





from jiwer import compute_measures

import datasets


lowerCAmelCase_    =				'''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

lowerCAmelCase_    =				'''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''

lowerCAmelCase_    =				'''
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = datasets.load_metric("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,     _KWARGS_DESCRIPTION     )
class 					__lowerCAmelCase       (			datasets.Metric     ):






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							return datasets.MetricInfo(
							    description=_DESCRIPTION					,							citation=_CITATION					,							inputs_description=_KWARGS_DESCRIPTION					,							features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''					,							id='''sequence'''    ),
							            '''references''': datasets.Value('''string'''					,							id='''sequence'''    ),
							        }    )					,							codebase_urls=['''https://github.com/jitsi/jiwer/''']					,							reference_urls=[
							        '''https://en.wikipedia.org/wiki/Word_error_rate''',
							    ]					,							)






			def 			lowerCamelCase  (self					,							__magic_name__=None					,							__magic_name__=None					,							__magic_name__=False    )     ->   int:


							'''simple docstring'''



							if concatenate_texts:
											return compute_measures(__magic_name__					,							__magic_name__    )["wer"]
							else:
											snake_case_						:    Optional[int]				=  0
											snake_case_						:    Any				=  0
											for prediction, reference in zip(__magic_name__					,							__magic_name__    ):
															snake_case_						:    Tuple				=  compute_measures(__magic_name__					,							__magic_name__    )
															incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
															total += measures["substitutions"] + measures["deletions"] + measures["hits"]
											return incorrect / total







import os

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

import datasets


lowerCAmelCase_    =				datasets.logging.get_logger(__name__)


lowerCAmelCase_    =				'''\
@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}
}
'''

lowerCAmelCase_    =				'''\
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.
'''

lowerCAmelCase_    =				'''
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]
'''

lowerCAmelCase_    =				{
    '''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    )     ->   Optional[int]:


							'''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					,							__magic_name__    )     ->   List[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\').'''    )
											snake_case_						:    Dict				=  '''bleurt-base-128'''

							if self.config_name.lower() in CHECKPOINT_URLS:
											snake_case_						:    Optional[int]				=  self.config_name.lower()

							elif self.config_name.upper() in CHECKPOINT_URLS:
											snake_case_						:    Union[str, Any]				=  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_						:    Any				=  dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]    )
							snake_case_						:    Dict				=  score.BleurtScorer(os.path.join(__magic_name__					,							__magic_name__    )    )






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


							'''simple docstring'''



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