code: 
  
  
  
  
  
  '''simple docstring'''
  
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForQuestionAnswering,
      AutoTokenizer,
      DataCollatorWithPadding,
      HfArgumentParser,
      SquadDataset,
      Trainer,
      TrainingArguments,
  )
  from transformers import SquadDataTrainingArguments as DataTrainingArguments
  from transformers.trainer_utils import is_main_process
  
  
  __lowerCAmelCase							:			Any				=logging.getLogger(__name__)
  
  
  
  
  
  
  @dataclass
  class      UpperCAmelCase			:
   __lowercase     	=       field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}       )
   __lowercase     	=       field(
       default=UpperCamelCase__    , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}       )
   __lowercase     	=       field(
       default=UpperCamelCase__    , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}       )
   __lowercase     	=       field(default=UpperCamelCase__    , metadata={"""help""": """Set this flag to use fast tokenization."""}       )
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   __lowercase     	=       field(
       default=UpperCamelCase__    , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    , )
  
  
  
  
  
  def 		UpperCamelCase       (							):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
  
      A__       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
  
      if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          A__,			A__,			A__       =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
      else:
          A__,			A__,			A__       =    parser.parse_args_into_dataclasses()
  
      if (
          os.path.exists(training_args.output_dir )
          and os.listdir(training_args.output_dir )
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
          raise ValueError(
              F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
              " --overwrite_output_dir to overcome." )
  
      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"		, datefmt="%m/%d/%Y %H:%M:%S"		, level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		, )
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"		, training_args.local_rank		, training_args.device		, training_args.n_gpu		, bool(training_args.local_rank != -1 )		, training_args.fpaa		, )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank ):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s"		, _lowerCamelCase )
  
      # Prepare Question-Answering task
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
  
      A__       =    AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path		, cache_dir=model_args.cache_dir		, )
      A__       =    AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		, cache_dir=model_args.cache_dir		, use_fast=_lowerCamelCase		, )
      A__       =    AutoModelForQuestionAnswering.from_pretrained(
          model_args.model_name_or_path		, from_tf=bool(".ckpt" in model_args.model_name_or_path )		, config=_lowerCamelCase		, cache_dir=model_args.cache_dir		, )
  
      # Get datasets
      A__       =    hasattr(model.config		, "lang2id" )
      A__       =    (
          SquadDataset(
              _lowerCamelCase		, tokenizer=_lowerCamelCase		, is_language_sensitive=_lowerCamelCase		, cache_dir=model_args.cache_dir )
          if training_args.do_train
          else None
      )
      A__       =    (
          SquadDataset(
              _lowerCamelCase		, tokenizer=_lowerCamelCase		, mode="dev"		, is_language_sensitive=_lowerCamelCase		, cache_dir=model_args.cache_dir		, )
          if training_args.do_eval
          else None
      )
  
      # Data collator
      A__       =    DataCollatorWithPadding(_lowerCamelCase		, pad_to_multiple_of=8 ) if training_args.fpaa else None
  
      # Initialize our Trainer
      A__       =    Trainer(
          model=_lowerCamelCase		, args=_lowerCamelCase		, train_dataset=_lowerCamelCase		, eval_dataset=_lowerCamelCase		, data_collator=_lowerCamelCase		, )
  
      # Training
      if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
              tokenizer.save_pretrained(training_args.output_dir )
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[str] ):
      # For xla_spawn (TPUs)
      main()
  
  
  if __name__ == "__main__":
    main()
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import sacrebleu as scb
  from packaging import version
  
  import datasets
  
  
  __lowerCAmelCase							:			Tuple				="\\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"
  
  __lowerCAmelCase							:			Dict				="\\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"
  
  __lowerCAmelCase							:			Dict				="\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       ):
   def   UpperCAmelCase_							(       self   :int							)->							Union[str, Any]:
       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   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int]		, lowercase_   :Optional[Any]		, lowercase_   :List[str]="exp"		, lowercase_   :str=None		, lowercase_   :Tuple=False		, lowercase_   :Any=False		, lowercase_   :Optional[Any]=None		, lowercase_   :Any=False		, )->							Tuple:
       A__       =    len(references[0]							)
       if any(len(lowercase_							) != 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(lowercase_							)]
       A__       =    scb.corpus_bleu(
           lowercase_		, lowercase_		, smooth_method=lowercase_		, smooth_value=lowercase_		, force=lowercase_		, lowercase=lowercase_		, use_effective_order=lowercase_		, **({"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
  
  
  
  