
import numpy as np
from matplotlib import pyplot as plt


def     a__			(    snake_case    ) ->    List[Any]:





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





class   __UpperCamelCase				:






     """simple docstring"""







     def __init__(							self    :    Union[str, Any]      ,		_A    :    str      ,		_A    :    str=None      ,		_A    :    int=None      ,		_A    :    Dict=False				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						units
       __SCREAMING_SNAKE_CASE     :					str			=						None
       __SCREAMING_SNAKE_CASE     :					str			=						None
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						activation
       if learning_rate is None:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						0.3
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						learning_rate
       __SCREAMING_SNAKE_CASE     :					str			=						is_input_layer



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						np.asmatrix(np.random.normal(0      ,		0.5      ,		(self.units, back_units)				)				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						np.asmatrix(np.random.normal(0      ,		0.5      ,		self.units				)				).T
       if self.activation is None:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						sigmoid



     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       if self.activation == sigmoid:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						np.dot(self.output      ,		(1 - self.output).T				)
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						np.diag(np.diag(_A				)				)
       else:
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						1
       return gradient_activation



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						xdata
       if self.is_input_layer:
         # input layer
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						xdata
         __SCREAMING_SNAKE_CASE     :					Tuple			=						xdata
         return xdata
       else:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						np.dot(self.weight      ,		self.xdata				) - self.bias
         __SCREAMING_SNAKE_CASE     :					Dict			=						self.activation(self.wx_plus_b				)
         return self.output




     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		_A    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						self.cal_gradient()  # i * i 维
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						np.asmatrix(np.dot(gradient.T      ,		_A				)				)

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						np.asmatrix(self.xdata				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						-1
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.weight

       __SCREAMING_SNAKE_CASE     :					List[str]			=						np.dot(gradient.T      ,		self._gradient_weight.T				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						gradient * self._gradient_bias
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						np.dot(_A      ,		self._gradient_x				).T
       # upgrade: the Negative gradient direction
       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.weight - self.learn_rate * self.gradient_weight
       __SCREAMING_SNAKE_CASE     :					Dict			=						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   __UpperCamelCase				:






     """simple docstring"""







     def __init__(							self    :    Any				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						[]
       __SCREAMING_SNAKE_CASE     :					Dict			=						[]
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						plt.figure()
       __SCREAMING_SNAKE_CASE     :					int			=						self.fig_loss.add_subplot(1      ,		1      ,		1				)



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    List[str]				):







       """simple docstring"""




       self.layers.append(_A				)



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """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    :    Any      ,		_A    :    Tuple      ,		_A    :    Union[str, Any]      ,		_A    :    Any      ,		_A    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						train_round
       __SCREAMING_SNAKE_CASE     :					int			=						accuracy

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

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

           # forward propagation
           for layer in self.layers:
             __SCREAMING_SNAKE_CASE     :					List[str]			=						layer.forward_propagation(_xdata				)

           __SCREAMING_SNAKE_CASE     :					int			=						self.cal_loss(_ydata      ,		_xdata				)
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						all_loss + loss

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

         __SCREAMING_SNAKE_CASE     :					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 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    List[Any]      ,		_A    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						np.sum(np.power((ydata - ydata_)      ,		2				)				)
       __SCREAMING_SNAKE_CASE     :					str			=						2 * (ydata_ - ydata)
       # vector (shape is the same as _ydata.shape)
       return self.loss, self.loss_gradient




     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """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__			(    ) ->    List[Any]:





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Dict			=						np.random.randn(10   ,	10    )
  __SCREAMING_SNAKE_CASE     :					int			=						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],
      ]    )
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						BPNN()
  for i in (10, 20, 30, 2):
    model.add_layer(DenseLayer(snake_case    )    )
  model.build()
  model.summary()
  model.train(xdata=snake_case   ,	ydata=snake_case   ,	train_round=100   ,	accuracy=0.01    )


if __name__ == "__main__":
   example()


import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types





class   __UpperCamelCase				(    unittest.TestCase				,		lowerCAmelCase__   ):






     """simple docstring"""







     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						load_tool('''translation'''				)
       self.tool.setup()
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						load_tool('''translation'''      ,		remote=_A				)



     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.tool('''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.remote_tool('''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						self.tool(text='''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.remote_tool(text='''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.tool(*_A				)

       # There is a single output
       if len(self.tool.outputs				) == 1:
         __SCREAMING_SNAKE_CASE     :					Any			=						[outputs]

       self.assertListEqual(output_types(_A				)      ,		self.tool.outputs				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.tool(*_A				)

       if not isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					Dict			=						[outputs]

       self.assertEqual(len(_A				)      ,		len(self.tool.outputs				)				)

       for output, output_type in zip(_A      ,		self.tool.outputs				):
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						AGENT_TYPE_MAPPING[output_type]
         self.assertTrue(isinstance(_A      ,		_A				)				)




     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						[]

       for _input, input_type in zip(_A      ,		self.tool.inputs				):
         if isinstance(_A      ,		_A				):
           _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
         else:
           _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.tool(*_A				)

       if not isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					Dict			=						[outputs]

       self.assertEqual(len(_A				)      ,		len(self.tool.outputs				)				)
