
"""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 					_snake_case (						snake_case__      :   Any		,					snake_case__      :   Dict   ):
	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__      :   List[str]		,					snake_case__      :   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 					_snake_case (						snake_case__      :   Union[str, Any]   ):
	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 					_snake_case (						snake_case__      :   int   ):
	# load dataset
	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__      :   List[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(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__":
		_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)
"""simple docstring"""






import math



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if (
				    not isinstance(lowerCamelCase					,			(int, float)       )
				    or power_factor < -1
				    or power_factor > 1
				):
								raise ValueError('power_factor must be a valid float value between -1 and 1.'       )
				return apparent_power * power_factor



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if (
				    not isinstance(lowerCamelCase					,			(int, float)       )
				    or power_factor < -1
				    or power_factor > 1
				):
								raise ValueError('power_factor must be a valid float value between -1 and 1.'       )
				return apparent_power * math.sqrt(1 - power_factor**2       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



