






"""simple docstring"""



import sacrebleu as scb
from packaging import version

import datasets


_UpperCamelCase						:    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'

_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						:    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 			a	(  datasets.Metric      ):







    def      UpperCamelCase_ (     self			):
      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				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase="exp"				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						_lowerCamelCase=False				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						):
      lowercase				       =			len(references[0]			)
      if any(len(_lowerCamelCase			) != references_per_prediction for refs in references			):
        raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
      lowercase				       =			[[refs[i] for refs in references] for i in range(_lowerCamelCase			)]
      lowercase				       =			scb.corpus_bleu(
          _lowerCamelCase				,						_lowerCamelCase				,						smooth_method=_lowerCamelCase				,						smooth_value=_lowerCamelCase				,						force=_lowerCamelCase				,						lowercase=_lowerCamelCase				,						use_effective_order=_lowerCamelCase				,						**({'tokenize': tokenize} if tokenize else {})				,						)
      lowercase				       =			{
          '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 argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


_lowerCAmelCase        =       get_logger(__name__)


def       a__      (					a							)		->	Optional[int]:
       return TestCommand(
           args.dataset  ,   args.name  ,   args.cache_dir  ,   args.data_dir  ,   args.all_configs  ,   args.save_info or args.save_infos  ,   args.ignore_verifications  ,   args.force_redownload  ,   args.clear_cache  ,   )






class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 __magic_name__          =   False  # to tell pytest it's not a test class
 @staticmethod
 def  UpperCAmelCase   (    __magic_name__      ):

        """simple docstring"""

        A_							:       Dict	      =				parser.add_parser('''test''' ,       help='''Test dataset implementation.'''      )
        test_parser.add_argument('''--name''' ,       type=__magic_name__ ,       default=__magic_name__ ,       help='''Dataset processing name'''      )
        test_parser.add_argument(
            '''--cache_dir''' ,       type=__magic_name__ ,       default=__magic_name__ ,       help='''Cache directory where the datasets are stored.''' ,       )
        test_parser.add_argument(
            '''--data_dir''' ,       type=__magic_name__ ,       default=__magic_name__ ,       help='''Can be used to specify a manual directory to get the files from.''' ,       )
        test_parser.add_argument('''--all_configs''' ,       action='''store_true''' ,       help='''Test all dataset configurations'''      )
        test_parser.add_argument(
            '''--save_info''' ,       action='''store_true''' ,       help='''Save the dataset infos in the dataset card (README.md)'''      )
        test_parser.add_argument(
            '''--ignore_verifications''' ,       action='''store_true''' ,       help='''Run the test without checksums and splits checks.''' ,       )
        test_parser.add_argument('''--force_redownload''' ,       action='''store_true''' ,       help='''Force dataset redownload'''      )
        test_parser.add_argument(
            '''--clear_cache''' ,       action='''store_true''' ,       help='''Remove downloaded files and cached datasets after each config test''' ,       )
        # aliases
        test_parser.add_argument('''--save_infos''' ,       action='''store_true''' ,       help='''alias to save_info'''      )
        test_parser.add_argument('''dataset''' ,       type=__magic_name__ ,       help='''Name of the dataset to download'''      )
        test_parser.set_defaults(func=_test_command_factory      )
 def __init__(    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        A_							:       Dict	      =				dataset
        A_							:       Any	      =				name
        A_							:       List[str]	      =				cache_dir
        A_							:       List[Any]	      =				data_dir
        A_							:       Any	      =				all_configs
        A_							:       int	      =				save_infos
        A_							:       Union[str, Any]	      =				ignore_verifications
        A_							:       List[Any]	      =				force_redownload
        A_							:       List[str]	      =				clear_cache
        if clear_cache and not cache_dir:
               print(
                   '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
                   '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
                   '''Please provide a --cache_dir that will be used to test the dataset script.'''      )
               exit(1      )
        if save_infos:
               A_							:       Optional[Any]	      =				True
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        fl_logger().setLevel(__magic_name__      )
        if self._name is not None and self._all_configs:
               print('''Both parameters `config` and `all_configs` can\'t be used at once.'''      )
               exit(1      )
        A_      ,							A_							:       Tuple	      =				self._dataset, self._name
        A_							:       Dict	      =				dataset_module_factory(__magic_name__      )
        A_							:       int	      =				import_main_class(module.module_path      )
        A_							:       str	      =				len(builder_cls.BUILDER_CONFIGS      ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

        def get_builders() -> Generator[DatasetBuilder, None, None]:
               if self._all_configs and builder_cls.BUILDER_CONFIGS:
                      for i, config in enumerate(builder_cls.BUILDER_CONFIGS      ):
                             if "config_name" in module.builder_kwargs:
                                    yield builder_cls(
                                        cache_dir=self._cache_dir ,       data_dir=self._data_dir ,       **module.builder_kwargs ,       )
                             else:
                                    yield builder_cls(
                                        config_name=config.name ,       cache_dir=self._cache_dir ,       data_dir=self._data_dir ,       **module.builder_kwargs ,       )
               else:
                      if "config_name" in module.builder_kwargs:
                             yield builder_cls(cache_dir=self._cache_dir ,       data_dir=self._data_dir ,       **module.builder_kwargs      )
                      else:
                             yield builder_cls(
                                 config_name=__magic_name__ ,       cache_dir=self._cache_dir ,       data_dir=self._data_dir ,       **module.builder_kwargs ,       )

        for j, builder in enumerate(get_builders()      ):
               print(f"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"""      )
               A_							:       str	      =				os.path.exists(
                   os.path.join(builder.get_imported_module_dir() ,       datasets.config.DATASETDICT_INFOS_FILENAME      )      )  # record checksums only if we need to update a (deprecated) dataset_infos.json
               builder.download_and_prepare(
                   download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                   if not self._force_redownload
                   else DownloadMode.FORCE_REDOWNLOAD ,       verification_mode=VerificationMode.NO_CHECKS
                   if self._ignore_verifications
                   else VerificationMode.ALL_CHECKS ,       try_from_hf_gcs=__magic_name__ ,       )
               builder.as_dataset()
               if self._save_infos:
                      builder._save_infos()

               # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
               # The dataset_infos are saved in the YAML part of the README.md

               # Let's move it to the original directory of the dataset script, to allow the user to
               # upload them on S3 at the same time afterwards.
               if self._save_infos:
                      A_							:       Union[str, Any]	      =				os.path.join(builder_cls.get_imported_module_dir() ,       '''README.md'''      )
                      A_							:       Union[str, Any]	      =				Path(__magic_name__      ).name + '''.py'''
                      A_							:       List[str]	      =				os.path.join(__magic_name__ ,       __magic_name__      )
                      if os.path.isfile(__magic_name__      ):
                             A_							:       str	      =				os.path.dirname(__magic_name__      )
                      elif os.path.isfile(__magic_name__      ):
                             A_							:       Any	      =				path
                      elif os.path.isdir(__magic_name__      ):  # for local directories containing only data files
                             A_							:       Optional[Any]	      =				path
                      else:  # in case of a remote dataset
                             A_							:       List[Any]	      =				None
                             print(f"""Dataset card saved at {dataset_readme_path}"""      )

                      # Move dataset_info back to the user
                      if dataset_dir is not None:
                             A_							:       Any	      =				os.path.join(__magic_name__ ,       '''README.md'''      )
                             copyfile(__magic_name__ ,       __magic_name__      )
                             print(f"""Dataset card saved at {user_dataset_readme_path}"""      )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
               if self._clear_cache:
                      if os.path.isdir(builder._cache_dir      ):
                             logger.warning(f"""Clearing cache at {builder._cache_dir}"""      )
                             rmtree(builder._cache_dir      )
                      A_							:       Optional[Any]	      =				os.path.join(self._cache_dir ,       datasets.config.DOWNLOADED_DATASETS_DIR      )
                      if os.path.isdir(__magic_name__      ):
                             logger.warning(f"""Clearing cache at {download_dir}"""      )
                             rmtree(__magic_name__      )

        print('''Test successful.'''      )


