




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__    (		_A     ,	_A							):







	'''simple docstring'''





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

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

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

	# print & log results
	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:
		snake_case_		    =     f"log_{dataset_id}_predictions.txt"
		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(_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 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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

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

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

	return text


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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

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

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

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

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

	# run inference on all examples
	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__":
		lowercase__				:     List[str] 		=						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.",
		)
		lowercase__				:     List[str] 		=						parser.parse_args()

		main(args)






def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     len(_A							)
	for i in range(_A							):
		for j in range(i + 1     ,	_A							):
			if numbers[j] < numbers[i]:
				snake_case_    ,      snake_case_		    =     numbers[j], numbers[i]
	return numbers


if __name__ == "__main__":
		lowercase__				:     int 		=						input("Enter numbers separated by a comma:\n").strip()
		lowercase__				:     Any 		=						[int(item) for item in user_input.split(",")]
		print(exchange_sort(unsorted))
