
import numpy as np
from matplotlib import pyplot as plt





def        lowerCAmelCase       (				snake_case__   :      Any )->			str:
    return 1 / (1 + np.exp(-1 * x ))


class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=False					):
      A_								=					units
      A_								=					None
      A_								=					None
      A_								=					activation
      if learning_rate is None:
          A_								=					0.3
      A_								=					learning_rate
      A_								=					is_input_layer

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

  def 					lowercase_						(					self					):
      # activation function may be sigmoid or linear
      if self.activation == sigmoid:
          A_								=					np.dot(self.output						,    (1 - self.output).T					)
          A_								=					np.diag(np.diag(__UpperCamelCase					)					)
      else:
          A_								=					1
      return gradient_activation

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

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					self.cal_gradient()  # i * i 维
      A_								=					np.asmatrix(np.dot(gradient.T						,    __UpperCamelCase					)					)

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

      A_								=					np.dot(gradient.T						,    self._gradient_weight.T					)
      A_								=					gradient * self._gradient_bias
      A_								=					np.dot(__UpperCamelCase						,    self._gradient_x					).T
      # upgrade: the Negative gradient direction
      A_								=					self.weight - self.learn_rate * self.gradient_weight
      A_								=					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					):
      A_								=					[]
      A_								=					[]
      A_								=					plt.figure()
      A_								=					self.fig_loss.add_subplot(1						,    1						,    1					)

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      self.layers.append(__UpperCamelCase					)

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

  def 					lowercase_						(					self					):
      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						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					train_round
      A_								=					accuracy

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

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

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

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

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

          A_								=					all_loss / x_shape[0]
          self.train_mse.append(__UpperCamelCase					)

          self.plot_loss()

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

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

  def 					lowercase_						(					self					):
      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        lowerCAmelCase       (				)->			Union[str, Any]:
    A_								=					np.random.randn(10				,					10 )
    A_								=					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],
        ] )
    A_								=					BPNN()
    for i in (10, 20, 30, 2):
        model.add_layer(DenseLayer(_SCREAMING_SNAKE_CASE ) )
    model.build()
    model.summary()
    model.train(xdata=_SCREAMING_SNAKE_CASE				,					ydata=_SCREAMING_SNAKE_CASE				,					train_round=100				,					accuracy=0.0_1 )


if __name__ == "__main__":
     example()








'''simple docstring'''







import os
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 GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCAmelCase			(					unittest.TestCase      ):




		@slow
		@unittest.skip("Model is not available."   )
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			"nvidia/megatron-gpt2-345m/"
								if "MYDIR" in os.environ:
														_SCREAMING_SNAKE_CASE					=			os.path.join(os.environ["MYDIR"]       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			GPTaLMHeadModel.from_pretrained(UpperCamelCase   )
								model.to(UpperCamelCase   )
								model.half()

								_SCREAMING_SNAKE_CASE					=			torch.tensor(
								    [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]       ,    device=UpperCamelCase       ,    dtype=torch.long       ,    )

								with torch.no_grad():
														_SCREAMING_SNAKE_CASE					=			model(UpperCamelCase   ).logits

								_SCREAMING_SNAKE_CASE					=			torch.Size((1, 9, 50_257)   )
								self.assertEqual(output.shape       ,    UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			torch.tensor(
								    [
								        4.94_14,
								        -0.29_20,
								        -1.21_48,
								        -4.02_73,
								        -0.51_61,
								        -5.21_09,
								        -1.24_12,
								        -1.83_01,
								        -1.77_34,
								        -4.71_48,
								        -0.23_17,
								        -1.08_11,
								        -2.17_77,
								        0.41_41,
								        -3.79_69,
								        -4.05_86,
								        -2.53_32,
								        -3.38_09,
								        4.38_67,
								    ]       ,    device=UpperCamelCase       ,    dtype=torch.half       ,    )

								for i in range(19   ):
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			8 * i // 17, 2_792 * i  # along the diagonal
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			output[0, r, c], expected_diag[i]
														_SCREAMING_SNAKE_CASE					=			F'row={r} col={c} computed={computed} expected={expected}'
														self.assertAlmostEqual(UpperCamelCase       ,    UpperCamelCase       ,    delta=1e-4       ,    msg=UpperCamelCase   )