






'''simple docstring'''







import os

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

import datasets


_A			:    int					  =datasets.logging.get_logger(__name__)


_A			:    Any					  ='''\
@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}
}
'''

_A			:    Optional[int]					  ='''\
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.
'''

_A			:    Tuple					  ='''
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]
'''

_A			:    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 _lowercase			(       datasets.Metric	):




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														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 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       Dict  ):
														# check that config name specifies a valid BLEURT model
														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__      :			str 						= """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__      :			List[Any] 						= 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__      :			Union[str, Any] 						= dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]  )
														lowerCamelCase__      :			Optional[int] 						= score.BleurtScorer(os.path.join(UpperCamelCase__    ,							UpperCamelCase__  )  )




							def 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Any  ):
														lowerCamelCase__      :			int 						= self.scorer.score(references=UpperCamelCase__    ,							candidates=UpperCamelCase__  )
														return {"scores": scores}









'''simple docstring'''







_A			:    List[str]					  ='''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
			from .utils import rich

