import os

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

import datasets


SCREAMING_SNAKE_CASE :	str							     =		datasets.logging.get_logger(__name__)


SCREAMING_SNAKE_CASE :	Tuple							     =		"""\
@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}
}
"""

SCREAMING_SNAKE_CASE :	List[Any]							     =		"""\
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.
"""

SCREAMING_SNAKE_CASE :	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]
"""

SCREAMING_SNAKE_CASE :	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    A_ (       datasets.Metric ):

  def 		_UpperCAmelCase   (		self :      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 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      List[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')."       )
    __a       =  "bleurt-base-128"

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

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

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self.scorer.score(references=__SCREAMING_SNAKE_CASE     ,							candidates=__SCREAMING_SNAKE_CASE       )
   return {"scores": scores}



from __future__ import annotations
class    A_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      int       ):
   __a       =  order

   # a_{0} ... a_{k}
   __a       =  [1.0] + [0.0] * order
   # b_{0} ... b_{k}
   __a       =  [1.0] + [0.0] * order

   # x[n-1] ... x[n-k]
   __a       =  [0.0] * self.order
   # y[n-1] ... y[n-k]
   __a       =  [0.0] * self.order

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      list[float]     ,							__SCREAMING_SNAKE_CASE :      list[float]       ):
   if len(__SCREAMING_SNAKE_CASE       ) < self.order:
    __a       =  [1.0, *a_coeffs]

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected a_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected b_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   __a       =  a_coeffs
   __a       =  b_coeffs

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      float       ):
   __a       =  0.0

   # Start at index 1 and do index 0 at the end.
   for i in range(1     ,							self.order + 1       ):
    result += (
        self.b_coeffs[i] * self.input_history[i - 1]
        - self.a_coeffs[i] * self.output_history[i - 1]
    )

   __a       =  (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

   __a       =  self.input_history[:-1]
   __a       =  self.output_history[:-1]

   __a       =  sample
   __a       =  result

   return result


