






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   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   args.log_outputs
				__magic_name__		:Tuple   =   '''_'''.join(args.dataset.split('''/'''			) + [args.config, args.split]			)

				# load metric
				__magic_name__		:Dict   =   load_metric('''wer'''			)
				__magic_name__		:List[Any]   =   load_metric('''cer'''			)

				# compute metrics
				__magic_name__		:Optional[Any]   =   wer.compute(references=result['''target'''],	predictions=result['''prediction''']			)
				__magic_name__		:Dict   =   cer.compute(references=result['''target'''],	predictions=result['''prediction''']			)

				# print & log results
				__magic_name__		:Tuple   =   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:
								__magic_name__		:Dict   =   f'''log_{dataset_id}_predictions.txt'''
								__magic_name__		:Union[str, Any]   =   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,	snake_case			):
																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   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

				__magic_name__		:Optional[Any]   =   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!
				__magic_name__		:Tuple   =   ['''\n\n''', '''\n''', '''   ''', '''  ''']

				for t in token_sequences_to_ignore:
								__magic_name__		:Any   =   ''' '''.join(text.split(snake_case			)			)

				return text

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   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
				__magic_name__		:str   =   AutoFeatureExtractor.from_pretrained(args.model_id			)
				__magic_name__		:Optional[int]   =   feature_extractor.sampling_rate

				# resample audio
				__magic_name__		:Dict   =   dataset.cast_column('''audio''',	Audio(sampling_rate=snake_case			)			)

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

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

								__magic_name__		:Any   =   prediction['''text''']
								__magic_name__		:Optional[int]   =   normalize_text(batch['''sentence''']			)
								return batch

				# run inference on all examples
				__magic_name__		:Any   =   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__":
					SCREAMING_SNAKE_CASE__   :      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.""",
					)
					SCREAMING_SNAKE_CASE__   :      int				=		parser.parse_args()

					main(args)














def   __lowercase       (			snake_case			):







				"""simple docstring"""






				return "".join([hex(snake_case			)[2:].zfill(2			).upper() for byte in list(snake_case			)]			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if (len(snake_case			) % 2) != 0:
								raise ValueError(
								    '''Base16 encoded data is invalid:
Data does not have an even number of hex digits.'''			)
				# Check the character set - the standard base16 alphabet
				# is uppercase according to RFC3548 section 6
				if not set(snake_case			) <= set('''0123456789ABCDEF'''			):
								raise ValueError(
								    '''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.'''			)
				# For every two hexadecimal digits (= a byte), turn it into an integer.
				# Then, string the result together into bytes, and return it.
				return bytes(int(data[i] + data[i + 1],	1_6			) for i in range(0,	len(snake_case			),	2			)			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






