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

			# load metric
			_snake_case          =      load_metric('wer'			)
			_snake_case          =      load_metric('cer'			)

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

			# print & log results
			_snake_case          =      f'''WER: {wer_result}\nCER: {cer_result}'''
			print(__lowercase			)

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

			# log all results in text file. Possibly interesting for analysis
			if log_outputs is not None:
						_snake_case          =      f'''log_{dataset_id}_predictions.txt'''
						_snake_case          =      f'''log_{dataset_id}_targets.txt'''

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

									result.map(__lowercase	, with_indices=__lowercase			)






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

			_snake_case          =      re.sub(__lowercase	, ''	, text.lower()			)

			# In addition, we can normalize the target text, e.g. removing new lines characters etc...
			# note that order is important here!
			_snake_case          =      ['\n\n', '\n', '   ', '  ']

			for t in token_sequences_to_ignore:
						_snake_case          =      ' '.join(text.split(__lowercase			)			)

			return text






def 	a_		(	__lowercase       :     List[Any]			)			->  List[str]:
			# load dataset
			_snake_case          =      load_dataset(args.dataset	, args.config	, split=args.split	, use_auth_token=__lowercase			)

			# for testing: only process the first two examples as a test
			# dataset = dataset.select(range(10))

			# load processor
			_snake_case          =      AutoFeatureExtractor.from_pretrained(args.model_id			)
			_snake_case          =      feature_extractor.sampling_rate

			# resample audio
			_snake_case          =      dataset.cast_column('audio'	, Audio(sampling_rate=__lowercase			)			)

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

			# map function to decode audio
			def map_to_pred(__lowercase       :     List[str]			):
						_snake_case          =      asr(
						    batch['audio']['array']	, chunk_length_s=args.chunk_length_s	, stride_length_s=args.stride_length_s			)

						_snake_case          =      prediction['text']
						_snake_case          =      normalize_text(batch['sentence']			)
						return batch

			# run inference on all examples
			_snake_case          =      dataset.map(__lowercase	, remove_columns=dataset.column_names			)

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


if __name__ == "__main__":
			_lowerCamelCase	:    Union[str, 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	:    Dict          =      parser.parse_args()

			main(args)

def 	a_		(	__lowercase       :     list[int]	, __lowercase       :     list[int]			)			->  tuple[float, float]:

			# Check if the input is valid
			if not len(__lowercase			) == len(__lowercase			) == 3:
						raise ValueError('Please enter a valid equation.'			)
			if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
						raise ValueError('Both a & b of two equations can\'t be zero.'			)

			# Extract the coefficients
			_snake_case     , _snake_case     , _snake_case          =      equationa
			_snake_case     , _snake_case     , _snake_case          =      equationa

			# Calculate the determinants of the matrices
			_snake_case          =      aa * ba - aa * ba
			_snake_case          =      ca * ba - ca * ba
			_snake_case          =      aa * ca - aa * ca

			# Check if the system of linear equations has a solution (using Cramer's rule)
			if determinant == 0:
						if determinant_x == determinant_y == 0:
									raise ValueError('Infinite solutions. (Consistent system)'			)
						else:
									raise ValueError('No solution. (Inconsistent system)'			)
			else:
						if determinant_x == determinant_y == 0:
									# Trivial solution (Inconsistent system)
									return (0.0, 0.0)
						else:
									_snake_case          =      determinant_x / determinant
									_snake_case          =      determinant_y / determinant
									# Non-Trivial Solution (Consistent system)
									return (x, y)