'''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  __lowerCamelCase       (      _UpperCamelCase  :  Dataset							,    _UpperCamelCase  :  Dict[str, str]					):


      '''simple docstring'''


      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(_UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  List[Any]					):
                        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       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      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       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      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(_UpperCamelCase  :  List[str]					):
            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__":
       lowercase__  :      Any        =					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.",
       )
       lowercase__  :      Union[str, Any]        =					parser.parse_args()

       main(args)






'''simple docstring'''
from __future__ import annotations



def  __lowerCamelCase       (      _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_      ,		UpperCAmelCase_            =  position
      UpperCAmelCase_            =  [
          (y + 1, x + 2),
          (y - 1, x + 2),
          (y + 1, x - 2),
          (y - 1, x - 2),
          (y + 2, x + 1),
          (y + 2, x - 1),
          (y - 2, x + 1),
          (y - 2, x - 1),
      ]
      UpperCAmelCase_            =  []

      for position in positions:
            UpperCAmelCase_      ,		UpperCAmelCase_            =  position
            if 0 <= y_test < n and 0 <= x_test < n:
                  permissible_positions.append(_UpperCamelCase					)

      return permissible_positions



def  __lowerCamelCase       (      _UpperCamelCase  :  list[list[int]]					):


      '''simple docstring'''


      return not any(elem == 0 for row in board for elem in row					)



def  __lowerCamelCase       (      _UpperCamelCase  :  list[list[int]]							,    _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if is_complete(_UpperCamelCase					):
            return True

      for position in get_valid_pos(_UpperCamelCase							,    len(_UpperCamelCase					)					):
            UpperCAmelCase_      ,		UpperCAmelCase_            =  position

            if board[y][x] == 0:
                  UpperCAmelCase_            =  curr + 1
                  if open_knight_tour_helper(_UpperCamelCase							,    _UpperCamelCase							,    curr + 1					):
                        return True
                  UpperCAmelCase_            =  0

      return False



def  __lowerCamelCase       (      _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  [[0 for i in range(_UpperCamelCase					)] for j in range(_UpperCamelCase					)]

      for i in range(_UpperCamelCase					):
            for j in range(_UpperCamelCase					):
                  UpperCAmelCase_            =  1
                  if open_knight_tour_helper(_UpperCamelCase							,    (i, j)							,    1					):
                        return board
                  UpperCAmelCase_            =  0

      UpperCAmelCase_            =  F"""Open Kight Tour cannot be performed on a board of size {n}"""
      raise ValueError(_UpperCamelCase					)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





