






"""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 _lowerCAmelCase	(  __lowerCamelCase:List[Any] ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:List[str]   ):

						'''simple docstring'''






						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:Optional[int] ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:int=True   ):

						'''simple docstring'''






						model.train()
						__magic_name__            =     model(__lowerCAmelCase   )
						__magic_name__            =     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:Tuple ,						__lowerCamelCase:List[Any]=False   ):

						'''simple docstring'''






						set_seed(4_2   )
						__magic_name__            =     RegressionModel()
						__magic_name__            =     deepcopy(__lowerCAmelCase   )
						__magic_name__            =     RegressionDataset(length=8_0   )
						__magic_name__            =     DataLoader(__lowerCAmelCase ,						batch_size=1_6   )
						model.to(accelerator.device   )
						if sched:
												__magic_name__            =     AdamW(params=model.parameters() ,						lr=1E-3   )
												__magic_name__            =     AdamW(params=ddp_model.parameters() ,						lr=1E-3   )
												__magic_name__            =     LambdaLR(__lowerCAmelCase ,						lr_lambda=lambda __lowerCamelCase   : epoch**0.6_5   )
												__magic_name__            =     LambdaLR(__lowerCAmelCase ,						lr_lambda=lambda __lowerCamelCase   : epoch**0.6_5   )
						# Make a copy of `model`
						if sched:
												__magic_name__            =     accelerator.prepare(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase   )
						else:
												__magic_name__            =     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:str   ):

						'''simple docstring'''






						__magic_name__            =     get_training_setup(__lowerCAmelCase   )
						# Use a single batch
						__magic_name__            =     next(iter(__lowerCAmelCase   )   ).values()
						for iteration in range(3   ):
												# Gather the distributed inputs and targs for the base model
												__magic_name__            =     accelerator.gather((ddp_input, ddp_target)   )
												__magic_name__            =     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   )
												__magic_name__            =     ddp_input[torch.randperm(len(__lowerCAmelCase   )   )]

def _lowerCAmelCase	(  __lowerCamelCase:Optional[Any]   ):

						'''simple docstring'''






						__magic_name__            =     get_training_setup(__lowerCAmelCase   )
						# Use a single batch
						__magic_name__            =     next(iter(__lowerCAmelCase   )   ).values()
						for iteration in range(3   ):
												# Gather the distributed inputs and targs for the base model
												__magic_name__            =     accelerator.gather((ddp_input, ddp_target)   )
												__magic_name__            =     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   )
												__magic_name__            =     ddp_input[torch.randperm(len(__lowerCAmelCase   )   )]

def _lowerCAmelCase	(  __lowerCamelCase:int=False ,						__lowerCamelCase:Tuple=False   ):

						'''simple docstring'''






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

def _lowerCAmelCase	(  __lowerCamelCase:Tuple=False ,						__lowerCamelCase:Union[str, Any]=False   ):

						'''simple docstring'''






						__magic_name__            =     Accelerator(
						    split_batches=__lowerCAmelCase ,						dispatch_batches=__lowerCAmelCase ,						gradient_accumulation_steps=2   )
						# Test that context manager behaves properly
						__magic_name__            =     get_training_setup(__lowerCAmelCase ,						__lowerCAmelCase   )
						for iteration, batch in enumerate(__lowerCAmelCase   ):
												__magic_name__            =     batch.values()
												# Gather the distributed inputs and targs for the base model
												__magic_name__            =     accelerator.gather((ddp_input, ddp_target)   )
												__magic_name__            =     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'''
												__magic_name__            =     (((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 _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     Accelerator()

						__magic_name__            =     RegressionDataset(length=8_0   )
						__magic_name__            =     DataLoader(__lowerCAmelCase ,						batch_size=1_6   )
						__magic_name__            =     RegressionDataset(length=9_6   )
						__magic_name__            =     DataLoader(__lowerCAmelCase ,						batch_size=1_6   )
						__magic_name__            =     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	(  ):

						'''simple docstring'''






						__magic_name__            =     Accelerator()
						__magic_name__            =     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:Union[str, Any]   ):

						'''simple docstring'''






						main()


if __name__ == "__main__":
			main()











"""simple docstring"""

from typing import TYPE_CHECKING

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


lowercase    			=       {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMRobertaForCausalLM''',
			    '''XLMRobertaForMaskedLM''',
			    '''XLMRobertaForMultipleChoice''',
			    '''XLMRobertaForQuestionAnswering''',
			    '''XLMRobertaForSequenceClassification''',
			    '''XLMRobertaForTokenClassification''',
			    '''XLMRobertaModel''',
			    '''XLMRobertaPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMRobertaForCausalLM''',
			    '''TFXLMRobertaForMaskedLM''',
			    '''TFXLMRobertaForMultipleChoice''',
			    '''TFXLMRobertaForQuestionAnswering''',
			    '''TFXLMRobertaForSequenceClassification''',
			    '''TFXLMRobertaForTokenClassification''',
			    '''TFXLMRobertaModel''',
			    '''TFXLMRobertaPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxXLMRobertaForMaskedLM''',
			    '''FlaxXLMRobertaForCausalLM''',
			    '''FlaxXLMRobertaForMultipleChoice''',
			    '''FlaxXLMRobertaForQuestionAnswering''',
			    '''FlaxXLMRobertaForSequenceClassification''',
			    '''FlaxXLMRobertaForTokenClassification''',
			    '''FlaxXLMRobertaModel''',
			    '''FlaxXLMRobertaPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta import (
			    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaConfig,
			    XLMRobertaOnnxConfig,
			)

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta import XLMRobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaForCausalLM,
						    XLMRobertaForMaskedLM,
						    XLMRobertaForMultipleChoice,
						    XLMRobertaForQuestionAnswering,
						    XLMRobertaForSequenceClassification,
						    XLMRobertaForTokenClassification,
						    XLMRobertaModel,
						    XLMRobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm_roberta import (
						    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMRobertaForCausalLM,
						    TFXLMRobertaForMaskedLM,
						    TFXLMRobertaForMultipleChoice,
						    TFXLMRobertaForQuestionAnswering,
						    TFXLMRobertaForSequenceClassification,
						    TFXLMRobertaForTokenClassification,
						    TFXLMRobertaModel,
						    TFXLMRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xlm_roberta import (
						    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxXLMRobertaForCausalLM,
						    FlaxXLMRobertaForMaskedLM,
						    FlaxXLMRobertaForMultipleChoice,
						    FlaxXLMRobertaForQuestionAnswering,
						    FlaxXLMRobertaForSequenceClassification,
						    FlaxXLMRobertaForTokenClassification,
						    FlaxXLMRobertaModel,
						    FlaxXLMRobertaPreTrainedModel,
						)

else:
			import sys

			lowercase    			=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



