





"""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 		__lowercase     (   snake_case_  :  int				,snake_case_  :  Any						)   ->Any:



    '''simple docstring'''







    __A    :						Any						 =     args.log_outputs
    __A    :						List[Any]						 =     '''_'''.join(args.dataset.split('''/'''						) + [args.config, args.split]						)

    # load metric
    __A    :						Any						 =     load_metric('''wer'''						)
    __A    :						Union[str, Any]						 =     load_metric('''cer'''						)

    # compute metrics
    __A    :						str						 =     wer.compute(references=result['''target''']				,predictions=result['''prediction''']						)
    __A    :						Optional[int]						 =     cer.compute(references=result['''target''']				,predictions=result['''prediction''']						)

    # print & log results
    __A    :						int						 =     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:
        __A    :						List[str]						 =     F"""log_{dataset_id}_predictions.txt"""
        __A    :						int						 =     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(snake_case_  :  Optional[Any]				,snake_case_  :  str						):
                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 		__lowercase     (   snake_case_  :  str						)   ->List[Any]:



    '''simple docstring'''







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

    __A    :						Optional[Any]						 =     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!
    __A    :						Tuple						 =     ['''\n\n''', '''\n''', '''   ''', '''  ''']

    for t in token_sequences_to_ignore:
        __A    :						List[Any]						 =     ''' '''.join(text.split(_A						)						)

    return text







def 		__lowercase     (   snake_case_  :  Dict						)   ->Optional[Any]:



    '''simple docstring'''







    __A    :						Optional[int]						 =     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
    __A    :						Union[str, Any]						 =     AutoFeatureExtractor.from_pretrained(args.model_id						)
    __A    :						List[Any]						 =     feature_extractor.sampling_rate

    # resample audio
    __A    :						Union[str, Any]						 =     dataset.cast_column('''audio'''				,Audio(sampling_rate=_A						)						)

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

    # map function to decode audio
    def map_to_pred(snake_case_  :  Tuple						):
        __A    :						Optional[Any]						 =     asr(
            batch['''audio''']['''array''']				,chunk_length_s=args.chunk_length_s				,stride_length_s=args.stride_length_s						)

        __A    :						Any						 =     prediction['''text''']
        __A    :						Tuple						 =     normalize_text(batch['''sentence''']						)
        return batch

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

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


if __name__ == "__main__":
       a_ =			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_ =			parser.parse_args()

       main(args)










from __future__ import annotations


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     str(_A							)
	return len(_A							) == 9 and set(_A							) == set("123456789"							)


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	for base_num in range(9999     ,	4999     ,	-1							):
		snake_case_		    =     100002 * base_num
		if is_9_pandigital(_A							):
			return candidate

	for base_num in range(333     ,	99     ,	-1							):
		snake_case_		    =     1002003 * base_num
		if is_9_pandigital(_A							):
			return candidate

	return None


if __name__ == "__main__":
		print(f'''{solution() = }''')
