





"""simple docstring"""






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase_			=							'''
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

lowerCAmelCase_			=							'''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


lowerCAmelCase_			=							'''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known ."]]
    >>> wiki_split = datasets.load_metric("wiki_split")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Tuple:






       """simple docstring"""




       def remove_articles(SCREAMING_SNAKE_CASE							):
              _UpperCAmelCase     =     re.compile(R'\b(a|an|the)\b',re.UNICODE							)
              return re.sub(SCREAMING_SNAKE_CASE,' ',SCREAMING_SNAKE_CASE							)

       def white_space_fix(SCREAMING_SNAKE_CASE							):
              return " ".join(text.split()							)

       def remove_punc(SCREAMING_SNAKE_CASE							):
              _UpperCAmelCase     =     set(string.punctuation							)
              return "".join(ch for ch in text if ch not in exclude							)

       def lower(SCREAMING_SNAKE_CASE							):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE							)							)							)							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Optional[Any]:






       """simple docstring"""




       return int(normalize_answer(SCREAMING_SNAKE_CASE							) == normalize_answer(SCREAMING_SNAKE_CASE							)							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     [any(compute_exact(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							) for ref in refs							) for pred, refs in zip(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)]
       return (sum(SCREAMING_SNAKE_CASE							) / len(SCREAMING_SNAKE_CASE							)) * 100

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     [rgram for rgrams in rgramslist for rgram in rgrams]
       _UpperCAmelCase     =     Counter(SCREAMING_SNAKE_CASE							)

       _UpperCAmelCase     =     Counter(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     Counter()
       for sgram, scount in sgramcounter.items():
              _UpperCAmelCase     =     scount * numref

       _UpperCAmelCase     =     Counter(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     Counter()
       for cgram, ccount in cgramcounter.items():
              _UpperCAmelCase     =     ccount * numref

       # KEEP
       _UpperCAmelCase     =     sgramcounter_rep & cgramcounter_rep
       _UpperCAmelCase     =     keepgramcounter_rep & rgramcounter
       _UpperCAmelCase     =     sgramcounter_rep & rgramcounter

       _UpperCAmelCase     =     0
       _UpperCAmelCase     =     0
       for keepgram in keepgramcountergood_rep:
              keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
              # Fix an alleged bug [2] in the keep score computation.
              # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
              keeptmpscorea += keepgramcountergood_rep[keepgram]
       # Define 0/0=1 instead of 0 to give higher scores for predictions that match
       #      a target exactly.
       _UpperCAmelCase     =     1
       _UpperCAmelCase     =     1
       if len(SCREAMING_SNAKE_CASE							) > 0:
              _UpperCAmelCase     =     keeptmpscorea / len(SCREAMING_SNAKE_CASE							)
       if len(SCREAMING_SNAKE_CASE							) > 0:
              # Fix an alleged bug [2] in the keep score computation.
              # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
              _UpperCAmelCase     =     keeptmpscorea / sum(keepgramcounterall_rep.values()							)
       _UpperCAmelCase     =     0
       if keepscore_precision > 0 or keepscore_recall > 0:
              _UpperCAmelCase     =     2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

       # DELETION
       _UpperCAmelCase     =     sgramcounter_rep - cgramcounter_rep
       _UpperCAmelCase     =     delgramcounter_rep - rgramcounter
       _UpperCAmelCase     =     sgramcounter_rep - rgramcounter
       _UpperCAmelCase     =     0
       _UpperCAmelCase     =     0
       for delgram in delgramcountergood_rep:
              deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
              deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
       # Define 0/0=1 instead of 0 to give higher scores for predictions that match
       # a target exactly.
       _UpperCAmelCase     =     1
       if len(SCREAMING_SNAKE_CASE							) > 0:
              _UpperCAmelCase     =     deltmpscorea / len(SCREAMING_SNAKE_CASE							)

       # ADDITION
       _UpperCAmelCase     =     set(SCREAMING_SNAKE_CASE							) - set(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     set(SCREAMING_SNAKE_CASE							) & set(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     set(SCREAMING_SNAKE_CASE							) - set(SCREAMING_SNAKE_CASE							)

       _UpperCAmelCase     =     0
       for addgram in addgramcountergood:
              addtmpscore += 1

       # Define 0/0=1 instead of 0 to give higher scores for predictions that match
       # a target exactly.
       _UpperCAmelCase     =     1
       _UpperCAmelCase     =     1
       if len(SCREAMING_SNAKE_CASE							) > 0:
              _UpperCAmelCase     =     addtmpscore / len(SCREAMING_SNAKE_CASE							)
       if len(SCREAMING_SNAKE_CASE							) > 0:
              _UpperCAmelCase     =     addtmpscore / len(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     0
       if addscore_precision > 0 or addscore_recall > 0:
              _UpperCAmelCase     =     2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

       return (keepscore, delscore_precision, addscore)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     len(SCREAMING_SNAKE_CASE							)

       _UpperCAmelCase     =     ssent.split(' '							)
       _UpperCAmelCase     =     csent.split(' '							)
       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     []

       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     []
       for rsent in rsents:
              _UpperCAmelCase     =     rsent.split(' '							)
              _UpperCAmelCase     =     []
              _UpperCAmelCase     =     []
              _UpperCAmelCase     =     []
              ragramslist.append(SCREAMING_SNAKE_CASE							)
              for i in range(0,len(SCREAMING_SNAKE_CASE							) - 1							):
                     if i < len(SCREAMING_SNAKE_CASE							) - 1:
                            _UpperCAmelCase     =     ragrams[i] + ' ' + ragrams[i + 1]
                            ragrams.append(SCREAMING_SNAKE_CASE							)
                     if i < len(SCREAMING_SNAKE_CASE							) - 2:
                            _UpperCAmelCase     =     ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                            ragrams.append(SCREAMING_SNAKE_CASE							)
                     if i < len(SCREAMING_SNAKE_CASE							) - 3:
                            _UpperCAmelCase     =     ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
                            ragrams.append(SCREAMING_SNAKE_CASE							)
              ragramslist.append(SCREAMING_SNAKE_CASE							)
              ragramslist.append(SCREAMING_SNAKE_CASE							)
              ragramslist.append(SCREAMING_SNAKE_CASE							)

       for i in range(0,len(SCREAMING_SNAKE_CASE							) - 1							):
              if i < len(SCREAMING_SNAKE_CASE							) - 1:
                     _UpperCAmelCase     =     sagrams[i] + ' ' + sagrams[i + 1]
                     sagrams.append(SCREAMING_SNAKE_CASE							)
              if i < len(SCREAMING_SNAKE_CASE							) - 2:
                     _UpperCAmelCase     =     sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
                     sagrams.append(SCREAMING_SNAKE_CASE							)
              if i < len(SCREAMING_SNAKE_CASE							) - 3:
                     _UpperCAmelCase     =     sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
                     sagrams.append(SCREAMING_SNAKE_CASE							)

       for i in range(0,len(SCREAMING_SNAKE_CASE							) - 1							):
              if i < len(SCREAMING_SNAKE_CASE							) - 1:
                     _UpperCAmelCase     =     cagrams[i] + ' ' + cagrams[i + 1]
                     cagrams.append(SCREAMING_SNAKE_CASE							)
              if i < len(SCREAMING_SNAKE_CASE							) - 2:
                     _UpperCAmelCase     =     cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
                     cagrams.append(SCREAMING_SNAKE_CASE							)
              if i < len(SCREAMING_SNAKE_CASE							) - 3:
                     _UpperCAmelCase     =     cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
                     cagrams.append(SCREAMING_SNAKE_CASE							)

       ((_UpperCAmelCase)    ,							(_UpperCAmelCase)    ,							(_UpperCAmelCase))     =     SARIngram(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       ((_UpperCAmelCase)    ,							(_UpperCAmelCase)    ,							(_UpperCAmelCase))     =     SARIngram(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       ((_UpperCAmelCase)    ,							(_UpperCAmelCase)    ,							(_UpperCAmelCase))     =     SARIngram(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       ((_UpperCAmelCase)    ,							(_UpperCAmelCase)    ,							(_UpperCAmelCase))     =     SARIngram(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     sum([keepascore, keepascore, keepascore, keepascore]							) / 4
       _UpperCAmelCase     =     sum([delascore, delascore, delascore, delascore]							) / 4
       _UpperCAmelCase     =     sum([addascore, addascore, addascore, addascore]							) / 4
       _UpperCAmelCase     =     (avgkeepscore + avgdelscore + avgaddscore) / 3
       return finalscore

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = True,SCREAMING_SNAKE_CASE = "13a",SCREAMING_SNAKE_CASE = True							)		->       str:






       """simple docstring"""




       if lowercase:
              _UpperCAmelCase     =     sentence.lower()

       if tokenizer in ["13a", "intl"]:
              if version.parse(sacrebleu.__version__							).major >= 2:
                     _UpperCAmelCase     =     sacrebleu.metrics.bleu._get_tokenizer(SCREAMING_SNAKE_CASE							)()(SCREAMING_SNAKE_CASE							)
              else:
                     _UpperCAmelCase     =     sacrebleu.TOKENIZERS[tokenizer]()(SCREAMING_SNAKE_CASE							)
       elif tokenizer == "moses":
              _UpperCAmelCase     =     sacremoses.MosesTokenizer().tokenize(SCREAMING_SNAKE_CASE,return_str=SCREAMING_SNAKE_CASE,escape=SCREAMING_SNAKE_CASE							)
       elif tokenizer == "penn":
              _UpperCAmelCase     =     sacremoses.MosesTokenizer().penn_tokenize(SCREAMING_SNAKE_CASE,return_str=SCREAMING_SNAKE_CASE							)
       else:
              _UpperCAmelCase     =     sentence

       if not return_str:
              _UpperCAmelCase     =     normalized_sent.split()

       return normalized_sent

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Optional[int]:






       """simple docstring"""




       if not (len(SCREAMING_SNAKE_CASE							) == len(SCREAMING_SNAKE_CASE							) == len(SCREAMING_SNAKE_CASE							)):
              raise ValueError('Sources length must match predictions and references lengths.'							)
       _UpperCAmelCase     =     0
       for src, pred, refs in zip(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							):
              sari_score += SARIsent(normalize(SCREAMING_SNAKE_CASE							),normalize(SCREAMING_SNAKE_CASE							),[normalize(SCREAMING_SNAKE_CASE							) for sent in refs]							)
       _UpperCAmelCase     =     sari_score / len(SCREAMING_SNAKE_CASE							)
       return 100 * sari_score

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE="exp",SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=False,SCREAMING_SNAKE_CASE=False,SCREAMING_SNAKE_CASE=False,)		->       Optional[int]:






       """simple docstring"""




       _UpperCAmelCase     =     len(references[0]							)
       if any(len(SCREAMING_SNAKE_CASE							) != references_per_prediction for refs in references							):
              raise ValueError('Sacrebleu requires the same number of references for each prediction'							)
       _UpperCAmelCase     =     [[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE							)]
       _UpperCAmelCase     =     sacrebleu.corpus_bleu(
           SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,smooth_method=SCREAMING_SNAKE_CASE,smooth_value=SCREAMING_SNAKE_CASE,force=SCREAMING_SNAKE_CASE,lowercase=SCREAMING_SNAKE_CASE,use_effective_order=SCREAMING_SNAKE_CASE,)
       return output.score



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






       def 							__A (   self     ):
              return datasets.MetricInfo(
                  description=_DESCRIPTION    ,  citation=_CITATION    ,  inputs_description=_KWARGS_DESCRIPTION    ,  features=datasets.Features(
                      {
                          'predictions': datasets.Value('string'    ,  id='sequence'     ),
                          'references': datasets.Sequence(datasets.Value('string'    ,  id='sequence'     )    ,  id='references'     ),
                      }     )    ,  codebase_urls=[
                      'https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py',
                      'https://github.com/cocoxu/simplification/blob/master/SARI.py',
                      'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
                      'https://github.com/mjpost/sacreBLEU',
                  ]    ,  reference_urls=[
                      'https://www.aclweb.org/anthology/Q16-1029.pdf',
                      'https://github.com/mjpost/sacreBLEU',
                      'https://en.wikipedia.org/wiki/BLEU',
                      'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
                  ]    ,  )






       def 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     {}
              result.update({'sari': compute_sari(sources=a__    ,  predictions=a__    ,  references=a__     )}     )
              result.update({'sacrebleu': compute_sacrebleu(predictions=a__    ,  references=a__     )}     )
              result.update({'exact': compute_em(predictions=a__    ,  references=a__     )}     )
              return result













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


lowerCAmelCase_			=							16
lowerCAmelCase_			=							32

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = 16							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     AutoTokenizer.from_pretrained('bert-base-cased'							)
       _UpperCAmelCase     =     load_dataset('glue','mrpc'							)

       def tokenize_function(SCREAMING_SNAKE_CASE							):
              # max_length=None => use the model max length (it's actually the default)
              _UpperCAmelCase     =     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():
              _UpperCAmelCase     =     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
       _UpperCAmelCase     =     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.
              _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(
                  SCREAMING_SNAKE_CASE,padding='longest',max_length=SCREAMING_SNAKE_CASE,pad_to_multiple_of=SCREAMING_SNAKE_CASE,return_tensors='pt',)

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

    lowerCAmelCase_			=							mocked_dataloaders  # noqa: F811

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Union[str, Any]:






       """simple docstring"""




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

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

       set_seed(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase    ,							_UpperCAmelCase     =     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)
       _UpperCAmelCase     =     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).
       _UpperCAmelCase     =     model.to(accelerator.device							)

       # Instantiate optimizer
       _UpperCAmelCase     =     AdamW(params=model.parameters(),lr=SCREAMING_SNAKE_CASE							)

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

              _UpperCAmelCase     =     metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:""",SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       )		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     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.'							)
       _UpperCAmelCase     =     parser.parse_args()
       _UpperCAmelCase     =     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
    main()






