


'''simple docstring'''







import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


lowerCAmelCase     :  List[Any]											=    logging.get_logger(__name__)
class 						UpperCamelCase__       :



     """simple docstring"""






     __magic_name__           =     None






@experimental
def       lowercase					(_A ,    _A ,    _A ,    _A ,    _A ,    _A ,    _A       ):







       """simple docstring"""







       if ParallelBackendConfig.backend_name is None:
              return _map_with_multiprocessing_pool(
                  _A ,    _A ,    _A ,    _A ,    _A ,    _A ,    _A       )

       return _map_with_joblib(_A ,    _A ,    _A ,    _A ,    _A ,    _A ,    _A       )






def       lowercase					(_A ,    _A ,    _A ,    _A ,    _A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							num_proc if num_proc <= len(_A       ) else len(_A       )
       _lowerCAmelCase     :				List[str]       =							[]  # We organize the splits ourselve (contiguous splits)
       for index in range(_A       ):
              _lowerCAmelCase     :				int       =							len(_A       ) // num_proc
              _lowerCAmelCase     :				List[str]       =							len(_A       ) % num_proc
              _lowerCAmelCase     :				List[str]       =							div * index + min(_A ,    _A       )
              _lowerCAmelCase     :				List[str]       =							start + div + (1 if index < mod else 0)
              split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)       )

       if len(_A       ) != sum(len(i[1]       ) for i in split_kwds       ):
              raise ValueError(
                  f'Error dividing inputs iterable among processes. '
                  f'Total number of objects {len(_A       )}, '
                  f'length: {sum(len(i[1]       ) for i in split_kwds       )}'       )

       logger.info(
           f'Spawning {num_proc} processes for {len(_A       )} objects in slices of {[len(i[1]       ) for i in split_kwds]}'       )
       _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							None, None
       if not disable_tqdm:
              _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							(RLock(),), tqdm.set_lock
       with Pool(_A ,    initargs=_A ,    initializer=_A       ) as pool:
              _lowerCAmelCase     :				Optional[int]       =							pool.map(_A ,    _A       )
       logger.info(f'Finished {num_proc} processes'       )
       _lowerCAmelCase     :				Tuple       =							[obj for proc_res in mapped for obj in proc_res]
       logger.info(f'Unpacked {len(_A       )} objects'       )

       return mapped






def       lowercase					(_A ,    _A ,    _A ,    _A ,    _A ,    _A ,    _A       ):







       """simple docstring"""







       import joblib

       with joblib.parallel_backend(ParallelBackendConfig.backend_name ,    n_jobs=_A       ):
              return joblib.Parallel()(
                  joblib.delayed(_A       )((function, obj, types, None, True, None)       ) for obj in iterable       )






@experimental
@contextlib.contextmanager
def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							backend_name

       if backend_name == "spark":
              from joblibspark import register_spark

              register_spark()

              # TODO: call create_cache_and_write_probe if "download" in steps
              # TODO: raise NotImplementedError when Dataset.map etc is called

       try:
              yield
       finally:
              _lowerCAmelCase     :				Any       =							None





'''simple docstring'''







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowerCAmelCase     :  Optional[int]											=    datasets.logging.get_logger(__name__)


lowerCAmelCase     :  List[str]											=    """\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
"""

lowerCAmelCase     :  List[Any]											=    """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

lowerCAmelCase     :  str											=    """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

lowerCAmelCase     :  Optional[Any]											=    {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,       _KWARGS_DESCRIPTION						)
class 						UpperCamelCase__       (			datasets.Metric						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            return datasets.MetricInfo(
                description=_DESCRIPTION   ,						citation=_CITATION   ,						homepage='https://github.com/google-research/bleurt'   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(
                    {
                        'predictions': datasets.Value('string'   ,						id='sequence' ),
                        'references': datasets.Value('string'   ,						id='sequence' ),
                    } )   ,						codebase_urls=['https://github.com/google-research/bleurt']   ,						reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696']   ,						)







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if self.config_name == "default":
                   logger.warning(
                       'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
                       'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').' )
                   _lowerCAmelCase     :				Tuple       =							'bleurt-base-128'

            if self.config_name.lower() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				int       =							self.config_name.lower()

            elif self.config_name.upper() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				str       =							self.config_name.upper()

            else:
                   raise KeyError(
                       F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}' )

            # download the model checkpoint specified by self.config_name and set up the scorer
            _lowerCAmelCase     :				Optional[Any]       =							dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
            _lowerCAmelCase     :				str       =							score.BleurtScorer(os.path.join(snake_case__   ,						snake_case__ ) )







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.scorer.score(references=snake_case__   ,						candidates=snake_case__ )
            return {"scores": scores}

