



'''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


lowerCAmelCase						:		Tuple						      = random.Random()



def 	__lowerCAmelCase       (				lowerCamelCase :    int					,		lowerCamelCase :    Optional[Any]=1.0					,		lowerCamelCase :    str=None					,		lowerCamelCase :    int=None       ):






	'''simple docstring'''




	if rng is None:
		__lowerCAmelCase       				=	global_rng

	__lowerCAmelCase       				=	[]
	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			):



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	"facebook/musicgen-small"
						__lowerCAmelCase       				=	tempfile.mkdtemp()



					def 							UpperCAmelCase_			(     self      ,					**UpperCamelCase  )  ->      List[str]:
						return TaTokenizer.from_pretrained(self.checkpoint      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					**UpperCamelCase  )  ->      int:
						return EncodecFeatureExtractor.from_pretrained(self.checkpoint      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						shutil.rmtree(self.tmpdirname  )



					def 							UpperCAmelCase_			(     self  )  ->      str:
						__lowerCAmelCase       				=	self.get_tokenizer()
						__lowerCAmelCase       				=	self.get_feature_extractor()

						__lowerCAmelCase       				=	MusicgenProcessor(tokenizer=UpperCamelCase      ,					feature_extractor=UpperCamelCase  )

						processor.save_pretrained(self.tmpdirname  )
						__lowerCAmelCase       				=	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  )  ->      str:
						__lowerCAmelCase       				=	MusicgenProcessor(tokenizer=self.get_tokenizer()      ,					feature_extractor=self.get_feature_extractor()  )
						processor.save_pretrained(self.tmpdirname  )

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

						__lowerCAmelCase       				=	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  )  ->      Any:
						__lowerCAmelCase       				=	self.get_feature_extractor()
						__lowerCAmelCase       				=	self.get_tokenizer()

						__lowerCAmelCase       				=	MusicgenProcessor(tokenizer=UpperCamelCase      ,					feature_extractor=UpperCamelCase  )

						__lowerCAmelCase       				=	floats_list((3, 1000)  )

						__lowerCAmelCase       				=	feature_extractor(UpperCamelCase      ,					return_tensors="np"  )
						__lowerCAmelCase       				=	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  )  ->      Dict:
						__lowerCAmelCase       				=	self.get_feature_extractor()
						__lowerCAmelCase       				=	self.get_tokenizer()

						__lowerCAmelCase       				=	MusicgenProcessor(tokenizer=UpperCamelCase      ,					feature_extractor=UpperCamelCase  )

						__lowerCAmelCase       				=	"This is a test string"

						__lowerCAmelCase       				=	processor(text=UpperCamelCase  )

						__lowerCAmelCase       				=	tokenizer(UpperCamelCase  )

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



					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						__lowerCAmelCase       				=	self.get_feature_extractor()
						__lowerCAmelCase       				=	self.get_tokenizer()

						__lowerCAmelCase       				=	MusicgenProcessor(tokenizer=UpperCamelCase      ,					feature_extractor=UpperCamelCase  )

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

						__lowerCAmelCase       				=	processor.batch_decode(sequences=UpperCamelCase  )
						__lowerCAmelCase       				=	tokenizer.batch_decode(UpperCamelCase  )

						self.assertListEqual(UpperCamelCase      ,					UpperCamelCase  )



					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						__lowerCAmelCase       				=	self.get_feature_extractor()
						__lowerCAmelCase       				=	self.get_tokenizer()

						__lowerCAmelCase       				=	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  )  ->      Any:
						__lowerCAmelCase       				=	self.get_feature_extractor(padding_side="left"  )
						__lowerCAmelCase       				=	self.get_tokenizer()

						__lowerCAmelCase       				=	MusicgenProcessor(tokenizer=UpperCamelCase      ,					feature_extractor=UpperCamelCase  )

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

						__lowerCAmelCase       				=	np.asarray(floats_list((3, 20)  )  )[:, None, :]
						__lowerCAmelCase       				=	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'''



lowerCAmelCase						:		Any						      = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase						:		Any						      = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase						:		str						      = {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}