



"""simple docstring"""







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel










"""simple docstring"""







import os

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

import datasets


lowercase__		:    Tuple				   =     datasets.logging.get_logger(__name__)


lowercase__		:    List[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}
}
"""

lowercase__		:    Tuple				   =     """\
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.
"""

lowercase__		:    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]
"""

lowercase__		:    List[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 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        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 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				str	):
        # 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_       :    List[Any]								=			'bleurt-base-128'

        if self.config_name.lower() in CHECKPOINT_URLS:
          lowerCAmelCase_       :    List[Any]								=			self.config_name.lower()

        elif self.config_name.upper() in CHECKPOINT_URLS:
          lowerCAmelCase_       :    Tuple								=			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_       :    List[Any]								=			dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]	)
        lowerCAmelCase_       :    List[str]								=			score.BleurtScorer(os.path.join(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Any	):
        lowerCAmelCase_       :    Tuple								=			self.scorer.score(references=SCREAMING_SNAKE_CASE_   ,      candidates=SCREAMING_SNAKE_CASE_	)
        return {"scores": scores}





