






import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


A__			:    List[Any]         =				'\\n@inproceedings{popovic-2015-chrf,\n    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n    month = sep,\n    year = "2015",\n    address = "Lisbon, Portugal",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W15-3049",\n    doi = "10.18653/v1/W15-3049",\n    pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n    title = "chr{F}++: words helping character n-grams",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Second Conference on Machine Translation",\n    month = sep,\n    year = "2017",\n    address = "Copenhagen, Denmark",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W17-4770",\n    doi = "10.18653/v1/W17-4770",\n    pages = "612--618",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

A__			:    Tuple         =				'\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

A__			:    Any         =				'\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,_KWARGS_DESCRIPTION			)
class    _UpperCAmelCase      ( datasets.Metric			):

				"""simple docstring"""




				def        lowercase__     (  self		:					List[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='''https://github.com/mjpost/sacreBLEU#chrf--chrf''',						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#chrf--chrf'''],						reference_urls=[
								        '''https://github.com/m-popovic/chrF''',
								    ],						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					int = CHRF.CHAR_ORDER,						lowerCamelCase		:					int = CHRF.WORD_ORDER,						lowerCamelCase		:					int = CHRF.BETA,						lowerCamelCase		:					bool = False,						lowerCamelCase		:					bool = False,						lowerCamelCase		:					bool = False,						):

								'''simple docstring'''
								lowercase__     				=    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'''						)
								lowercase__     				=    [[refs[i] for refs in references] for i in range(lowerCamelCase						)]

								lowercase__     				=    CHRF(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)
								lowercase__     				=    sb_chrf.corpus_score(lowerCamelCase,						lowerCamelCase						)

								return {
								    "score": output.score,
								    "char_order": output.char_order,
								    "word_order": output.word_order,
								    "beta": output.beta,
								}












import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    args.log_outputs
				lowercase__     				=    '''_'''.join(args.dataset.split('''/'''       ) + [args.config, args.split]       )

				# load metric
				lowercase__     				=    load_metric('''wer'''       )
				lowercase__     				=    load_metric('''cer'''       )

				# compute metrics
				lowercase__     				=    wer.compute(references=result['''target''']	,     predictions=result['''prediction''']       )
				lowercase__     				=    cer.compute(references=result['''target''']	,     predictions=result['''prediction''']       )

				# print & log results
				lowercase__     				=    F"""WER: {wer_result}\nCER: {cer_result}"""
				print(lowerCamelCase_       )

				with open(F"""{dataset_id}_eval_results.txt"""	,     '''w'''       ) as f:
								f.write(lowerCamelCase_       )

				# log all results in text file. Possibly interesting for analysis
				if log_outputs is not None:
								lowercase__     				=    F"""log_{dataset_id}_predictions.txt"""
								lowercase__     				=    F"""log_{dataset_id}_targets.txt"""

								with open(lowerCamelCase_	,     '''w'''       ) as p, open(lowerCamelCase_	,     '''w'''       ) as t:
												# mapping function to write output
												def write_to_file(lowerCamelCase_	,     lowerCamelCase_       ):
																p.write(F"""{i}""" + '''\n'''       )
																p.write(batch['''prediction'''] + '''\n'''       )
																t.write(F"""{i}""" + '''\n'''       )
																t.write(batch['''target'''] + '''\n'''       )

												result.map(lowerCamelCase_	,     with_indices=lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

				lowercase__     				=    re.sub(lowerCamelCase_	,     ''''''	,     text.lower()       )

				# In addition, we can normalize the target text, e.g. removing new lines characters etc...
				# note that order is important here!
				lowercase__     				=    ['''\n\n''', '''\n''', '''   ''', '''  ''']

				for t in token_sequences_to_ignore:
								lowercase__     				=    ''' '''.join(text.split(lowerCamelCase_       )       )

				return text






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				# load dataset
				lowercase__     				=    load_dataset(args.dataset	,     args.config	,     split=args.split	,     use_auth_token=lowerCamelCase_       )

				# for testing: only process the first two examples as a test
				# dataset = dataset.select(range(10))

				# load processor
				lowercase__     				=    AutoFeatureExtractor.from_pretrained(args.model_id       )
				lowercase__     				=    feature_extractor.sampling_rate

				# resample audio
				lowercase__     				=    dataset.cast_column('''audio'''	,     Audio(sampling_rate=lowerCamelCase_       )       )

				# load eval pipeline
				if args.device is None:
								lowercase__     				=    0 if torch.cuda.is_available() else -1
				lowercase__     				=    pipeline('''automatic-speech-recognition'''	,     model=args.model_id	,     device=args.device       )

				# map function to decode audio
				def map_to_pred(lowerCamelCase_       ):
								lowercase__     				=    asr(
								    batch['''audio''']['''array''']	,     chunk_length_s=args.chunk_length_s	,     stride_length_s=args.stride_length_s       )

								lowercase__     				=    prediction['''text''']
								lowercase__     				=    normalize_text(batch['''sentence''']       )
								return batch

				# run inference on all examples
				lowercase__     				=    dataset.map(lowerCamelCase_	,     remove_columns=dataset.column_names       )

				# compute and log_results
				# do not change function below
				log_results(lowerCamelCase_	,     lowerCamelCase_       )


if __name__ == "__main__":
							A__			:    int         =				argparse.ArgumentParser()

							parser.add_argument(
							    '--model_id', type=str, required=True, help='Model identifier. Should be loadable with 🤗 Transformers'
							)
							parser.add_argument(
							    '--dataset',
							    type=str,
							    required=True,
							    help='Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets',
							)
							parser.add_argument(
							    '--config', type=str, required=True, help='Config of the dataset. *E.g.* `\'en\'`  for Common Voice'
							)
							parser.add_argument('--split', type=str, required=True, help='Split of the dataset. *E.g.* `\'test\'`')
							parser.add_argument(
							    '--chunk_length_s', type=float, default=None, help='Chunk length in seconds. Defaults to 5 seconds.'
							)
							parser.add_argument(
							    '--stride_length_s', type=float, default=None, help='Stride of the audio chunks. Defaults to 1 second.'
							)
							parser.add_argument(
							    '--log_outputs', action='store_true', help='If defined, write outputs to log file for analysis.'
							)
							parser.add_argument(
							    '--device',
							    type=int,
							    default=None,
							    help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.',
							)
							A__			:    Union[str, Any]         =				parser.parse_args()

							main(args)




