






"""simple docstring"""

import os

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

import datasets


lowercase    			=       datasets.logging.get_logger(__name__)


lowercase    			=       '''\
@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}
}
'''

lowercase    			=       '''\
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.
'''

lowercase    			=       '''
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]
'''

lowercase    			=       {
    '''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							:						List[Any]	)				->					Optional[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     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						str	)				->					Union[str, Any]:
										# 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')."	)
																__magic_name__            =     "bleurt-base-128"

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

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







				def     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Optional[Any]	)				->					Tuple:
										__magic_name__            =     self.scorer.score(references=__lowerCamelCase						,    candidates=__lowerCamelCase	)
										return {"scores": scores}











"""simple docstring"""

from copy import deepcopy

class 						A_							:







				def __init__(							self							:						List[str]						,    __lowerCamelCase							:						list[int] | None = None						,    __lowerCamelCase							:						int | None = None	)				->					None:

										if arr is None and size is not None:
																__magic_name__            =     size
																__magic_name__            =     [0] * size
										elif arr is not None:
																self.init(__lowerCamelCase	)
										else:
																raise ValueError("Either arr or size must be specified"	)







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						list[int]	)				->					None:
										__magic_name__            =     len(__lowerCamelCase	)
										__magic_name__            =     deepcopy(__lowerCamelCase	)
										for i in range(1						,    self.size	):
																__magic_name__            =     self.next_(__lowerCamelCase	)
																if j < self.size:
																						self.tree[j] += self.tree[i]







				def     _snake_case					(							self							:						Any	)				->					list[int]:
										__magic_name__            =     self.tree[:]
										for i in range(self.size - 1						,    0						,    -1	):
																__magic_name__            =     self.next_(__lowerCamelCase	)
																if j < self.size:
																						arr[j] -= arr[i]
										return arr







				@staticmethod
				def     _snake_case					(							__lowerCamelCase							:						int	)				->					int:
										return index + (index & (-index))







				@staticmethod
				def     _snake_case					(							__lowerCamelCase							:						int	)				->					int:
										return index - (index & (-index))







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int	)				->					None:
										if index == 0:
																self.tree[0] += value
																return
										while index < self.size:
																self.tree[index] += value
																__magic_name__            =     self.next_(__lowerCamelCase	)







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int	)				->					None:
										self.add(__lowerCamelCase						,    value - self.get(__lowerCamelCase	)	)







				def     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						int	)				->					int:
										if right == 0:
																return 0
										__magic_name__            =     self.tree[0]
										right -= 1  # make right inclusive
										while right > 0:
																result += self.tree[right]
																__magic_name__            =     self.prev(__lowerCamelCase	)
										return result







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int	)				->					int:
										return self.prefix(__lowerCamelCase	) - self.prefix(__lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						int	)				->					int:
										return self.query(__lowerCamelCase						,    index + 1	)







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						int	)				->					int:
										value -= self.tree[0]
										if value < 0:
																return -1

										__magic_name__            =     1  # Largest power of 2 <= size
										while j * 2 < self.size:
																j *= 2

										__magic_name__            =     0

										while j > 0:
																if i + j < self.size and self.tree[i + j] <= value:
																						value -= self.tree[i + j]
																						i += j
																j //= 2
										return i


if __name__ == "__main__":
			import doctest

			doctest.testmod()



