






def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]     ):
 snake_case__				:   List[str]     =						[int(lowerCAmelCase__     ) for i in ip_va_address.split("."     ) if i.isdigit()]
 return len(lowerCAmelCase__     ) == 4 and all(0 <= int(lowerCAmelCase__     ) <= 254 for octet in octets     )


if __name__ == "__main__":
 __lowerCamelCase      :    Any   		=   input().strip()
 __lowerCamelCase      :    List[Any]   		=   """valid""" if is_ip_va_address_valid(ip) else """invalid"""
 print(f"{ip} is a {valid_or_invalid} IP v4 address.")











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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Dataset  ,						snake_case_      :							Dict[str, str]     ):
	snake_case__				:   Tuple     =						args.log_outputs
	snake_case__				:   Union[str, Any]     =						"_".join(args.dataset.split("/"     ) + [args.config, args.split]     )

	# load metric
	snake_case__				:   List[str]     =						load_metric("wer"     )
	snake_case__				:   List[str]     =						load_metric("cer"     )

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

	# print & log results
	snake_case__				:   Dict     =						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:
		snake_case__				:   Union[str, Any]     =						F'''log_{dataset_id}_predictions.txt'''
		snake_case__				:   int     =						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_      :							Union[str, Any]  ,						snake_case_      :							Any     ):
				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   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	snake_case__				:   List[Any]     =						"[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	snake_case__				:   Optional[int]     =						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!
	snake_case__				:   Optional[Any]     =						["\n\n", "\n", "   ", "  "]

	for t in token_sequences_to_ignore:
		snake_case__				:   Optional[int]     =						" ".join(text.split(snake_case_     )     )

	return text







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	# load dataset
	snake_case__				:   int     =						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
	snake_case__				:   List[str]     =						AutoFeatureExtractor.from_pretrained(args.model_id     )
	snake_case__				:   List[Any]     =						feature_extractor.sampling_rate

	# resample audio
	snake_case__				:   Dict     =						dataset.cast_column("audio"  ,						Audio(sampling_rate=snake_case_     )     )

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

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

		snake_case__				:   Optional[int]     =						prediction["text"]
		snake_case__				:   Optional[Any]     =						normalize_text(batch["sentence"]     )
		return batch

	# run inference on all examples
	snake_case__				:   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__":
	__lowerCamelCase      :    Dict   		=   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      :    str   		=   parser.parse_args()

	main(args)



