




import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
		pass

if is_speech_available():
		from transformers import EncodecFeatureExtractor, MusicgenProcessor


__lowerCAmelCase						:							Union[str, Any]			     =						random.Random()




def      __magic_name__							(					A						:   int,    A						:   str=1.0,    A						:   Dict=None,    A						:   Optional[Any]=None  ):






			'''simple docstring'''







			if rng is None:
						a       =     global_rng

			a       =     []
			for batch_idx in range(shape[0]  ):
						values.append([]  )
						for _ in range(shape[1]  ):
									values[-1].append(rng.random() * scale  )

			return values






@require_torch
@require_sentencepiece
class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	Any				)   ->	int:
							a       =     "facebook/musicgen-small"
							a       =     tempfile.mkdtemp()





				def 				__UpperCAmelCase				( self							:	Optional[Any]   , **__lowerCamelCase							:	List[str]				)   ->	Union[str, Any]:
							return TaTokenizer.from_pretrained(self.checkpoint   , **__UpperCAmelCase				)





				def 				__UpperCAmelCase				( self							:	str   , **__lowerCamelCase							:	int				)   ->	Any:
							return EncodecFeatureExtractor.from_pretrained(self.checkpoint   , **__UpperCAmelCase				)





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Optional[int]:
							shutil.rmtree(self.tmpdirname				)





				def 				__UpperCAmelCase				( self							:	str				)   ->	Dict:
							a       =     self.get_tokenizer()
							a       =     self.get_feature_extractor()

							a       =     MusicgenProcessor(tokenizer=__UpperCAmelCase   , feature_extractor=__UpperCAmelCase				)

							processor.save_pretrained(self.tmpdirname				)
							a       =     MusicgenProcessor.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 				__UpperCAmelCase				( self							:	List[Any]				)   ->	int:
							a       =     MusicgenProcessor(tokenizer=self.get_tokenizer()   , feature_extractor=self.get_feature_extractor()				)
							processor.save_pretrained(self.tmpdirname				)

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

							a       =     MusicgenProcessor.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 				__UpperCAmelCase				( self							:	str				)   ->	List[Any]:
							a       =     self.get_feature_extractor()
							a       =     self.get_tokenizer()

							a       =     MusicgenProcessor(tokenizer=__UpperCAmelCase   , feature_extractor=__UpperCAmelCase				)

							a       =     floats_list((3, 10_00)				)

							a       =     feature_extractor(__UpperCAmelCase   , return_tensors="np"				)
							a       =     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 				__UpperCAmelCase				( self							:	Any				)   ->	Tuple:
							a       =     self.get_feature_extractor()
							a       =     self.get_tokenizer()

							a       =     MusicgenProcessor(tokenizer=__UpperCAmelCase   , feature_extractor=__UpperCAmelCase				)

							a       =     "This is a test string"

							a       =     processor(text=__UpperCAmelCase				)

							a       =     tokenizer(__UpperCAmelCase				)

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





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

							a       =     MusicgenProcessor(tokenizer=__UpperCAmelCase   , feature_extractor=__UpperCAmelCase				)

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

							a       =     processor.batch_decode(sequences=__UpperCAmelCase				)
							a       =     tokenizer.batch_decode(__UpperCAmelCase				)

							self.assertListEqual(__UpperCAmelCase   , __UpperCAmelCase				)





				def 				__UpperCAmelCase				( self							:	str				)   ->	Optional[int]:
							a       =     self.get_feature_extractor()
							a       =     self.get_tokenizer()

							a       =     MusicgenProcessor(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"   , )





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	int:
							a       =     self.get_feature_extractor(padding_side="left"				)
							a       =     self.get_tokenizer()

							a       =     MusicgenProcessor(tokenizer=__UpperCAmelCase   , feature_extractor=__UpperCAmelCase				)

							a       =     [floats_list((1, x)				)[0] for x in range(5   , 20   , 5				)]
							a       =     processor(__UpperCAmelCase				).padding_mask

							a       =     np.asarray(floats_list((3, 20)				)				)[:, None, :]
							a       =     processor.batch_decode(__UpperCAmelCase   , padding_mask=__UpperCAmelCase				)

							self.assertIsInstance(__UpperCAmelCase   , __UpperCAmelCase				)

							for audio in decoded_audios:
										self.assertIsInstance(__UpperCAmelCase   , np.ndarray				)

							self.assertTrue(decoded_audios[0].shape == (1, 10)				)
							self.assertTrue(decoded_audios[1].shape == (1, 15)				)
							self.assertTrue(decoded_audios[2].shape == (1, 20)				)








"""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() = }""")



