






import numpy as np
from matplotlib import pyplot as plt


def __A   (				__lowerCAmelCase     )->  int:





						"""simple docstring"""



						return 1 / (1 + np.exp(-1 * x     ))





class __lowerCamelCase			:





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase	,   UpperCAmelCase=None	,   UpperCAmelCase=None	,   UpperCAmelCase=False   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







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

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

							_UpperCAmelCase     =      np.dot(gradient.T	,   self._gradient_weight.T   )
							_UpperCAmelCase     =      gradient * self._gradient_bias
							_UpperCAmelCase     =      np.dot(__lowerCamelCase	,   self._gradient_x   ).T
							# upgrade: the Negative gradient direction
							_UpperCAmelCase     =      self.weight - self.learn_rate * self.gradient_weight
							_UpperCAmelCase     =      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   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							self.layers.append(__lowerCamelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							for i, layer in enumerate(self.layers[:]   ):
													if i < 1:
																			_UpperCAmelCase     =      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	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      train_round
							_UpperCAmelCase     =      accuracy

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

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

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

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

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

													_UpperCAmelCase     =      all_loss / x_shape[0]
													self.train_mse.append(__lowerCamelCase   )

													self.plot_loss()

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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      np.sum(np.power((ydata - ydata_)	,   2   )   )
							_UpperCAmelCase     =      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 __A   (				)->  Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase     =      np.random.randn(10			,		10     )
						_UpperCAmelCase     =      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],
						    ]     )
						_UpperCAmelCase     =      BPNN()
						for i in (10, 20, 30, 2):
												model.add_layer(DenseLayer(A_     )     )
						model.build()
						model.summary()
						model.train(xdata=A_			,		ydata=A_			,		train_round=100			,		accuracy=0.01     )


if __name__ == "__main__":
		example()








import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir





@dataclass
class __lowerCamelCase			:





	"""simple docstring"""






	UpperCamelCase__								=				42





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece.model'   )
							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   0   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   1   )

							self.assertIn(
							    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
							    ' which is not implemented in the fast tokenizers.'	,   str(w[0].message   )	,   )
