






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      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Any   ):
				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(UpperCAmelCase   )

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

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

												result.map(UpperCAmelCase,						with_indices=UpperCAmelCase   )







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

				A	 =      re.sub(UpperCAmelCase,						'',						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(UpperCAmelCase   )   )

				return text







def      snake_case		(							UpperCAmelCase	:       Dict   ):
				A	 =      load_dataset(args.dataset,						args.config,						split=args.split,						use_auth_token=UpperCAmelCase   )

				# 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=UpperCAmelCase   )   )

				# 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(UpperCAmelCase	:       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(UpperCAmelCase,						remove_columns=dataset.column_names   )

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


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)













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_           = [int(item) for item in user_input.split(',')]
					lowerCAmelCase_           = int(input('Enter the number to be searched:\n'))
					lowerCAmelCase_           = jump_search(arr, x)
					if res == -1:
										print('Number not found!')
					else:
										print(f'''Number {x} is at index {res}''')





