




# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


__lowerCAmelCase		:				str       			=		re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')




@total_ordering
@dataclass
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							str
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					None
				SCREAMING_SNAKE_CASE_       :							Optional[Union[str, int]]							 =					None
				SCREAMING_SNAKE_CASE_       :							Optional[Union[str, int]]							 =					None
				SCREAMING_SNAKE_CASE_       :							Optional[Union[str, int]]							 =					None





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[Any]:
							a    ,		a    ,		a    =			_str_to_version_tuple(self.version_str							)





				def __repr__(      self				:     List[str]							)					->     Dict:
							return f"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""





				@property
				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Any:
							return self.major, self.minor, self.patch





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Optional[int]							)					->     Optional[Any]:
							if isinstance(__lowerCamelCase					,      __lowerCamelCase							):
										return Version(__lowerCamelCase							)
							elif isinstance(__lowerCamelCase					,      __lowerCamelCase							):
										return other
							raise TypeError(f"""{other} (type {type(__lowerCamelCase							)}) cannot be compared to version."""							)





				def __eq__(      self				:     int					,      __lowerCamelCase				:     Any							)					->     Optional[int]:
							try:
										a    =			self._validate_operand(__lowerCamelCase							)
							except (TypeError, ValueError):
										return False
							else:
										return self.tuple == other.tuple





				def __lt__(      self				:     Optional[Any]					,      __lowerCamelCase				:     Union[str, Any]							)					->     Dict:
							a    =			self._validate_operand(__lowerCamelCase							)
							return self.tuple < other.tuple





				def __hash__(      self				:     Any							)					->     Optional[Any]:
							return hash(_version_tuple_to_str(self.tuple							)							)





				@classmethod
				def 							__UpperCAmelCase							(      cls				:     Optional[Any]					,      __lowerCamelCase				:     Dict							)					->     Any:
							a    =			{f.name for f in dataclasses.fields(cls							)}
							return cls(**{k: v for k, v in dic.items() if k in field_names}							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     str:
							return self.version_str




def 			__magic_name__				( A		:					Optional[int]   ):






			'''simple docstring'''







			a    =			_VERSION_REG.match(A   )
			if not res:
						raise ValueError(F"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."""   )
			return tuple(int(A   ) for v in [res.group("major"   ), res.group("minor"   ), res.group("patch"   )]   )






def 			__magic_name__				( A		:					Optional[Any]   ):






			'''simple docstring'''







			return ".".join(str(A   ) for v in version_tuple   )











'''simple docstring'''





import os

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

import datasets


_lowerCAmelCase      						=	datasets.logging.get_logger(__name__)


_lowerCAmelCase      						=	'''\
@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}
}
'''

_lowerCAmelCase      						=	'''\
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.
'''

_lowerCAmelCase      						=	'''
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]
'''

_lowerCAmelCase      						=	{
    '''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        lowerCAmelCase_(				datasets.Metric     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      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  ,__UpperCAmelCase      )    ->		Tuple:
      # 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__ :  str							      =					"""bleurt-base-128"""

      if self.config_name.lower() in CHECKPOINT_URLS:
          lowerCAmelCase__ :  Union[str, Any]							      =					self.config_name.lower()

      elif self.config_name.upper() in CHECKPOINT_URLS:
          lowerCAmelCase__ :  List[Any]							      =					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__ :  int							      =					dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]      )
      lowerCAmelCase__ :  Dict							      =					score.BleurtScorer(os.path.join(__UpperCAmelCase  ,__UpperCAmelCase      )      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Union[str, Any]							      =					self.scorer.score(references=__UpperCAmelCase  ,candidates=__UpperCAmelCase      )
      return {"scores": scores}


