






"""simple docstring"""


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def 					UpperCAmelCase			(				):





      '''simple docstring'''






      def filter_log(a__   ):
            return False if "This IS expected if you are initializing" in record.msg else True

      lowerCAmelCase      :Tuple				  = datasets.utils.logging.get_logger('transformers.modeling_utils'   )
      logger.addFilter(a__   )
      try:
            yield
      finally:
            logger.removeFilter(a__   )


__SCREAMING_SNAKE_CASE      =      '\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

__SCREAMING_SNAKE_CASE      =      '\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

__SCREAMING_SNAKE_CASE      =      '\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,    _KWARGS_DESCRIPTION	)
class 	__UpperCamelCase    (    datasets.Metric	):
     def 					UpperCAmelCase__   (       self       :  Optional[int]      )  ->					Tuple:
           return datasets.MetricInfo(
               description=_DESCRIPTION    ,   citation=_CITATION    ,   homepage='https://github.com/Tiiiger/bert_score'    ,   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/Tiiiger/bert_score']    ,   reference_urls=[
                   'https://github.com/Tiiiger/bert_score',
                   'https://arxiv.org/abs/1904.09675',
               ]    ,   )
     def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  Optional[int]    ,   UpperCAmelCase       :  Union[str, Any]    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  str=None    ,   UpperCAmelCase       :  Optional[Any]=None    ,   UpperCAmelCase       :  List[str]=False    ,   UpperCAmelCase       :  Optional[int]=False    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Any=64    ,   UpperCAmelCase       :  Optional[int]=4    ,   UpperCAmelCase       :  List[str]=False    ,   UpperCAmelCase       :  List[Any]=False    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  Any=False    ,   )  ->					int:
           lowerCAmelCase      :str				  = bert_score.utils.get_hash
           lowerCAmelCase      :int				  = bert_score.BERTScorer

           if version.parse(bert_score.__version__      ) >= version.parse('0.3.10'      ):
                 lowerCAmelCase      :List[Any]				  = functools.partial(UpperCAmelCase    ,   use_fast_tokenizer=UpperCAmelCase      )
                 lowerCAmelCase      :List[Any]				  = functools.partial(UpperCAmelCase    ,   use_fast_tokenizer=UpperCAmelCase      )
           elif use_fast_tokenizer:
                 raise ImportWarning(
                     'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
                     'You can install it with `pip install "bert-score>=0.3.10"`.'      )

           if model_type is None:
                 assert lang is not None, "either lang or model_type should be specified"
                 lowerCAmelCase      :str				  = bert_score.utils.langamodel[lang.lower()]

           if num_layers is None:
                 lowerCAmelCase      :Optional[Any]				  = bert_score.utils.modelalayers[model_type]

           lowerCAmelCase      :List[str]				  = get_hash(
               model=UpperCAmelCase    ,   num_layers=UpperCAmelCase    ,   idf=UpperCAmelCase    ,   rescale_with_baseline=UpperCAmelCase    ,   use_custom_baseline=baseline_path is not None    ,   )

           with filter_logging_context():
                 if not hasattr(self    ,   'cached_bertscorer'      ) or self.cached_bertscorer.hash != hashcode:
                       lowerCAmelCase      :Tuple				  = scorer(
                           model_type=UpperCAmelCase    ,   num_layers=UpperCAmelCase    ,   batch_size=UpperCAmelCase    ,   nthreads=UpperCAmelCase    ,   all_layers=UpperCAmelCase    ,   idf=UpperCAmelCase    ,   device=UpperCAmelCase    ,   lang=UpperCAmelCase    ,   rescale_with_baseline=UpperCAmelCase    ,   baseline_path=UpperCAmelCase    ,   )

           (lowerCAmelCase)      :str				  = self.cached_bertscorer.score(
               cands=UpperCAmelCase    ,   refs=UpperCAmelCase    ,   verbose=UpperCAmelCase    ,   batch_size=UpperCAmelCase    ,   )
           lowerCAmelCase      :Any				  = {
               'precision': P.tolist(),
               'recall': R.tolist(),
               'f1': F.tolist(),
               'hashcode': hashcode,
           }
           return output_dict
     def 					UpperCAmelCase__   (       self       :  int    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  List[str]=None    ,   **UpperCAmelCase       :  Any      )  ->					Optional[Any]:
           # References can be strings or lists of strings
           # Let's change strings to lists of strings with one element
           if references is not None:
                 lowerCAmelCase      :List[Any]				  = [[ref] if isinstance(UpperCAmelCase    ,   UpperCAmelCase      ) else ref for ref in references]
           super().add_batch(predictions=UpperCAmelCase    ,   references=UpperCAmelCase    ,   **UpperCAmelCase      )
     def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  int=None    ,   UpperCAmelCase       :  Any=None    ,   **UpperCAmelCase       :  List[Any]      )  ->					int:
           # References can be strings or lists of strings
           # Let's change strings to lists of strings with one element
           if isinstance(UpperCAmelCase    ,   UpperCAmelCase      ):
                 lowerCAmelCase      :Optional[Any]				  = [reference]
           super().add(prediction=UpperCAmelCase    ,   reference=UpperCAmelCase    ,   **UpperCAmelCase      )







"""simple docstring"""


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


__SCREAMING_SNAKE_CASE      =      get_logger(__name__)


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						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    (    UpperCamelCase	):
					lowercase_						:							str			=     False  # to tell pytest it's not a test class
					@staticmethod
					def 					UpperCAmelCase__   (       UpperCAmelCase       :  ArgumentParser      )  ->					Optional[int]:
											lowerCAmelCase      :Union[str, Any]				  = parser.add_parser('test'    ,   help='Test dataset implementation.'      )
											test_parser.add_argument('--name'    ,   type=UpperCAmelCase    ,   default=UpperCAmelCase    ,   help='Dataset processing name'      )
											test_parser.add_argument(
											    '--cache_dir'    ,   type=UpperCAmelCase    ,   default=UpperCAmelCase    ,   help='Cache directory where the datasets are stored.'    ,   )
											test_parser.add_argument(
											    '--data_dir'    ,   type=UpperCAmelCase    ,   default=UpperCAmelCase    ,   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=UpperCAmelCase    ,   help='Name of the dataset to download'      )
											test_parser.set_defaults(func=_test_command_factory      )
					def __init__(       self       :  List[Any]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   )  ->					Any:
											lowerCAmelCase      :int				  = dataset
											lowerCAmelCase      :Tuple				  = name
											lowerCAmelCase      :Tuple				  = cache_dir
											lowerCAmelCase      :List[Any]				  = data_dir
											lowerCAmelCase      :int				  = all_configs
											lowerCAmelCase      :Dict				  = save_infos
											lowerCAmelCase      :Union[str, Any]				  = ignore_verifications
											lowerCAmelCase      :int				  = force_redownload
											lowerCAmelCase      :Union[str, Any]				  = 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:
																	lowerCAmelCase      :List[str]				  = True
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					int:
											fl_logger().setLevel(UpperCAmelCase      )
											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      )
											lowerCAmelCase  ,   lowerCAmelCase      :Dict				  = self._dataset, self._name
											lowerCAmelCase      :int				  = dataset_module_factory(UpperCAmelCase      )
											lowerCAmelCase      :int				  = import_main_class(module.module_path      )
											lowerCAmelCase      :Dict				  = 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=UpperCAmelCase    ,   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})"""      )
																	lowerCAmelCase      :int				  = 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=UpperCAmelCase    ,   )
																	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:
																							lowerCAmelCase      :Optional[Any]				  = os.path.join(builder_cls.get_imported_module_dir()    ,   'README.md'      )
																							lowerCAmelCase      :List[str]				  = Path(UpperCAmelCase      ).name + '.py'
																							lowerCAmelCase      :Dict				  = os.path.join(UpperCAmelCase    ,   UpperCAmelCase      )
																							if os.path.isfile(UpperCAmelCase      ):
																													lowerCAmelCase      :Any				  = os.path.dirname(UpperCAmelCase      )
																							elif os.path.isfile(UpperCAmelCase      ):
																													lowerCAmelCase      :Any				  = path
																							elif os.path.isdir(UpperCAmelCase      ):  # for local directories containing only data files
																													lowerCAmelCase      :Dict				  = path
																							else:  # in case of a remote dataset
																													lowerCAmelCase      :int				  = None
																													print(f"""Dataset card saved at {dataset_readme_path}"""      )

																							# Move dataset_info back to the user
																							if dataset_dir is not None:
																													lowerCAmelCase      :Any				  = os.path.join(UpperCAmelCase    ,   'README.md'      )
																													copyfile(UpperCAmelCase    ,   UpperCAmelCase      )
																													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      )
																							lowerCAmelCase      :str				  = os.path.join(self._cache_dir    ,   datasets.config.DOWNLOADED_DATASETS_DIR      )
																							if os.path.isdir(UpperCAmelCase      ):
																													logger.warning(f"""Clearing cache at {download_dir}"""      )
																													rmtree(UpperCAmelCase      )

											print('Test successful.'      )