


from __future__ import annotations

from enum import Enum

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: Tuple       =					24
							_lowerCamelCase				: Optional[int]       =					21
							_lowerCamelCase				: int       =					18
							_lowerCamelCase				: List[Any]       =					15
							_lowerCamelCase				: Any       =					12
							_lowerCamelCase				: int       =					9
							_lowerCamelCase				: Optional[Any]       =					6
							_lowerCamelCase				: List[Any]       =					3
							_lowerCamelCase				: str       =					2
							_lowerCamelCase				: Any       =					1
							_lowerCamelCase				: int       =					-1
							_lowerCamelCase				: str       =					-2
							_lowerCamelCase				: int       =					-3
							_lowerCamelCase				: Union[str, Any]       =					-6
							_lowerCamelCase				: Any       =					-9
							_lowerCamelCase				: Dict       =					-12
							_lowerCamelCase				: Optional[int]       =					-15
							_lowerCamelCase				: Union[str, Any]       =					-18
							_lowerCamelCase				: Dict       =					-21
							_lowerCamelCase				: Dict       =					-24







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: List[Any]       =					8
							_lowerCamelCase				: int       =					7
							_lowerCamelCase				: Tuple       =					6
							_lowerCamelCase				: Union[str, Any]       =					5
							_lowerCamelCase				: List[str]       =					4
							_lowerCamelCase				: Optional[int]       =					3
							_lowerCamelCase				: Tuple       =					2
							_lowerCamelCase				: Dict       =					1







def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      float     ,					UpperCamelCase		:      str | SIUnit     ,					UpperCamelCase		:      str | SIUnit     ,					)     ->	float:



	"""simple docstring"""






	if isinstance(UpperCamelCase     ,					UpperCamelCase	):
		a_     			=   SIUnit[known_prefix.lower()]
	if isinstance(UpperCamelCase     ,					UpperCamelCase	):
		a_     			=   SIUnit[unknown_prefix.lower()]
	a_     			=   known_amount * (
	    10 ** (known_prefix.value - unknown_prefix.value)
	)
	return unknown_amount

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      float     ,					UpperCamelCase		:      str | BinaryUnit     ,					UpperCamelCase		:      str | BinaryUnit     ,					)     ->	float:



	"""simple docstring"""






	if isinstance(UpperCamelCase     ,					UpperCamelCase	):
		a_     			=   BinaryUnit[known_prefix.lower()]
	if isinstance(UpperCamelCase     ,					UpperCamelCase	):
		a_     			=   BinaryUnit[unknown_prefix.lower()]
	a_     			=   known_amount * (
	    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
	)
	return unknown_amount


if __name__ == "__main__":
			import doctest

			doctest.testmod()



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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Any=1.0     ,					UpperCamelCase		:      Union[str, Any]=None     ,					UpperCamelCase		:      Union[str, Any]=None	)     ->	List[Any]:



	"""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 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self    ):
								a_     			=   """facebook/musicgen-small"""
								a_     			=   tempfile.mkdtemp()





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return TaTokenizer.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								shutil.rmtree(self.tmpdirname    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_tokenizer()
								a_     			=   self.get_feature_extractor()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								processor.save_pretrained(self.tmpdirname    )
								a_     			=   MusicgenProcessor.from_pretrained(self.tmpdirname    )

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

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





							def   __magic_name__      (							self    ):
								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=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

								a_     			=   MusicgenProcessor.from_pretrained(
								    self.tmpdirname			,       bos_token="""(BOS)"""			,       eos_token="""(EOS)"""			,       do_normalize=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

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

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   floats_list((3, 1000)    )

								a_     			=   feature_extractor(_SCREAMING_SNAKE_CASE			,       return_tensors="""np"""    )
								a_     			=   processor(_SCREAMING_SNAKE_CASE			,       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   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   """This is a test string"""

								a_     			=   processor(text=_SCREAMING_SNAKE_CASE    )

								a_     			=   tokenizer(_SCREAMING_SNAKE_CASE    )

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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

								a_     			=   processor.batch_decode(sequences=_SCREAMING_SNAKE_CASE    )
								a_     			=   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    )

								self.assertListEqual(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor(padding_side="""left"""    )
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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

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

								self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

								for audio in decoded_audios:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       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)    )