'''simple docstring'''




import sacrebleu as scb
from packaging import version

import datasets


__snake_case  : List[str]		       =    '\\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'

__snake_case  : Tuple		       =    '\\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'

__snake_case  : Optional[Any]		       =    '\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       __UpperCAmelCase							(							datasets.Metric	):





       '''simple docstring'''



       def    __A			( self					)						->	Optional[int]:
           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    __A			( self       ,	_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=None       ,	_SCREAMING_SNAKE_CASE=False       ,	)						->	List[Any]:
           A_      =  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'''					)
           A_      =  [[refs[i] for refs in references] for i in range(_SCREAMING_SNAKE_CASE					)]
           A_      =  scb.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       ,	**({'''tokenize''': tokenize} if tokenize else {})       ,	)
           A_      =  {
               '''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







'''simple docstring'''




import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
  __snake_case  : Any		       =    argparse.ArgumentParser()

  # Hyperparameters sent by the client are passed as command-line arguments to the script.
  parser.add_argument('--epochs', type=int, default=1)
  parser.add_argument('--per_device_train_batch_size', type=int, default=16)
  parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
  parser.add_argument('--model_name_or_path', type=str)
  parser.add_argument('--learning_rate', type=str, default=5E-5)
  parser.add_argument('--do_train', type=bool, default=True)
  parser.add_argument('--do_eval', type=bool, default=True)
  parser.add_argument('--output_dir', type=str)

  __snake_case       ,       __snake_case  : Dict		       =    parser.parse_known_args()

  # overwrite batch size until we have tf_glue.py
  __snake_case  : Any		       =    16
  __snake_case  : Optional[Any]		       =    16

  # Set up logging
  __snake_case  : Union[str, Any]		       =    logging.getLogger(__name__)

  logging.basicConfig(
      level=logging.getLevelName('INFO'),
      handlers=[logging.StreamHandler(sys.stdout)],
      format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
  )

  # Load model and tokenizer
  __snake_case  : Optional[Any]		       =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  __snake_case  : Optional[Any]		       =    AutoTokenizer.from_pretrained(args.model_name_or_path)

  # Load dataset
  __snake_case       ,       __snake_case  : Optional[int]		       =    load_dataset('imdb', split=['train', 'test'])
  __snake_case  : List[str]		       =    train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
  __snake_case  : List[str]		       =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

  # Preprocess train dataset
  __snake_case  : int		       =    train_dataset.map(
      lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
  )
  train_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

  __snake_case  : Dict		       =    {
      x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case  : str		       =    tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
      args.per_device_train_batch_size
  )

  # Preprocess test dataset
  __snake_case  : Tuple		       =    test_dataset.map(
      lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
  )
  test_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

  __snake_case  : Optional[int]		       =    {
      x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case  : List[str]		       =    tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
      args.per_device_eval_batch_size
  )

  # fine optimizer and loss
  __snake_case  : Optional[int]		       =    tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  __snake_case  : Union[str, Any]		       =    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  __snake_case  : int		       =    [tf.keras.metrics.SparseCategoricalAccuracy()]
  model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

  __snake_case  : Any		       =    time.time()
  __snake_case  : int		       =    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  __snake_case  : Union[str, Any]		       =    time.time() - start_train_time

  logger.info('*** Train ***')
  logger.info(F"""train_runtime = {end_train_time}""")
  for key, value in train_results.history.items():
    logger.info(F"""  {key} = {value}""")






