




from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class 				UpperCamelCase_    :

    '''simple docstring'''






    UpperCAmelCase__							  =      42
    UpperCAmelCase__							  =      42


class 				UpperCamelCase_    :

    '''simple docstring'''






    def __init__(			self      :	str					, UpperCAmelCase__      :	int)						->str:





           '''simple docstring'''

           A__       =    [[] for _ in range(UpperCAmelCase__)]
           A__       =    size


    def __getitem__(			self      :	List[Any]					, UpperCAmelCase__      :	int)						->Iterator[Edge]:





           '''simple docstring'''

           return iter(self._graph[vertex])


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Union[str, Any]:





           '''simple docstring'''

           return self._size


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	int)						->Optional[Any]:





           '''simple docstring'''

           if weight not in (0, 1):
                  raise ValueError('''Edge weight must be either 0 or 1.''')

           if to_vertex < 0 or to_vertex >= self.size:
                  raise ValueError('''Vertex indexes must be in [0; size).''')

           self._graph[from_vertex].append(Edge(UpperCAmelCase__					, UpperCAmelCase__))





    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any]					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	int)						->int | None:





           '''simple docstring'''

           A__       =    deque([start_vertex])
           A__       =    [None] * self.size
           A__       =    0

           while queue:
                  A__       =    queue.popleft()
                  A__       =    distances[current_vertex]
                  if current_distance is None:
                         continue

                  for edge in self[current_vertex]:
                         A__       =    current_distance + edge.weight
                         A__       =    distances[edge.destination_vertex]
                         if (
                             isinstance(UpperCAmelCase__					, UpperCAmelCase__)
                             and new_distance >= dest_vertex_distance
                         ):
                                continue
                         A__       =    new_distance
                         if edge.weight == 0:
                                queue.appendleft(edge.destination_vertex)
                         else:
                                queue.append(edge.destination_vertex)

           if distances[finish_vertex] is None:
                  raise ValueError('''No path from start_vertex to finish_vertex.''')

           return distances[finish_vertex]


if __name__ == "__main__":
  import doctest

  doctest.testmod()








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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

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

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

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

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

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

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

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

                     result.map(lowercase_ ,				with_indices=lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """simple docstring"""

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

       A__       =    re.sub(lowercase_ ,				'''''' ,				text.lower()						)

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

       for t in token_sequences_to_ignore:
              A__       =    ''' '''.join(text.split(lowercase_						)						)

       return text







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       A__       =    load_dataset(args.dataset ,				args.config ,				split=args.split ,				use_auth_token=lowercase_						)

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

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

       # resample audio
       A__       =    dataset.cast_column('''audio''' ,				Audio(sampling_rate=lowercase_						)						)

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

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

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

       # run inference on all examples
       A__       =    dataset.map(lowercase_ ,				remove_columns=dataset.column_names						)

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


if __name__ == "__main__":
  _lowerCamelCase	:		List[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.""",
  )
  _lowerCamelCase	:		str							       =   parser.parse_args()

  main(args)


