

"""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__			:					Dict							  =					random.Random()
def  UpperCamelCase   (     _lowerCAmelCase       :  Union[str, Any],					_lowerCAmelCase       :  Optional[int]=1.0,					_lowerCAmelCase       :  Tuple=None,					_lowerCAmelCase       :  str=None	)			->     List[Any]:
	if rng is None:
		_UpperCAmelCase      :							List[str]       =   global_rng

	_UpperCAmelCase      :							Any       =   []
	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 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   """facebook/musicgen-small"""
				_UpperCAmelCase      :							Tuple       =   tempfile.mkdtemp()
			def 							__snake_case	(						self ,						**_A       )      ->      int:






				'''simple docstring'''







				return TaTokenizer.from_pretrained(self.checkpoint ,						**_A       )
			def 							__snake_case	(						self ,						**_A       )      ->      Optional[int]:






				'''simple docstring'''







				return EncodecFeatureExtractor.from_pretrained(self.checkpoint ,						**_A       )
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







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






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   self.get_tokenizer()
				_UpperCAmelCase      :							List[Any]       =   self.get_feature_extractor()

				_UpperCAmelCase      :							Dict       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

				processor.save_pretrained(self.tmpdirname       )
				_UpperCAmelCase      :							int       =   MusicgenProcessor.from_pretrained(self.tmpdirname       )

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

				self.assertEqual(processor.feature_extractor.to_json_string() ,						feature_extractor.to_json_string()       )
				self.assertIsInstance(processor.feature_extractor ,						_A       )
			def 							__snake_case	(						self       )      ->      Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   MusicgenProcessor(tokenizer=self.get_tokenizer() ,						feature_extractor=self.get_feature_extractor()       )
				processor.save_pretrained(self.tmpdirname       )

				_UpperCAmelCase      :							List[str]       =   self.get_tokenizer(bos_token="""(BOS)""" ,						eos_token="""(EOS)"""       )
				_UpperCAmelCase      :							Dict       =   self.get_feature_extractor(do_normalize=_A ,						padding_value=1.0       )

				_UpperCAmelCase      :							Optional[Any]       =   MusicgenProcessor.from_pretrained(
				    self.tmpdirname ,						bos_token="""(BOS)""" ,						eos_token="""(EOS)""" ,						do_normalize=_A ,						padding_value=1.0       )

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

				self.assertEqual(processor.feature_extractor.to_json_string() ,						feature_extractor_add_kwargs.to_json_string()       )
				self.assertIsInstance(processor.feature_extractor ,						_A       )
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				_UpperCAmelCase      :							Dict       =   self.get_feature_extractor()
				_UpperCAmelCase      :							Dict       =   self.get_tokenizer()

				_UpperCAmelCase      :							Any       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

				_UpperCAmelCase      :							int       =   floats_list((3, 10_00)       )

				_UpperCAmelCase      :							List[str]       =   feature_extractor(_A ,						return_tensors="""np"""       )
				_UpperCAmelCase      :							Tuple       =   processor(_A ,						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 							__snake_case	(						self       )      ->      Any:






				'''simple docstring'''







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

				_UpperCAmelCase      :							Any       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

				_UpperCAmelCase      :							Dict       =   """This is a test string"""

				_UpperCAmelCase      :							List[Any]       =   processor(text=_A       )

				_UpperCAmelCase      :							str       =   tokenizer(_A       )

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key] ,						encoded_processor[key]       )
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Tuple       =   self.get_feature_extractor()
				_UpperCAmelCase      :							Optional[int]       =   self.get_tokenizer()

				_UpperCAmelCase      :							Optional[int]       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

				_UpperCAmelCase      :							str       =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				_UpperCAmelCase      :							Union[str, Any]       =   processor.batch_decode(sequences=_A       )
				_UpperCAmelCase      :							List[str]       =   tokenizer.batch_decode(_A       )

				self.assertListEqual(_A ,						_A       )
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Tuple       =   self.get_feature_extractor()
				_UpperCAmelCase      :							Tuple       =   self.get_tokenizer()

				_UpperCAmelCase      :							List[Any]       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

				self.assertListEqual(
				    processor.model_input_names ,						feature_extractor.model_input_names ,						msg="""`processor` and `feature_extractor` model input names do not match""" ,						)
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







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

				_UpperCAmelCase      :							Union[str, Any]       =   MusicgenProcessor(tokenizer=_A ,						feature_extractor=_A       )

				_UpperCAmelCase      :							Any       =   [floats_list((1, x)       )[0] for x in range(5 ,						20 ,						5       )]
				_UpperCAmelCase      :							Optional[int]       =   processor(_A       ).padding_mask

				_UpperCAmelCase      :							Optional[Any]       =   np.asarray(floats_list((3, 20)       )       )[:, None, :]
				_UpperCAmelCase      :							Union[str, Any]       =   processor.batch_decode(_A ,						padding_mask=_A       )

				self.assertIsInstance(_A ,						_A       )

				for audio in decoded_audios:
					self.assertIsInstance(_A ,						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"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__			:					int							  =					{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					Optional[int]							  =					[
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

						lowerCamelCase__			:					Optional[Any]							  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





