



'''simple docstring'''





import numpy as np
from matplotlib import pyplot as plt

def  a_  (							_lowerCAmelCase )   ->   Optional[Any]:
  return 1 / (1 + np.exp(-1 * x ))






class       lowerCamelCase_					:
  """simple docstring"""




  def __init__(				self   :			List[Any]    ,			_a   :			int    ,			_a   :			Union[str, Any]=None    ,			_a   :			Optional[int]=None    ,			_a   :			Dict=False     )      ->      Union[str, Any]:
    __lowerCamelCase : Union[str, Any]					 =				units
    __lowerCamelCase : List[str]					 =				None
    __lowerCamelCase : Union[str, Any]					 =				None
    __lowerCamelCase : str					 =				activation
    if learning_rate is None:
      __lowerCamelCase : Dict					 =				0.3
    __lowerCamelCase : Union[str, Any]					 =				learning_rate
    __lowerCamelCase : List[Any]					 =				is_input_layer




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			Any     )      ->      Optional[Any]:
    __lowerCamelCase : str					 =				np.asmatrix(np.random.normal(0    ,			0.5    ,			(self.units, back_units)     )     )
    __lowerCamelCase : int					 =				np.asmatrix(np.random.normal(0    ,			0.5    ,			self.units     )     ).T
    if self.activation is None:
      __lowerCamelCase : List[Any]					 =				sigmoid




  def 		_lowercase  (				self   :			List[str]     )      ->      List[Any]:
    # activation function may be sigmoid or linear
    if self.activation == sigmoid:
      __lowerCamelCase : int					 =				np.dot(self.output    ,			(1 - self.output).T     )
      __lowerCamelCase : List[str]					 =				np.diag(np.diag(_a     )     )
    else:
      __lowerCamelCase : int					 =				1
    return gradient_activation




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			List[Any]     )      ->      Any:
    __lowerCamelCase : Optional[Any]					 =				xdata
    if self.is_input_layer:
      # input layer
      __lowerCamelCase : str					 =				xdata
      __lowerCamelCase : Optional[Any]					 =				xdata
      return xdata
    else:
      __lowerCamelCase : List[Any]					 =				np.dot(self.weight    ,			self.xdata     ) - self.bias
      __lowerCamelCase : Optional[int]					 =				self.activation(self.wx_plus_b     )
      return self.output




  def 		_lowercase  (				self   :			str    ,			_a   :			Optional[Any]     )      ->      Union[str, Any]:
    __lowerCamelCase : Optional[Any]					 =				self.cal_gradient()  # i * i 维
    __lowerCamelCase : int					 =				np.asmatrix(np.dot(gradient.T    ,			_a     )     )

    __lowerCamelCase : Tuple					 =				np.asmatrix(self.xdata     )
    __lowerCamelCase : List[str]					 =				-1
    __lowerCamelCase : Optional[int]					 =				self.weight

    __lowerCamelCase : Union[str, Any]					 =				np.dot(gradient.T    ,			self._gradient_weight.T     )
    __lowerCamelCase : int					 =				gradient * self._gradient_bias
    __lowerCamelCase : List[str]					 =				np.dot(_a    ,			self._gradient_x     ).T
    # upgrade: the Negative gradient direction
    __lowerCamelCase : Optional[Any]					 =				self.weight - self.learn_rate * self.gradient_weight
    __lowerCamelCase : Any					 =				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       lowerCamelCase_					:
  """simple docstring"""




  def __init__(				self   :			str     )      ->      int:
    __lowerCamelCase : int					 =				[]
    __lowerCamelCase : Optional[Any]					 =				[]
    __lowerCamelCase : Union[str, Any]					 =				plt.figure()
    __lowerCamelCase : Optional[Any]					 =				self.fig_loss.add_subplot(1    ,			1    ,			1     )




  def 		_lowercase  (				self   :			List[Any]    ,			_a   :			Optional[int]     )      ->      Optional[Any]:
    self.layers.append(_a     )




  def 		_lowercase  (				self   :			Dict     )      ->      Dict:
    for i, layer in enumerate(self.layers[:]     ):
      if i < 1:
        __lowerCamelCase : List[str]					 =				True
      else:
        layer.initializer(self.layers[i - 1].units     )




  def 		_lowercase  (				self   :			Dict     )      ->      Tuple:
    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 		_lowercase  (				self   :			Tuple    ,			_a   :			Optional[int]    ,			_a   :			str    ,			_a   :			str    ,			_a   :			Any     )      ->      Optional[Any]:
    __lowerCamelCase : int					 =				train_round
    __lowerCamelCase : Optional[Any]					 =				accuracy

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

    __lowerCamelCase : List[Any]					 =				np.shape(_a     )
    for _ in range(_a     ):
      __lowerCamelCase : Dict					 =				0
      for row in range(x_shape[0]     ):
        __lowerCamelCase : str					 =				np.asmatrix(xdata[row, :]     ).T
        __lowerCamelCase : Optional[Any]					 =				np.asmatrix(ydata[row, :]     ).T

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

        __lowerCamelCase ,__lowerCamelCase : Any					 =				self.cal_loss(_ydata    ,			_xdata     )
        __lowerCamelCase : str					 =				all_loss + loss

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

      __lowerCamelCase : Optional[int]					 =				all_loss / x_shape[0]
      self.train_mse.append(_a     )

      self.plot_loss()

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




  def 		_lowercase  (				self   :			List[Any]    ,			_a   :			List[Any]    ,			_a   :			Dict     )      ->      str:
    __lowerCamelCase : List[Any]					 =				np.sum(np.power((ydata - ydata_)    ,			2     )     )
    __lowerCamelCase : int					 =				2 * (ydata_ - ydata)
    # vector (shape is the same as _ydata.shape)
    return self.loss, self.loss_gradient




  def 		_lowercase  (				self   :			List[str]     )      ->      Tuple:
    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  a_  (							)   ->   Any:
  __lowerCamelCase : List[str]					 =				np.random.randn(10 ,10 )
  __lowerCamelCase : List[str]					 =				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 : Dict					 =				BPNN()
  for i in (10, 20, 30, 2):
    model.add_layer(DenseLayer(_lowerCAmelCase ) )
  model.build()
  model.summary()
  model.train(xdata=_lowerCAmelCase ,ydata=_lowerCAmelCase ,train_round=100 ,accuracy=0.01 )


if __name__ == "__main__":
   example()











'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import CamembertModel






@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase_					(       unittest.TestCase  ):
  """simple docstring"""




  @slow
  def 		_lowercase  (				self   :			str     )      ->      List[Any]:
    __lowerCamelCase : str					 =				CamembertModel.from_pretrained('camembert-base'     )
    model.to(_a     )

    __lowerCamelCase : List[Any]					 =				torch.tensor(
        [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]    ,			device=_a    ,			dtype=torch.long    ,			)  # J'aime le camembert !
    with torch.no_grad():
      __lowerCamelCase : int					 =				model(_a     )['last_hidden_state']
    __lowerCamelCase : Optional[int]					 =				torch.Size((1, 10, 768)     )
    self.assertEqual(output.shape    ,			_a     )
    # compare the actual values for a slice.
    __lowerCamelCase : Tuple					 =				torch.tensor(
        [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,			device=_a    ,			dtype=torch.float    ,			)
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(torch.allclose(output[:, :3, :3]    ,			_a    ,			atol=1e-4     )     )






