


import argparse
from collections import defaultdict


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   F'{file}_{class_name}_{test_name}'
			done_test[_id] += 1

			with open(__lowerCAmelCase       ,			'''r'''  ) as f:
						__lowercase 					=   f.readlines()

			__lowercase 					=   F'class {class_name}('
			__lowercase 					=   F'{4 * " "}def {test_name}('
			__lowercase 					=   F'{8 * " "}{correct_line.split()[0]}'
			__lowercase 					=   F'{16 * " "}{correct_line.split()[0]}'
			__lowercase 					=   False
			__lowercase 					=   False
			__lowercase 					=   False
			__lowercase 					=   False
			__lowercase 					=   0
			__lowercase 					=   0

			__lowercase 					=   []
			for line in lines:
						if line.startswith(__lowerCAmelCase  ):
									__lowercase 					=   True
						elif in_class and line.startswith(__lowerCAmelCase  ):
									__lowercase 					=   True
						elif in_class and in_func and (line.startswith(__lowerCAmelCase  ) or line.startswith(__lowerCAmelCase  )):
									__lowercase 					=   len(line.split(correct_line.split()[0]  )[0]  )
									count += 1

									if count == done_test[_id]:
												__lowercase 					=   True

						if in_class and in_func and in_line:
									if ")" not in line:
												continue
									else:
												__lowercase 					=   True

						if in_class and in_func and in_line and insert_line:
									new_lines.append(F'{spaces * " "}{correct_line}'  )
									__lowercase 					=   __lowercase 					=   __lowercase 					=   __lowercase 					=   False
						else:
									new_lines.append(__lowerCAmelCase  )

			with open(__lowerCAmelCase       ,			'''w'''  ) as f:
						for line in new_lines:
									f.write(__lowerCAmelCase  )


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







			if fail is not None:
						with open(__lowerCAmelCase       ,			'''r'''  ) as f:
									__lowercase 					=   {l.strip() for l in f.readlines()}
			else:
						__lowercase 					=   None

			with open(__lowerCAmelCase       ,			'''r'''  ) as f:
						__lowercase 					=   f.readlines()

			__lowercase 					=   defaultdict(__lowerCAmelCase  )
			for line in correct_lines:
						__lowercase					, __lowercase					, __lowercase					, __lowercase 					=   line.split(''';'''  )
						if test_failures is None or "::".join([file, class_name, test_name]  ) in test_failures:
									overwrite_file(__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase  )


if __name__ == "__main__":
					a : str		=				argparse.ArgumentParser()
					parser.add_argument('''--correct_filename''', help='''filename of tests with expected result''')
					parser.add_argument('''--fail_filename''', help='''filename of test failures''', type=str, default=None)
					a : str		=				parser.parse_args()

					main(args.correct_filename, args.fail_filename)






import os

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

import datasets


a : Optional[int]		=				datasets.logging.get_logger(__name__)


a : 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}
}
'''

a : Union[str, 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.
'''

a : Union[str, 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]
'''

a : Tuple		=				{
    '''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				):



					'''simple docstring'''







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







								'''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 A      (				self ,       snake_case_						)   ->			List[str]:







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

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

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






					def A      (				self ,       snake_case_ ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

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


