




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  :		Tuple		     =   random.Random()
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=1.0    ,SCREAMING_SNAKE_CASE_=None    ,SCREAMING_SNAKE_CASE_=None    )      ->			List[Any]:
							if rng is None:
														lowercase__  :	Union[str, Any]        =							global_rng

							lowercase__  :	str        =							[]
							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 				UpperCAmelCase(					unittest.TestCase					):



	"""simple docstring"""






	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	str        =							"facebook/musicgen-small"
								lowercase__  :	Optional[Any]        =							tempfile.mkdtemp()

	def    __a     (					self	,				**lowerCamelCase    ) ->      int:





								"""simple docstring"""


								return TaTokenizer.from_pretrained(self.checkpoint	,				**lowerCamelCase    )

	def    __a     (					self	,				**lowerCamelCase    ) ->      Union[str, Any]:





								"""simple docstring"""


								return EncodecFeatureExtractor.from_pretrained(self.checkpoint	,				**lowerCamelCase    )

	def    __a     (					self    ) ->      List[Any]:





								"""simple docstring"""


								shutil.rmtree(self.tmpdirname    )

	def    __a     (					self    ) ->      Union[str, Any]:





								"""simple docstring"""


								lowercase__  :	List[str]        =							self.get_tokenizer()
								lowercase__  :	Tuple        =							self.get_feature_extractor()

								lowercase__  :	int        =							MusicgenProcessor(tokenizer=lowerCamelCase	,				feature_extractor=lowerCamelCase    )

								processor.save_pretrained(self.tmpdirname    )
								lowercase__  :	Tuple        =							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    ) ->      Optional[int]:





								"""simple docstring"""


								lowercase__  :	Dict        =							MusicgenProcessor(tokenizer=self.get_tokenizer()	,				feature_extractor=self.get_feature_extractor()    )
								processor.save_pretrained(self.tmpdirname    )

								lowercase__  :	List[str]        =							self.get_tokenizer(bos_token="(BOS)"	,				eos_token="(EOS)"    )
								lowercase__  :	List[Any]        =							self.get_feature_extractor(do_normalize=lowerCamelCase	,				padding_value=1.0    )

								lowercase__  :	str        =							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    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	Any        =							self.get_feature_extractor()
								lowercase__  :	str        =							self.get_tokenizer()

								lowercase__  :	Optional[Any]        =							MusicgenProcessor(tokenizer=lowerCamelCase	,				feature_extractor=lowerCamelCase    )

								lowercase__  :	List[str]        =							floats_list((3, 1000)    )

								lowercase__  :	Dict        =							feature_extractor(lowerCamelCase	,				return_tensors="np"    )
								lowercase__  :	str        =							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__  :	Optional[int]        =							self.get_feature_extractor()
								lowercase__  :	List[Any]        =							self.get_tokenizer()

								lowercase__  :	List[Any]        =							MusicgenProcessor(tokenizer=lowerCamelCase	,				feature_extractor=lowerCamelCase    )

								lowercase__  :	int        =							"This is a test string"

								lowercase__  :	Tuple        =							processor(text=lowerCamelCase    )

								lowercase__  :	Tuple        =							tokenizer(lowerCamelCase    )

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

	def    __a     (					self    ) ->      str:





								"""simple docstring"""


								lowercase__  :	List[str]        =							self.get_feature_extractor()
								lowercase__  :	int        =							self.get_tokenizer()

								lowercase__  :	List[str]        =							MusicgenProcessor(tokenizer=lowerCamelCase	,				feature_extractor=lowerCamelCase    )

								lowercase__  :	Tuple        =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								lowercase__  :	List[str]        =							processor.batch_decode(sequences=lowerCamelCase    )
								lowercase__  :	List[str]        =							tokenizer.batch_decode(lowerCamelCase    )

								self.assertListEqual(lowerCamelCase	,				lowerCamelCase    )

	def    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	int        =							self.get_feature_extractor()
								lowercase__  :	Optional[Any]        =							self.get_tokenizer()

								lowercase__  :	List[Any]        =							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    ) ->      int:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							self.get_feature_extractor(padding_side="left"    )
								lowercase__  :	Any        =							self.get_tokenizer()

								lowercase__  :	Any        =							MusicgenProcessor(tokenizer=lowerCamelCase	,				feature_extractor=lowerCamelCase    )

								lowercase__  :	Any        =							[floats_list((1, x)    )[0] for x in range(5	,				20	,				5    )]
								lowercase__  :	List[str]        =							processor(lowerCamelCase    ).padding_mask

								lowercase__  :	Optional[int]        =							np.asarray(floats_list((3, 20)    )    )[:, None, :]
								lowercase__  :	List[Any]        =							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)    )





import numpy as np
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			int:
							lowercase__  :	str        =							n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							lowercase__  :	Union[str, Any]        =							np.floor(SCREAMING_SNAKE_CASE_    )
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							lowercase__  :	Tuple        =							int(n_draws - floored.sum()    )
							if need_to_add > 0:
														lowercase__  :	Union[str, Any]        =							continuous - floored
														lowercase__  :	Optional[Any]        =							np.sort(np.unique(SCREAMING_SNAKE_CASE_    )    )[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					((lowercase__) ,       )  :	Optional[Any]        =							np.where(remainder == value    )
																					# if we need_to_add less than what's in inds
																					# we draw randomly from them.
																					# if we need to add more, we add them all and
																					# go to the next value
																					lowercase__  :	Dict        =							min(len(SCREAMING_SNAKE_CASE_    )    ,SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Any        =							rng.choice(SCREAMING_SNAKE_CASE_    ,size=SCREAMING_SNAKE_CASE_    ,replace=SCREAMING_SNAKE_CASE_    )
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa    )




def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=10    )      ->			List[str]:
							lowercase__ ,       lowercase__  :	int        =							np.unique(SCREAMING_SNAKE_CASE_    ,return_inverse=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	int        =							classes.shape[0]
							lowercase__  :	Union[str, Any]        =							np.bincount(SCREAMING_SNAKE_CASE_    )
							if np.min(SCREAMING_SNAKE_CASE_    ) < 2:
														raise ValueError("Minimum class count error"    )
							if n_train < n_classes:
														raise ValueError(
														    "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)    )
							if n_test < n_classes:
														raise ValueError(
														    "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)    )
							lowercase__  :	List[Any]        =							np.split(np.argsort(SCREAMING_SNAKE_CASE_    ,kind="mergesort"    )    ,np.cumsum(SCREAMING_SNAKE_CASE_    )[:-1]    )
							for _ in range(SCREAMING_SNAKE_CASE_    ):
														lowercase__  :	str        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
														lowercase__  :	int        =							class_counts - n_i
														lowercase__  :	int        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

														lowercase__  :	Tuple        =							[]
														lowercase__  :	int        =							[]

														for i in range(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Union[str, Any]        =							rng.permutation(class_counts[i]    )
																					lowercase__  :	List[Any]        =							class_indices[i].take(SCREAMING_SNAKE_CASE_    ,mode="clip"    )
																					train.extend(perm_indices_class_i[: n_i[i]]    )
																					test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]    )
														lowercase__  :	Tuple        =							rng.permutation(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Dict        =							rng.permutation(SCREAMING_SNAKE_CASE_    )

														yield train, test