






'''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
#
########################################################################


lowerCamelCase_				: List[Any]    	=							16
lowerCamelCase_				: List[Any]    	=							32







def 	__magic_name__( _A						,      _A = 16					):
						'''simple docstring'''





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

						def tokenize_function(_A					):
												# max_length=None => use the model max length (it's actually the default)
												UpperCamelCase__         = 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():
												UpperCamelCase__         = 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
						UpperCamelCase__         = 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.
												UpperCamelCase__         = 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":
																		UpperCamelCase__         = 16
												elif accelerator.mixed_precision != "no":
																		UpperCamelCase__         = 8
												else:
																		UpperCamelCase__         = None

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

						# Instantiate dataloaders.
						UpperCamelCase__         = DataLoader(
						    tokenized_datasets["""train"""]						,      shuffle=_A						,      collate_fn=_A						,      batch_size=_A					)
						UpperCamelCase__         = 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

					lowerCamelCase_				: Union[str, Any]    	=							mocked_dataloaders  # noqa: F811







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,      _A					) == "1":
												UpperCamelCase__         = 2
						# New Code #
						UpperCamelCase__         = int(args.gradient_accumulation_steps					)
						UpperCamelCase__         = int(args.local_sgd_steps					)
						# Initialize accelerator
						UpperCamelCase__         = 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
						UpperCamelCase__         = config["""lr"""]
						UpperCamelCase__         = int(config["""num_epochs"""]					)
						UpperCamelCase__         = int(config["""seed"""]					)
						UpperCamelCase__         = int(config["""batch_size"""]					)

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

						set_seed(_A					)
						UpperCamelCase__    ,   UpperCamelCase__         = get_dataloaders(_A						,      _A					)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCamelCase__         = 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).
						UpperCamelCase__         = model.to(accelerator.device					)

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

						# Instantiate scheduler
						UpperCamelCase__         = 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.
						UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = 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					):
																														UpperCamelCase__         = model(**_A					)
																														UpperCamelCase__         = 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():
																								UpperCamelCase__         = model(**_A					)
																		UpperCamelCase__         = outputs.logits.argmax(dim=-1					)
																		UpperCamelCase__    ,   UpperCamelCase__         = accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
																		metric.add_batch(
																		    predictions=_A						,      references=_A						,      )

												UpperCamelCase__         = metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:"						,      _A					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = 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."""					)
						UpperCamelCase__         = parser.parse_args()
						UpperCamelCase__         = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(_A						,      _A					)


if __name__ == "__main__":
					main()








'''simple docstring'''


import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowerCamelCase_				: Any    	=							get_logger(__name__)
class    _SCREAMING_SNAKE_CASE							(		enum.Enum	):


			'''simple docstring'''





			__a							:							Tuple					     =					"all_checks"
			__a							:							List[Any]					     =					"basic_checks"
			__a							:							Dict					     =					"no_checks"
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''












def 	__magic_name__( _A						,      _A						,      _A=None					):
						'''simple docstring'''





						if expected_checksums is None:
												logger.info("""Unable to verify checksums."""					)
												return
						if len(set(_A					) - set(_A					)					) > 0:
												raise ExpectedMoreDownloadedFiles(str(set(_A					) - set(_A					)					)					)
						if len(set(_A					) - set(_A					)					) > 0:
												raise UnexpectedDownloadedFile(str(set(_A					) - set(_A					)					)					)
						UpperCamelCase__         = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
						UpperCamelCase__         = """ for """ + verification_name if verification_name is not None else """"""
						if len(_A					) > 0:
												raise NonMatchingChecksumError(
												    f"Checksums didn't match{for_verification_name}:\n"
												    f"{bad_urls}\n"
												    """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""					)
						logger.info("""All the checksums matched successfully""" + for_verification_name					)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''












def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						if expected_splits is None:
												logger.info("""Unable to verify splits sizes."""					)
												return
						if len(set(_A					) - set(_A					)					) > 0:
												raise ExpectedMoreSplits(str(set(_A					) - set(_A					)					)					)
						if len(set(_A					) - set(_A					)					) > 0:
												raise UnexpectedSplits(str(set(_A					) - set(_A					)					)					)
						UpperCamelCase__         = [
						    {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
						    for name in expected_splits
						    if expected_splits[name].num_examples != recorded_splits[name].num_examples
						]
						if len(_A					) > 0:
												raise NonMatchingSplitsSizesError(str(_A					)					)
						logger.info("""All the splits matched successfully."""					)







def 	__magic_name__( _A						,      _A = True					):
						'''simple docstring'''





						if record_checksum:
												UpperCamelCase__         = shaaaa()
												with open(_A						,      """rb"""					) as f:
																		for chunk in iter(lambda: f.read(1 << 20					)						,      B""""""					):
																								m.update(_A					)
																		UpperCamelCase__         = m.hexdigest()
						else:
												UpperCamelCase__         = None
						return {"num_bytes": os.path.getsize(_A					), "checksum": checksum}







def 	__magic_name__( _A					):
						'''simple docstring'''





						if dataset_size and config.IN_MEMORY_MAX_SIZE:
												return dataset_size < config.IN_MEMORY_MAX_SIZE
						else:
												return False
