



'''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 					__lowerCAmelCase		(  __lowerCamelCase	:    Dataset				,   __lowerCamelCase	:    Dict[str, str]	)  ->						str:
					__lowerCAmelCase	   =args.log_outputs
					__lowerCAmelCase	   ="""_""".join(args.dataset.split("""/"""	) + [args.config, args.split]	)

					# load metric
					__lowerCAmelCase	   =load_metric("""wer"""	)
					__lowerCAmelCase	   =load_metric("""cer"""	)

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

					# print & log results
					__lowerCAmelCase	   =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:
										__lowerCAmelCase	   =f"""log_{dataset_id}_predictions.txt"""
										__lowerCAmelCase	   =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(__lowerCamelCase	:    int				,   __lowerCamelCase	:    Optional[int]	):
																				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 					__lowerCAmelCase		(  __lowerCamelCase	:    str	)  ->						str:
					__lowerCAmelCase	   ="""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

					__lowerCAmelCase	   =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!
					__lowerCAmelCase	   =["""\n\n""", """\n""", """   """, """  """]

					for t in token_sequences_to_ignore:
										__lowerCAmelCase	   =""" """.join(text.split(_A	)	)

					return text






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						Tuple:
					__lowerCAmelCase	   =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
					__lowerCAmelCase	   =AutoFeatureExtractor.from_pretrained(args.model_id	)
					__lowerCAmelCase	   =feature_extractor.sampling_rate

					# resample audio
					__lowerCAmelCase	   =dataset.cast_column("""audio"""				,   Audio(sampling_rate=_A	)	)

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

					# map function to decode audio
					def map_to_pred(__lowerCamelCase	:    Dict	):
										__lowerCAmelCase	   =asr(
										    batch["""audio"""]["""array"""]				,   chunk_length_s=args.chunk_length_s				,   stride_length_s=args.stride_length_s	)

										__lowerCAmelCase	   =prediction["""text"""]
										__lowerCAmelCase	   =normalize_text(batch["""sentence"""]	)
										return batch

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

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


if __name__ == "__main__":
				lowercase_	       =			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.''',
				)
				lowercase_	       =			parser.parse_args()

				main(args)









def 					__lowerCAmelCase		(  __lowerCamelCase	:    int = 3				,   __lowerCamelCase	:    int = 7				,   __lowerCamelCase	:    int = 1000000	)  ->						int:
					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =1

					for current_denominator in range(1				,   limit + 1	):
										__lowerCAmelCase	   =current_denominator * numerator // denominator
										if current_denominator % denominator == 0:
															current_numerator -= 1
										if current_numerator * max_denominator > current_denominator * max_numerator:
															__lowerCAmelCase	   =current_numerator
															__lowerCAmelCase	   =current_denominator
					return max_numerator


if __name__ == "__main__":
				print(solution(numerator=3, denominator=7, limit=1_00_00_00))




