

'''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			)				->     Optional[int]:






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

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

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

     # print & log results
     __magic_name__						       =    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:
          __magic_name__						       =    F'''log_{dataset_id}_predictions.txt'''
          __magic_name__						       =    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			)				->     str:






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

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

     for t in token_sequences_to_ignore:
          __magic_name__						       =    ''' '''.join(text.split(a			)			)

     return text




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     # load dataset
     __magic_name__						       =    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
     __magic_name__						       =    AutoFeatureExtractor.from_pretrained(args.model_id			)
     __magic_name__						       =    feature_extractor.sampling_rate

     # resample audio
     __magic_name__						       =    dataset.cast_column('''audio'''		,						Audio(sampling_rate=a			)			)

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

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

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

     # run inference on all examples
     __magic_name__						       =    dataset.map(a		,						remove_columns=dataset.column_names			)

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


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)









'''simple docstring'''




import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_lowerCAmelCase         =						get_tests_dir("fixtures")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy-config.json")






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    0



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      with tempfile.TemporaryDirectory() as tmpdirname:
           __magic_name__						       =    WavaVecaConfig()

           # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      ).to_dict()

           config_dict.pop('''feature_extractor_type'''      )
           __magic_name__						       =    WavaVecaFeatureExtractor(**a__      )

           # save in new folder
           model_config.save_pretrained(a__      )
           config.save_pretrained(a__      )

           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )

           # make sure private variable is not incorrectly saved
           __magic_name__						       =    json.loads(config.to_json_string()      )
           self.assertTrue('''_processor_class''' not in dict_as_saved      )

      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				'''bert-base is not a local folder and is not a valid model identifier'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''bert-base'''      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__			,				revision='''aaaaaa'''      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      with self.assertRaisesRegex(
          a__			,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,				):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''      )



 def 			snake_case__  (   self		:   Dict      ):
      # If remote code is not set, we will time out when asking whether to load the model.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''      )
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )

      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
          '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
      self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )

      # Test feature extractor can be reloaded.
      with tempfile.TemporaryDirectory() as tmp_dir:
           feature_extractor.save_pretrained(a__      )
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__			,				trust_remote_code=a__      )
      self.assertEqual(reloaded_feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )



 def 			snake_case__  (   self		:   int      ):
      try:
           AutoConfig.register('''custom'''			,				a__      )
           AutoFeatureExtractor.register(a__			,				a__      )
           # Trying to register something existing in the Transformers library will raise an error
           with self.assertRaises(a__      ):
                AutoFeatureExtractor.register(a__			,				a__      )

           # Now that the config is registered, it can be used as any other config with the auto-API
           __magic_name__						       =    CustomFeatureExtractor.from_pretrained(a__      )
           with tempfile.TemporaryDirectory() as tmp_dir:
                feature_extractor.save_pretrained(a__      )
                __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
                self.assertIsInstance(a__			,				a__      )

      finally:
           if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]



 def 			snake_case__  (   self		:   int      ):






      class   _SCREAMING_SNAKE_CASE   (   __a     ):
       __SCREAMING_SNAKE_CASE					:Optional[int]      	=					True

      try:
           AutoConfig.register('''custom'''			,				a__      )
           AutoFeatureExtractor.register(a__			,				a__      )
           # If remote code is not set, the default is to use local
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(feature_extractor.is_local      )

           # If remote code is disabled, we load the local one.
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(feature_extractor.is_local      )

           # If remote is enabled, we load from the Hub
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(not hasattr(a__			,				'''is_local'''      )      )

      finally:
           if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]






