
"""simple docstring"""


import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
			from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__A					=			get_tests_dir('fixtures/test_sentencepiece.model')







@require_torch
@require_torchaudio
@require_sentencepiece
class 						lowerCamelCase__   (   unittest.TestCase							):





			'''simple docstring'''



			def    _lowerCAmelCase						(				self    )					->	List[str]:
				_lowerCAmelCase								=tempfile.mkdtemp()

				_lowerCAmelCase								=["""<s>""", """<pad>""", """</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est"""]
				_lowerCAmelCase								=dict(zip(__UpperCAmelCase	,	range(len(__UpperCAmelCase    )    )    )    )
				_lowerCAmelCase								=Path(self.tmpdirname    )
				save_json(__UpperCAmelCase	,	save_dir / VOCAB_FILES_NAMES["""vocab_file"""]    )
				if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
					copyfile(__UpperCAmelCase	,	save_dir / VOCAB_FILES_NAMES["""spm_file"""]    )

				_lowerCAmelCase								=SpeechaTextTokenizer.from_pretrained(self.tmpdirname    )
				tokenizer.save_pretrained(self.tmpdirname    )

				_lowerCAmelCase								={
				    """feature_size""": 24,
				    """num_mel_bins""": 24,
				    """padding_value""": 0.0,
				    """sampling_rate""": 1_60_00,
				    """return_attention_mask""": False,
				    """do_normalize""": True,
				}
				save_json(__UpperCAmelCase	,	save_dir / FEATURE_EXTRACTOR_NAME    )



			def    _lowerCAmelCase						(				self	,	**__UpperCAmelCase    )					->	str:
				return SpeechaTextTokenizer.from_pretrained(self.tmpdirname	,	**__UpperCAmelCase    )



			def    _lowerCAmelCase						(				self	,	**__UpperCAmelCase    )					->	Any:
				return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname	,	**__UpperCAmelCase    )



			def    _lowerCAmelCase						(				self    )					->	Dict:
				shutil.rmtree(self.tmpdirname    )



			def    _lowerCAmelCase						(				self    )					->	int:
				_lowerCAmelCase								=self.get_tokenizer()
				_lowerCAmelCase								=self.get_feature_extractor()

				_lowerCAmelCase								=SpeechaTextProcessor(tokenizer=__UpperCAmelCase	,	feature_extractor=__UpperCAmelCase    )

				processor.save_pretrained(self.tmpdirname    )
				_lowerCAmelCase								=SpeechaTextProcessor.from_pretrained(self.tmpdirname    )

				self.assertEqual(processor.tokenizer.get_vocab()	,	tokenizer.get_vocab()    )
				self.assertIsInstance(processor.tokenizer	,	__UpperCAmelCase    )

				self.assertEqual(processor.feature_extractor.to_json_string()	,	feature_extractor.to_json_string()    )
				self.assertIsInstance(processor.feature_extractor	,	__UpperCAmelCase    )



			def    _lowerCAmelCase						(				self    )					->	int:
				_lowerCAmelCase								=SpeechaTextProcessor(
				    tokenizer=self.get_tokenizer()	,	feature_extractor=self.get_feature_extractor()    )
				processor.save_pretrained(self.tmpdirname    )

				_lowerCAmelCase								=self.get_tokenizer(bos_token="""(BOS)"""	,	eos_token="""(EOS)"""    )
				_lowerCAmelCase								=self.get_feature_extractor(do_normalize=__UpperCAmelCase	,	padding_value=1.0    )

				_lowerCAmelCase								=SpeechaTextProcessor.from_pretrained(
				    self.tmpdirname	,	bos_token="""(BOS)"""	,	eos_token="""(EOS)"""	,	do_normalize=__UpperCAmelCase	,	padding_value=1.0    )

				self.assertEqual(processor.tokenizer.get_vocab()	,	tokenizer_add_kwargs.get_vocab()    )
				self.assertIsInstance(processor.tokenizer	,	__UpperCAmelCase    )

				self.assertEqual(processor.feature_extractor.to_json_string()	,	feature_extractor_add_kwargs.to_json_string()    )
				self.assertIsInstance(processor.feature_extractor	,	__UpperCAmelCase    )



			def    _lowerCAmelCase						(				self    )					->	Dict:
				_lowerCAmelCase								=self.get_feature_extractor()
				_lowerCAmelCase								=self.get_tokenizer()

				_lowerCAmelCase								=SpeechaTextProcessor(tokenizer=__UpperCAmelCase	,	feature_extractor=__UpperCAmelCase    )

				_lowerCAmelCase								=floats_list((3, 10_00)    )

				_lowerCAmelCase								=feature_extractor(__UpperCAmelCase	,	return_tensors="""np"""    )
				_lowerCAmelCase								=processor(__UpperCAmelCase	,	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    _lowerCAmelCase						(				self    )					->	Optional[Any]:
				_lowerCAmelCase								=self.get_feature_extractor()
				_lowerCAmelCase								=self.get_tokenizer()

				_lowerCAmelCase								=SpeechaTextProcessor(tokenizer=__UpperCAmelCase	,	feature_extractor=__UpperCAmelCase    )

				_lowerCAmelCase								="""This is a test string"""

				_lowerCAmelCase								=processor(text=__UpperCAmelCase    )

				_lowerCAmelCase								=tokenizer(__UpperCAmelCase    )

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



			def    _lowerCAmelCase						(				self    )					->	Any:
				_lowerCAmelCase								=self.get_feature_extractor()
				_lowerCAmelCase								=self.get_tokenizer()

				_lowerCAmelCase								=SpeechaTextProcessor(tokenizer=__UpperCAmelCase	,	feature_extractor=__UpperCAmelCase    )

				_lowerCAmelCase								=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				_lowerCAmelCase								=processor.batch_decode(__UpperCAmelCase    )
				_lowerCAmelCase								=tokenizer.batch_decode(__UpperCAmelCase    )

				self.assertListEqual(__UpperCAmelCase	,	__UpperCAmelCase    )



			def    _lowerCAmelCase						(				self    )					->	List[str]:
				_lowerCAmelCase								=self.get_feature_extractor()
				_lowerCAmelCase								=self.get_tokenizer()

				_lowerCAmelCase								=SpeechaTextProcessor(tokenizer=__UpperCAmelCase	,	feature_extractor=__UpperCAmelCase    )

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





"""simple docstring"""


import math



def        _lowerCamelCase(__UpperCamelCase			)							->					bool:

	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5       ,					int(math.sqrt(__UpperCamelCase			) + 1			)       ,					6			):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True



def        _lowerCamelCase(__UpperCamelCase = 600851475143			)							->					int:

	try:
		_lowerCAmelCase								=int(__UpperCamelCase			)
	except (TypeError, ValueError):
		raise TypeError("""Parameter n must be int or castable to int."""			)
	if n <= 0:
		raise ValueError("""Parameter n must be greater than or equal to one."""			)
	_lowerCAmelCase								=0
	if is_prime(__UpperCamelCase			):
		return n
	while n % 2 == 0:
		n //= 2
	if is_prime(__UpperCamelCase			):
		return n
	for i in range(3       ,					int(math.sqrt(__UpperCamelCase			)			) + 1       ,					2			):
		if n % i == 0:
			if is_prime(n // i			):
				_lowerCAmelCase								=n // i
				break
			elif is_prime(__UpperCamelCase			):
				_lowerCAmelCase								=i
	return max_number


if __name__ == "__main__":
			print(F"""{solution() = }""")



