

'''simple docstring'''




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			)				->     List[str]:






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

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

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

     # print & log results
     __magic_name__						       =F'''WER: {wer_result}\nCER: {cer_result}'''
     print(_UpperCAmelCase			)

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

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

          with open(_UpperCAmelCase		,						'''w'''			) as p, open(_UpperCAmelCase		,						'''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(_UpperCAmelCase		,						with_indices=_UpperCAmelCase			)




def 						UpperCamelCase	(			a			)				->     str:






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

     __magic_name__						       =re.sub(_UpperCAmelCase		,						''''''		,						text.lower()			)

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

     for t in token_sequences_to_ignore:
          __magic_name__						       =' '.join(text.split(_UpperCAmelCase			)			)

     return text




def 						UpperCamelCase	(			a			)				->     Union[str, Any]:






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

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

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

     # resample audio
     __magic_name__						       =dataset.cast_column('''audio'''		,						Audio(sampling_rate=_UpperCAmelCase			)			)

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

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

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

     # run inference on all examples
     __magic_name__						       =dataset.map(_UpperCAmelCase		,						remove_columns=dataset.column_names			)

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


if __name__ == "__main__":
      _lowerCAmelCase         =						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.",
      )
      _lowerCAmelCase         =						parser.parse_args()

      main(args)









'''simple docstring'''




from pathlib import Path

import fire
from tqdm import tqdm




def 						UpperCamelCase	(			a="ro"		,						a="en"		,						a="wmt16"		,						a=None			)				->     None:






     '''simple docstring'''
     try:
          import datasets
     except (ModuleNotFoundError, ImportError):
          raise ImportError('''run pip install datasets'''			)
     __magic_name__						       =    F'''{src_lang}-{tgt_lang}'''
     print(F'''Converting {dataset}-{pair}'''			)
     __magic_name__						       =    datasets.load_dataset(a		,						a			)
     if save_dir is None:
          __magic_name__						       =    F'''{dataset}-{pair}'''
     __magic_name__						       =    Path(a			)
     save_dir.mkdir(exist_ok=a			)

     for split in ds.keys():
          print(F'''Splitting {split} with {ds[split].num_rows} records'''			)

          # to save to val.source, val.target like summary datasets
          __magic_name__						       =    '''val''' if split == '''validation''' else split
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.source'''			)
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.target'''			)
          __magic_name__						       =    src_path.open('''w+'''			)
          __magic_name__						       =    tgt_path.open('''w+'''			)

          # reader is the bottleneck so writing one record at a time doesn't slow things down
          for x in tqdm(ds[split]			):
               __magic_name__						       =    x['''translation''']
               src_fp.write(ex[src_lang] + '''\n'''			)
               tgt_fp.write(ex[tgt_lang] + '''\n'''			)

     print(F'''Saved {dataset} dataset to {save_dir}'''			)


if __name__ == "__main__":
      fire.Fire(download_wmt_dataset)






