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
  
  
  __A          =    random.Random()
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=1.0		,		lowercase__=None		,		lowercase__=None	):
  						if rng is None:
  												lowercase__							 =       global_rng
  
  						lowercase__							 =       []
  						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				):
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       """facebook/musicgen-small"""
  									lowercase__							 =       tempfile.mkdtemp()
  
  
  
  			def A__ (					self       ,     **lowerCamelCase__       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return TaTokenizer.from_pretrained(self.checkpoint       ,     **lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     **lowerCamelCase__       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return EncodecFeatureExtractor.from_pretrained(self.checkpoint       ,     **lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									shutil.rmtree(self.tmpdirname       )
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.get_tokenizer()
  									lowercase__							 =       self.get_feature_extractor()
  
  									lowercase__							 =       MusicgenProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )
  
  									processor.save_pretrained(self.tmpdirname       )
  									lowercase__							 =       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 A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       MusicgenProcessor(tokenizer=self.get_tokenizer()       ,     feature_extractor=self.get_feature_extractor()       )
  									processor.save_pretrained(self.tmpdirname       )
  
  									lowercase__							 =       self.get_tokenizer(bos_token="""(BOS)"""       ,     eos_token="""(EOS)"""       )
  									lowercase__							 =       self.get_feature_extractor(do_normalize=lowerCamelCase__       ,     padding_value=1.0       )
  
  									lowercase__							 =       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 A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.get_feature_extractor()
  									lowercase__							 =       self.get_tokenizer()
  
  									lowercase__							 =       MusicgenProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )
  
  									lowercase__							 =       floats_list((3, 1_000)       )
  
  									lowercase__							 =       feature_extractor(lowerCamelCase__       ,     return_tensors="""np"""       )
  									lowercase__							 =       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 A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.get_feature_extractor()
  									lowercase__							 =       self.get_tokenizer()
  
  									lowercase__							 =       MusicgenProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )
  
  									lowercase__							 =       """This is a test string"""
  
  									lowercase__							 =       processor(text=lowerCamelCase__       )
  
  									lowercase__							 =       tokenizer(lowerCamelCase__       )
  
  									for key in encoded_tok.keys():
  															self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]       )
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.get_feature_extractor()
  									lowercase__							 =       self.get_tokenizer()
  
  									lowercase__							 =       MusicgenProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )
  
  									lowercase__							 =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  									lowercase__							 =       processor.batch_decode(sequences=lowerCamelCase__       )
  									lowercase__							 =       tokenizer.batch_decode(lowerCamelCase__       )
  
  									self.assertListEqual(lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.get_feature_extractor()
  									lowercase__							 =       self.get_tokenizer()
  
  									lowercase__							 =       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 A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.get_feature_extractor(padding_side="""left"""       )
  									lowercase__							 =       self.get_tokenizer()
  
  									lowercase__							 =       MusicgenProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )
  
  									lowercase__							 =       [floats_list((1, x)       )[0] for x in range(5       ,     20       ,     5       )]
  									lowercase__							 =       processor(lowerCamelCase__       ).padding_mask
  
  									lowercase__							 =       np.asarray(floats_list((3, 20)       )       )[:, None, :]
  									lowercase__							 =       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'''
  
  
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       copy.deepcopy(lowercase__	)
  						lowercase__							 =       sorted(lowercase__	)
  						lowercase__							 =       [bisect.bisect_right(lowercase__		,		lowercase__	) for y in x]
  						return quantized
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=0	):
  						lowercase__							 =       np.arange(start=3		,		stop=lowercase__		,		step=4	).tolist() if k > 0 else [10]
  						lowercase__							 =       _quantize(lowercase__		,		lowercase__	)
  						# count number of elements per group
  						lowercase__							 =       np.unique(lowercase__		,		return_counts=lowercase__	)[1]
  						lowercase__							 =       [0] + bins + [np.inf]
  						logger.info("""Using {} as bins for aspect lengths quantization""".format(lowercase__	)	)
  						logger.info("""Count of instances per bin: {}""".format(lowercase__	)	)
  						return groups
  
  
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  															raise ValueError(
  															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
  									lowercase__							 =       sampler
  									lowercase__							 =       group_ids
  									lowercase__							 =       batch_size
  
  
  
  			def __iter__(					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       defaultdict(lowerCamelCase__       )
  									lowercase__							 =       defaultdict(lowerCamelCase__       )
  
  									lowercase__							 =       0
  									for idx in self.sampler:
  															lowercase__							 =       self.group_ids[idx]
  															buffer_per_group[group_id].append(lowerCamelCase__       )
  															samples_per_group[group_id].append(lowerCamelCase__       )
  															if len(buffer_per_group[group_id]       ) == self.batch_size:
  																					yield buffer_per_group[group_id]  # TODO
  																					num_batches += 1
  																					del buffer_per_group[group_id]
  															assert len(buffer_per_group[group_id]       ) < self.batch_size
  
  									# now we have run out of elements that satisfy
  									# the group criteria, let's return the remaining
  									# elements so that the size of the sampler is
  									# deterministic
  									lowercase__							 =       len(self       )
  									lowercase__							 =       expected_num_batches - num_batches
  									if num_remaining > 0:
  															# for the remaining batches, group the batches by similar lengths
  															lowercase__							 =       []
  															for group_id, idxs in sorted(buffer_per_group.items()       ,     key=lambda lowerCamelCase__       : x[0]       ):
  																					batch_idx.extend(lowerCamelCase__       )
  																					if len(lowerCamelCase__       ) >= self.batch_size:
  																											yield batch_idx[: self.batch_size]
  																											lowercase__							 =       batch_idx[self.batch_size :]
  																											num_remaining -= 1
  															if len(lowerCamelCase__       ) > 0:
  																					yield batch_idx
  																					num_remaining -= 1
  									assert num_remaining == 0
  
  
  
  			def __len__(					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size
  
  
  
  
  
  
  