code: 
  
  
  
  
  
  
  """simple docstring"""
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import normalizers
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_mpnet import MPNetTokenizer
  
  
  snake_case   :   Dict    		=		logging.get_logger(__name__)
  
  snake_case   :   Tuple    		=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
  
  snake_case   :   Any    		=		{
      'vocab_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
      },
      'tokenizer_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
      },
  }
  
  snake_case   :   str    		=		{
      'microsoft/mpnet-base': 5_1_2,
  }
  
  snake_case   :   Union[str, Any]    		=		{
      'microsoft/mpnet-base': {'do_lower_case': True},
  }
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   VOCAB_FILES_NAMES
      __UpperCAmelCase											=   PRETRAINED_VOCAB_FILES_MAP
      __UpperCAmelCase											=   PRETRAINED_INIT_CONFIGURATION
      __UpperCAmelCase											=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCAmelCase											=   MPNetTokenizer
      __UpperCAmelCase											=   ["""input_ids""", """attention_mask"""]
  
  
  
  
  
  
  
      def __init__(		self   :  List[str]							,						UpperCamelCase_   :  Any=None							,						UpperCamelCase_   :  Tuple=None							,						UpperCamelCase_   :  Any=True							,						UpperCamelCase_   :  int="<s>"							,						UpperCamelCase_   :  Optional[int]="</s>"							,						UpperCamelCase_   :  str="</s>"							,						UpperCamelCase_   :  List[Any]="<s>"							,						UpperCamelCase_   :  Tuple="[UNK]"							,						UpperCamelCase_   :  Optional[Any]="<pad>"							,						UpperCamelCase_   :  List[Any]="<mask>"							,						UpperCamelCase_   :  Any=True							,						UpperCamelCase_   :  Optional[int]=None							,						**UpperCamelCase_   :  Union[str, Any]							,						):
  
  
  
  
  
             '''simple docstring'''
  
             super().__init__(
                 UpperCamelCase_							,						tokenizer_file=UpperCamelCase_							,						do_lower_case=UpperCamelCase_							,						bos_token=UpperCamelCase_							,						eos_token=UpperCamelCase_							,						sep_token=UpperCamelCase_							,						cls_token=UpperCamelCase_							,						unk_token=UpperCamelCase_							,						pad_token=UpperCamelCase_							,						mask_token=UpperCamelCase_							,						tokenize_chinese_chars=UpperCamelCase_							,						strip_accents=UpperCamelCase_							,						**UpperCamelCase_							,						)
  
             __magic_name__										=						json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
             if (
                 pre_tok_state.get('lowercase'							,						UpperCamelCase_   ) != do_lower_case
                 or pre_tok_state.get('strip_accents'							,						UpperCamelCase_   ) != strip_accents
             ):
                    __magic_name__										=						getattr(UpperCamelCase_							,						pre_tok_state.pop('type'   )   )
                    __magic_name__										=						do_lower_case
                    __magic_name__										=						strip_accents
                    __magic_name__										=						pre_tok_class(**UpperCamelCase_   )
  
             __magic_name__										=						do_lower_case
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self._mask_token is None:
                    if self.verbose:
                           logger.error('Using mask_token, but it is not set yet.'   )
                    return None
             return str(self._mask_token   )
  
  
  
  
  
  
  
      @mask_token.setter
      def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						AddedToken(UpperCamelCase_							,						lstrip=UpperCamelCase_							,						rstrip=UpperCamelCase_   ) if isinstance(UpperCamelCase_							,						UpperCamelCase_   ) else value
             __magic_name__										=						value
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  int=None   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                    return output
  
             return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Tuple = None   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						[self.sep_token_id]
             __magic_name__										=						[self.cls_token_id]
  
             if token_ids_a is None:
                    return len(cls + token_ids_a + sep   ) * [0]
             return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  List[str] = None   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self._tokenizer.model.save(UpperCamelCase_							,						name=UpperCamelCase_   )
             return tuple(UpperCamelCase_   )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  from matplotlib import pyplot as plt
  
  
  def        A	(							__snake_case:   List[Any]     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         return 1 / (1 + np.exp(-1 * x     ))
  
  
  
  class 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Tuple							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  Optional[int]=None							,						UpperCamelCase_   :  List[str]=None							,						UpperCamelCase_   :  Tuple=False   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						units
             __magic_name__										=						None
             __magic_name__										=						None
             __magic_name__										=						activation
             if learning_rate is None:
                    __magic_name__										=						0.3
             __magic_name__										=						learning_rate
             __magic_name__										=						is_input_layer
  
  
  
  
  
  
  
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						np.asmatrix(np.random.normal(0							,						0.5							,						(self.units, back_units)   )   )
             __magic_name__										=						np.asmatrix(np.random.normal(0							,						0.5							,						self.units   )   ).T
             if self.activation is None:
                    __magic_name__										=						sigmoid
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.activation == sigmoid:
                    __magic_name__										=						np.dot(self.output							,						(1 - self.output).T   )
                    __magic_name__										=						np.diag(np.diag(UpperCamelCase_   )   )
             else:
                    __magic_name__										=						1
             return gradient_activation
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						xdata
             if self.is_input_layer:
                    # input layer
                    __magic_name__										=						xdata
                    __magic_name__										=						xdata
                    return xdata
             else:
                    __magic_name__										=						np.dot(self.weight							,						self.xdata   ) - self.bias
                    __magic_name__										=						self.activation(self.wx_plus_b   )
                    return self.output
  
  
  
  
  
  
  
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.cal_gradient()  # i * i 维
             __magic_name__										=						np.asmatrix(np.dot(gradient.T							,						UpperCamelCase_   )   )
  
             __magic_name__										=						np.asmatrix(self.xdata   )
             __magic_name__										=						-1
             __magic_name__										=						self.weight
  
             __magic_name__										=						np.dot(gradient.T							,						self._gradient_weight.T   )
             __magic_name__										=						gradient * self._gradient_bias
             __magic_name__										=						np.dot(UpperCamelCase_							,						self._gradient_x   ).T
             # upgrade: the Negative gradient direction
             __magic_name__										=						self.weight - self.learn_rate * self.gradient_weight
             __magic_name__										=						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   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						[]
             __magic_name__										=						[]
             __magic_name__										=						plt.figure()
             __magic_name__										=						self.fig_loss.add_subplot(1							,						1							,						1   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             self.layers.append(UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             for i, layer in enumerate(self.layers[:]   ):
                    if i < 1:
                           __magic_name__										=						True
                    else:
                           layer.initializer(self.layers[i - 1].units   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any   ):
  
  
  
  
  
             '''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 							a__ (		self   :  str							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						train_round
             __magic_name__										=						accuracy
  
             self.ax_loss.hlines(self.accuracy							,						0							,						self.train_round * 1.1   )
  
             __magic_name__										=						np.shape(UpperCamelCase_   )
             for _ in range(UpperCamelCase_   ):
                    __magic_name__										=						0
                    for row in range(x_shape[0]   ):
                           __magic_name__										=						np.asmatrix(xdata[row, :]   ).T
                           __magic_name__										=						np.asmatrix(ydata[row, :]   ).T
  
                           # forward propagation
                           for layer in self.layers:
                                  __magic_name__										=						layer.forward_propagation(_xdata   )
  
                           __magic_name__							,     __magic_name__										=						self.cal_loss(_ydata							,						_xdata   )
                           __magic_name__										=						all_loss + loss
  
                           # back propagation: the input_layer does not upgrade
                           for layer in self.layers[:0:-1]:
                                  __magic_name__										=						layer.back_propagation(UpperCamelCase_   )
  
                    __magic_name__										=						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 							a__ (		self   :  Union[str, Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						np.sum(np.power((ydata - ydata_)							,						2   )   )
             __magic_name__										=						2 * (ydata_ - ydata)
             # vector (shape is the same as _ydata.shape)
             return self.loss, self.loss_gradient
  
  
  
  
  
  
  
      def 							a__ (		self   :  str   ):
  
  
  
  
  
             '''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	(							)						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						np.random.randn(1_0   ,    1_0     )
         __magic_name__										=						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],
             ]     )
         __magic_name__										=						BPNN()
         for i in (1_0, 2_0, 3_0, 2):
                model.add_layer(DenseLayer(__snake_case     )     )
         model.build()
         model.summary()
         model.train(xdata=__snake_case   ,    ydata=__snake_case   ,    train_round=1_0_0   ,    accuracy=0.01     )
  
  
  if __name__ == "__main__":
   example()