





from jiwer import compute_measures

import datasets


lowercase_		:				Union[str, Any]				=						'''\
@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.}
}
'''

lowercase_		:				Any				=						'''\
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.
'''

lowercase_		:				Optional[int]				=						'''
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 			lowercase				(							datasets.Metric     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''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 		__UpperCAmelCase       (    self			:		int ,				lowerCamelCase_			:		List[str]=None ,				lowerCamelCase_			:		Union[str, Any]=None ,				lowerCamelCase_			:		Dict=False					):





										'''simple docstring'''







										if concatenate_texts:
														return compute_measures(lowerCamelCase_ ,				lowerCamelCase_					)["wer"]
										else:
														_snake_case :			str  						=   0
														_snake_case :			Dict  						=   0
														for prediction, reference in zip(lowerCamelCase_ ,				lowerCamelCase_					):
																		_snake_case :			List[Any]  						=   compute_measures(lowerCamelCase_ ,				lowerCamelCase_					)
																		incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																		total += measures["substitutions"] + measures["deletions"] + measures["hits"]
														return incorrect / total













import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


lowercase_		:				List[str]				=						'''\
@inproceedings{snover-etal-2006-study,
    title = "A Study of Translation Edit Rate with Targeted Human Annotation",
    author = "Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John",
    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = aug # " 8-12",
    year = "2006",
    address = "Cambridge, Massachusetts, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2006.amta-papers.25",
    pages = "223--231",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

lowercase_		:				Optional[int]				=						'''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

lowercase_		:				Any				=						'''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''


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



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										if version.parse(scb.__version__					) < version.parse('1.4.12'					):
														raise ImportWarning(
														    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
														    'You can install it with `pip install "sacrebleu>=1.4.12"`.'					)
										return datasets.MetricInfo(
										    description=_DESCRIPTION ,				citation=_CITATION ,				homepage='http://www.cs.umd.edu/~snover/tercom/' ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
										        {
										            'predictions': datasets.Value('string' ,				id='sequence'					),
										            'references': datasets.Sequence(datasets.Value('string' ,				id='sequence'					) ,				id='references'					),
										        }					) ,				codebase_urls=['https://github.com/mjpost/sacreBLEU#ter'] ,				reference_urls=[
										        'https://github.com/jhclark/tercom',
										    ] ,				)







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







										_snake_case :			str  						=   len(references[0]					)
										if any(len(lowerCamelCase_					) != references_per_prediction for refs in references					):
														raise ValueError('Sacrebleu requires the same number of references for each prediction'					)
										_snake_case :			int  						=   [[refs[i] for refs in references] for i in range(lowerCamelCase_					)]

										_snake_case :			Optional[int]  						=   TER(
										    normalized=lowerCamelCase_ ,				no_punct=lowerCamelCase_ ,				asian_support=lowerCamelCase_ ,				case_sensitive=lowerCamelCase_ ,				)
										_snake_case :			Optional[Any]  						=   sb_ter.corpus_score(lowerCamelCase_ ,				lowerCamelCase_					)

										return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}






