


"""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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       args.log_outputs
					__SCREAMING_SNAKE_CASE		     =       "_".join(args.dataset.split("/"     ) + [args.config, args.split]     )

					# load metric
					__SCREAMING_SNAKE_CASE		     =       load_metric("wer"     )
					__SCREAMING_SNAKE_CASE		     =       load_metric("cer"     )

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

					# print & log results
					__SCREAMING_SNAKE_CASE		     =       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:
										__SCREAMING_SNAKE_CASE		     =       f"""log_{dataset_id}_predictions.txt"""
										__SCREAMING_SNAKE_CASE		     =       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(lowerCAmelCase_						,		lowerCAmelCase_     ):
																				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      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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

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

					for t in token_sequences_to_ignore:
										__SCREAMING_SNAKE_CASE		     =       " ".join(text.split(_A     )     )

					return text




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       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
					__SCREAMING_SNAKE_CASE		     =       AutoFeatureExtractor.from_pretrained(args.model_id     )
					__SCREAMING_SNAKE_CASE		     =       feature_extractor.sampling_rate

					# resample audio
					__SCREAMING_SNAKE_CASE		     =       dataset.cast_column("audio"						,		Audio(sampling_rate=_A     )     )

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

					# map function to decode audio
					def map_to_pred(lowerCAmelCase_     ):
										__SCREAMING_SNAKE_CASE		     =       asr(
										    batch["audio"]["array"]						,		chunk_length_s=args.chunk_length_s						,		stride_length_s=args.stride_length_s     )

										__SCREAMING_SNAKE_CASE		     =       prediction["text"]
										__SCREAMING_SNAKE_CASE		     =       normalize_text(batch["sentence"]     )
										return batch

					# run inference on all examples
					__SCREAMING_SNAKE_CASE		     =       dataset.map(_A						,		remove_columns=dataset.column_names     )

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


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

							main(args)




"""simple docstring"""







from __future__ import annotations




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					if len(lowerCAmelCase_     ) < 2:
										raise ValueError("Monogons and Digons are not polygons in the Euclidean space"     )
					if any(i <= 0 for i in nums     ):
										raise ValueError("All values must be greater than 0"     )
					__SCREAMING_SNAKE_CASE		     =       nums.copy()
					copy_nums.sort()
					return copy_nums[-1] < sum(copy_nums[:-1]     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
