




'''simple docstring'''



import numpy as np
from matplotlib import pyplot as plt
def  SCREAMING_SNAKE_CASE_ (							__A				: Optional[int]  ) -> List[str]:
   return 1 / (1 + np.exp(-1 * x  ))







class 		lowercase_    :


   """simple docstring"""

   def __init__(    self					:	List[Any]  ,      __lowerCamelCase					:	Dict  ,      __lowerCamelCase					:	Dict=None  ,      __lowerCamelCase					:	Tuple=None  ,      __lowerCamelCase					:	Union[str, Any]=False    ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      units
      _SCREAMING_SNAKE_CASE       =      None
      _SCREAMING_SNAKE_CASE       =      None
      _SCREAMING_SNAKE_CASE       =      activation
      if learning_rate is None:
         _SCREAMING_SNAKE_CASE       =      0.3
      _SCREAMING_SNAKE_CASE       =      learning_rate
      _SCREAMING_SNAKE_CASE       =      is_input_layer



   def 							lowerCAmelCase_			(    self					:	Union[str, Any]  ,      __lowerCamelCase					:	Optional[Any]    ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      np.asmatrix(np.random.normal(0  ,      0.5  ,      (self.units, back_units)    )    )
      _SCREAMING_SNAKE_CASE       =      np.asmatrix(np.random.normal(0  ,      0.5  ,      self.units    )    ).T
      if self.activation is None:
         _SCREAMING_SNAKE_CASE       =      sigmoid



   def 							lowerCAmelCase_			(    self					:	List[Any]    ):

      """simple docstring"""






      if self.activation == sigmoid:
         _SCREAMING_SNAKE_CASE       =      np.dot(self.output  ,      (1 - self.output).T    )
         _SCREAMING_SNAKE_CASE       =      np.diag(np.diag(_snake_case    )    )
      else:
         _SCREAMING_SNAKE_CASE       =      1
      return gradient_activation



   def 							lowerCAmelCase_			(    self					:	Optional[Any]  ,      __lowerCamelCase					:	Optional[Any]    ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      xdata
      if self.is_input_layer:
         # input layer
         _SCREAMING_SNAKE_CASE       =      xdata
         _SCREAMING_SNAKE_CASE       =      xdata
         return xdata
      else:
         _SCREAMING_SNAKE_CASE       =      np.dot(self.weight  ,      self.xdata    ) - self.bias
         _SCREAMING_SNAKE_CASE       =      self.activation(self.wx_plus_b    )
         return self.output



   def 							lowerCAmelCase_			(    self					:	Optional[int]  ,      __lowerCamelCase					:	List[str]    ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      self.cal_gradient()  # i * i 维
      _SCREAMING_SNAKE_CASE       =      np.asmatrix(np.dot(gradient.T  ,      _snake_case    )    )

      _SCREAMING_SNAKE_CASE       =      np.asmatrix(self.xdata    )
      _SCREAMING_SNAKE_CASE       =      -1
      _SCREAMING_SNAKE_CASE       =      self.weight

      _SCREAMING_SNAKE_CASE       =      np.dot(gradient.T  ,      self._gradient_weight.T    )
      _SCREAMING_SNAKE_CASE       =      gradient * self._gradient_bias
      _SCREAMING_SNAKE_CASE       =      np.dot(_snake_case  ,      self._gradient_x    ).T
      # upgrade: the Negative gradient direction
      _SCREAMING_SNAKE_CASE       =      self.weight - self.learn_rate * self.gradient_weight
      _SCREAMING_SNAKE_CASE       =      self.bias - self.learn_rate * self.gradient_bias.T
      # updates the weights and bias according to learning rate (0.3 if undefined)
      return self.gradient







class 		lowercase_    :


   """simple docstring"""

   def __init__(    self					:	Tuple    ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      []
      _SCREAMING_SNAKE_CASE       =      []
      _SCREAMING_SNAKE_CASE       =      plt.figure()
      _SCREAMING_SNAKE_CASE       =      self.fig_loss.add_subplot(1  ,      1  ,      1    )



   def 							lowerCAmelCase_			(    self					:	Optional[int]  ,      __lowerCamelCase					:	Dict    ):

      """simple docstring"""
      self.layers.append(_snake_case    )



   def 							lowerCAmelCase_			(    self					:	Optional[int]    ):

      """simple docstring"""






      for i, layer in enumerate(self.layers[:]    ):
         if i < 1:
            _SCREAMING_SNAKE_CASE       =      True
         else:
            layer.initializer(self.layers[i - 1].units    )



   def 							lowerCAmelCase_			(    self					:	List[str]    ):

      """simple docstring"""






      for i, layer in enumerate(self.layers[:]    ):
         print(F"""------- layer {i} -------"""    )
         print("weight.shape "  ,      np.shape(layer.weight    )    )
         print("bias.shape "  ,      np.shape(layer.bias    )    )



   def 							lowerCAmelCase_			(    self					:	int  ,      __lowerCamelCase					:	int  ,      __lowerCamelCase					:	Union[str, Any]  ,      __lowerCamelCase					:	Tuple  ,      __lowerCamelCase					:	List[str]    ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      train_round
      _SCREAMING_SNAKE_CASE       =      accuracy

      self.ax_loss.hlines(self.accuracy  ,      0  ,      self.train_round * 1.1    )

      _SCREAMING_SNAKE_CASE       =      np.shape(_snake_case    )
      for _ in range(_snake_case    ):
         _SCREAMING_SNAKE_CASE       =      0
         for row in range(x_shape[0]    ):
            _SCREAMING_SNAKE_CASE       =      np.asmatrix(xdata[row, :]    ).T
            _SCREAMING_SNAKE_CASE       =      np.asmatrix(ydata[row, :]    ).T

            # forward propagation
            for layer in self.layers:
               _SCREAMING_SNAKE_CASE       =      layer.forward_propagation(_xdata    )

            _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE       =      self.cal_loss(_ydata  ,      _xdata    )
            _SCREAMING_SNAKE_CASE       =      all_loss + loss

            # back propagation: the input_layer does not upgrade
            for layer in self.layers[:0:-1]:
               _SCREAMING_SNAKE_CASE       =      layer.back_propagation(_snake_case    )

         _SCREAMING_SNAKE_CASE       =      all_loss / x_shape[0]
         self.train_mse.append(_snake_case    )

         self.plot_loss()

         if mse < self.accuracy:
            print("----达到精度----"    )
            return mse
      return None



   def 							lowerCAmelCase_			(    self					:	Optional[Any]  ,      __lowerCamelCase					:	List[str]  ,      __lowerCamelCase					:	Optional[Any]    ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE       =      np.sum(np.power((ydata - ydata_)  ,      2    )    )
      _SCREAMING_SNAKE_CASE       =      2 * (ydata_ - ydata)
      # vector (shape is the same as _ydata.shape)
      return self.loss, self.loss_gradient



   def 							lowerCAmelCase_			(    self					:	Optional[int]    ):

      """simple docstring"""






      if self.ax_loss.lines:
         self.ax_loss.lines.remove(self.ax_loss.lines[0]    )
      self.ax_loss.plot(self.train_mse  ,      "r-"    )
      plt.ion()
      plt.xlabel("step"    )
      plt.ylabel("loss"    )
      plt.show()
      plt.pause(0.1    )


def  SCREAMING_SNAKE_CASE_ (							) -> Optional[Any]:
   _SCREAMING_SNAKE_CASE       =      np.random.randn(10       ,     10  )
   _SCREAMING_SNAKE_CASE       =      np.asarray(
       [
           [0.8, 0.4],
           [0.4, 0.3],
           [0.3_4, 0.4_5],
           [0.6_7, 0.3_2],
           [0.8_8, 0.6_7],
           [0.7_8, 0.7_7],
           [0.5_5, 0.6_6],
           [0.5_5, 0.4_3],
           [0.5_4, 0.1],
           [0.1, 0.5],
       ]  )
   _SCREAMING_SNAKE_CASE       =      BPNN()
   for i in (10, 20, 30, 2):
      model.add_layer(DenseLayer(_UpperCAmelCase  )  )
   model.build()
   model.summary()
   model.train(xdata=_UpperCAmelCase       ,     ydata=_UpperCAmelCase       ,     train_round=1_00       ,     accuracy=0.0_1  )


if __name__ == "__main__":
 example()








"""simple docstring"""
import unittest
from unittest import skip

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed


if is_torch_available():
       import torch

       from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer







@require_torch
class 	a				(					unittest.TestCase   ):
 snake_case__    	=    (JukeboxModel,) if is_torch_available() else ()
 snake_case__    	=    '''openai/jukebox-1b-lyrics'''
 snake_case__    	=    {
     '''artist''': '''Zac Brown Band''',
     '''genres''': '''Country''',
     '''lyrics''': '''I met a traveller from an antique land,
    Who said "Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away
    ''',
 }
 # fmt: off
 snake_case__    	=    [
     1_8_6_4, 1_5_3_6, 1_2_1_3, 1_8_7_0, 1_3_5_7, 1_5_3_6, 5_1_9, 8_8_0, 1_3_2_3, 7_8_9, 1_0_8_2, 5_3_4,
     1_0_0_0, 1_4_4_5, 1_1_0_5, 1_1_3_0, 9_6_7, 5_1_5, 1_4_3_4, 1_6_2_0, 5_3_4, 1_4_9_5, 2_8_3, 1_4_4_5,
     3_3_3, 1_3_0_7, 5_3_9, 1_6_3_1, 1_5_2_8, 3_7_5, 1_4_3_4, 6_7_3, 6_2_7, 7_1_0, 7_7_8, 1_8_8_3,
     1_4_0_5, 1_2_7_6, 1_4_5_5, 1_2_2_8
 ]

 snake_case__    	=    [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]

 snake_case__    	=    [
     1_1_2_5, 1_7_5_1, 6_9_7, 1_7_7_6, 1_1_4_1, 1_4_7_6, 3_9_1, 6_9_7, 1_1_2_5, 6_8_4, 8_6_7, 4_1_6,
     8_4_4, 1_3_7_2, 1_2_7_4, 7_1_7, 1_2_7_4, 8_4_4, 1_2_9_9, 1_4_1_9, 6_9_7, 1_3_7_0, 3_1_7, 1_1_2_5,
     1_9_1, 1_4_4_0, 1_3_7_0, 1_4_4_0, 1_3_7_0, 2_8_2, 1_6_2_1, 1_3_7_0, 3_6_8, 3_4_9, 8_6_7, 1_8_7_2,
     1_2_6_2, 8_6_9, 1_7_2_8, 7_4_7
 ]
 snake_case__    	=    [
     4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]

 snake_case__    	=    [
     1_7_5_5, 8_4_2, 3_0_7, 1_8_4_3, 1_0_2_2, 1_3_9_5, 2_3_4, 1_5_5_4, 8_0_6, 7_3_9, 1_0_2_2, 4_4_2,
     6_1_6, 5_5_6, 2_6_8, 1_4_9_9, 9_3_3, 4_5_7, 1_4_4_0, 1_8_3_7, 7_5_5, 9_8_5, 3_0_8, 9_0_2,
     2_9_3, 1_4_4_3, 1_6_7_1, 1_1_4_1, 1_5_3_3, 5_5_5, 1_5_6_2, 1_0_6_1, 2_8_7, 4_1_7, 1_0_2_2, 2_0_0_8,
     1_1_8_6, 1_0_1_5, 1_7_7_7, 2_6_8
 ]
 snake_case__    	=    [
     8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2,
     1_8_5, 4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 3_0_7, 8_4_2,
     5_9_1, 8_4_2, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2,
     1_8_5, 8_4_2, 5_9_1, 8_9
 ]

 snake_case__    	=    [1_0_5_8_3_0_4, 0, 7_8_6_4_3_2, 7_1_6_9, 5_0_7, 7_6, 2_7, 4_0, 3_0, 7_6]

 snake_case__    	=    [
     3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
     1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
     1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
     1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
 ]
 snake_case__    	=    [
     1_2_3_6, 1_6_6_8, 1_4_8_4, 1_9_2_0, 1_8_4_8, 1_4_0_9, 1_3_9, 8_6_4, 1_8_2_8, 1_2_7_2, 1_5_9_9, 8_2_4,
     1_6_7_2, 1_3_9, 5_5_5, 1_4_8_4, 8_2_4, 1_9_2_0, 5_5_5, 5_9_6, 1_5_7_9, 1_5_9_9, 1_2_3_1, 1_5_9_9,
     1_6_3_7, 1_4_0_7, 2_1_2, 8_2_4, 1_5_9_9, 1_1_6, 1_4_3_3, 8_2_4, 2_5_8, 1_5_9_9, 1_4_3_3, 1_8_9_5,
     1_0_6_3, 1_4_3_3, 1_4_3_3, 1_5_9_9
 ]
 snake_case__    	=    [
     1_6_8_4, 1_8_7_3, 1_1_1_9, 1_1_8_9, 3_9_5, 6_1_1, 1_9_0_1, 9_7_2, 8_9_0, 1_3_3_7, 1_3_9_2, 1_9_2_7,
     9_6, 9_7_2, 6_7_2, 7_8_0, 1_1_1_9, 8_9_0, 1_5_8, 7_7_1, 1_0_7_3, 1_9_2_7, 3_5_3, 1_3_3_1,
     1_2_6_9, 1_4_5_9, 1_3_3_3, 1_6_4_5, 8_1_2, 1_5_7_7, 1_3_3_7, 6_0_6, 3_5_3, 9_8_1, 1_4_6_6, 6_1_9,
     1_9_7, 3_9_1, 3_0_2, 1_9_3_0
 ]
 snake_case__    	=    [
     3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
     1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
     1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
     1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
 ]
 snake_case__    	=    [
     -0.04_92, -0.05_24, -0.05_65, -0.06_40, -0.06_86, -0.06_84, -0.06_77, -0.06_64,
     -0.06_05, -0.04_90, -0.03_30, -0.01_68, -0.00_83, -0.00_75, -0.00_51, 0.00_25,
     0.01_36, 0.02_61, 0.03_86, 0.04_97, 0.05_80, 0.05_99, 0.05_83, 0.06_14,
     0.07_40, 0.08_89, 0.10_23, 0.11_62, 0.12_11, 0.12_12, 0.12_51, 0.13_36,
     0.15_02, 0.16_86, 0.18_83, 0.21_48, 0.23_63, 0.24_58, 0.25_07, 0.25_31
 ]
 snake_case__    	=    [
     0.02_56, -0.05_44, 0.16_00, -0.00_32, 0.10_66, 0.08_25, -0.00_13, 0.34_40,
     0.02_10, 0.04_12, -0.17_77, -0.08_92, -0.01_64, 0.02_85, -0.06_13, -0.06_17,
     -0.01_37, -0.02_01, -0.01_75, 0.02_15, -0.06_27, 0.05_20, -0.07_30, 0.09_70,
     -0.01_00, 0.04_42, -0.05_86, 0.02_07, -0.00_15, -0.00_82
 ]
 snake_case__    	=    [
     0.04_15, 0.08_77, 0.00_22, -0.00_55, 0.07_51, 0.03_34, 0.03_24, -0.00_68,
     0.00_11, 0.00_17, -0.06_76, 0.06_55, -0.01_43, 0.03_99, 0.03_03, 0.07_43,
     -0.01_68, -0.03_94, -0.11_13, 0.01_24, 0.04_42, 0.02_67, -0.00_03, -0.15_36,
     -0.01_16, -0.18_37, -0.01_80, -0.10_26, -0.07_77, -0.04_56
 ]
 snake_case__    	=    [
     7_6, 2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5, 4_5, 7_6, 3_8, 3_1, 3_3,
     4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6, 4_1, 4_0, 3_1, 7_8, 7_6
 ]



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      JukeboxTokenizer.from_pretrained(self.model_id			)
  lowerCAmelCase  							=      tokenizer(**self.metas			)['input_ids']
  return tokens



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      JukeboxModel.from_pretrained(self.model_id							,    min_duration=0			).eval()
  lowerCAmelCase  							=      self.prepare_inputs()

  set_seed(0			)
  lowerCAmelCase  							=      [torch.zeros(1							,    0							,    dtype=torch.long			).cpu() for _ in range(3			)]
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [0]							,    sample_length=40 * model.priors[0].raw_to_tokens							,    save_results=_snake_case			)
  self.assertIn(zs[0][0].detach().cpu().tolist()							,    [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]			)

  set_seed(0			)
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [1]							,    sample_length=40 * model.priors[1].raw_to_tokens							,    save_results=_snake_case			)
  self.assertIn(zs[1][0].detach().cpu().tolist()							,    [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]			)

  set_seed(0			)
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [2]							,    sample_length=40 * model.priors[2].raw_to_tokens							,    save_results=_snake_case			)
  self.assertIn(zs[2][0].detach().cpu().tolist()							,    [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      False
  lowerCAmelCase  							=      JukeboxModel.from_pretrained(self.model_id							,    min_duration=0			).eval()

  lowerCAmelCase  							=      self.prepare_inputs()
  set_seed(0			)
  lowerCAmelCase  							=      [torch.zeros(1							,    0							,    dtype=torch.long			) for _ in range(3			)]

  lowerCAmelCase  							=      model.priors[0]
  lowerCAmelCase  							=      0
  lowerCAmelCase  							=      top_prior.get_music_tokens_conds(_snake_case							,    start=_snake_case							,    end=start + top_prior.n_ctx			)
  lowerCAmelCase  							=      top_prior.get_metadata(labels[0].clone()							,    _snake_case							,    1_05_83_04							,    0			)

  self.assertIsNone(_snake_case			)
  self.assertListEqual(metadata.numpy()[0][:10].tolist()							,    self.EXPECTED_Y_COND			)

  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase  							=      top_prior.get_cond(_snake_case							,    _snake_case			)
  torch.testing.assert_allclose(
      audio_conditioning[0][0][:30].detach()							,    torch.tensor(self.EXPECTED_AUDIO_COND			)							,    atol=1E-4							,    rtol=1E-4			)
  torch.testing.assert_allclose(
      metadata_conditioning[0][0][:30].detach()							,    torch.tensor(self.EXPECTED_META_COND			)							,    atol=1E-4							,    rtol=1E-4			)
  torch.testing.assert_allclose(
      lyric_tokens[0, :30].detach()							,    torch.tensor(self.EXPECTED_LYRIC_COND			)							,    atol=1E-4							,    rtol=1E-4			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      False

  lowerCAmelCase  							=      JukeboxModel.from_pretrained(self.model_id							,    min_duration=0			).eval()
  set_seed(0			)
  lowerCAmelCase  							=      torch.rand((1, 51_20, 1)			)
  lowerCAmelCase  							=      list(self.prepare_inputs()			)

  lowerCAmelCase  							=      [model.vqvae.encode(_snake_case							,    start_level=2							,    bs_chunks=waveform.shape[0]			)[0], None, None]
  lowerCAmelCase  							=      model._sample(
      _snake_case							,    _snake_case							,    sample_levels=[0]							,    save_results=_snake_case							,    sample_length=40 * model.priors[0].raw_to_tokens			)
  torch.testing.assert_allclose(zs[0][0][:40]							,    torch.tensor(self.EXPECTED_PRIMED_0			)			)

  lowerCAmelCase  							=      torch.cat((zs[0], torch.zeros(1							,    20_48 - zs[0].shape[-1]			))							,    dim=-1			).long()
  lowerCAmelCase  							=      [upper_a, model.vqvae.encode(_snake_case							,    start_level=1							,    bs_chunks=waveform.shape[0]			)[0], None]
  lowerCAmelCase  							=      model._sample(
      _snake_case							,    _snake_case							,    sample_levels=[1]							,    save_results=_snake_case							,    sample_length=40 * model.priors[1].raw_to_tokens			)
  torch.testing.assert_allclose(zs[1][0][:40]							,    torch.tensor(self.EXPECTED_PRIMED_1			)			)

  lowerCAmelCase  							=      torch.cat((zs[1], torch.zeros(1							,    20_48 - zs[1].shape[-1]			))							,    dim=-1			).long()
  lowerCAmelCase  							=      [upper_a, upper_a, model.vqvae.encode(_snake_case							,    start_level=0							,    bs_chunks=waveform.shape[0]			)[0]]
  lowerCAmelCase  							=      model._sample(
      _snake_case							,    _snake_case							,    sample_levels=[2]							,    save_results=_snake_case							,    sample_length=40 * model.priors[2].raw_to_tokens			)
  torch.testing.assert_allclose(zs[2][0][:40].cpu()							,    torch.tensor(self.EXPECTED_PRIMED_2			)			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      JukeboxModel.from_pretrained(self.model_id							,    min_duration=0			).eval()
  set_seed(0			)
  lowerCAmelCase  							=      torch.rand((1, 51_20, 1)			)
  with torch.no_grad():
   lowerCAmelCase  							=      model.vqvae.encode(_snake_case							,    start_level=2							,    bs_chunks=x.shape[0]			)
  torch.testing.assert_allclose(zs[0][0]							,    torch.tensor(self.EXPECTED_VQVAE_ENCODE			)			)

  with torch.no_grad():
   lowerCAmelCase  							=      model.vqvae.decode(_snake_case							,    start_level=2							,    bs_chunks=x.shape[0]			)
  torch.testing.assert_allclose(x[0, :40, 0]							,    torch.tensor(self.EXPECTED_VQVAE_DECODE			)							,    atol=1E-4							,    rtol=1E-4			)







@require_torch
class 	a				(					unittest.TestCase   ):
 snake_case__    	=    (JukeboxModel,) if is_torch_available() else ()
 snake_case__    	=    '''openai/jukebox-5b-lyrics'''
 snake_case__    	=    {
     '''artist''': '''Zac Brown Band''',
     '''genres''': '''Country''',
     '''lyrics''': '''I met a traveller from an antique land,
    Who said "Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away
    ''',
 }

 # fmt: off
 snake_case__    	=    [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     1_4_8_9, 1_4_8_9, 1_4_8_9, 1_4_8_9, 1_1_5_0, 1_8_5_3, 1_5_0_9, 1_1_5_0, 1_3_5_7, 1_5_0_9, 6, 1_2_7_2
 ]
 snake_case__    	=    [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]

 snake_case__    	=    [
     1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]
 snake_case__    	=    [
     4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]

 snake_case__    	=    [
     1_7_5_5, 1_0_6_1, 2_3_4, 1_7_5_5, 1_0_6_1, 1_7_5_5, 1_8_5, 2_9_0, 3_0_7, 3_0_7, 6_1_6, 6_1_6,
     6_1_6, 6_1_6, 6_1_6, 6_1_6, 3_0_7, 2_9_0, 4_1_7, 1_7_5_5, 2_3_4, 1_7_5_5, 1_8_5, 2_9_0,
     2_9_0, 2_9_0, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0, 2_3_4, 2_3_4, 1_7_5_5,
     2_3_4, 2_3_4, 1_7_5_5, 2_3_4, 1_8_5, 1_8_5, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0,
     1_7_5_5, 1_7_5_5, 1_7_5_5, 2_3_4, 2_3_4, 1_7_5_5, 1_5_7_2, 2_9_0, 3_0_7, 6_1_6, 3_4, 6_1_6
 ]
 snake_case__    	=    [
     8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2, 1_8_5,
     4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2,
     1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1,
     8_9, 5_9_1, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 4_1_7, 1_3_7_2, 8_4_2, 1_3_7_2, 8_4_2, 3_4, 8_4_2,
     1_8_5, 8_9, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 6_3_2
 ]

 snake_case__    	=    [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]
 snake_case__    	=    [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 1_8_5_3, 1_1_7_7, 1_5_3_6, 1_2_2_8,
     7_1_0, 4_7_5, 1_4_8_9, 1_2_2_9, 1_2_2_4, 2_3_1, 1_2_2_4, 2_5_2, 1_4_3_4, 6_5_3, 4_7_5,
     1_1_0_6, 1_8_7_7, 1_5_9_9, 1_2_2_8, 1_6_0_0, 1_6_8_3, 1_1_8_2, 1_8_5_3, 4_7_5, 1_8_6_4,
     2_5_2, 1_2_2_9, 1_4_3_4, 2_0_0_1
 ]

 snake_case__    	=    [
     1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 1_1_2_5, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]
 snake_case__    	=    [
     4_9_1, 1_7_5_5, 3_4, 1_6_1_3, 1_7_5_5, 4_1_7, 9_9_2, 1_6_1_3, 2_2_2, 8_4_2, 1_3_5_3, 1_6_1_3,
     8_4_4, 6_3_2, 1_8_5, 1_6_1_3, 8_4_4, 6_3_2, 1_8_5, 1_6_1_3, 1_8_5, 8_4_2, 6_7_7, 1_6_1_3,
     1_8_5, 1_1_4, 1_3_5_3, 1_6_1_3, 3_0_7, 8_9, 8_4_4, 1_6_1_3, 3_0_7, 1_3_3_2, 2_3_4, 1_9_7_9,
     3_0_7, 8_9, 1_3_5_3, 6_1_6, 3_4, 8_4_2, 1_8_5, 8_4_2, 3_4, 8_4_2, 1_8_5, 8_4_2,
     3_0_7, 1_1_4, 1_8_5, 8_9, 3_4, 1_2_6_8, 1_8_5, 8_9, 3_4, 8_4_2, 1_8_5, 8_9
 ]



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      JukeboxTokenizer.from_pretrained(_snake_case			)
  lowerCAmelCase  							=      tokenizer(**self.metas			)['input_ids']
  return tokens



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      JukeboxModel.from_pretrained(self.model_id							,    min_duration=0			).eval()
  lowerCAmelCase  							=      self.prepare_inputs(self.model_id			)

  set_seed(0			)
  lowerCAmelCase  							=      [torch.zeros(1							,    0							,    dtype=torch.long			).cpu() for _ in range(3			)]
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [0]							,    sample_length=60 * model.priors[0].raw_to_tokens							,    save_results=_snake_case			)
  self.assertIn(zs[0][0].detach().cpu().tolist()							,    [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]			)

  set_seed(0			)
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [1]							,    sample_length=60 * model.priors[1].raw_to_tokens							,    save_results=_snake_case			)
  self.assertIn(zs[1][0].detach().cpu().tolist()							,    [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]			)

  set_seed(0			)
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [2]							,    sample_length=60 * model.priors[2].raw_to_tokens							,    save_results=_snake_case			)
  self.assertIn(zs[2][0].detach().cpu().tolist()							,    [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]			)



 @slow
 @skip('Not enough GPU memory on CI runners'			)
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      JukeboxModel.from_pretrained(self.model_id							,    min_duration=0			).eval()
  lowerCAmelCase  							=      [i.cuda() for i in self.prepare_inputs(self.model_id			)]

  set_seed(0			)
  model.priors[0].cuda()
  lowerCAmelCase  							=      [torch.zeros(1							,    0							,    dtype=torch.long			).cuda() for _ in range(3			)]
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [0]							,    sample_length=60 * model.priors[0].raw_to_tokens							,    save_results=_snake_case			)
  torch.testing.assert_allclose(zs[0][0].cpu()							,    torch.tensor(self.EXPECTED_GPU_OUTPUTS_2			)			)
  model.priors[0].cpu()

  set_seed(0			)
  model.priors[1].cuda()
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [1]							,    sample_length=60 * model.priors[1].raw_to_tokens							,    save_results=_snake_case			)
  torch.testing.assert_allclose(zs[1][0].cpu()							,    torch.tensor(self.EXPECTED_GPU_OUTPUTS_1			)			)
  model.priors[1].cpu()

  set_seed(0			)
  model.priors[2].cuda()
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [2]							,    sample_length=60 * model.priors[2].raw_to_tokens							,    save_results=_snake_case			)
  torch.testing.assert_allclose(zs[2][0].cpu()							,    torch.tensor(self.EXPECTED_GPU_OUTPUTS_0			)			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      'ArthurZ/jukebox_prior_0'
  lowerCAmelCase  							=      JukeboxPrior.from_pretrained(_snake_case							,    min_duration=0			).eval().half().to('cuda'			)

  lowerCAmelCase  							=      self.prepare_inputs(_snake_case			)[0].cuda()
  lowerCAmelCase  							=      model.get_metadata(_snake_case							,    0							,    76_80							,    0			)
  set_seed(0			)
  lowerCAmelCase  							=      model.sample(1							,    metadata=_snake_case							,    sample_tokens=60			)
  self.assertIn(outputs[0].cpu().tolist()							,    [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]			)





