

import numpy as np
from matplotlib import pyplot as plt


def  __a    (				SCREAMING_SNAKE_CASE ) ->       Any:






    '''simple docstring'''
    return 1 / (1 + np.exp(-1 * x ))
class    A_				:


    '''simple docstring'''






    def __init__(self       , lowercase__       , lowercase__=None       , lowercase__=None       , lowercase__=False					)       -> Tuple:
        __UpperCAmelCase			   =     units
        __UpperCAmelCase			   =     None
        __UpperCAmelCase			   =     None
        __UpperCAmelCase			   =     activation
        if learning_rate is None:
            __UpperCAmelCase			   =     0.3
        __UpperCAmelCase			   =     learning_rate
        __UpperCAmelCase			   =     is_input_layer


    def        lowerCAmelCase_  (self       , lowercase__					)       -> Union[str, Any]:
        __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        lowerCAmelCase_  (self					)       -> Optional[int]:
        # activation function may be sigmoid or linear
        if self.activation == sigmoid:
            __UpperCAmelCase			   =     np.dot(self.output       , (1 - self.output).T					)
            __UpperCAmelCase			   =     np.diag(np.diag(lowercase__					)					)
        else:
            __UpperCAmelCase			   =     1
        return gradient_activation


    def        lowerCAmelCase_  (self       , lowercase__					)       -> Optional[Any]:
        __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        lowerCAmelCase_  (self       , lowercase__					)       -> str:
        __UpperCAmelCase			   =     self.cal_gradient()  # i * i 维
        __UpperCAmelCase			   =     np.asmatrix(np.dot(gradient.T       , lowercase__					)					)

        __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(lowercase__       , 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    A_				:


    '''simple docstring'''






    def __init__(self					)       -> List[Any]:
        __UpperCAmelCase			   =     []
        __UpperCAmelCase			   =     []
        __UpperCAmelCase			   =     plt.figure()
        __UpperCAmelCase			   =     self.fig_loss.add_subplot(1       , 1       , 1					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        self.layers.append(lowercase__					)


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


    def        lowerCAmelCase_  (self					)       -> Optional[int]:
        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       , lowercase__       , lowercase__       , lowercase__       , lowercase__					)       -> str:
        __UpperCAmelCase			   =     train_round
        __UpperCAmelCase			   =     accuracy

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

        __UpperCAmelCase			   =     np.shape(lowercase__					)
        for _ in range(lowercase__					):
            __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   ,       __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(lowercase__					)

            __UpperCAmelCase			   =     all_loss / x_shape[0]
            self.train_mse.append(lowercase__					)

            self.plot_loss()

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


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__					)       -> List[str]:
        __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        lowerCAmelCase_  (self					)       -> List[str]:
        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:






    '''simple docstring'''
    __UpperCAmelCase			   =     np.random.randn(1_0    ,				1_0 )
    __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 (1_0, 2_0, 3_0, 2):
        model.add_layer(DenseLayer(SCREAMING_SNAKE_CASE ) )
    model.build()
    model.summary()
    model.train(xdata=SCREAMING_SNAKE_CASE    ,				ydata=SCREAMING_SNAKE_CASE    ,				train_round=1_0_0    ,				accuracy=0.01 )


if __name__ == "__main__":
       example()







# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/




