import numpy as np
from matplotlib import pyplot as plt







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						str:
     return 1 / (1 + np.exp(-1 * x							))






class 				_SCREAMING_SNAKE_CASE:
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_=False     )						->Dict:
        a	:     Optional[Any]						      =    units
        a	:     List[str]						      =    None
        a	:     List[Any]						      =    None
        a	:     int						      =    activation
        if learning_rate is None:
             a	:     Optional[Any]						      =    0.3
        a	:     Dict						      =    learning_rate
        a	:     Any						      =    is_input_layer
   def _lowercase(     self				,  lowerCAmelCase_     )						->Optional[Any]:
        a	:     Dict						      =    np.asmatrix(np.random.normal(0				,  0.5				,  (self.units, back_units)     )     )
        a	:     Dict						      =    np.asmatrix(np.random.normal(0				,  0.5				,  self.units     )     ).T
        if self.activation is None:
             a	:     Optional[Any]						      =    sigmoid
   def _lowercase(     self     )						->int:
        # activation function may be sigmoid or linear
        if self.activation == sigmoid:
             a	:     List[str]						      =    np.dot(self.output				,  (1 - self.output).T     )
             a	:     int						      =    np.diag(np.diag(lowerCAmelCase_     )     )
        else:
             a	:     List[Any]						      =    1
        return gradient_activation
   def _lowercase(     self				,  lowerCAmelCase_     )						->Any:
        a	:     int						      =    xdata
        if self.is_input_layer:
             # input layer
             a	:     str						      =    xdata
             a	:     Optional[int]						      =    xdata
             return xdata
        else:
             a	:     Dict						      =    np.dot(self.weight				,  self.xdata     ) - self.bias
             a	:     Optional[int]						      =    self.activation(self.wx_plus_b     )
             return self.output


   def _lowercase(     self				,  lowerCAmelCase_     )						->List[Any]:
        a	:     Tuple						      =    self.cal_gradient()  # i * i 维
        a	:     Union[str, Any]						      =    np.asmatrix(np.dot(gradient.T				,  lowerCAmelCase_     )     )

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

        a	:     str						      =    np.dot(gradient.T				,  self._gradient_weight.T     )
        a	:     str						      =    gradient * self._gradient_bias
        a	:     Tuple						      =    np.dot(lowerCAmelCase_				,  self._gradient_x     ).T
        # upgrade: the Negative gradient direction
        a	:     str						      =    self.weight - self.learn_rate * self.gradient_weight
        a	:     Optional[int]						      =    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 				_SCREAMING_SNAKE_CASE:
   def __init__(     self     )						->Tuple:
        a	:     Optional[int]						      =    []
        a	:     str						      =    []
        a	:     Union[str, Any]						      =    plt.figure()
        a	:     int						      =    self.fig_loss.add_subplot(1				,  1				,  1     )
   def _lowercase(     self				,  lowerCAmelCase_     )						->str:
        self.layers.append(lowerCAmelCase_     )
   def _lowercase(     self     )						->List[Any]:
        for i, layer in enumerate(self.layers[:]     ):
             if i < 1:
                  a	:     int						      =    True
             else:
                  layer.initializer(self.layers[i - 1].units     )
   def _lowercase(     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 _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        a	:     Optional[Any]						      =    train_round
        a	:     Optional[Any]						      =    accuracy

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

        a	:     str						      =    np.shape(lowerCAmelCase_     )
        for _ in range(lowerCAmelCase_     ):
             a	:     Union[str, Any]						      =    0
             for row in range(x_shape[0]     ):
                  a	:     Union[str, Any]						      =    np.asmatrix(xdata[row, :]     ).T
                  a	:     List[Any]						      =    np.asmatrix(ydata[row, :]     ).T

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

                  a      ,				a	:     str						      =    self.cal_loss(_ydata				,  _xdata     )
                  a	:     List[Any]						      =    all_loss + loss

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

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


   def _lowercase(     self     )						->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 						UpperCAmelCase							(		)						->						List[Any]:
     a	:     List[str]						      =    np.random.randn(1_0    ,   1_0							)
     a	:     Union[str, Any]						      =    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],
         ]							)
     a	:     Optional[int]						      =    BPNN()
     for i in (1_0, 2_0, 3_0, 2):
          model.add_layer(DenseLayer(lowerCAmelCase_							)							)
     model.build()
     model.summary()
     model.train(xdata=lowerCAmelCase_    ,   ydata=lowerCAmelCase_    ,   train_round=1_0_0    ,   accuracy=0.01							)


if __name__ == "__main__":
  example()
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase				 =   logging.get_logger(__name__)

UpperCAmelCase				 =   {
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    List[str]			      = 'opt'
   UpperCamelCase_					:    List[str]			      = ['past_key_values']
   def __init__(     self				,  lowerCAmelCase_=5_02_72				,  lowerCAmelCase_=7_68				,  lowerCAmelCase_=12				,  lowerCAmelCase_=30_72				,  lowerCAmelCase_=20_48				,  lowerCAmelCase_=True				,  lowerCAmelCase_=False				,  lowerCAmelCase_=None				,  lowerCAmelCase_=0.1				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=12				,  lowerCAmelCase_="relu"				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=0.02				,  lowerCAmelCase_=True				,  lowerCAmelCase_=1				,  lowerCAmelCase_=2				,  lowerCAmelCase_=2				,  lowerCAmelCase_=True				,  lowerCAmelCase_=True				,  **lowerCAmelCase_				,  )						->Any:
        super().__init__(
            pad_token_id=lowerCAmelCase_				,  bos_token_id=lowerCAmelCase_				,  eos_token_id=lowerCAmelCase_				,  **lowerCAmelCase_				,  )
        a	:     Dict						      =    vocab_size
        a	:     int						      =    max_position_embeddings
        a	:     List[Any]						      =    num_attention_heads
        a	:     Union[str, Any]						      =    word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        a	:     Any						      =    ffn_dim
        a	:     Optional[int]						      =    hidden_size
        a	:     Optional[Any]						      =    num_hidden_layers
        a	:     Optional[int]						      =    dropout
        a	:     Optional[Any]						      =    attention_dropout
        a	:     Union[str, Any]						      =    activation_function
        a	:     Any						      =    init_std
        a	:     List[Any]						      =    layerdrop
        a	:     str						      =    use_cache
        a	:     Any						      =    do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        a	:     List[Any]						      =    enable_bias
        a	:     List[str]						      =    layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        a	:     Dict						      =    _remove_final_layer_norm