'''simple docstring'''




import os

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

import datasets


_a   :	Union[str, Any] 							=					datasets.logging.get_logger(__name__)


_a   :	Tuple 							=					"\\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"

_a   :	Optional[int] 							=					"\\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"

_a   :	Tuple 							=					"\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"

_a   :	Optional[int] 							=					{
    "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 		__A       (datasets.Metric   ):






       def 			_snake_case						(      self      ):
              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 			_snake_case						(      self				,     UpperCamelCase_      ):
              # 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')."      )
                     __UpperCAmelCase  :				Any       				=		"bleurt-base-128"

              if self.config_name.lower() in CHECKPOINT_URLS:
                     __UpperCAmelCase  :				Dict       				=		self.config_name.lower()

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







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				str       				=		self.scorer.score(references=UpperCamelCase_				,     candidates=UpperCamelCase_      )
              return {"scores": scores}







'''simple docstring'''




def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       List[str]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		(boundary[1] - boundary[0]) / steps
       __UpperCAmelCase  :				Tuple       				=		boundary[0]
       __UpperCAmelCase  :				List[str]       				=		boundary[1]
       __UpperCAmelCase  :				List[Any]       				=		make_points(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)
       __UpperCAmelCase  :				int       				=		0.0
       y += (h / 2.0) * f(lowerCamelCase__						)
       for i in x_i:
              # print(i)
              y += h * f(lowerCamelCase__						)
       y += (h / 2.0) * f(lowerCamelCase__						)
       return y

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		a + h
       while x < (b - h):
              yield x
              __UpperCAmelCase  :				List[str]       				=		x + h

def _lowercase	(      lowerCamelCase__						)					->       Optional[Any]:  # enter your function here





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		(x - 0) * (x - 0)
       return y

def _lowercase	(      )					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Tuple       				=		0.0  # Lower bound of integration
       __UpperCAmelCase  :				Union[str, Any]       				=		1.0  # Upper bound of integration
       __UpperCAmelCase  :				Union[str, Any]       				=		10.0  # define number of steps or resolution
       __UpperCAmelCase  :				Dict       				=		[a, b]  # define boundary of integration
       __UpperCAmelCase  :				Optional[int]       				=		method_a(lowerCamelCase__							,    lowerCamelCase__						)
       print(f"""y = {y}"""						)


if __name__ == "__main__":
   main()






