





"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Optional[int]       ):
 return 1 / (1 + np.exp(-1 * x       ))







class 	a				:



 def __init__(			self							,    _snake_case							,    _snake_case=None							,    _snake_case=None							,    _snake_case=False			):







  """simple docstring"""






  lowerCAmelCase  							=      units
  lowerCAmelCase  							=      None
  lowerCAmelCase  							=      None
  lowerCAmelCase  							=      activation
  if learning_rate is None:
   lowerCAmelCase  							=      0.3
  lowerCAmelCase  							=      learning_rate
  lowerCAmelCase  							=      is_input_layer



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.cal_gradient()  # i * i 维
  lowerCAmelCase  							=      np.asmatrix(np.dot(gradient.T							,    _snake_case			)			)

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

  lowerCAmelCase  							=      np.dot(gradient.T							,    self._gradient_weight.T			)
  lowerCAmelCase  							=      gradient * self._gradient_bias
  lowerCAmelCase  							=      np.dot(_snake_case							,    self._gradient_x			).T
  # upgrade: the Negative gradient direction
  lowerCAmelCase  							=      self.weight - self.learn_rate * self.gradient_weight
  lowerCAmelCase  							=      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 	a				:



 def __init__(			self			):







  """simple docstring"""






  lowerCAmelCase  							=      []
  lowerCAmelCase  							=      []
  lowerCAmelCase  							=      plt.figure()
  lowerCAmelCase  							=      self.fig_loss.add_subplot(1							,    1							,    1			)



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






  self.layers.append(_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """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 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      train_round
  lowerCAmelCase  							=      accuracy

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

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

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

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

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

   lowerCAmelCase  							=      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 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """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       ():
 lowerCAmelCase  							=      np.random.randn(10   ,			10       )
 lowerCAmelCase  							=      np.asarray(
     [
         [0.8, 0.4],
         [0.4, 0.3],
         [0.34, 0.45],
         [0.67, 0.32],
         [0.88, 0.67],
         [0.78, 0.77],
         [0.55, 0.66],
         [0.55, 0.43],
         [0.54, 0.1],
         [0.1, 0.5],
     ]       )
 lowerCAmelCase  							=      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=100   ,			accuracy=0.01       )


if __name__ == "__main__":
       example()












"""simple docstring"""
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	a				(					a__   ):
 snake_case__    	=    (HeunDiscreteScheduler,)
 snake_case__    	=    1_0



 def 					UpperCamelCase__   (			self							,    **_snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      {
      'num_train_timesteps': 11_00,
      'beta_start': 0.0_001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
  }

  config.update(**_snake_case			)
  return config



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for timesteps in [10, 50, 1_00, 10_00]:
   self.check_over_configs(num_train_timesteps=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]							,    [0.0_002, 0.002, 0.02]			):
   self.check_over_configs(beta_start=_snake_case							,    beta_end=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for schedule in ["linear", "scaled_linear", "exp"]:
   self.check_over_configs(beta_schedule=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for clip_sample_range in [1.0, 2.0, 3.0]:
   self.check_over_configs(clip_sample_range=_snake_case							,    clip_sample=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for prediction_type in ["epsilon", "v_prediction", "sample"]:
   self.check_over_configs(prediction_type=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

  scheduler.set_timesteps(self.num_inference_steps			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for i, t in enumerate(scheduler.timesteps			):
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if torch_device in ["cpu", "mps"]:
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3
  else:
   # CUDA
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config(prediction_type='v_prediction'			)
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

  scheduler.set_timesteps(self.num_inference_steps			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for i, t in enumerate(scheduler.timesteps			):
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if torch_device in ["cpu", "mps"]:
   assert abs(result_sum.item() - 4.69_34E-07			) < 1E-2
   assert abs(result_mean.item() - 6.11_12E-10			) < 1E-3
  else:
   # CUDA
   assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

  scheduler.set_timesteps(self.num_inference_steps							,    device=_snake_case			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

  for t in scheduler.timesteps:
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if str(_snake_case			).startswith('cpu'			):
   # The following sum varies between 148 and 156 on mps. Why?
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3
  elif str(_snake_case			).startswith('mps'			):
   # Larger tolerance on mps
   assert abs(result_mean.item() - 0.0_002			) < 1E-2
  else:
   # CUDA
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case							,    use_karras_sigmas=_snake_case			)

  scheduler.set_timesteps(self.num_inference_steps							,    device=_snake_case			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for t in scheduler.timesteps:
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  assert abs(result_sum.item() - 0.00_015			) < 1E-2
  assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07			) < 1E-2





