



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


_lowercase				:					Tuple     					=random.Random()




def    UpperCAmelCase					(  lowercase__						:					Union[str, Any]				,					lowercase__						:					Optional[int]=1.0				,					lowercase__						:					List[Any]=None				,					lowercase__						:					Union[str, Any]=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 lowerCAmelCase_       (   unittest.TestCase			):







	'''simple docstring'''




	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  """facebook/musicgen-small"""
				a__					=  tempfile.mkdtemp()

	def 				_A			(	self   ,       **lowerCamelCase       ):



				'''simple docstring'''


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

	def 				_A			(	self   ,       **lowerCamelCase       ):



				'''simple docstring'''


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

	def 				_A			(	self       ):



				'''simple docstring'''


				shutil.rmtree(self.tmpdirname       )

	def 				_A			(	self       ):



				'''simple docstring'''


				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 				_A			(	self       ):



				'''simple docstring'''


				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 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_feature_extractor()
				a__					=  self.get_tokenizer()

				a__					=  MusicgenProcessor(tokenizer=lowerCamelCase   ,       feature_extractor=lowerCamelCase       )

				a__					=  floats_list((3, 1000)       )

				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 				_A			(	self       ):



				'''simple docstring'''


				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 				_A			(	self       ):



				'''simple docstring'''


				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 				_A			(	self       ):



				'''simple docstring'''


				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 				_A			(	self       ):



				'''simple docstring'''


				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)       )






from .prime_check import Test

Test()

