


import os

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

import datasets


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


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

__UpperCAmelCase : Any 			=				"\\nBLEURT 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)\nand 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\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

__UpperCAmelCase : List[Any] 			=				"\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

__UpperCAmelCase : 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  _lowercase   (     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  _lowercase   (     self ,					__SCREAMING_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\').'''						)
                     UpperCamelCase    :	int   	=		'''bleurt-base-128'''

              if self.config_name.lower() in CHECKPOINT_URLS:
                     UpperCamelCase    :	Optional[Any]   	=		self.config_name.lower()

              elif self.config_name.upper() in CHECKPOINT_URLS:
                     UpperCamelCase    :	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
              UpperCamelCase    :	Union[str, Any]   	=		dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]						)
              UpperCamelCase    :	Optional[Any]   	=		score.BleurtScorer(os.path.join(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		self.scorer.score(references=__SCREAMING_SNAKE_CASE ,					candidates=__SCREAMING_SNAKE_CASE						)
              return {"scores": scores}





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


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )

