






"""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
#
########################################################################


_SCREAMING_SNAKE_CASE					:				List[str]					    =						16
_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						32







def      lowerCamelCase__       (							_lowerCamelCase       :		Accelerator		,				_lowerCamelCase       :		int = 16 )      ->				Dict:
  lowerCamelCase_         =						AutoTokenizer.from_pretrained('bert-base-cased' )
  lowerCamelCase_         =						load_dataset('glue'		,				'mrpc' )

  def tokenize_function(_lowerCamelCase       :		Optional[int] ):
    # max_length=None => use the model max length (it's actually the default)
    lowerCamelCase_         =						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():
    lowerCamelCase_         =						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
  lowerCamelCase_         =						tokenized_datasets.rename_column('label'		,				'labels' )

  def collate_fn(_lowerCamelCase       :		Dict ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    lowerCamelCase_         =						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":
      lowerCamelCase_         =						16
    elif accelerator.mixed_precision != "no":
      lowerCamelCase_         =						8
    else:
      lowerCamelCase_         =						None

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

  # Instantiate dataloaders.
  lowerCamelCase_         =						DataLoader(
      tokenized_datasets['train']		,				shuffle=_lowerCamelCase		,				collate_fn=_lowerCamelCase		,				batch_size=_lowerCamelCase )
  lowerCamelCase_         =						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

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







def      lowerCamelCase__       (							_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		Any )      ->				Tuple:
  # For testing only
  if os.environ.get('TESTING_MOCKED_DATALOADERS'		,				_lowerCamelCase ) == "1":
    lowerCamelCase_         =						2
  # New Code #
  lowerCamelCase_         =						int(args.gradient_accumulation_steps )
  lowerCamelCase_         =						int(args.local_sgd_steps )
  # Initialize accelerator
  lowerCamelCase_         =						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
  lowerCamelCase_         =						config['lr']
  lowerCamelCase_         =						int(config['num_epochs'] )
  lowerCamelCase_         =						int(config['seed'] )
  lowerCamelCase_         =						int(config['batch_size'] )

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

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

  # Instantiate optimizer
  lowerCamelCase_         =						AdamW(params=model.parameters()		,				lr=_lowerCamelCase )

  # Instantiate scheduler
  lowerCamelCase_         =						get_linear_schedule_with_warmup(
      optimizer=_lowerCamelCase		,				num_warmup_steps=100		,				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.
  lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						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 ):
          lowerCamelCase_         =						model(**_lowerCamelCase )
          lowerCamelCase_         =						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():
        lowerCamelCase_         =						model(**_lowerCamelCase )
      lowerCamelCase_         =						outputs.logits.argmax(dim=-1 )
      lowerCamelCase_     ,	lowerCamelCase_         =						accelerator.gather_for_metrics((predictions, batch['labels']) )
      metric.add_batch(
          predictions=_lowerCamelCase		,				references=_lowerCamelCase		,				)

    lowerCamelCase_         =						metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'''epoch {epoch}:'''		,				_lowerCamelCase )







def      lowerCamelCase__       (							)      ->				Optional[int]:
  lowerCamelCase_         =						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.' )
  lowerCamelCase_         =						parser.parse_args()
  lowerCamelCase_         =						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(_lowerCamelCase		,				_lowerCamelCase )


if __name__ == "__main__":
  main()












"""simple docstring"""





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_SCREAMING_SNAKE_CASE					:				Tuple					    =						version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
  from nltk import word_tokenize


_SCREAMING_SNAKE_CASE					:				Tuple					    =						'''\
@inproceedings{banarjee2005,
  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
  author    = {Banerjee, Satanjeev  and Lavie, Alon},
  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
  month     = jun,
  year      = {2005},
  address   = {Ann Arbor, Michigan},
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/W05-0909},
  pages     = {65--72},
}
'''

_SCREAMING_SNAKE_CASE					:				int					    =						'''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''

_SCREAMING_SNAKE_CASE					:				List[Any]					    =						'''
Computes METEOR score of translated segments against one or more references.
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    \'meteor\': meteor score.
Examples:

    >>> meteor = datasets.load_metric(\'meteor\')
    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results["meteor"], 4))
    0.6944
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,      _KWARGS_DESCRIPTION		)
class      a						(					datasets.Metric		):






    def    UpperCamelCase       (       self     :							List[str]					)     ->		List[str]:
      return datasets.MetricInfo(
          description=_DESCRIPTION       ,						citation=_CITATION       ,						inputs_description=_KWARGS_DESCRIPTION       ,						features=datasets.Features(
              {
                  'predictions': datasets.Value('string'       ,						id='sequence'					),
                  'references': datasets.Value('string'       ,						id='sequence'					),
              }					)       ,						codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']       ,						reference_urls=[
              'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
              'https://en.wikipedia.org/wiki/METEOR',
          ]       ,						)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]					)     ->		Dict:
      import nltk

      nltk.download('wordnet'					)
      if NLTK_VERSION >= version.Version('3.6.5'					):
        nltk.download('punkt'					)
      if NLTK_VERSION >= version.Version('3.6.6'					):
        nltk.download('omw-1.4'					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=0.9       ,						__SCREAMING_SNAKE_CASE     :							Dict=3       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=0.5					)     ->		int:
      if NLTK_VERSION >= version.Version('3.6.5'					):
        lowerCamelCase_         =						[
            meteor_score.single_meteor_score(
                word_tokenize(__SCREAMING_SNAKE_CASE					)       ,						word_tokenize(__SCREAMING_SNAKE_CASE					)       ,						alpha=__SCREAMING_SNAKE_CASE       ,						beta=__SCREAMING_SNAKE_CASE       ,						gamma=__SCREAMING_SNAKE_CASE					)
            for ref, pred in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        ]
      else:
        lowerCamelCase_         =						[
            meteor_score.single_meteor_score(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						alpha=__SCREAMING_SNAKE_CASE       ,						beta=__SCREAMING_SNAKE_CASE       ,						gamma=__SCREAMING_SNAKE_CASE					)
            for ref, pred in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        ]

      return {"meteor": np.mean(__SCREAMING_SNAKE_CASE					)}




