
import os

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

import datasets


UpperCamelCase_						=						datasets.logging.get_logger(__name__)


UpperCamelCase_						=						"""\
@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}
}
"""

UpperCamelCase_						=						"""\
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.
"""

UpperCamelCase_						=						"""
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]
"""

UpperCamelCase_						=						{
    """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      _SCREAMING_SNAKE_CASE				(		datasets.Metric				):





				def 							_UpperCAmelCase    (		self			:		Any				):


								"""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			:		Tuple       ,   snake_case_			:		Optional[Any]				):


								"""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\').'''				)
												A  :	int				=					"bleurt-base-128"

								if self.config_name.lower() in CHECKPOINT_URLS:
												A  :	Optional[int]				=					self.config_name.lower()

								elif self.config_name.upper() in CHECKPOINT_URLS:
												A  :	Dict				=					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
								A  :	List[str]				=					dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]				)
								A  :	Optional[Any]				=					score.BleurtScorer(os.path.join(snake_case_       ,   snake_case_				)				)






				def 							_UpperCAmelCase    (		self			:		List[str]       ,   snake_case_			:		Optional[Any]       ,   snake_case_			:		str				):


								"""simple docstring"""

								A  :	List[str]				=					self.scorer.score(references=snake_case_       ,   candidates=snake_case_				)
								return {"scores": scores}






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__							:      Tuple         =    [
      """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AutoformerForPrediction""",
      """AutoformerModel""",
      """AutoformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

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

