"""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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE = 16       )     ->Any:
  a__:					List[Any]      							=     AutoTokenizer.from_pretrained('bert-base-cased'       )
  a__:					Tuple      							=     load_dataset('glue'		,					'mrpc'       )

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

  def collate_fn(_SCREAMING_SNAKE_CASE       ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a__:					Union[str, Any]      							=     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":
      a__:					Dict      							=     16
    elif accelerator.mixed_precision != "no":
      a__:					List[Any]      							=     8
    else:
      a__:					List[Any]      							=     None

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

  # Instantiate dataloaders.
  a__:					Optional[int]      							=     DataLoader(
      tokenized_datasets['train']		,					shuffle=_SCREAMING_SNAKE_CASE		,					collate_fn=_SCREAMING_SNAKE_CASE		,					batch_size=_SCREAMING_SNAKE_CASE       )
  a__:					Optional[Any]      							=     DataLoader(
      tokenized_datasets['validation']		,					shuffle=_SCREAMING_SNAKE_CASE		,					collate_fn=_SCREAMING_SNAKE_CASE		,					batch_size=_SCREAMING_SNAKE_CASE       )

  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 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->int:
  # For testing only
  if os.environ.get('TESTING_MOCKED_DATALOADERS'		,					_SCREAMING_SNAKE_CASE       ) == "1":
    a__:					Union[str, Any]      							=     2
  # New Code #
  a__:					Any      							=     int(args.gradient_accumulation_steps       )
  # Initialize accelerator
  a__:					str      							=     Accelerator(
      cpu=args.cpu		,					mixed_precision=args.mixed_precision		,					gradient_accumulation_steps=_SCREAMING_SNAKE_CASE       )
  if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
    raise NotImplementedError(
        'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'       )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a__:					Any      							=     config['lr']
  a__:					Dict      							=     int(config['num_epochs']       )
  a__:					Dict      							=     int(config['seed']       )
  a__:					Union[str, Any]      							=     int(config['batch_size']       )

  a__:					Optional[Any]      							=     evaluate.load('glue'		,					'mrpc'       )

  set_seed(_SCREAMING_SNAKE_CASE       )
  a__							,   a__:					List[str]      							=     get_dataloaders(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a__:					Optional[int]      							=     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,					return_dict=_SCREAMING_SNAKE_CASE       )

  # 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).
  a__:					List[Any]      							=     model.to(accelerator.device       )

  # Instantiate optimizer
  a__:					Union[str, Any]      							=     AdamW(params=model.parameters()		,					lr=_SCREAMING_SNAKE_CASE       )

  # Instantiate scheduler
  a__:					Union[str, Any]      							=     get_linear_schedule_with_warmup(
      optimizer=_SCREAMING_SNAKE_CASE		,					num_warmup_steps=100		,					num_training_steps=(len(_SCREAMING_SNAKE_CASE       ) * 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.
  a__							,   a__							,   a__							,   a__							,   a__:					List[Any]      							=     accelerator.prepare(
      _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  # Now we train the model
  for epoch in range(_SCREAMING_SNAKE_CASE       ):
    model.train()
    for step, batch in enumerate(_SCREAMING_SNAKE_CASE       ):
      # 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(_SCREAMING_SNAKE_CASE       ):
        a__:					List[Any]      							=     model(**_SCREAMING_SNAKE_CASE       )
        a__:					int      							=     output.loss
        accelerator.backward(_SCREAMING_SNAKE_CASE       )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

    a__:					Union[str, Any]      							=     metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'epoch {epoch}:'		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     )     ->str:
  a__:					Optional[int]      							=     argparse.ArgumentParser(description='Simple example of training script.'       )
  parser.add_argument(
      '--mixed_precision'		,					type=_SCREAMING_SNAKE_CASE		,					default=_SCREAMING_SNAKE_CASE		,					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=_SCREAMING_SNAKE_CASE		,					default=1		,					help='The number of minibatches to be ran before gradients are accumulated.'		,					)
  parser.add_argument('--cpu'		,					action='store_true'		,					help='If passed, will train on the CPU.'       )
  a__:					List[str]      							=     parser.parse_args()
  a__:					Union[str, Any]      							=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
 main()



"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

lowercase__ 	=     '2.13.1'

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('3.7'):
 raise ImportWarning(
     'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'
 )

if version.parse(pyarrow.__version__).major < 8:
 raise ImportWarning(
     'To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'
     'If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'
 )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

lowercase__ 	=     concatenate_datasets
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadManager
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


