



import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch



class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Union[str, Any]       					=  JukeboxTokenizer
       UpperCAmelCase__    : Optional[int]       					=  {
           "artist": "Zac Brown Band",
           "genres": "Country",
           "lyrics": "I met a traveller from an antique land,\n        Who said \"Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ",
       }
       @require_torch
       def  snake_case_	(      self	)					->		Optional[Any]:
          import torch

          UpperCamelCase   :  Tuple						     =     JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics'	)
          UpperCamelCase   :  List[str]						     =     tokenizer(**self.metas	)['input_ids']
          # fmt: off
          UpperCamelCase   :  Dict						     =     [
              torch.tensor([[
                  0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
                  76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                  44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                  47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                  76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                  30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
                  27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
                  45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
                  41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                  19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                  76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                  76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                  64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                  30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                  27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                  34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                  27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                  41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                  76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                  44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                  76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                  32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                  40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                  20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                  45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                  31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                  45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                  76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                  34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                  31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                  40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                  38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                  76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                  41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                  27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                  46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                  76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                  41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                  46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                  41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                  40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                  27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                  76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                  76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                  41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                  76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                  27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                  34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                  44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                  40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                  46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                  38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                  40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                  27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                  20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                  76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                  76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                  76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                  76, 76]]	),
              torch.tensor([[0, 0, 0, 1069, 11]]	),
              torch.tensor([[0, 0, 0, 1069, 11]]	),
          ]
          # fmt: on
          self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]	)	)
          self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]	)	)
          self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]	)	)






       @require_torch
       def  snake_case_	(      self	)					->		Optional[Any]:
          import torch

          UpperCamelCase   :  str						     =     JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics'	)
          UpperCamelCase   :  Dict						     =     tokenizer(**self.metas	)['input_ids']
          # fmt: off
          UpperCamelCase   :  Optional[int]						     =     [
              torch.tensor([[
                  0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
                  31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                  31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                  40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                  79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                  77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                  27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                  37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                  32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                  77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                  77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                  77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                  46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                  77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                  77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                  77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                  77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                  64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                  40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                  40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                  38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                  31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                  41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                  77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                  46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                  41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                  31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                  31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                  23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                  44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                  31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                  38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                  40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                  27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                  31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                  34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                  31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                  1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                  31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                  45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                  31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                  15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                  11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                  45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                  41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                  44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                  46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                  27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                  35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                  77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                  31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                  41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                  77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                  40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                  77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                  77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                  27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                  77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                  77, 77, 77, 77, 77, 77]]	),
              torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]	),
              torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]	),
          ]
          # fmt: on
          self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]	)	)
          self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]	)	)
          self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]	)	)








import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__UpperCAmelCase	       =	logging.get_logger(__name__)



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : int       					=  ["input_values", "attention_mask"]
       def __init__(      self,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = 1_6000,      SCREAMING_SNAKE_CASE_ = 0.0,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = 80,      SCREAMING_SNAKE_CASE_ = 16,      SCREAMING_SNAKE_CASE_ = 64,      SCREAMING_SNAKE_CASE_ = "hann_window",      SCREAMING_SNAKE_CASE_ = 1.0,      SCREAMING_SNAKE_CASE_ = 80,      SCREAMING_SNAKE_CASE_ = 7600,      SCREAMING_SNAKE_CASE_ = 1e-10,      SCREAMING_SNAKE_CASE_ = 2,      SCREAMING_SNAKE_CASE_ = True,      **SCREAMING_SNAKE_CASE_,      )					->		Union[str, Any]:
          super().__init__(feature_size=SCREAMING_SNAKE_CASE_,      sampling_rate=SCREAMING_SNAKE_CASE_,      padding_value=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     do_normalize
          UpperCamelCase   :  Optional[Any]						     =     return_attention_mask

          UpperCamelCase   :  Union[str, Any]						     =     num_mel_bins
          UpperCamelCase   :  int						     =     hop_length
          UpperCamelCase   :  Any						     =     win_length
          UpperCamelCase   :  Dict						     =     win_function
          UpperCamelCase   :  Any						     =     frame_signal_scale
          UpperCamelCase   :  str						     =     fmin
          UpperCamelCase   :  int						     =     fmax
          UpperCamelCase   :  Dict						     =     mel_floor
          UpperCamelCase   :  Any						     =     reduction_factor

          UpperCamelCase   :  List[str]						     =     win_length * sampling_rate // 1000
          UpperCamelCase   :  Union[str, Any]						     =     hop_length * sampling_rate // 1000
          UpperCamelCase   :  Tuple						     =     optimal_fft_length(self.sample_size	)
          UpperCamelCase   :  int						     =     (self.n_fft // 2) + 1

          UpperCamelCase   :  Any						     =     window_function(window_length=self.sample_size,      name=self.win_function,      periodic=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  int						     =     mel_filter_bank(
              num_frequency_bins=self.n_freqs,      num_mel_filters=self.num_mel_bins,      min_frequency=self.fmin,      max_frequency=self.fmax,      sampling_rate=self.sampling_rate,      norm='slaney',      mel_scale='slaney',      )

          if frame_signal_scale != 1.0:
             warnings.warn(
                 'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers',      SCREAMING_SNAKE_CASE_,      )
          if reduction_factor != 2.0:
             warnings.warn(
                 'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers',      SCREAMING_SNAKE_CASE_,      )
       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def  snake_case_	(      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 0.0	)					->		List[np.ndarray]:
          if attention_mask is not None:
             UpperCamelCase   :  Dict						     =     np.array(SCREAMING_SNAKE_CASE_,      np.intaa	)
             UpperCamelCase   :  int						     =     []

             for vector, length in zip(SCREAMING_SNAKE_CASE_,      attention_mask.sum(-1	)	):
                UpperCamelCase   :  Any						     =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7	)
                if length < normed_slice.shape[0]:
                   UpperCamelCase   :  Optional[Any]						     =     padding_value

                normed_input_values.append(SCREAMING_SNAKE_CASE_	)
          else:
             UpperCamelCase   :  str						     =     [(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

          return normed_input_values
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      )					->		np.ndarray:
          UpperCamelCase   :  int						     =     spectrogram(
              SCREAMING_SNAKE_CASE_,      window=self.window,      frame_length=self.sample_size,      hop_length=self.sample_stride,      fft_length=self.n_fft,      mel_filters=self.mel_filters,      mel_floor=self.mel_floor,      log_mel='log10',      )
          return log_mel_spec.T
       def __call__(      self,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		BatchFeature:
          if audio is None and audio_target is None:
             raise ValueError('You must provide either `audio` or `audio_target` values.'	)

          if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                raise ValueError(
                    F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                    F""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
                    F""" {self.sampling_rate} and not {sampling_rate}."""	)
          else:
             logger.warning(
                 'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
                 'Failing to do so can result in silent errors that might be hard to debug.'	)

          if audio is not None:
             UpperCamelCase   :  Dict						     =     self._process_audio(
                 SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
          else:
             UpperCamelCase   :  str						     =     None

          if audio_target is not None:
             UpperCamelCase   :  str						     =     self._process_audio(
                 SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

             if inputs is None:
                return inputs_target
             else:
                UpperCamelCase   :  Dict						     =     inputs_target['input_values']
                UpperCamelCase   :  str						     =     inputs_target.get('attention_mask'	)
                if decoder_attention_mask is not None:
                   UpperCamelCase   :  str						     =     decoder_attention_mask

          return inputs
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		BatchFeature:
          UpperCamelCase   :  Any						     =     isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and len(speech.shape	) > 1
          if is_batched_numpy and len(speech.shape	) > 2:
             raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""	)
          UpperCamelCase   :  Any						     =     is_batched_numpy or (
              isinstance(SCREAMING_SNAKE_CASE_,      (list, tuple)	) and (isinstance(speech[0],      (np.ndarray, tuple, list)	))
          )

          if is_batched:
             UpperCamelCase   :  str						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	) for speech in speech]
          elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	):
             UpperCamelCase   :  int						     =     np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	)
          elif isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and speech.dtype is np.dtype(np.floataa	):
             UpperCamelCase   :  Any						     =     speech.astype(np.floataa	)

          # always return batch
          if not is_batched:
             UpperCamelCase   :  str						     =     [speech]

          # needed to make pad() work on spectrogram inputs
          UpperCamelCase   :  Optional[Any]						     =     self.feature_size

          # convert into correct format for padding
          if is_target:
             UpperCamelCase   :  Optional[int]						     =     [self._extract_mel_features(SCREAMING_SNAKE_CASE_	) for waveform in speech]
             UpperCamelCase   :  Union[str, Any]						     =     BatchFeature({'input_values': features}	)
             UpperCamelCase   :  List[str]						     =     self.num_mel_bins
          else:
             UpperCamelCase   :  Dict						     =     BatchFeature({'input_values': speech}	)

          UpperCamelCase   :  Tuple						     =     self.pad(
              SCREAMING_SNAKE_CASE_,      padding=SCREAMING_SNAKE_CASE_,      max_length=SCREAMING_SNAKE_CASE_,      truncation=SCREAMING_SNAKE_CASE_,      pad_to_multiple_of=SCREAMING_SNAKE_CASE_,      return_attention_mask=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          UpperCamelCase   :  int						     =     feature_size_hack

          # convert input values to correct format
          UpperCamelCase   :  Optional[int]						     =     padded_inputs['input_values']
          if not isinstance(input_values[0],      np.ndarray	):
             UpperCamelCase   :  Union[str, Any]						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	) for array in input_values]
          elif (
              not isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)
              and isinstance(input_values[0],      np.ndarray	)
              and input_values[0].dtype is np.dtype(np.floataa	)
          ):
             UpperCamelCase   :  Optional[int]						     =     [array.astype(np.floataa	) for array in input_values]
          elif isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
             UpperCamelCase   :  Dict						     =     input_values.astype(np.floataa	)

          # convert attention_mask to correct format
          UpperCamelCase   :  Dict						     =     padded_inputs.get('attention_mask'	)
          if attention_mask is not None:
             UpperCamelCase   :  int						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.intaa	) for array in attention_mask]

          # zero-mean and unit-variance normalization
          if not is_target and self.do_normalize:
             UpperCamelCase   :  Dict						     =     (
                 attention_mask
                 if self._get_padding_strategies(SCREAMING_SNAKE_CASE_,      max_length=SCREAMING_SNAKE_CASE_	) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             UpperCamelCase   :  Tuple						     =     self.zero_mean_unit_var_norm(
                 padded_inputs['input_values'],      attention_mask=SCREAMING_SNAKE_CASE_,      padding_value=self.padding_value	)

          if return_tensors is not None:
             UpperCamelCase   :  int						     =     padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_	)

          return padded_inputs






       def  snake_case_	(      self	)					->		Dict[str, Any]:
          UpperCamelCase   :  Any						     =     super().to_dict()

          # Don't serialize these as they are derived from the other properties.
          UpperCamelCase   :  Any						     =     ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
          for name in names:
             if name in output:
                del output[name]

          return output



