






"""simple docstring"""







import os

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

import datasets


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


a			:    int				 =		"""\
@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			:    Union[str, 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.
"""

a			:    List[Any]				 =		"""
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			:    Dict				 =		{
    """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 		UpperCAmelCase_	(       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 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												# 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')."      )
																		lowercase__    :				Optional[int]=	"bleurt-base-128"

												if self.config_name.lower() in CHECKPOINT_URLS:
																		lowercase__    :				Union[str, Any]=	self.config_name.lower()

												elif self.config_name.upper() in CHECKPOINT_URLS:
																		lowercase__    :				Optional[int]=	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
												lowercase__    :				Optional[Any]=	dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]      )
												lowercase__    :				Dict=	score.BleurtScorer(os.path.join(snake_case__	,  snake_case__      )      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	self.scorer.score(references=snake_case__	,  candidates=snake_case__      )
												return {"scores": scores}














"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a			:    Union[str, Any]				 =		{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    int				 =		[
			    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PegasusXForConditionalGeneration""",
			    """PegasusXModel""",
			    """PegasusXPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_pegasus_x import (
						    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PegasusXForConditionalGeneration,
						    PegasusXModel,
						    PegasusXPreTrainedModel,
						)


else:
			import sys

			a			:    Optional[Any]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






