






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
#
########################################################################


__UpperCamelCase					:		List[Any]	  =						16
__UpperCamelCase					:		Tuple	  =						32
def      a_ (      _A      ,   _A = 16  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     AutoTokenizer.from_pretrained('bert-base-cased'  )
				snake_case__									=     load_dataset('glue'      ,   'mrpc'  )

				def tokenize_function(_A  ):
								# max_length=None => use the model max length (it's actually the default)
								snake_case__									=     tokenizer(examples['sentence1']      ,   examples['sentence2']      ,   truncation=_A      ,   max_length=_A  )
								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():
								snake_case__									=     datasets.map(
								    _A      ,   batched=_A      ,   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
				snake_case__									=     tokenized_datasets.rename_column('label'      ,   'labels'  )

				def collate_fn(_A  ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								snake_case__									=     128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												snake_case__									=     16
								elif accelerator.mixed_precision != "no":
												snake_case__									=     8
								else:
												snake_case__									=     None

								return tokenizer.pad(
								    _A      ,   padding='longest'      ,   max_length=_A      ,   pad_to_multiple_of=_A      ,   return_tensors='pt'      ,   )

				# Instantiate dataloaders.
				snake_case__									=     DataLoader(
				    tokenized_datasets['train']      ,   shuffle=_A      ,   collate_fn=_A      ,   batch_size=_A  )
				snake_case__									=     DataLoader(
				    tokenized_datasets['validation']      ,   shuffle=_A      ,   collate_fn=_A      ,   batch_size=_A  )

				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

							__UpperCamelCase					:		List[Any]	  =						mocked_dataloaders  # noqa: F811
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				# For testing only
				if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,   _A  ) == "1":
								snake_case__									=     2
				# New Code #
				snake_case__									=     int(args.gradient_accumulation_steps  )
				snake_case__									=     int(args.local_sgd_steps  )
				# Initialize accelerator
				snake_case__									=     Accelerator(
				    cpu=args.cpu      ,   mixed_precision=args.mixed_precision      ,   gradient_accumulation_steps=_A  )
				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
				snake_case__									=     config['lr']
				snake_case__									=     int(config['num_epochs']  )
				snake_case__									=     int(config['seed']  )
				snake_case__									=     int(config['batch_size']  )

				snake_case__									=     evaluate.load('glue'      ,   'mrpc'  )

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

				# 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).
				snake_case__									=     model.to(accelerator.device  )

				# Instantiate optimizer
				snake_case__									=     AdamW(params=model.parameters()      ,   lr=_A  )

				# Instantiate scheduler
				snake_case__									=     get_linear_schedule_with_warmup(
				    optimizer=_A      ,   num_warmup_steps=100      ,   num_training_steps=(len(_A  ) * 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.
				snake_case__							,						snake_case__							,						snake_case__							,						snake_case__							,						snake_case__									=     accelerator.prepare(
				    _A      ,   _A      ,   _A      ,   _A      ,   _A  )

				# Now we train the model
				for epoch in range(_A  ):
								model.train()
								with LocalSGD(
								    accelerator=_A      ,   model=_A      ,   local_sgd_steps=_A      ,   enabled=local_sgd_steps is not None  ) as local_sgd:
												for step, batch in enumerate(_A  ):
																# 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(_A  ):
																				snake_case__									=     model(**_A  )
																				snake_case__									=     output.loss
																				accelerator.backward(_A  )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

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

								snake_case__									=     metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''      ,   _A  )
def      a_ (      )			->			List[str]:







				"""simple docstring"""
				snake_case__									=     argparse.ArgumentParser(description='Simple example of training script.'  )
				parser.add_argument(
				    '--mixed_precision'      ,   type=_A      ,   default=_A      ,   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=_A      ,   default=1      ,   help='The number of minibatches to be ran before gradients are accumulated.'      ,   )
				parser.add_argument(
				    '--local_sgd_steps'      ,   type=_A      ,   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.'  )
				snake_case__									=     parser.parse_args()
				snake_case__									=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(_A      ,   _A  )


if __name__ == "__main__":
							main()












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,
)


__UpperCamelCase					:		int	  =						{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""XGLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						["""XGLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		List[str]	  =						[
							    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XGLMForCausalLM""",
							    """XGLMModel""",
							    """XGLMPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Any	  =						[
							    """FlaxXGLMForCausalLM""",
							    """FlaxXGLMModel""",
							    """FlaxXGLMPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXGLMForCausalLM""",
							    """TFXGLMModel""",
							    """TFXGLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)




