code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  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
  
  
  UpperCAmelCase__      =							random.Random()
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Dict=1.0,_SCREAMING_SNAKE_CASE				:   Dict=None,_SCREAMING_SNAKE_CASE				:   List[str]=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 	a__				(				unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		Dict:
  						__A=  "facebook/musicgen-small"
  						__A=  tempfile.mkdtemp()
  			def 				lowerCAmelCase    (     self       : Dict ,						**lowerCAmelCase_       : List[str]  )			->		Tuple:
  						return TaTokenizer.from_pretrained(self.checkpoint ,						**lowerCamelCase__  )
  			def 				lowerCAmelCase    (     self       : List[str] ,						**lowerCAmelCase_       : Optional[Any]  )			->		List[Any]:
  						return EncodecFeatureExtractor.from_pretrained(self.checkpoint ,						**lowerCamelCase__  )
  			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Dict:
  						shutil.rmtree(self.tmpdirname  )
  			def 				lowerCAmelCase    (     self       : int  )			->		List[Any]:
  						__A=  self.get_tokenizer()
  						__A=  self.get_feature_extractor()
  
  						__A=  MusicgenProcessor(tokenizer=lowerCamelCase__ ,						feature_extractor=lowerCamelCase__  )
  
  						processor.save_pretrained(self.tmpdirname  )
  						__A=  MusicgenProcessor.from_pretrained(self.tmpdirname  )
  
  						self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer.get_vocab()  )
  						self.assertIsInstance(processor.tokenizer ,						lowerCamelCase__  )
  
  						self.assertEqual(processor.feature_extractor.to_json_string() ,						feature_extractor.to_json_string()  )
  						self.assertIsInstance(processor.feature_extractor ,						lowerCamelCase__  )
  			def 				lowerCAmelCase    (     self       : List[Any]  )			->		List[str]:
  						__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=lowerCamelCase__ ,						padding_value=1.0  )
  
  						__A=  MusicgenProcessor.from_pretrained(
  						    self.tmpdirname ,						bos_token='(BOS)' ,						eos_token='(EOS)' ,						do_normalize=lowerCamelCase__ ,						padding_value=1.0  )
  
  						self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer_add_kwargs.get_vocab()  )
  						self.assertIsInstance(processor.tokenizer ,						lowerCamelCase__  )
  
  						self.assertEqual(processor.feature_extractor.to_json_string() ,						feature_extractor_add_kwargs.to_json_string()  )
  						self.assertIsInstance(processor.feature_extractor ,						lowerCamelCase__  )
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		List[str]:
  						__A=  self.get_feature_extractor()
  						__A=  self.get_tokenizer()
  
  						__A=  MusicgenProcessor(tokenizer=lowerCamelCase__ ,						feature_extractor=lowerCamelCase__  )
  
  						__A=  floats_list((3, 1_000)  )
  
  						__A=  feature_extractor(lowerCamelCase__ ,						return_tensors='np'  )
  						__A=  processor(lowerCamelCase__ ,						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       : str  )			->		Tuple:
  						__A=  self.get_feature_extractor()
  						__A=  self.get_tokenizer()
  
  						__A=  MusicgenProcessor(tokenizer=lowerCamelCase__ ,						feature_extractor=lowerCamelCase__  )
  
  						__A=  "This is a test string"
  
  						__A=  processor(text=lowerCamelCase__  )
  
  						__A=  tokenizer(lowerCamelCase__  )
  
  						for key in encoded_tok.keys():
  									self.assertListEqual(encoded_tok[key] ,						encoded_processor[key]  )
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		Union[str, Any]:
  						__A=  self.get_feature_extractor()
  						__A=  self.get_tokenizer()
  
  						__A=  MusicgenProcessor(tokenizer=lowerCamelCase__ ,						feature_extractor=lowerCamelCase__  )
  
  						__A=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  						__A=  processor.batch_decode(sequences=lowerCamelCase__  )
  						__A=  tokenizer.batch_decode(lowerCamelCase__  )
  
  						self.assertListEqual(lowerCamelCase__ ,						lowerCamelCase__  )
  			def 				lowerCAmelCase    (     self       : Any  )			->		Optional[Any]:
  						__A=  self.get_feature_extractor()
  						__A=  self.get_tokenizer()
  
  						__A=  MusicgenProcessor(tokenizer=lowerCamelCase__ ,						feature_extractor=lowerCamelCase__  )
  
  						self.assertListEqual(
  						    processor.model_input_names ,						feature_extractor.model_input_names ,						msg='`processor` and `feature_extractor` model input names do not match' ,						)
  
  			def 				lowerCAmelCase    (     self       : str  )			->		int:
  						__A=  self.get_feature_extractor(padding_side='left'  )
  						__A=  self.get_tokenizer()
  
  						__A=  MusicgenProcessor(tokenizer=lowerCamelCase__ ,						feature_extractor=lowerCamelCase__  )
  
  						__A=  [floats_list((1, x)  )[0] for x in range(5 ,						20 ,						5  )]
  						__A=  processor(lowerCamelCase__  ).padding_mask
  
  						__A=  np.asarray(floats_list((3, 20)  )  )[:, None, :]
  						__A=  processor.batch_decode(lowerCamelCase__ ,						padding_mask=lowerCamelCase__  )
  
  						self.assertIsInstance(lowerCamelCase__ ,						lowerCamelCase__  )
  
  						for audio in decoded_audios:
  									self.assertIsInstance(lowerCamelCase__ ,						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)  )
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  UpperCAmelCase__      =							[[0, 1, 1], [0, 0, 1], [1, 0, 0]]
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Any ,						lowerCAmelCase_       : Optional[int]  )			->		Tuple:
  						__A=  name
  						__A=  []
  						__A=  []
  			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : Dict  )			->		Any:
  						self.inbound.append(lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : str ,						lowerCAmelCase_       : Any  )			->		List[Any]:
  						self.outbound.append(lowerCAmelCase_  )
  
  			def __repr__(     self       : int  )			->		Union[str, Any]:
  						return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   Optional[int]=3,_SCREAMING_SNAKE_CASE				:   Dict=0.85   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  {}
  			for node in nodes:
  						__A=  1
  
  			__A=  {}
  			for node in nodes:
  						__A=  len(node.outbound   )
  
  			for i in range(_SCREAMING_SNAKE_CASE   ):
  						print(f"""======= Iteration {i + 1} ======="""   )
  						for _, node in enumerate(_SCREAMING_SNAKE_CASE   ):
  									__A=  (1 - d) + d * sum(
  									    ranks[ib] / outbounds[ib] for ib in node.inbound   )
  						print(_SCREAMING_SNAKE_CASE   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  list(input('Enter Names of the Nodes: '   ).split()   )
  
  			__A=  [Node(_SCREAMING_SNAKE_CASE   ) for name in names]
  
  			for ri, row in enumerate(_SCREAMING_SNAKE_CASE   ):
  						for ci, col in enumerate(_SCREAMING_SNAKE_CASE   ):
  									if col == 1:
  												nodes[ci].add_inbound(names[ri]   )
  												nodes[ri].add_outbound(names[ci]   )
  
  			print('======= Nodes ======='   )
  			for node in nodes:
  						print(_SCREAMING_SNAKE_CASE   )
  
  			page_rank(_SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  