"""simple docstring"""






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__A			   = "\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\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			   = "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__A			   = "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n"



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Optional[Any]:

   def remove_articles(__UpperCAmelCase ):
      lowercase__:   str					=      re.compile(R'''\b(a|an|the)\b'''       ,    re.UNICODE )
      return re.sub(_A       ,    ''' '''       ,    _A )

   def white_space_fix(__UpperCAmelCase ):
      return " ".join(text.split() )

   def remove_punc(__UpperCAmelCase ):
      lowercase__:   str					=      set(string.punctuation )
      return "".join(ch for ch in text if ch not in exclude )

   def lower(__UpperCAmelCase ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_A ) ) ) )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Dict:
   return int(normalize_answer(_A ) == normalize_answer(_A ) )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:
   lowercase__:   Dict					=      [any(compute_exact(_A       ,    _A ) for ref in refs ) for pred, refs in zip(_A       ,    _A )]
   return (sum(_A ) / len(_A )) * 1_0_0



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Any:
   lowercase__:   Any					=      [rgram for rgrams in rgramslist for rgram in rgrams]
   lowercase__:   Any					=      Counter(_A )

   lowercase__:   Tuple					=      Counter(_A )
   lowercase__:   Optional[Any]					=      Counter()
   for sgram, scount in sgramcounter.items():
      lowercase__:   str					=      scount * numref

   lowercase__:   int					=      Counter(_A )
   lowercase__:   Optional[Any]					=      Counter()
   for cgram, ccount in cgramcounter.items():
      lowercase__:   int					=      ccount * numref

   # KEEP
   lowercase__:   Any					=      sgramcounter_rep & cgramcounter_rep
   lowercase__:   int					=      keepgramcounter_rep & rgramcounter
   lowercase__:   str					=      sgramcounter_rep & rgramcounter

   lowercase__:   List[str]					=      0
   lowercase__:   int					=      0
   for keepgram in keepgramcountergood_rep:
      keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
      # Fix an alleged bug [2] in the keep score computation.
      # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
      keeptmpscorea += keepgramcountergood_rep[keepgram]
   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   #      a target exactly.
   lowercase__:   List[Any]					=      1
   lowercase__:   int					=      1
   if len(_A ) > 0:
      lowercase__:   str					=      keeptmpscorea / len(_A )
   if len(_A ) > 0:
      # Fix an alleged bug [2] in the keep score computation.
      # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
      lowercase__:   Optional[Any]					=      keeptmpscorea / sum(keepgramcounterall_rep.values() )
   lowercase__:   Tuple					=      0
   if keepscore_precision > 0 or keepscore_recall > 0:
      lowercase__:   Optional[Any]					=      2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

   # DELETION
   lowercase__:   Any					=      sgramcounter_rep - cgramcounter_rep
   lowercase__:   Dict					=      delgramcounter_rep - rgramcounter
   lowercase__:   Dict					=      sgramcounter_rep - rgramcounter
   lowercase__:   Union[str, Any]					=      0
   lowercase__:   List[Any]					=      0
   for delgram in delgramcountergood_rep:
      deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
      deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   # a target exactly.
   lowercase__:   Optional[int]					=      1
   if len(_A ) > 0:
      lowercase__:   Any					=      deltmpscorea / len(_A )

   # ADDITION
   lowercase__:   Optional[Any]					=      set(_A ) - set(_A )
   lowercase__:   str					=      set(_A ) & set(_A )
   lowercase__:   Union[str, Any]					=      set(_A ) - set(_A )

   lowercase__:   str					=      0
   for addgram in addgramcountergood:
      addtmpscore += 1

   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   # a target exactly.
   lowercase__:   Union[str, Any]					=      1
   lowercase__:   List[Any]					=      1
   if len(_A ) > 0:
      lowercase__:   Optional[Any]					=      addtmpscore / len(_A )
   if len(_A ) > 0:
      lowercase__:   Dict					=      addtmpscore / len(_A )
   lowercase__:   List[Any]					=      0
   if addscore_precision > 0 or addscore_recall > 0:
      lowercase__:   List[str]					=      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

   return (keepscore, delscore_precision, addscore)



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  str:
   lowercase__:   Optional[Any]					=      len(_A )

   lowercase__:   Optional[Any]					=      ssent.split(''' ''' )
   lowercase__:   Any					=      csent.split(''' ''' )
   lowercase__:   str					=      []
   lowercase__:   Dict					=      []
   lowercase__:   str					=      []
   lowercase__:   List[str]					=      []
   lowercase__:   str					=      []
   lowercase__:   List[str]					=      []

   lowercase__:   str					=      []
   lowercase__:   Any					=      []
   lowercase__:   List[str]					=      []
   lowercase__:   Union[str, Any]					=      []
   for rsent in rsents:
      lowercase__:   int					=      rsent.split(''' ''' )
      lowercase__:   Union[str, Any]					=      []
      lowercase__:   int					=      []
      lowercase__:   List[Any]					=      []
      ragramslist.append(_A )
      for i in range(0       ,    len(_A ) - 1 ):
         if i < len(_A ) - 1:
            lowercase__:   List[Any]					=      ragrams[i] + ''' ''' + ragrams[i + 1]
            ragrams.append(_A )
         if i < len(_A ) - 2:
            lowercase__:   Any					=      ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
            ragrams.append(_A )
         if i < len(_A ) - 3:
            lowercase__:   int					=      ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
            ragrams.append(_A )
      ragramslist.append(_A )
      ragramslist.append(_A )
      ragramslist.append(_A )

   for i in range(0       ,    len(_A ) - 1 ):
      if i < len(_A ) - 1:
         lowercase__:   List[str]					=      sagrams[i] + ''' ''' + sagrams[i + 1]
         sagrams.append(_A )
      if i < len(_A ) - 2:
         lowercase__:   Union[str, Any]					=      sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
         sagrams.append(_A )
      if i < len(_A ) - 3:
         lowercase__:   Optional[int]					=      sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
         sagrams.append(_A )

   for i in range(0       ,    len(_A ) - 1 ):
      if i < len(_A ) - 1:
         lowercase__:   Tuple					=      cagrams[i] + ''' ''' + cagrams[i + 1]
         cagrams.append(_A )
      if i < len(_A ) - 2:
         lowercase__:   Dict					=      cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
         cagrams.append(_A )
      if i < len(_A ) - 3:
         lowercase__:   int					=      cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
         cagrams.append(_A )

   ((lowercase__), (lowercase__), (lowercase__)):   Optional[int]					=      SARIngram(_A       ,    _A       ,    _A       ,    _A )
   ((lowercase__), (lowercase__), (lowercase__)):   Dict					=      SARIngram(_A       ,    _A       ,    _A       ,    _A )
   ((lowercase__), (lowercase__), (lowercase__)):   Dict					=      SARIngram(_A       ,    _A       ,    _A       ,    _A )
   ((lowercase__), (lowercase__), (lowercase__)):   Dict					=      SARIngram(_A       ,    _A       ,    _A       ,    _A )
   lowercase__:   Any					=      sum([keepascore, keepascore, keepascore, keepascore] ) / 4
   lowercase__:   int					=      sum([delascore, delascore, delascore, delascore] ) / 4
   lowercase__:   Union[str, Any]					=      sum([addascore, addascore, addascore, addascore] ) / 4
   lowercase__:   str					=      (avgkeepscore + avgdelscore + avgaddscore) / 3
   return finalscore



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase = True       ,    __UpperCAmelCase = "13a"       ,    __UpperCAmelCase = True )  ->  Dict:
   # Normalization is requried for the ASSET dataset (one of the primary
   # datasets in sentence simplification) to allow using space
   # to split the sentence. Even though Wiki-Auto and TURK datasets,
   # do not require normalization, we do it for consistency.
   # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
   # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

   if lowercase:
      lowercase__:   Optional[int]					=      sentence.lower()

   if tokenizer in ["13a", "intl"]:
      if version.parse(sacrebleu.__version__ ).major >= 2:
         lowercase__:   Any					=      sacrebleu.metrics.bleu._get_tokenizer(_A )()(_A )
      else:
         lowercase__:   Tuple					=      sacrebleu.TOKENIZERS[tokenizer]()(_A )
   elif tokenizer == "moses":
      lowercase__:   int					=      sacremoses.MosesTokenizer().tokenize(_A       ,    return_str=_A       ,    escape=_A )
   elif tokenizer == "penn":
      lowercase__:   Any					=      sacremoses.MosesTokenizer().penn_tokenize(_A       ,    return_str=_A )
   else:
      lowercase__:   List[Any]					=      sentence

   if not return_str:
      lowercase__:   Any					=      normalized_sent.split()

   return normalized_sent



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  str:
   if not (len(_A ) == len(_A ) == len(_A )):
      raise ValueError('''Sources length must match predictions and references lengths.''' )
   lowercase__:   Any					=      0
   for src, pred, refs in zip(_A       ,    _A       ,    _A ):
      sari_score += SARIsent(normalize(_A )       ,    normalize(_A )       ,    [normalize(_A ) for sent in refs] )
   lowercase__:   List[str]					=      sari_score / len(_A )
   return 1_0_0 * sari_score



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase="exp"       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=False       ,    __UpperCAmelCase=False       ,    __UpperCAmelCase=False       ,    )  ->  Optional[Any]:
   lowercase__:   int					=      len(references[0] )
   if any(len(_A ) != references_per_prediction for refs in references ):
      raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
   lowercase__:   List[Any]					=      [[refs[i] for refs in references] for i in range(_A )]
   lowercase__:   Dict					=      sacrebleu.corpus_bleu(
       _A       ,    _A       ,    smooth_method=_A       ,    smooth_value=_A       ,    force=_A       ,    lowercase=_A       ,    use_effective_order=_A       ,    )
   return output.score




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



    """simple docstring"""

    def _snake_case			(   self							):
       return datasets.MetricInfo(
           description=_DESCRIPTION    ,			citation=_CITATION    ,			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/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
               '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
               '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
               '''https://github.com/mjpost/sacreBLEU''',
           ]    ,			reference_urls=[
               '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
               '''https://github.com/mjpost/sacreBLEU''',
               '''https://en.wikipedia.org/wiki/BLEU''',
               '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
           ]    ,			)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Optional[int]					=      {}
       result.update({'''sari''': compute_sari(sources=UpperCamelCase__    ,			predictions=UpperCamelCase__    ,			references=UpperCamelCase__							)}							)
       result.update({'''sacrebleu''': compute_sacrebleu(predictions=UpperCamelCase__    ,			references=UpperCamelCase__							)}							)
       result.update({'''exact''': compute_em(predictions=UpperCamelCase__    ,			references=UpperCamelCase__							)}							)
       return result






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 __UpperCamelCase			(       _A ,  _A  ):
						lowerCAmelCase_      			=  args.log_outputs
						lowerCAmelCase_      			=  '''_'''.join(args.dataset.split('''/'''  ) + [args.config, args.split]  )

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

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

						# print & log results
						lowerCAmelCase_      			=  f"WER: {wer_result}\nCER: {cer_result}"
						print(_A  )

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

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

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

																		result.map(_A ,  with_indices=_A  )





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

						lowerCAmelCase_      			=  re.sub(_A ,  '''''' ,  text.lower()  )

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

						for t in token_sequences_to_ignore:
												lowerCAmelCase_      			=  ''' '''.join(text.split(_A  )  )

						return text





def __UpperCamelCase			(       _A  ):
						# load dataset
						lowerCAmelCase_      			=  load_dataset(args.dataset ,  args.config ,  split=args.split ,  use_auth_token=_A  )

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

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

						# resample audio
						lowerCAmelCase_      			=  dataset.cast_column('''audio''' ,  Audio(sampling_rate=_A  )  )

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

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

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

						# run inference on all examples
						lowerCAmelCase_      			=  dataset.map(_A ,  remove_columns=dataset.column_names  )

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


if __name__ == "__main__":
						_A     =			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     =			parser.parse_args()

						main(args)




