UpperCAmelCase__             =							RegressionDataset(length=9_6       )
				UpperCAmelCase__             =							DataLoader(SCREAMING_SNAKE_CASE__					,			batch_size=1_6       )
				UpperCAmelCase__             =							accelerator.prepare(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )
				assert accelerator.gradient_state.active_dataloader is None
				for iteration, _ in enumerate(SCREAMING_SNAKE_CASE__       ):
								assert id(accelerator.gradient_state.active_dataloader       ) == id(SCREAMING_SNAKE_CASE__       )
								if iteration < len(SCREAMING_SNAKE_CASE__       ) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
												if iteration == 1:
																for batch_num, _ in enumerate(SCREAMING_SNAKE_CASE__       ):
																				assert id(accelerator.gradient_state.active_dataloader       ) == id(SCREAMING_SNAKE_CASE__       )
																				if batch_num < len(SCREAMING_SNAKE_CASE__       ) - 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_						(					):
				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(SCREAMING_SNAKE_CASE__       )
				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(SCREAMING_SNAKE_CASE__       )
				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(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )

    # 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(SCREAMING_SNAKE_CASE__					,			SCREAMING_SNAKE_CASE__       )



def   a_						(					lowerCamelCase       ):
				main()


if __name__ == "__main__":
						main()







'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       str     		=      0
				for ch in input_str:
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      ord(SCREAMING_SNAKE_CASE__     )
								_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      pow(2							,      SCREAMING_SNAKE_CASE__     )

								# If we already turned on bit for current character's unicode
								if bitmap >> ch_unicode & 1 == 1:
												return False
								bitmap |= ch_bit_index_on
				return True


if __name__ == "__main__":
				import doctest

				doctest.testmod()




