



"""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_(   lowercase_     :							Dataset						,   lowercase_     :							Dict[str, str]							)	->       Tuple:
				_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(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:
								_lowerCamelCase        =				F"""log_{dataset_id}_predictions.txt"""
								_lowerCamelCase        =				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_     :							List[Any]							):
																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        lowerCAmelCase_(   lowercase_     :							str							)	->       str:
				_lowerCamelCase        =				'''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

				_lowerCamelCase        =				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!
				_lowerCamelCase        =				['''\n\n''', '''\n''', '''   ''', '''  ''']

				for t in token_sequences_to_ignore:
								_lowerCamelCase        =				''' '''.join(text.split(lowercase_							)							)

				return text



def        lowerCAmelCase_(   lowercase_     :							Any							)	->       List[Any]:
				# load dataset
				_lowerCamelCase        =				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
				_lowerCamelCase        =				AutoFeatureExtractor.from_pretrained(args.model_id							)
				_lowerCamelCase        =				feature_extractor.sampling_rate

				# resample audio
				_lowerCamelCase        =				dataset.cast_column('''audio'''						,   Audio(sampling_rate=lowercase_							)							)

				# 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(lowercase_     :							Optional[Any]							):
								_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(lowercase_						,   remove_columns=dataset.column_names							)

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


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      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.''',
			)
			__SCREAMING_SNAKE_CASE  :						Any    							=      parser.parse_args()

			main(args)





"""simple docstring"""
from __future__ import annotations

from math import pow, sqrt



def        lowerCAmelCase_(   lowercase_     :							float						,   lowercase_     :							float						,   lowercase_     :							float							)	->       dict[str, float]:
				if (resistance, reactance, impedance).count(0							) != 1:
								raise ValueError('''One and only one argument must be 0'''							)
				if resistance == 0:
								return {"resistance": sqrt(pow(lowercase_						,   2							) - pow(lowercase_						,   2							)							)}
				elif reactance == 0:
								return {"reactance": sqrt(pow(lowercase_						,   2							) - pow(lowercase_						,   2							)							)}
				elif impedance == 0:
								return {"impedance": sqrt(pow(lowercase_						,   2							) + pow(lowercase_						,   2							)							)}
				else:
								raise ValueError('''Exactly one argument must be 0'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
