




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


snake_case_					:					Dict       						=			random.Random()


def   A		(__A  :       str  ,							__A  :       int=1.0  ,							__A  :       Dict=None  ,							__A  :       Tuple=None			)  -> int:







     """simple docstring"""


     if rng is None:
          UpperCAmelCase_		=				global_rng

     UpperCAmelCase_		=				[]
     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 __snake_case     (     unittest.TestCase						):







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				'''facebook/musicgen-small'''
        UpperCAmelCase_		=				tempfile.mkdtemp()







   def lowerCamelCase		( self						:		int   ,		**_snake_case						:		List[Any]):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		int   ,		**_snake_case						:		str):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		List[str]):

        """simple docstring"""


        shutil.rmtree(self.tmpdirname)







   def lowerCamelCase		( self						:		int):

        """simple docstring"""


        UpperCAmelCase_		=				self.get_tokenizer()
        UpperCAmelCase_		=				self.get_feature_extractor()

        UpperCAmelCase_		=				MusicgenProcessor(tokenizer=_snake_case   ,		feature_extractor=_snake_case)

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

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

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







   def lowerCamelCase		( self						:		int):

        """simple docstring"""


        UpperCAmelCase_		=				MusicgenProcessor(tokenizer=self.get_tokenizer()   ,		feature_extractor=self.get_feature_extractor())
        processor.save_pretrained(self.tmpdirname)

        UpperCAmelCase_		=				self.get_tokenizer(bos_token='''(BOS)'''   ,		eos_token='''(EOS)''')
        UpperCAmelCase_		=				self.get_feature_extractor(do_normalize=_snake_case   ,		padding_value=1.0)

        UpperCAmelCase_		=				MusicgenProcessor.from_pretrained(
            self.tmpdirname   ,		bos_token='''(BOS)'''   ,		eos_token='''(EOS)'''   ,		do_normalize=_snake_case   ,		padding_value=1.0)

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

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







   def lowerCamelCase		( self						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_		=				self.get_feature_extractor()
        UpperCAmelCase_		=				self.get_tokenizer()

        UpperCAmelCase_		=				MusicgenProcessor(tokenizer=_snake_case   ,		feature_extractor=_snake_case)

        UpperCAmelCase_		=				floats_list((3, 1000))

        UpperCAmelCase_		=				feature_extractor(_snake_case   ,		return_tensors='''np''')
        UpperCAmelCase_		=				processor(_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 lowerCamelCase		( self						:		Any):

        """simple docstring"""


        UpperCAmelCase_		=				self.get_feature_extractor()
        UpperCAmelCase_		=				self.get_tokenizer()

        UpperCAmelCase_		=				MusicgenProcessor(tokenizer=_snake_case   ,		feature_extractor=_snake_case)

        UpperCAmelCase_		=				'''This is a test string'''

        UpperCAmelCase_		=				processor(text=_snake_case)

        UpperCAmelCase_		=				tokenizer(_snake_case)

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







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.get_feature_extractor()
        UpperCAmelCase_		=				self.get_tokenizer()

        UpperCAmelCase_		=				MusicgenProcessor(tokenizer=_snake_case   ,		feature_extractor=_snake_case)

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

        UpperCAmelCase_		=				processor.batch_decode(sequences=_snake_case)
        UpperCAmelCase_		=				tokenizer.batch_decode(_snake_case)

        self.assertListEqual(_snake_case   ,		_snake_case)







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        UpperCAmelCase_		=				self.get_feature_extractor()
        UpperCAmelCase_		=				self.get_tokenizer()

        UpperCAmelCase_		=				MusicgenProcessor(tokenizer=_snake_case   ,		feature_extractor=_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 lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				self.get_feature_extractor(padding_side='''left''')
        UpperCAmelCase_		=				self.get_tokenizer()

        UpperCAmelCase_		=				MusicgenProcessor(tokenizer=_snake_case   ,		feature_extractor=_snake_case)

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

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

        self.assertIsInstance(_snake_case   ,		_snake_case)

        for audio in decoded_audios:
             self.assertIsInstance(_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))












import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


snake_case_					:					Dict       						=			logging.get_logger(__name__)







class __snake_case     (     a						):







   def __init__( self						:		Union[str, Any]   ,		*_snake_case						:		int   ,		**_snake_case						:		List[str]):

        """simple docstring"""


        warnings.warn(
            '''The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
            ''' use LevitImageProcessor instead.'''   ,		_snake_case   ,		)
        super().__init__(*_snake_case   ,		**_snake_case)






