
import os

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

import datasets


__magic_name__  							=	datasets.logging.get_logger(__name__)


__magic_name__  							=	'''\
@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}
}
'''

__magic_name__  							=	'''\
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.
'''

__magic_name__  							=	'''
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]
'''

__magic_name__  							=	{
    '''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 A_      (      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 A_      (      self		,						lowerCamelCase			):
          # 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')."			)
              snake_case__    =					"bleurt-base-128"

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

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



      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					self.scorer.score(references=lowerCamelCase		,						candidates=lowerCamelCase			)
          return {"scores": scores}





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if any(not isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				) or x < 0 for x in sequence				):
        raise TypeError("Sequence must be list of non-negative integers"				)
    for _ in range(len(__lowerCAmelCase				)				):
        for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase     ,		sequence[1:]				)				):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]



