



"""simple docstring"""
import os

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

import datasets


UpperCAmelCase						:		Optional[Any]			= datasets.logging.get_logger(__name__)


UpperCAmelCase						:		Dict			= "\\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						:		List[str]			= "\\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[str]			= "\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						:		Tuple			= {
    "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			:				Optional[Any] ,  lowerCAmelCase_			:				Union[str, 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').""")
        lowercase_								= """bleurt-base-128"""

      if self.config_name.lower() in CHECKPOINT_URLS:
        lowercase_								= self.config_name.lower()

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




    def 					_UpperCAmelCase	(				self			:				str ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      lowercase_								= self.scorer.score(references=lowerCAmelCase_ ,  candidates=lowerCAmelCase_)
      return {"scores": scores}






"""simple docstring"""
import os
from collections.abc import Iterator


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase = "."							) ->						Iterator[str]:







  '''simple docstring'''


  for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase							):
    lowercase_								= [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
    for filename in filenames:
      if filename == "__init__.py":
        continue
      if os.path.splitext(__lowerCAmelCase							)[1] in (".py", ".ipynb"):
        yield os.path.join(__lowerCAmelCase ,  __lowerCAmelCase							).lstrip("""./"""							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  return F'''{i * "  "}*''' if i else "\n##"


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						str:







  '''simple docstring'''


  lowercase_								= old_path.split(os.sep							)
  for i, new_part in enumerate(new_path.split(os.sep							)							):
    if (i + 1 > len(__lowerCAmelCase							) or old_parts[i] != new_part) and new_part:
      print(F'''{md_prefix(__lowerCAmelCase							)} {new_part.replace("_" ,  " "							).title()}'''							)
  return new_path


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase = "."							) ->						None:







  '''simple docstring'''


  lowercase_								= """"""
  for filepath in sorted(good_file_paths(__lowerCAmelCase							)							):
    lowercase_					,    lowercase_								= os.path.split(__lowerCAmelCase							)
    if filepath != old_path:
      lowercase_								= print_path(__lowerCAmelCase ,  __lowerCAmelCase							)
    lowercase_								= (filepath.count(os.sep							) + 1) if filepath else 0
    lowercase_								= F'''{filepath}/{filename}'''.replace(""" """ ,  """%20"""							)
    lowercase_								= os.path.splitext(filename.replace("""_""" ,  """ """							).title()							)[0]
    print(F'''{md_prefix(__lowerCAmelCase							)} [{filename}]({url})'''							)


if __name__ == "__main__":
       print_directory_md(".")

