






import sacrebleu as scb
from packaging import version

import datasets


_UpperCAmelCase				:	Any       =    '\\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

_UpperCAmelCase				:	List[Any]       =    '\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich\'s `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n'

_UpperCAmelCase				:	List[str]       =    '\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `\'exp\'`. Possible values are:\n        - `\'none\'`: no smoothing\n        - `\'floor\'`: increment zero counts\n        - `\'add-k\'`: increment num/denom by k for n>1\n        - `\'exp\'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method=\'floor\'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method=\'add-k\'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `\'zh\'` for Chinese, `\'ja-mecab\'` for Japanese and `\'13a\'` (mteval) otherwise. Possible values are:\n        - `\'none\'`: No tokenization.\n        - `\'zh\'`: Chinese tokenization.\n        - `\'13a\'`: mimics the `mteval-v13a` script from Moses.\n        - `\'intl\'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `\'char\'`: Language-agnostic character-level tokenization.\n        - `\'ja-mecab\'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    \'score\': BLEU score,\n    \'counts\': Counts,\n    \'totals\': Totals,\n    \'precisions\': Precisions,\n    \'bp\': Brevity penalty,\n    \'sys_len\': predictions length,\n    \'ref_len\': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = ["hello there general kenobi", "foo bar foobar"]\n        >>> references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = ["hello there general kenobi",\n        ...                 "on our way to ankh morpork"]\n        >>> references = [["hello there general kenobi", "hello there !"],\n        ...                 ["goodbye ankh morpork", "ankh morpork"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        39.8\n'


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





     def 	lowerCamelCase       (  self     )		->							Tuple:
        if version.parse(scb.__version__     ) < version.parse("""1.4.12"""     ):
           raise ImportWarning(
               """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
               """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""     )
        return datasets.MetricInfo(
            description=_DESCRIPTION       ,   citation=_CITATION       ,   homepage="""https://github.com/mjpost/sacreBLEU"""       ,   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/mjpost/sacreBLEU"""]       ,   reference_urls=[
                """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 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase="exp"       ,   _UpperCAmelCase=None       ,   _UpperCAmelCase=False       ,   _UpperCAmelCase=False       ,   _UpperCAmelCase=None       ,   _UpperCAmelCase=False       ,   )		->							Any:
        lowerCamelCase											=len(references[0]     )
        if any(len(_UpperCAmelCase     ) != references_per_prediction for refs in references     ):
           raise ValueError("""Sacrebleu requires the same number of references for each prediction"""     )
        lowerCamelCase											=[[refs[i] for refs in references] for i in range(_UpperCAmelCase     )]
        lowerCamelCase											=scb.corpus_bleu(
            _UpperCAmelCase       ,   _UpperCAmelCase       ,   smooth_method=_UpperCAmelCase       ,   smooth_value=_UpperCAmelCase       ,   force=_UpperCAmelCase       ,   lowercase=_UpperCAmelCase       ,   use_effective_order=_UpperCAmelCase       ,   **({"""tokenize""": tokenize} if tokenize else {})       ,   )
        lowerCamelCase											={
            """score""": output.score,
            """counts""": output.counts,
            """totals""": output.totals,
            """precisions""": output.precisions,
            """bp""": output.bp,
            """sys_len""": output.sys_len,
            """ref_len""": output.ref_len,
        }
        return output_dict







import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
   print('#################### AVAILABLE DEVICES ####################')
   print(jax.devices())
   print('###########################################################')

   # setup for wandb sweep
   _UpperCAmelCase				:	Optional[Any]    						=    Args()
   _UpperCAmelCase				:	Tuple    						=    wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   _UpperCAmelCase				:	Optional[Any]    						=    dict(logger.config)
   del wandb_args["batch_size"]
   _UpperCAmelCase				:	str    						=    replace(args, **wandb_args)
   _UpperCAmelCase				:	Dict    						=    args.base_dir + '-' + wandb.run.id
   _UpperCAmelCase				:	Tuple    						=    replace(args, base_dir=base_dir)
   print(args)

   _UpperCAmelCase				:	List[Any]    						=    load_dataset('json', data_files=args.tr_data_path)['train']
   _UpperCAmelCase				:	Dict    						=    load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   _UpperCAmelCase				:	List[str]    						=    range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   _UpperCAmelCase				:	List[Any]    						=    tr_dataset.shuffle().select(indices)
   _UpperCAmelCase				:	Any    						=    range(len(val_dataset) - len(val_dataset) % args.batch_size)
   _UpperCAmelCase				:	Union[str, Any]    						=    val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _UpperCAmelCase				:	Optional[Any]    						=    tr_dataset.shuffle().select(range(8_0000))
      _UpperCAmelCase				:	List[Any]    						=    val_dataset.shuffle().select(range(8000))

   print(tr_dataset)
   print(val_dataset)

   _UpperCAmelCase				:	int    						=    FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _UpperCAmelCase				:	Union[str, Any]    						=    BigBirdTokenizerFast.from_pretrained(args.model_id)
   _UpperCAmelCase				:	List[str]    						=    DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

   _UpperCAmelCase				:	Any    						=    {
       'lr': args.lr,
       'init_lr': args.init_lr,
       'warmup_steps': args.warmup_steps,
       'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
       'weight_decay': args.weight_decay,
   }
   _UpperCAmelCase						,						_UpperCAmelCase				:	Optional[int]    						=    build_tx(**tx_args)

   _UpperCAmelCase				:	List[Any]    						=    Trainer(
       args=args,
       data_collator=data_collator,
       model_save_fn=model.save_pretrained,
       train_step_fn=train_step,
       val_step_fn=val_step,
       logger=logger,
       scheduler_fn=lr,
   )

   _UpperCAmelCase				:	Union[str, Any]    						=    None
   _UpperCAmelCase				:	Optional[Any]    						=    trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
   try:
      trainer.train(state, tr_dataset, val_dataset)
   except KeyboardInterrupt:
      print('Oooops; TRAINING STOPPED UNFORTUNATELY')

   print('SAVING WEIGHTS IN `final-weights`')
   _UpperCAmelCase				:	Optional[int]    						=    jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)