



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 (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			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 (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=True  ):
			model.train()
			lowercase__					  =				model(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False  ):
			set_seed(42  )
			lowercase__					  =				RegressionModel()
			lowercase__					  =				deepcopy(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				RegressionDataset(length=80  )
			lowercase__					  =				DataLoader(SCREAMING_SNAKE_CASE_						,  batch_size=16  )
			model.to(accelerator.device  )
			if sched:
						lowercase__					  =				AdamW(params=model.parameters()						,  lr=1e-3  )
						lowercase__					  =				AdamW(params=ddp_model.parameters()						,  lr=1e-3  )
						lowercase__					  =				LambdaLR(SCREAMING_SNAKE_CASE_						,  lr_lambda=lambda SCREAMING_SNAKE_CASE_  : epoch**0.65  )
						lowercase__					  =				LambdaLR(SCREAMING_SNAKE_CASE_						,  lr_lambda=lambda SCREAMING_SNAKE_CASE_  : epoch**0.65  )
			# Make a copy of `model`
			if sched:
						lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				accelerator.prepare(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			else:
						lowercase__  ,       lowercase__					  =				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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			# Test when on a single CPU or GPU that the context manager does nothing
			lowercase__  ,       lowercase__  ,       lowercase__					  =				get_training_setup(SCREAMING_SNAKE_CASE_  )
			# Use a single batch
			lowercase__  ,       lowercase__					  =				next(iter(SCREAMING_SNAKE_CASE_  )  ).values()
			for iteration in range(3  ):
						# Gather the distributed inputs and targs for the base model
						lowercase__  ,       lowercase__					  =				accelerator.gather((ddp_input, ddp_target)  )
						lowercase__  ,       lowercase__					  =				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(1337 + iteration  )
						lowercase__					  =				ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE_  )  )]



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			# Test on distributed setup that context manager behaves properly
			lowercase__  ,       lowercase__  ,       lowercase__					  =				get_training_setup(SCREAMING_SNAKE_CASE_  )
			# Use a single batch
			lowercase__  ,       lowercase__					  =				next(iter(SCREAMING_SNAKE_CASE_  )  ).values()
			for iteration in range(3  ):
						# Gather the distributed inputs and targs for the base model
						lowercase__  ,       lowercase__					  =				accelerator.gather((ddp_input, ddp_target)  )
						lowercase__  ,       lowercase__					  =				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(1337 + iteration  )
						lowercase__					  =				ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE_  )  )]



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_=False  ):
			lowercase__					  =				Accelerator(
			    split_batches=SCREAMING_SNAKE_CASE_						,  dispatch_batches=SCREAMING_SNAKE_CASE_						,  gradient_accumulation_steps=2  )
			# Test that context manager behaves properly
			lowercase__  ,       lowercase__  ,       lowercase__					  =				get_training_setup(SCREAMING_SNAKE_CASE_  )
			for iteration, batch in enumerate(SCREAMING_SNAKE_CASE_  ):
						lowercase__  ,       lowercase__					  =				batch.values()
						# Gather the distributed inputs and targs for the base model
						lowercase__  ,       lowercase__					  =				accelerator.gather((ddp_input, ddp_target)  )
						lowercase__  ,       lowercase__					  =				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(1337 + iteration  )
						lowercase__					  =				ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE_  )  )]
			GradientState._reset_state()



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_=False  ):
			lowercase__					  =				Accelerator(
			    split_batches=SCREAMING_SNAKE_CASE_						,  dispatch_batches=SCREAMING_SNAKE_CASE_						,  gradient_accumulation_steps=2  )
			# Test that context manager behaves properly
			lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				get_training_setup(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			for iteration, batch in enumerate(SCREAMING_SNAKE_CASE_  ):
						lowercase__  ,       lowercase__					  =				batch.values()
						# Gather the distributed inputs and targs for the base model
						lowercase__  ,       lowercase__					  =				accelerator.gather((ddp_input, ddp_target)  )
						lowercase__  ,       lowercase__					  =				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'''
						lowercase__					  =				(((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(1337 + iteration  )
			GradientState._reset_state()



def __lowerCAmelCase (     ):
			lowercase__					  =				Accelerator()

			lowercase__					  =				RegressionDataset(length=80  )
			lowercase__					  =				DataLoader(SCREAMING_SNAKE_CASE_						,  batch_size=16  )
			lowercase__					  =				RegressionDataset(length=96  )
			lowercase__					  =				DataLoader(SCREAMING_SNAKE_CASE_						,  batch_size=16  )
			lowercase__  ,       lowercase__					  =				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 __lowerCAmelCase (     ):
			lowercase__					  =				Accelerator()
			lowercase__					  =				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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_								=     {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RobertaPreLayerNormForCausalLM""",
							    """RobertaPreLayerNormForMaskedLM""",
							    """RobertaPreLayerNormForMultipleChoice""",
							    """RobertaPreLayerNormForQuestionAnswering""",
							    """RobertaPreLayerNormForSequenceClassification""",
							    """RobertaPreLayerNormForTokenClassification""",
							    """RobertaPreLayerNormModel""",
							    """RobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRobertaPreLayerNormForCausalLM""",
							    """TFRobertaPreLayerNormForMaskedLM""",
							    """TFRobertaPreLayerNormForMultipleChoice""",
							    """TFRobertaPreLayerNormForQuestionAnswering""",
							    """TFRobertaPreLayerNormForSequenceClassification""",
							    """TFRobertaPreLayerNormForTokenClassification""",
							    """TFRobertaPreLayerNormMainLayer""",
							    """TFRobertaPreLayerNormModel""",
							    """TFRobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """FlaxRobertaPreLayerNormForCausalLM""",
							    """FlaxRobertaPreLayerNormForMaskedLM""",
							    """FlaxRobertaPreLayerNormForMultipleChoice""",
							    """FlaxRobertaPreLayerNormForQuestionAnswering""",
							    """FlaxRobertaPreLayerNormForSequenceClassification""",
							    """FlaxRobertaPreLayerNormForTokenClassification""",
							    """FlaxRobertaPreLayerNormModel""",
							    """FlaxRobertaPreLayerNormPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roberta_prelayernorm import (
							    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    RobertaPreLayerNormConfig,
							    RobertaPreLayerNormOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta_prelayernorm import (
														    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaPreLayerNormForCausalLM,
														    RobertaPreLayerNormForMaskedLM,
														    RobertaPreLayerNormForMultipleChoice,
														    RobertaPreLayerNormForQuestionAnswering,
														    RobertaPreLayerNormForSequenceClassification,
														    RobertaPreLayerNormForTokenClassification,
														    RobertaPreLayerNormModel,
														    RobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta_prelayernorm import (
														    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaPreLayerNormForCausalLM,
														    TFRobertaPreLayerNormForMaskedLM,
														    TFRobertaPreLayerNormForMultipleChoice,
														    TFRobertaPreLayerNormForQuestionAnswering,
														    TFRobertaPreLayerNormForSequenceClassification,
														    TFRobertaPreLayerNormForTokenClassification,
														    TFRobertaPreLayerNormMainLayer,
														    TFRobertaPreLayerNormModel,
														    TFRobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta_prelayernorm import (
														    FlaxRobertaPreLayerNormForCausalLM,
														    FlaxRobertaPreLayerNormForMaskedLM,
														    FlaxRobertaPreLayerNormForMultipleChoice,
														    FlaxRobertaPreLayerNormForQuestionAnswering,
														    FlaxRobertaPreLayerNormForSequenceClassification,
														    FlaxRobertaPreLayerNormForTokenClassification,
														    FlaxRobertaPreLayerNormModel,
														    FlaxRobertaPreLayerNormPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




