






"""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


lowercase_								=	random.Random()



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase=1.0						,			__UpperCamelCase=None						,			__UpperCamelCase=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    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					'''facebook/musicgen-small'''
     __A		     =					tempfile.mkdtemp()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			**_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     return TaTokenizer.from_pretrained(self.checkpoint,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			**_lowerCamelCase					:	Optional[Any]    ):

     '''simple docstring'''
     return EncodecFeatureExtractor.from_pretrained(self.checkpoint,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     shutil.rmtree(self.tmpdirname    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''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 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''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 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     __A		     =					self.get_feature_extractor()
     __A		     =					self.get_tokenizer()

     __A		     =					MusicgenProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

     __A		     =					floats_list((3, 10_00)    )

     __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 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''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 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''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 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''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)    )












"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


lowercase_								=	logging.get_logger(__name__)


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	Optional[int],			*_lowerCamelCase					:	int,			**_lowerCamelCase					:	str    ):

     '''simple docstring'''
     warnings.warn(
         '''The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
         ''' use ViltImageProcessor instead.''',			_lowerCamelCase,			)
     super().__init__(*_lowerCamelCase,			**_lowerCamelCase    )




