





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      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  args.log_outputs
  UpperCAmelCase					      =  "_".join(args.dataset.split("/"			) + [args.config, args.split]			)

  # load metric
  UpperCAmelCase					      =  load_metric("wer"			)
  UpperCAmelCase					      =  load_metric("cer"			)

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

  # print & log results
  UpperCAmelCase					      =  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:
    UpperCAmelCase					      =  F"log_{dataset_id}_predictions.txt"
    UpperCAmelCase					      =  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      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

  UpperCAmelCase					      =  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!
  UpperCAmelCase					      =  ["\n\n", "\n", "   ", "  "]

  for t in token_sequences_to_ignore:
    UpperCAmelCase					      =  " ".join(text.split(UpperCAmelCase__			)			)

  return text
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  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
  UpperCAmelCase					      =  AutoFeatureExtractor.from_pretrained(args.model_id			)
  UpperCAmelCase					      =  feature_extractor.sampling_rate

  # resample audio
  UpperCAmelCase					      =  dataset.cast_column("audio"   ,       Audio(sampling_rate=UpperCAmelCase__			)			)

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

  # map function to decode audio
  def map_to_pred(__A			):
    UpperCAmelCase					      =  asr(
        batch["audio"]["array"]   ,       chunk_length_s=args.chunk_length_s   ,       stride_length_s=args.stride_length_s			)

    UpperCAmelCase					      =  prediction["text"]
    UpperCAmelCase					      =  normalize_text(batch["sentence"]			)
    return batch

  # run inference on all examples
  UpperCAmelCase					      =  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)






def      _lowerCAmelCase(	__A			):

  if not isinstance(__A   ,       __A			):
    raise TypeError("only integers accepted as input"			)
  else:
    UpperCAmelCase					      =  str(abs(__A			)			)
    UpperCAmelCase					      =  [list(__A			) for char in range(len(__A			)			)]
    for index in range(len(__A			)			):
      num_transpositions[index].pop(__A			)
    return max(
        int("".join(list(__A			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
      __import__("doctest").testmod()



