


'''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 	_A				(lowerCAmelCase__  :Dataset				,						lowerCAmelCase__  :Dict[str, str]		)   ->  Optional[int]:




     '''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(lowerCAmelCase__		)

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

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

               result.map(lowerCAmelCase__				,						with_indices=lowerCAmelCase__		)






def 	_A				(lowerCAmelCase__  :str		)   ->  str:




     '''simple docstring'''



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

     _a			      =							re.sub(lowerCAmelCase__				,						''				,						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(lowerCAmelCase__		)		)

     return text






def 	_A				(lowerCAmelCase__  :Dict		)   ->  Tuple:




     '''simple docstring'''



     _a			      =							load_dataset(args.dataset				,						args.config				,						split=args.split				,						use_auth_token=lowerCAmelCase__		)

     # 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=lowerCAmelCase__		)		)

     # 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(lowerCAmelCase__  :str		):
          _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(lowerCAmelCase__				,						remove_columns=dataset.column_names		)

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


if __name__ == "__main__":
       a_					:    Optional[int]          =    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.",
       )
       a_					:    Tuple          =    parser.parse_args()

       main(args)





'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict






@pytest.mark.parametrize(
    'split_dict'				,						[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42				,						dataset_name='my_dataset'		)}		),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42		)}		),
        SplitDict({'train': SplitInfo()}		),
    ]				,						)
def 	_A				(lowerCAmelCase__  :SplitDict		)   ->  Any:




     '''simple docstring'''



     _a			      =							split_dict._to_yaml_list()
     assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)
     _a			      =							SplitDict._from_yaml_list(lowerCAmelCase__		)
     for split_name, split_info in split_dict.items():
          # dataset_name field is deprecated, and is therefore not part of the YAML dump
          _a			      =							None
          # the split name of split_dict takes over the name of the split info object
          _a			      =							split_name
     assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'				,						[SplitInfo(), SplitInfo(dataset_name=lowerCAmelCase__		), SplitInfo(dataset_name='my_dataset'		)]		)
def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							asdict(SplitDict({'train': split_info}		)		)
     assert "dataset_name" in split_dict_asdict["train"]
     assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

