

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_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						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_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=True    ):
						model.train()
						SCREAMING_SNAKE_CASE_: Any  		=       model(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       F.mse_loss(_UpperCAmelCase				,	target.to(output.device    )    )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						set_seed(42    )
						SCREAMING_SNAKE_CASE_: Tuple  		=       RegressionModel()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       deepcopy(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       RegressionDataset(length=80    )
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       DataLoader(_UpperCAmelCase				,	batch_size=16    )
						model.to(accelerator.device    )
						if sched:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       AdamW(params=model.parameters()				,	lr=1e-3    )
												SCREAMING_SNAKE_CASE_: str  		=       AdamW(params=ddp_model.parameters()				,	lr=1e-3    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       LambdaLR(_UpperCAmelCase				,	lr_lambda=lambda _UpperCAmelCase    : epoch**0.6_5    )
												SCREAMING_SNAKE_CASE_: List[Any]  		=       LambdaLR(_UpperCAmelCase				,	lr_lambda=lambda _UpperCAmelCase    : epoch**0.6_5    )
						# Make a copy of `model`
						if sched:
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       accelerator.prepare(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
						else:
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       accelerator.prepare(_UpperCAmelCase				,	_UpperCAmelCase    )
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader







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

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







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

												# 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    )
												SCREAMING_SNAKE_CASE_: Any  		=       ddp_input[torch.randperm(len(_UpperCAmelCase    )    )]







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

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







def 			A_      (    _UpperCAmelCase=False				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_: List[Any]  		=       Accelerator(
						    split_batches=_UpperCAmelCase				,	dispatch_batches=_UpperCAmelCase				,	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_: Union[str, Any]  		=       get_training_setup(_UpperCAmelCase				,	_UpperCAmelCase    )
						for iteration, batch in enumerate(_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       batch.values()
												# Gather the distributed inputs and targs for the base model
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       accelerator.gather((ddp_input, ddp_target)    )
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       input.to(accelerator.device    ), target.to(accelerator.device    )
												# Perform our initial ground truth step in non "DDP"
												model.train()
												ddp_model.train()
												step_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
												opt.step()

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







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: List[Any]  		=       Accelerator()

						SCREAMING_SNAKE_CASE_: Dict  		=       RegressionDataset(length=80    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       DataLoader(_UpperCAmelCase				,	batch_size=16    )
						SCREAMING_SNAKE_CASE_: Any  		=       RegressionDataset(length=96    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       DataLoader(_UpperCAmelCase				,	batch_size=16    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       accelerator.prepare(_UpperCAmelCase				,	_UpperCAmelCase    )
						assert accelerator.gradient_state.active_dataloader is None
						for iteration, _ in enumerate(_UpperCAmelCase    ):
												assert id(accelerator.gradient_state.active_dataloader    ) == id(_UpperCAmelCase    )
												if iteration < len(_UpperCAmelCase    ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
																		if iteration == 1:
																								for batch_num, _ in enumerate(_UpperCAmelCase    ):
																														assert id(accelerator.gradient_state.active_dataloader    ) == id(_UpperCAmelCase    )
																														if batch_num < len(_UpperCAmelCase    ) - 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_: Union[str, Any]  		=       Accelerator()
						SCREAMING_SNAKE_CASE_: int  		=       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(_UpperCAmelCase    )
						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(_UpperCAmelCase    )
						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(_UpperCAmelCase				,	_UpperCAmelCase    )

    # 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(_UpperCAmelCase				,	_UpperCAmelCase    )







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


if __name__ == "__main__":
						main()










def 	a_      (    SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      str     ):
       '''simple docstring'''





       assert x is not None
       assert y is not None

       _lowerCamelCase       :		Dict		=len(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Optional[Any]		=len(SCREAMING_SNAKE_CASE__     )

       # declaring the array for storing the dp values
       _lowerCamelCase       :		Optional[int]		=[[0] * (n + 1) for _ in range(m + 1     )]  # noqa: E741

       for i in range(1		,			m + 1     ):
              for j in range(1		,			n + 1     ):
                     _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

                     _lowerCamelCase       :		Union[str, Any]		=max(l[i - 1][j]		,			l[i][j - 1]		,			l[i - 1][j - 1] + match     )

       _lowerCamelCase       :		Optional[int]		=''
       _lowerCamelCase	,     _lowerCamelCase       :		Dict		=m, n
       while i > 0 and j > 0:
              _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

              if l[i][j] == l[i - 1][j - 1] + match:
                     if match == 1:
                            _lowerCamelCase       :		Union[str, Any]		=x[i - 1] + seq
                     i -= 1
                     j -= 1
              elif l[i][j] == l[i - 1][j]:
                     i -= 1
              else:
                     j -= 1

       return l[m][n], seq


if __name__ == "__main__":
 lowerCamelCase											=       'AGGTAB'
 lowerCamelCase											=       'GXTXAYB'
 lowerCamelCase											=       4
 lowerCamelCase											=       'GTAB'

 lowerCamelCase					,				lowerCamelCase											=       longest_common_subsequence(a, b)
 print('len =', ln, ', sub-sequence =', subseq)
 import doctest

 doctest.testmod()


