


'''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				(      __a			, __a       )  ->     Optional[int]:



					'''simple docstring'''






					UpperCamelCase__       :Optional[int]							 =			args.log_outputs
					UpperCamelCase__       :List[str]							 =			"""_""".join(args.dataset.split('''/'''       ) + [args.config, args.split]       )

					# load metric
					UpperCamelCase__       :int							 =			load_metric('''wer'''       )
					UpperCamelCase__       :Tuple							 =			load_metric('''cer'''       )

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

					# print & log results
					UpperCamelCase__       :Any							 =			f'''WER: {wer_result}\nCER: {cer_result}'''
					print(_a       )

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

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

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

															result.map(_a			, with_indices=_a       )


def       a				(      __a       )  ->     str:



					'''simple docstring'''






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

					UpperCamelCase__       :Optional[int]							 =			re.sub(_a			, ''''''			, text.lower()       )

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

					for t in token_sequences_to_ignore:
										UpperCamelCase__       :List[Any]							 =			""" """.join(text.split(_a       )       )

					return text


def       a				(      __a       )  ->     str:



					'''simple docstring'''






					UpperCamelCase__       :List[str]							 =			load_dataset(args.dataset			, args.config			, split=args.split			, use_auth_token=_a       )

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

					# load processor
					UpperCamelCase__       :List[Any]							 =			AutoFeatureExtractor.from_pretrained(args.model_id       )
					UpperCamelCase__       :str							 =			feature_extractor.sampling_rate

					# resample audio
					UpperCamelCase__       :int							 =			dataset.cast_column('''audio'''			, Audio(sampling_rate=_a       )       )

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

					# map function to decode audio
					def map_to_pred(__a       ):
										UpperCamelCase__       :Tuple							 =			asr(
										    batch['''audio''']['''array''']			, chunk_length_s=args.chunk_length_s			, stride_length_s=args.stride_length_s       )

										UpperCamelCase__       :List[Any]							 =			prediction["""text"""]
										UpperCamelCase__       :int							 =			normalize_text(batch['''sentence''']       )
										return batch

					# run inference on all examples
					UpperCamelCase__       :Optional[Any]							 =			dataset.map(_a			, remove_columns=dataset.column_names       )

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


if __name__ == "__main__":
		__snake_case											=					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.''',
		)
		__snake_case											=					parser.parse_args()

		main(args)



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import _LazyModule


__snake_case											=					{'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)