
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
							from huggingface_hub import snapshot_download
							from pyctcdecode import BeamSearchDecoderCTC

							from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
							from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
							from transformers import WavaVecaForCTC




@require_pyctcdecode
class   __lowercase						(						unittest.TestCase       ):






	def       _a(self				: Optional[int]    ) ->			Any:
							_lowercase       :	str           =   "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
							_lowercase       :	Dict           =   dict(zip(snake_case      ,	range(len(snake_case    )    )    )    )

							_lowercase       :	List[str]           =   {
							    "unk_token": "<unk>",
							    "bos_token": "<s>",
							    "eos_token": "</s>",
							}
							_lowercase       :	str           =   {
							    "feature_size": 1,
							    "padding_value": 0.0,
							    "sampling_rate": 1_6000,
							    "return_attention_mask": False,
							    "do_normalize": True,
							}

							_lowercase       :	Optional[int]           =   tempfile.mkdtemp()
							_lowercase       :	str           =   os.path.join(self.tmpdirname      ,	VOCAB_FILES_NAMES["vocab_file"]    )
							_lowercase       :	str           =   os.path.join(self.tmpdirname      ,	snake_case    )
							with open(self.vocab_file      ,	"w"      ,	encoding="utf-8"    ) as fp:
													fp.write(json.dumps(snake_case    ) + "\n"    )

							with open(self.feature_extraction_file      ,	"w"      ,	encoding="utf-8"    ) as fp:
													fp.write(json.dumps(snake_case    ) + "\n"    )

							# load decoder from hub
							_lowercase       :	Union[str, Any]           =   "hf-internal-testing/ngram-beam-search-decoder"






	def       _a(self				: Optional[int]      ,	**snake_case				: Union[str, Any]    ) ->			Tuple:
							_lowercase       :	Tuple           =   self.add_kwargs_tokens_map.copy()
							kwargs.update(snake_case    )
							return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,	**snake_case    )






	def       _a(self				: Optional[int]      ,	**snake_case				: int    ) ->			List[str]:
							return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,	**snake_case    )






	def       _a(self				: Union[str, Any]      ,	**snake_case				: int    ) ->			int:
							return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name      ,	**snake_case    )






	def       _a(self				: Union[str, Any]    ) ->			Any:
							shutil.rmtree(self.tmpdirname    )






	def       _a(self				: Optional[Any]    ) ->			Union[str, Any]:
							_lowercase       :	int           =   self.get_tokenizer()
							_lowercase       :	List[str]           =   self.get_feature_extractor()
							_lowercase       :	int           =   self.get_decoder()

							_lowercase       :	Tuple           =   WavaVecaProcessorWithLM(tokenizer=snake_case      ,	feature_extractor=snake_case      ,	decoder=snake_case    )

							processor.save_pretrained(self.tmpdirname    )
							_lowercase       :	Union[str, Any]           =   WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname    )

							# tokenizer
							self.assertEqual(processor.tokenizer.get_vocab()      ,	tokenizer.get_vocab()    )
							self.assertIsInstance(processor.tokenizer      ,	snake_case    )

							# feature extractor
							self.assertEqual(processor.feature_extractor.to_json_string()      ,	feature_extractor.to_json_string()    )
							self.assertIsInstance(processor.feature_extractor      ,	snake_case    )

							# decoder
							self.assertEqual(processor.decoder._alphabet.labels      ,	decoder._alphabet.labels    )
							self.assertEqual(
							    processor.decoder.model_container[decoder._model_key]._unigram_set      ,	decoder.model_container[decoder._model_key]._unigram_set      ,	)
							self.assertIsInstance(processor.decoder      ,	snake_case    )






	def       _a(self				: Optional[Any]    ) ->			Optional[Any]:
							_lowercase       :	Tuple           =   WavaVecaProcessorWithLM(
							    tokenizer=self.get_tokenizer()      ,	feature_extractor=self.get_feature_extractor()      ,	decoder=self.get_decoder()    )
							processor.save_pretrained(self.tmpdirname    )

							# make sure that error is thrown when decoder alphabet doesn't match
							_lowercase       :	Any           =   WavaVecaProcessorWithLM.from_pretrained(
							    self.tmpdirname      ,	alpha=5.0      ,	beta=3.0      ,	score_boundary=-7.0      ,	unk_score_offset=3    )

							# decoder
							self.assertEqual(processor.language_model.alpha      ,	5.0    )
							self.assertEqual(processor.language_model.beta      ,	3.0    )
							self.assertEqual(processor.language_model.score_boundary      ,	-7.0    )
							self.assertEqual(processor.language_model.unk_score_offset      ,	3    )






	def       _a(self				: str    ) ->			Optional[int]:
							_lowercase       :	Dict           =   self.get_tokenizer()
							# add token to trigger raise
							tokenizer.add_tokens(["xx"]    )
							with self.assertRaisesRegex(snake_case      ,	"include"    ):
													WavaVecaProcessorWithLM(
													    tokenizer=snake_case      ,	feature_extractor=self.get_feature_extractor()      ,	decoder=self.get_decoder()    )






	def       _a(self				: Dict    ) ->			Optional[int]:
							_lowercase       :	str           =   self.get_feature_extractor()
							_lowercase       :	Tuple           =   self.get_tokenizer()
							_lowercase       :	Tuple           =   self.get_decoder()

							_lowercase       :	Any           =   WavaVecaProcessorWithLM(tokenizer=snake_case      ,	feature_extractor=snake_case      ,	decoder=snake_case    )

							_lowercase       :	Optional[int]           =   floats_list((3, 1000)    )

							_lowercase       :	List[Any]           =   feature_extractor(snake_case      ,	return_tensors="np"    )
							_lowercase       :	Dict           =   processor(snake_case      ,	return_tensors="np"    )

							for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()      ,	input_processor[key].sum()      ,	delta=1e-2    )






	def       _a(self				: Optional[Any]    ) ->			Tuple:
							_lowercase       :	Dict           =   self.get_feature_extractor()
							_lowercase       :	int           =   self.get_tokenizer()
							_lowercase       :	Optional[int]           =   self.get_decoder()

							_lowercase       :	Any           =   WavaVecaProcessorWithLM(tokenizer=snake_case      ,	feature_extractor=snake_case      ,	decoder=snake_case    )

							_lowercase       :	Dict           =   "This is a test string"

							_lowercase       :	Union[str, Any]           =   processor(text=snake_case    )

							_lowercase       :	Tuple           =   tokenizer(snake_case    )

							for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]      ,	encoded_processor[key]    )






	def       _a(self				: Optional[int]      ,	snake_case				: Dict=(2, 10, 16)      ,	snake_case				: Optional[Any]=77    ) ->			Optional[int]:
							np.random.seed(snake_case    )
							return np.random.rand(*snake_case    )






	def       _a(self				: Dict    ) ->			str:
							_lowercase       :	int           =   self.get_feature_extractor()
							_lowercase       :	str           =   self.get_tokenizer()
							_lowercase       :	int           =   self.get_decoder()

							_lowercase       :	str           =   WavaVecaProcessorWithLM(tokenizer=snake_case      ,	feature_extractor=snake_case      ,	decoder=snake_case    )

							_lowercase       :	Optional[Any]           =   self._get_dummy_logits(shape=(10, 16)      ,	seed=13    )

							_lowercase       :	Dict           =   processor.decode(snake_case    )

							_lowercase       :	Any           =   decoder.decode_beams(snake_case    )[0]

							self.assertEqual(decoded_decoder[0]      ,	decoded_processor.text    )
							self.assertEqual("</s> <s> </s>"      ,	decoded_processor.text    )
							self.assertEqual(decoded_decoder[-2]      ,	decoded_processor.logit_score    )
							self.assertEqual(decoded_decoder[-1]      ,	decoded_processor.lm_score    )






	@parameterized.expand([[None], ["fork"], ["spawn"]]    )
	def       _a(self				: List[str]      ,	snake_case				: Any    ) ->			Optional[int]:
							_lowercase       :	int           =   self.get_feature_extractor()
							_lowercase       :	Tuple           =   self.get_tokenizer()
							_lowercase       :	List[str]           =   self.get_decoder()

							_lowercase       :	List[Any]           =   WavaVecaProcessorWithLM(tokenizer=snake_case      ,	feature_extractor=snake_case      ,	decoder=snake_case    )

							_lowercase       :	int           =   self._get_dummy_logits()

							# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
							#       otherwise, the LM won't be available to the pool's sub-processes.
							# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
							if pool_context is None:
													_lowercase       :	Any           =   processor.batch_decode(snake_case    )
							else:
													with get_context(snake_case    ).Pool() as pool:
																			_lowercase       :	str           =   processor.batch_decode(snake_case      ,	snake_case    )

							_lowercase       :	Any           =   list(snake_case    )

							with get_context("fork"    ).Pool() as p:
													_lowercase       :	Optional[int]           =   decoder.decode_beams_batch(snake_case      ,	snake_case    )

							_lowercase      ,    _lowercase      ,    _lowercase       :	Dict           =   [], [], []
							for beams in decoded_beams:
													texts_decoder.append(beams[0][0]    )
													logit_scores_decoder.append(beams[0][-2]    )
													lm_scores_decoder.append(beams[0][-1]    )

							self.assertListEqual(snake_case      ,	decoded_processor.text    )
							self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]      ,	decoded_processor.text    )
							self.assertListEqual(snake_case      ,	decoded_processor.logit_score    )
							self.assertListEqual(snake_case      ,	decoded_processor.lm_score    )






	def       _a(self				: Any    ) ->			List[Any]:
							_lowercase       :	Union[str, Any]           =   self.get_feature_extractor()
							_lowercase       :	Any           =   self.get_tokenizer()
							_lowercase       :	int           =   self.get_decoder()

							_lowercase       :	Dict           =   WavaVecaProcessorWithLM(tokenizer=snake_case      ,	feature_extractor=snake_case      ,	decoder=snake_case    )

							_lowercase       :	Tuple           =   self._get_dummy_logits()

							_lowercase       :	List[str]           =   15
							_lowercase       :	Union[str, Any]           =   -20.0
							_lowercase       :	List[str]           =   -4.0

							_lowercase       :	Optional[int]           =   processor.batch_decode(
							    snake_case      ,	beam_width=snake_case      ,	beam_prune_logp=snake_case      ,	token_min_logp=snake_case      ,	)
							_lowercase       :	Any           =   decoded_processor_out.text

							_lowercase       :	List[str]           =   list(snake_case    )

							with get_context("fork"    ).Pool() as pool:
													_lowercase       :	int           =   decoder.decode_beams_batch(
													    snake_case      ,	snake_case      ,	beam_width=snake_case      ,	beam_prune_logp=snake_case      ,	token_min_logp=snake_case      ,	)

							_lowercase       :	Union[str, Any]           =   [d[0][0] for d in decoded_decoder_out]
							_lowercase       :	Tuple           =   [d[0][2] for d in decoded_decoder_out]
							_lowercase       :	Optional[int]           =   [d[0][3] for d in decoded_decoder_out]

							self.assertListEqual(snake_case      ,	snake_case    )
							self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"]      ,	snake_case    )

							self.assertTrue(np.array_equal(snake_case      ,	decoded_processor_out.logit_score    )    )
							self.assertTrue(np.allclose([-20.0_54, -18.4_47]      ,	snake_case      ,	atol=1e-3    )    )

							self.assertTrue(np.array_equal(snake_case      ,	decoded_processor_out.lm_score    )    )
							self.assertTrue(np.allclose([-15.5_54, -13.94_74]      ,	snake_case      ,	atol=1e-3    )    )






	def       _a(self				: Union[str, Any]    ) ->			List[str]:
							_lowercase       :	List[Any]           =   self.get_feature_extractor()
							_lowercase       :	str           =   self.get_tokenizer()
							_lowercase       :	List[str]           =   self.get_decoder()

							_lowercase       :	Optional[Any]           =   WavaVecaProcessorWithLM(tokenizer=snake_case      ,	feature_extractor=snake_case      ,	decoder=snake_case    )

							_lowercase       :	Optional[Any]           =   self._get_dummy_logits()

							_lowercase       :	Union[str, Any]           =   2.0
							_lowercase       :	str           =   5.0
							_lowercase       :	int           =   -20.0
							_lowercase       :	Dict           =   True

							_lowercase       :	str           =   processor.batch_decode(
							    snake_case      ,	alpha=snake_case      ,	beta=snake_case      ,	unk_score_offset=snake_case      ,	lm_score_boundary=snake_case      ,	)
							_lowercase       :	Optional[Any]           =   decoded_processor_out.text

							_lowercase       :	Dict           =   list(snake_case    )
							decoder.reset_params(
							    alpha=snake_case      ,	beta=snake_case      ,	unk_score_offset=snake_case      ,	lm_score_boundary=snake_case      ,	)

							with get_context("fork"    ).Pool() as pool:
													_lowercase       :	List[str]           =   decoder.decode_beams_batch(
													    snake_case      ,	snake_case      ,	)

							_lowercase       :	List[Any]           =   [d[0][0] for d in decoded_decoder_out]

							self.assertListEqual(snake_case      ,	snake_case    )
							self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]      ,	snake_case    )
							_lowercase       :	Optional[int]           =   processor.decoder.model_container[processor.decoder._model_key]
							self.assertEqual(lm_model.alpha      ,	2.0    )
							self.assertEqual(lm_model.beta      ,	5.0    )
							self.assertEqual(lm_model.unk_score_offset      ,	-20.0    )
							self.assertEqual(lm_model.score_boundary      ,	snake_case    )






	def       _a(self				: Optional[int]    ) ->			int:
							_lowercase       :	Optional[int]           =   WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )

							_lowercase       :	Optional[Any]           =   processor.decoder.model_container[processor.decoder._model_key]
							_lowercase       :	Dict           =   Path(language_model._kenlm_model.path.decode("utf-8"    )    ).parent.parent.absolute()

							_lowercase       :	Optional[int]           =   os.listdir(snake_case    )
							_lowercase       :	str           =   ["alphabet.json", "language_model"]

							downloaded_decoder_files.sort()
							expected_decoder_files.sort()

							# test that only decoder relevant files from
							# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
							# are downloaded and none of the rest (e.g. README.md, ...)
							self.assertListEqual(snake_case      ,	snake_case    )






	def       _a(self				: Any    ) ->			Optional[Any]:
							_lowercase       :	Any           =   snapshot_download("hf-internal-testing/processor_with_lm"    )

							_lowercase       :	int           =   WavaVecaProcessorWithLM.from_pretrained(snake_case    )

							_lowercase       :	Union[str, Any]           =   processor.decoder.model_container[processor.decoder._model_key]
							_lowercase       :	Tuple           =   Path(language_model._kenlm_model.path.decode("utf-8"    )    ).parent.parent.absolute()

							_lowercase       :	Tuple           =   os.listdir(snake_case    )
							_lowercase       :	int           =   os.listdir(snake_case    )

							local_decoder_files.sort()
							expected_decoder_files.sort()

							# test that both decoder form hub and local files in cache are the same
							self.assertListEqual(snake_case      ,	snake_case    )






	def       _a(self				: Optional[int]    ) ->			List[Any]:
							_lowercase       :	List[Any]           =   WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )
							_lowercase       :	List[str]           =   AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"    )

							_lowercase       :	List[Any]           =   floats_list((3, 1000)    )

							_lowercase       :	List[str]           =   processor_wavaveca(snake_case      ,	return_tensors="np"    )
							_lowercase       :	Dict           =   processor_auto(snake_case      ,	return_tensors="np"    )

							for key in input_wavaveca.keys():
													self.assertAlmostEqual(input_wavaveca[key].sum()      ,	input_auto[key].sum()      ,	delta=1e-2    )

							_lowercase       :	List[Any]           =   self._get_dummy_logits()

							_lowercase       :	str           =   processor_wavaveca.batch_decode(snake_case    )
							_lowercase       :	int           =   processor_auto.batch_decode(snake_case    )

							self.assertListEqual(decoded_wavaveca.text      ,	decoded_auto.text    )






	def       _a(self				: int    ) ->			Optional[int]:
							_lowercase       :	Optional[int]           =   self.get_feature_extractor()
							_lowercase       :	List[Any]           =   self.get_tokenizer()
							_lowercase       :	Dict           =   self.get_decoder()

							_lowercase       :	Optional[int]           =   WavaVecaProcessorWithLM(tokenizer=snake_case      ,	feature_extractor=snake_case      ,	decoder=snake_case    )

							self.assertListEqual(
							    processor.model_input_names      ,	feature_extractor.model_input_names      ,	msg="`processor` and `feature_extractor` model input names do not match"      ,	)






	@staticmethod
	def       _a(snake_case				: Dict      ,	snake_case				: Dict    ) ->			int:
							_lowercase       :	Tuple           =   [d[key] for d in offsets]
							return retrieved_list






	def       _a(self				: Optional[int]    ) ->			Any:
							_lowercase       :	List[Any]           =   WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )
							_lowercase       :	Any           =   self._get_dummy_logits()[0]

							_lowercase       :	int           =   processor.decode(snake_case      ,	output_word_offsets=snake_case    )
							# check Wav2Vec2CTCTokenizerOutput keys for word
							self.assertEqual(len(outputs.keys()    )      ,	4    )
							self.assertTrue("text" in outputs    )
							self.assertTrue("word_offsets" in outputs    )
							self.assertTrue(isinstance(snake_case      ,	snake_case    )    )

							self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"]      ,	"word"    )    )      ,	outputs.text    )
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]      ,	"word"    )      ,	["<s>", "<s>", "</s>"]    )
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]      ,	"start_offset"    )      ,	[0, 2, 4]    )
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]      ,	"end_offset"    )      ,	[1, 3, 5]    )






	def       _a(self				: str    ) ->			Tuple:
							_lowercase       :	Tuple           =   WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )
							_lowercase       :	Optional[int]           =   self._get_dummy_logits()

							_lowercase       :	Any           =   processor.batch_decode(snake_case      ,	output_word_offsets=snake_case    )

							# check Wav2Vec2CTCTokenizerOutput keys for word
							self.assertEqual(len(outputs.keys()    )      ,	4    )
							self.assertTrue("text" in outputs    )
							self.assertTrue("word_offsets" in outputs    )
							self.assertTrue(isinstance(snake_case      ,	snake_case    )    )

							self.assertListEqual(
							    [" ".join(self.get_from_offsets(snake_case      ,	"word"    )    ) for o in outputs["word_offsets"]]      ,	outputs.text    )
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]      ,	"word"    )      ,	["<s>", "<s>", "</s>"]    )
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]      ,	"start_offset"    )      ,	[0, 2, 4]    )
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]      ,	"end_offset"    )      ,	[1, 3, 5]    )






	@slow
	@require_torch
	@require_torchaudio
	def       _a(self				: int    ) ->			str:
							import torch

							_lowercase       :	Union[str, Any]           =   load_dataset("common_voice"      ,	"en"      ,	split="train"      ,	streaming=snake_case    )
							_lowercase       :	Optional[Any]           =   ds.cast_column("audio"      ,	datasets.Audio(sampling_rate=1_6000    )    )
							_lowercase       :	List[str]           =   iter(snake_case    )
							_lowercase       :	Union[str, Any]           =   next(snake_case    )

							_lowercase       :	Tuple           =   AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"    )
							_lowercase       :	int           =   WavaVecaForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"    )

							# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
							_lowercase       :	Union[str, Any]           =   processor(sample["audio"]["array"]      ,	return_tensors="pt"    ).input_values

							with torch.no_grad():
													_lowercase       :	List[str]           =   model(snake_case    ).logits.cpu().numpy()

							_lowercase       :	Any           =   processor.decode(logits[0]      ,	output_word_offsets=snake_case    )

							_lowercase       :	List[Any]           =   model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
							_lowercase       :	List[str]           =   [
							    {
							        "start_time": d["start_offset"] * time_offset,
							        "end_time": d["end_offset"] * time_offset,
							        "word": d["word"],
							    }
							    for d in output["word_offsets"]
							]

							_lowercase       :	List[Any]           =   "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"

							# output words
							self.assertEqual(" ".join(self.get_from_offsets(snake_case      ,	"word"    )    )      ,	snake_case    )
							self.assertEqual(" ".join(self.get_from_offsets(snake_case      ,	"word"    )    )      ,	output.text    )

							# output times
							_lowercase       :	Dict           =   torch.tensor(self.get_from_offsets(snake_case      ,	"start_time"    )    )
							_lowercase       :	List[Any]           =   torch.tensor(self.get_from_offsets(snake_case      ,	"end_time"    )    )

							# fmt: off
							_lowercase       :	Union[str, Any]           =   torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]    )
							_lowercase       :	List[Any]           =   torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]    )
							# fmt: on

							self.assertTrue(torch.allclose(snake_case      ,	snake_case      ,	atol=0.01    )    )
							self.assertTrue(torch.allclose(snake_case      ,	snake_case      ,	atol=0.01    )    )






def 		UpperCamelCase     (       _UpperCAmelCase  :   str  )   ->			bool:






						'''simple docstring'''

						_lowercase       :	List[str]           =   [int(_UpperCAmelCase  ) for i in ip_va_address.split("."  ) if i.isdigit()]
						return len(_UpperCAmelCase  ) == 4 and all(0 <= int(_UpperCAmelCase  ) <= 254 for octet in octets  )


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




