



import os

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

import datasets


UpperCAmelCase__     = datasets.logging.get_logger(__name__)


UpperCAmelCase__     = "\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

UpperCAmelCase__     = "\\nBLEURT 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)\nand 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\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

UpperCAmelCase__     = "\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

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     __lowerCAmelCase					(    datasets.Metric		):



			def 					_lowerCamelCase (		self     :						str)     ->					Tuple:



						"""simple docstring"""






						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 					_lowerCamelCase (		self     :						Dict				,	A     :						Union[str, Any])     ->					Any:



						"""simple docstring"""






						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\').')
									_UpperCAmelCase	 =     'bleurt-base-128'

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

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



			def 					_lowerCamelCase (		self     :						str				,	A     :						List[Any]				,	A     :						Optional[int])     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.scorer.score(references=A				,	candidates=A)
						return {"scores": scores}





import os

# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase__     = [int(0.5 * n * (n + 1)) for n in range(1, 101)]





def 							A			(  )       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     os.path.dirname(os.path.realpath(_UpperCAmelCase					)					)
			_UpperCAmelCase	 =     os.path.join(_UpperCAmelCase							,     'words.txt'					)

			_UpperCAmelCase	 =     ''
			with open(_UpperCAmelCase					) as f:
						_UpperCAmelCase	 =     f.readline()

			_UpperCAmelCase	 =     [word.strip('"'					) for word in words.strip('\r\n'					).split(','					)]
			_UpperCAmelCase	 =     [
			    word
			    for word in [sum(ord(_UpperCAmelCase					) - 64 for x in word					) for word in words]
			    if word in TRIANGULAR_NUMBERS
			]
			return len(_UpperCAmelCase					)


if __name__ == "__main__":
							print(solution())
