


'''simple docstring'''






import os

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

import datasets


__UpperCAmelCase					       =							datasets.logging.get_logger(__name__)


__UpperCAmelCase					       =							"""\
@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}
}
"""

__UpperCAmelCase					       =							"""\
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.
"""

__UpperCAmelCase					       =							"""
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]
"""

__UpperCAmelCase					       =							{
    """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						):





       '''simple docstring'''






       def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							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 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Any:







              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__      =			'''bleurt-base-128'''

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

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







       def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							str:
              lowerCAmelCase__      =			self.scorer.score(references=_lowerCAmelCase      ,					candidates=_lowerCAmelCase     )
              return {"scores": scores}



'''simple docstring'''






import doctest
from collections import deque

import numpy as np




class 	a__       :





							'''simple docstring'''






							def __init__(	self     ) ->							None:
														lowerCAmelCase__      =			[2, 1, 2, -1]
														lowerCAmelCase__      =			[1, 2, 3, 4]







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							list[float]:
														lowerCAmelCase__      =			len(self.first_signal     )
														lowerCAmelCase__      =			len(self.second_signal     )

														lowerCAmelCase__      =			max(lowerCamelCase_      ,					lowerCamelCase_     )

														# create a zero matrix of max_length x max_length
														lowerCAmelCase__      =			[[0] * max_length for i in range(lowerCamelCase_     )]

														# fills the smaller signal with zeros to make both signals of same length
														if length_first_signal < length_second_signal:
																					self.first_signal += [0] * (max_length - length_first_signal)
														elif length_first_signal > length_second_signal:
																					self.second_signal += [0] * (max_length - length_second_signal)
														for i in range(lowerCamelCase_     ):
																					lowerCAmelCase__      =			deque(self.second_signal     )
																					rotated_signal.rotate(lowerCamelCase_     )
																					for j, item in enumerate(lowerCamelCase_     ):
																												matrix[i][j] += item

        # multiply the matrix with the first signal
														lowerCAmelCase__      =			np.matmul(np.transpose(lowerCamelCase_     )      ,					np.transpose(self.first_signal     )     )

														# rounding-off to two decimal places
														return [round(lowerCamelCase_      ,					2     ) for i in final_signal]


if __name__ == "__main__":
		doctest.testmod()