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

     UpperCamelCase__										=      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!
     UpperCamelCase__										=      ['''\n\n''', '''\n''', '''   ''', '''  ''']

     for t in token_sequences_to_ignore:
          UpperCamelCase__										=      ''' '''.join(text.split(A							)							)

     return text







def  __UpperCamelCase    (						A							):
     # load dataset
     UpperCamelCase__										=      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
     UpperCamelCase__										=      AutoFeatureExtractor.from_pretrained(args.model_id							)
     UpperCamelCase__										=      feature_extractor.sampling_rate

     # resample audio
     UpperCamelCase__										=      dataset.cast_column('''audio''' ,							Audio(sampling_rate=A							)							)

     # 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(A ,							remove_columns=dataset.column_names							)

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


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

     main(args)




import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	List[str]						=""
  SCREAMING_SNAKE_CASE_	:	str						=(
      None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
  )
  SCREAMING_SNAKE_CASE_	:	str						=None  # compression type in fsspec. ex: "gzip"
  SCREAMING_SNAKE_CASE_	:	str						=None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz






  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = ""   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       super().__init__(self   ,				**SCREAMING_SNAKE_CASE_	)
       # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
       UpperCamelCase__										=      fsspec.open(
           SCREAMING_SNAKE_CASE_   ,				mode='''rb'''   ,				protocol=SCREAMING_SNAKE_CASE_   ,				compression=self.compression   ,				client_kwargs={
               '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
               '''trust_env''': True,  # Enable reading proxy env variables.
               **(target_options or {}).pop('''client_kwargs'''   ,				{}	),  # To avoid issues if it was already passed.
           }   ,				**(target_options or {})   ,				)
       UpperCamelCase__										=      os.path.basename(self.file.path.split('''::'''	)[0]	)
       UpperCamelCase__										=      (
           self.compressed_name[: self.compressed_name.rindex('''.'''	)]
           if '''.''' in self.compressed_name
           else self.compressed_name
       )
       UpperCamelCase__										=      None






  @classmethod
  def 		_a					(cls   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       return super()._strip_protocol(SCREAMING_SNAKE_CASE_	).lstrip('''/'''	)






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       if self.dir_cache is None:
            UpperCamelCase__										=      {**self.file.fs.info(self.file.path	), '''name''': self.uncompressed_name}
            UpperCamelCase__										=      {f['''name''']: f}






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       return self.file.open().read()






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "rb"   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self._strip_protocol(SCREAMING_SNAKE_CASE_	)
       if mode != "rb":
            raise ValueError(F"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"	)
       return self.file.open()



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						="bz2"
  SCREAMING_SNAKE_CASE_	:	List[str]						="bz2"
  SCREAMING_SNAKE_CASE_	:	List[Any]						=".bz2"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						="gzip"
  SCREAMING_SNAKE_CASE_	:	Dict						="gzip"
  SCREAMING_SNAKE_CASE_	:	List[Any]						=".gz"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	List[Any]						="lz4"
  SCREAMING_SNAKE_CASE_	:	str						="lz4"
  SCREAMING_SNAKE_CASE_	:	Any						=".lz4"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						="xz"
  SCREAMING_SNAKE_CASE_	:	List[str]						="xz"
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=".xz"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						="zstd"
  SCREAMING_SNAKE_CASE_	:	Optional[int]						="zstd"
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=".zst"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "rb"   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = DEFAULT_BLOCK_SIZE   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      List[Any]:





       '''simple docstring'''





       super().__init__(
           fo=SCREAMING_SNAKE_CASE_   ,				mode=SCREAMING_SNAKE_CASE_   ,				target_protocol=SCREAMING_SNAKE_CASE_   ,				target_options=SCREAMING_SNAKE_CASE_   ,				block_size=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_   ,				)
       # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
       #
       # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
       #     out.close = close
       # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
       #
       # see https://github.com/intake/filesystem_spec/issues/725
       UpperCamelCase__										=      self.file.__enter__



       class     _A   :






         def __init__(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





              '''simple docstring'''





              UpperCamelCase__										=      file_






         def __enter__(self	)		->      Union[str, Any]:





              '''simple docstring'''





              self._file.__enter__()
              return self






         def __exit__(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





              '''simple docstring'''





              self._file.__exit__(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)






         def __iter__(self	)		->      Dict:





              '''simple docstring'''





              return iter(self._file	)






         def 		_a					(self	)		->      Dict:





              '''simple docstring'''





              return next(self._file	)






         def __getattr__(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





              '''simple docstring'''





              return getattr(self._file   ,				SCREAMING_SNAKE_CASE_	)

       def fixed_enter(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	):
            return WrappedFile(_enter(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)	)

       UpperCamelCase__										=      fixed_enter



