





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

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

				for t in token_sequences_to_ignore:
								_a				      =					""" """.join(text.split(lowercase		)		)

				return text






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

				# resample audio
				_a				      =					dataset.cast_column("audio"       ,				Audio(sampling_rate=lowercase		)		)

				# 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(lowercase		: Optional[int]		):
								_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(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_   :       int       =     parser.parse_args()

					main(args)







"""simple docstring"""


import math

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return math.pow(SCREAMING_SNAKE_CASE							,     2     ) - a

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return 2 * x

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Union[str, Any]				   =			2.0

						while start <= a:
												UpperCamelCase				:		Optional[Any]				   =			math.pow(SCREAMING_SNAKE_CASE							,     2     )

						return start

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE = 9999							,     SCREAMING_SNAKE_CASE = 0.00_00_00_00_00_00_01     ):

						if a < 0:
												raise ValueError("""math domain error"""     )

						UpperCamelCase				:		Optional[Any]				   =			get_initial_point(SCREAMING_SNAKE_CASE     )

						for _ in range(SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[str]				   =			value
												UpperCamelCase				:		Tuple				   =			value - fx(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) / fx_derivative(SCREAMING_SNAKE_CASE     )
												if abs(prev_value - value     ) < tolerance:
																		return value

						return value


if __name__ == "__main__":
		from doctest import testmod

		testmod()



