

import argparse
import os
import re

import packaging.version


_SCREAMING_SNAKE_CASE					:  List[str]             =      'examples/'
_SCREAMING_SNAKE_CASE					:  List[Any]             =      {
    'examples': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), 'release = "VERSION"\n'),
}
_SCREAMING_SNAKE_CASE					:  str             =      {
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
_SCREAMING_SNAKE_CASE					:  str             =      'README.md'





def     UpperCAmelCase_       (						_A				,		_A				,		_A    ):





     '''simple docstring'''




     with open(_A				,		'''r'''				,		encoding='''utf-8'''				,		newline='''\n'''    ) as f:
          SCREAMING_SNAKE_CASE__   =   f.read()
     SCREAMING_SNAKE_CASE__   =   REPLACE_PATTERNS[pattern]
     SCREAMING_SNAKE_CASE__   =   replace.replace('''VERSION'''				,		_A    )
     SCREAMING_SNAKE_CASE__   =   re_pattern.sub(_A				,		_A    )
     with open(_A				,		'''w'''				,		encoding='''utf-8'''				,		newline='''\n'''    ) as f:
          f.write(_A    )





def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     for folder, directories, fnames in os.walk(_A    ):
          # Removing some of the folders with non-actively maintained examples from the walk
          if "research_projects" in directories:
               directories.remove('''research_projects'''    )
          if "legacy" in directories:
               directories.remove('''legacy'''    )
          for fname in fnames:
               if fname.endswith('''.py'''    ):
                    update_version_in_file(os.path.join(_A				,		_A    )				,		_A				,		pattern='''examples'''    )





def     UpperCAmelCase_       (						_A				,		_A=False    ):





     '''simple docstring'''




     for pattern, fname in REPLACE_FILES.items():
          update_version_in_file(_A				,		_A				,		_A    )
     if not patch:
          update_version_in_examples(_A    )





def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   '🤗 Transformers currently provides the following architectures'
     SCREAMING_SNAKE_CASE__   =   '1. Want to contribute a new model?'
     with open(_A				,		'''r'''				,		encoding='''utf-8'''				,		newline='''\n'''    ) as f:
          SCREAMING_SNAKE_CASE__   =   f.readlines()

     # Find the start of the list.
     SCREAMING_SNAKE_CASE__   =   0
     while not lines[start_index].startswith(_start_prompt    ):
          start_index += 1
     start_index += 1

     SCREAMING_SNAKE_CASE__   =   start_index
     # Update the lines in the model list.
     while not lines[index].startswith(_end_prompt    ):
          if lines[index].startswith('''1.'''    ):
               SCREAMING_SNAKE_CASE__   =   lines[index].replace(
                   '''https://huggingface.co/docs/transformers/main/model_doc'''				,		'''https://huggingface.co/docs/transformers/model_doc'''				,		)
          index += 1

     with open(_A				,		'''w'''				,		encoding='''utf-8'''				,		newline='''\n'''    ) as f:
          f.writelines(_A    )





def     UpperCAmelCase_       (						):





     '''simple docstring'''




     with open(REPLACE_FILES['''init''']				,		'''r'''    ) as f:
          SCREAMING_SNAKE_CASE__   =   f.read()
     SCREAMING_SNAKE_CASE__   =   REPLACE_PATTERNS['init'][0].search(_A    ).groups()[0]
     return packaging.version.parse(_A    )





def     UpperCAmelCase_       (						_A=False    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   get_version()
     if patch and default_version.is_devrelease:
          raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''    )
     if default_version.is_devrelease:
          SCREAMING_SNAKE_CASE__   =   default_version.base_version
     elif patch:
          SCREAMING_SNAKE_CASE__   =   F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
     else:
          SCREAMING_SNAKE_CASE__   =   F'''{default_version.major}.{default_version.minor + 1}.0'''

     # Now let's ask nicely if that's the right one.
     SCREAMING_SNAKE_CASE__   =   input(F'''Which version are you releasing? [{default_version}]'''    )
     if len(_A    ) == 0:
          SCREAMING_SNAKE_CASE__   =   default_version

     print(F'''Updating version to {version}.'''    )
     global_version_update(_A				,		patch=_A    )
     if not patch:
          print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''    )
          clean_main_ref_in_model_list()





def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   get_version()
     SCREAMING_SNAKE_CASE__   =   F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
     SCREAMING_SNAKE_CASE__   =   current_version.base_version

     # Check with the user we got that right.
     SCREAMING_SNAKE_CASE__   =   input(F'''Which version are we developing now? [{dev_version}]'''    )
     if len(_A    ) == 0:
          SCREAMING_SNAKE_CASE__   =   dev_version

     print(F'''Updating version to {version}.'''    )
     global_version_update(_A    )
     print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''    )
     clean_main_ref_in_model_list()


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Tuple             =      argparse.ArgumentParser()
       parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
       parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
       _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      parser.parse_args()
       if not args.post_release:
              pre_release_work(patch=args.patch)
       elif args.patch:
              print('''Nothing to do after a patch :-)''')
       else:
              post_release_work()





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    ):





     '''simple docstring'''




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

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

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

     # print & log results
     SCREAMING_SNAKE_CASE__   =   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:
          SCREAMING_SNAKE_CASE__   =   F'''log_{dataset_id}_predictions.txt'''
          SCREAMING_SNAKE_CASE__   =   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    ):





     '''simple docstring'''




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

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

     for t in token_sequences_to_ignore:
          SCREAMING_SNAKE_CASE__   =   ''' '''.join(text.split(_A    )    )

     return text
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   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
     SCREAMING_SNAKE_CASE__   =   AutoFeatureExtractor.from_pretrained(args.model_id    )
     SCREAMING_SNAKE_CASE__   =   feature_extractor.sampling_rate

     # resample audio
     SCREAMING_SNAKE_CASE__   =   dataset.cast_column('''audio'''				,		Audio(sampling_rate=_A    )    )

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

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

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

     # run inference on all examples
     SCREAMING_SNAKE_CASE__   =   dataset.map(_A				,		remove_columns=dataset.column_names    )

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  List[str]             =      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.''',
       )
       _SCREAMING_SNAKE_CASE					:  List[Any]             =      parser.parse_args()

       main(args)


