






"""simple docstring"""

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowercase    			=       16
lowercase    			=       32






def _lowerCAmelCase	(  __lowerCamelCase:Accelerator ,						__lowerCamelCase:int = 1_6   ):

						'''simple docstring'''






						__magic_name__            =     AutoTokenizer.from_pretrained("bert-base-cased"   )
						__magic_name__            =     load_dataset("glue" ,						"mrpc"   )

						def tokenize_function(__lowerCamelCase:Optional[int]   ):
												# max_length=None => use the model max length (it's actually the default)
												__magic_name__            =     tokenizer(examples["sentence1"] ,						examples["sentence2"] ,						truncation=__lowerCamelCase ,						max_length=__lowerCamelCase   )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												__magic_name__            =     datasets.map(
												    __lowerCamelCase ,						batched=__lowerCamelCase ,						remove_columns=["idx", "sentence1", "sentence2"] ,						)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__magic_name__            =     tokenized_datasets.rename_column("label" ,						"labels"   )

						def collate_fn(__lowerCamelCase:Any   ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__magic_name__            =     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__magic_name__            =     1_6
												elif accelerator.mixed_precision != "no":
																		__magic_name__            =     8
												else:
																		__magic_name__            =     None

												return tokenizer.pad(
												    __lowerCamelCase ,						padding="longest" ,						max_length=__lowerCamelCase ,						pad_to_multiple_of=__lowerCamelCase ,						return_tensors="pt" ,						)

						# Instantiate dataloaders.
						__magic_name__            =     DataLoader(
						    tokenized_datasets["train"] ,						shuffle=__lowerCamelCase ,						collate_fn=__lowerCamelCase ,						batch_size=__lowerCamelCase   )
						__magic_name__            =     DataLoader(
						    tokenized_datasets["validation"] ,						shuffle=__lowerCamelCase ,						collate_fn=__lowerCamelCase ,						batch_size=__lowerCamelCase   )

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
			from accelerate.test_utils.training import mocked_dataloaders

			lowercase    			=       mocked_dataloaders  # noqa: F811






def _lowerCAmelCase	(  __lowerCamelCase:Tuple ,						__lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						if os.environ.get("TESTING_MOCKED_DATALOADERS" ,						__lowerCamelCase   ) == "1":
												__magic_name__            =     2
						# New Code #
						__magic_name__            =     int(args.gradient_accumulation_steps   )
						__magic_name__            =     int(args.local_sgd_steps   )
						# Initialize accelerator
						__magic_name__            =     Accelerator(
						    cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						gradient_accumulation_steps=__lowerCamelCase   )
						if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
												raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"   )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__magic_name__            =     config["lr"]
						__magic_name__            =     int(config["num_epochs"]   )
						__magic_name__            =     int(config["seed"]   )
						__magic_name__            =     int(config["batch_size"]   )

						__magic_name__            =     evaluate.load("glue" ,						"mrpc"   )

						set_seed(__lowerCamelCase   )
						__magic_name__      ,    __magic_name__            =     get_dataloaders(__lowerCamelCase ,						__lowerCamelCase   )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__magic_name__            =     AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,						return_dict=__lowerCamelCase   )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						__magic_name__            =     model.to(accelerator.device   )

						# Instantiate optimizer
						__magic_name__            =     AdamW(params=model.parameters() ,						lr=__lowerCamelCase   )

						# Instantiate scheduler
						__magic_name__            =     get_linear_schedule_with_warmup(
						    optimizer=__lowerCamelCase ,						num_warmup_steps=1_0_0 ,						num_training_steps=(len(__lowerCamelCase   ) * num_epochs) ,						)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     accelerator.prepare(
						    __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

						# Now we train the model
						for epoch in range(__lowerCamelCase   ):
												model.train()
												with LocalSGD(
												    accelerator=__lowerCamelCase ,						model=__lowerCamelCase ,						local_sgd_steps=__lowerCamelCase ,						enabled=local_sgd_steps is not None   ) as local_sgd:
																		for step, batch in enumerate(__lowerCamelCase   ):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device   )
																								# New code #
																								# We use the new `accumulate` context manager to perform gradient accumulation
																								# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																								with accelerator.accumulate(__lowerCamelCase   ):
																														__magic_name__            =     model(**__lowerCamelCase   )
																														__magic_name__            =     output.loss
																														accelerator.backward(__lowerCamelCase   )
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# LocalSGD-specific line
																														local_sgd.step()

												model.eval()
												for step, batch in enumerate(__lowerCamelCase   ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device   )
																		with torch.no_grad():
																								__magic_name__            =     model(**__lowerCamelCase   )
																		__magic_name__            =     outputs.logits.argmax(dim=-1   )
																		__magic_name__      ,    __magic_name__            =     accelerator.gather_for_metrics((predictions, batch["labels"])   )
																		metric.add_batch(
																		    predictions=__lowerCamelCase ,						references=__lowerCamelCase ,						)

												__magic_name__            =     metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:''' ,						__lowerCamelCase   )






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     argparse.ArgumentParser(description="Simple example of training script."   )
						parser.add_argument(
						    "--mixed_precision" ,						type=__lowerCamelCase ,						default=__lowerCamelCase ,						choices=["no", "fp16", "bf16", "fp8"] ,						help="Whether to use mixed precision. Choose"
						    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
						    "and an Nvidia Ampere GPU." ,						)
						# New Code #
						parser.add_argument(
						    "--gradient_accumulation_steps" ,						type=__lowerCamelCase ,						default=1 ,						help="The number of minibatches to be ran before gradients are accumulated." ,						)
						parser.add_argument(
						    "--local_sgd_steps" ,						type=__lowerCamelCase ,						default=8 ,						help="Number of local SGD steps or None to disable local SGD"   )
						parser.add_argument("--cpu" ,						action="store_true" ,						help="If passed, will train on the CPU."   )
						__magic_name__            =     parser.parse_args()
						__magic_name__            =     {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
						training_function(__lowerCamelCase ,						__lowerCamelCase   )


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__)



