

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_(							_snake_case   :  Dataset       ,     _snake_case   :  Dict[str, str]	):




			"""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(_snake_case	)

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

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

									result.map(_snake_case       ,     with_indices=_snake_case	)






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






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

			__a        =re.sub(_snake_case       ,     ''       ,     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(_snake_case	)	)

			return text






def        UpperCamelCase_(							_snake_case   :  List[str]	):




			"""simple docstring"""






			__a        =load_dataset(args.dataset       ,     args.config       ,     split=args.split       ,     use_auth_token=_snake_case	)

			# 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=_snake_case	)	)

			# 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(_snake_case   :  Union[str, Any]	):
						__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(_snake_case       ,     remove_columns=dataset.column_names	)

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


if __name__ == "__main__":
						_lowerCAmelCase					:			Optional[Any]  				=			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					:			Optional[Any]  				=			parser.parse_args()

						main(args)









from __future__ import annotations






def        UpperCamelCase_(							_snake_case   :  int	):




			"""simple docstring"""






			__a        =str(_snake_case	)
			return len(_snake_case	) == 9 and set(_snake_case	) == set('123456789'	)






def        UpperCamelCase_(							):




			"""simple docstring"""






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

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

			return None


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






