code: 
  
  
  
  
  
  
  """simple docstring"""
  import json
  import os
  from collections.abc import Mapping
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  import torch
  
  from .imports import is_safetensors_available
  
  
  def        A	(							__snake_case:   List[Any]   ,    __snake_case:   List[Any]   ,    __snake_case:   List[Any]   ,    __snake_case:   Optional[int]=None     )						->		List[str]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						None
         # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
         if str(weight.dtype     ) == "torch.bfloat16":
                # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
                __magic_name__										=						weight.view(torch.intaa     )
                __magic_name__										=						'bfloat16'
         __magic_name__										=						weight.cpu().numpy()
         __magic_name__										=						os.path.join(__snake_case   ,    F"""{weight_name}.dat"""     )
         if index is not None:
                if dtype is None:
                       __magic_name__										=						str(array.dtype     )
                __magic_name__										=						{'dtype': dtype, 'shape': list(array.shape     )}
         if array.ndim == 0:
                __magic_name__										=						array[None]
         __magic_name__										=						np.memmap(__snake_case   ,    dtype=array.dtype   ,    mode='w+'   ,    shape=array.shape     )
         __magic_name__										=						array[:]
         file_array.flush()
         return index
  
  
  def        A	(							__snake_case:   int   ,    __snake_case:   str     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						tuple(weight_info['shape']     )
         if shape == ():
                # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
                __magic_name__										=						(1,)
  
         __magic_name__										=						weight_info['dtype']
         if dtype == "bfloat16":
                # NumPy does not support bfloat16 so this was saved as a int16
                __magic_name__										=						'int16'
  
         __magic_name__										=						np.memmap(__snake_case   ,    dtype=__snake_case   ,    shape=__snake_case   ,    mode='r'     )
  
         if len(weight_info['shape']     ) == 0:
                __magic_name__										=						weight[0]
         __magic_name__										=						torch.tensor(__snake_case     )
         if weight_info["dtype"] == "bfloat16":
                __magic_name__										=						weight.view(torch.bfloataa     )
  
         return weight
  
  
  def        A	(							__snake_case:   int   ,    __snake_case:   str     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         if index is None or len(__snake_case     ) == 0:
                # Nothing to save
                return
  
         __magic_name__										=						os.path.join(__snake_case   ,    'index.json'     )
         if os.path.isfile(__snake_case     ):
                with open(__snake_case   ,    'r'   ,    encoding='utf-8'     ) as f:
                       __magic_name__										=						json.load(__snake_case     )
         else:
                __magic_name__										=						{}
         current_index.update(__snake_case     )
  
         with open(__snake_case   ,    'w'   ,    encoding='utf-8'     ) as f:
                json.dump(__snake_case   ,    __snake_case   ,    indent=2     )
  
  
  def        A	(							__snake_case:   Union[str, os.PathLike]   ,    __snake_case:   Dict[str, torch.Tensor]     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         os.makedirs(__snake_case   ,    exist_ok=__snake_case     )
         __magic_name__										=						{}
         for name, parameter in state_dict.items():
                __magic_name__										=						offload_weight(__snake_case   ,    __snake_case   ,    __snake_case   ,    index=__snake_case     )
  
         # Update index
         save_offload_index(__snake_case   ,    __snake_case     )
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Optional[Any]							,						UpperCamelCase_   :  Mapping							,						UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						dataset
             __magic_name__										=						prefix
  
  
  
  
  
  
  
      def __getitem__(		self   :  Tuple							,						UpperCamelCase_   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             return self.dataset[f"""{self.prefix}{key}"""]
  
  
  
  
  
  
  
      def __iter__(		self   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             return iter([key for key in self.dataset if key.startswith(self.prefix   )]   )
  
  
  
  
  
  
  
      def __len__(		self   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return len(self.dataset   )
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  str							,						UpperCamelCase_   :  Dict[str, torch.Tensor] = None							,						UpperCamelCase_   :  Optional[Union[str, os.PathLike]] = None							,						UpperCamelCase_   :  Mapping = None							,						UpperCamelCase_   :  Optional[Any]=None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if state_dict is None and save_folder is None:
                    raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'   )
  
             __magic_name__										=						{} if state_dict is None else state_dict
             __magic_name__										=						save_folder
             if index is None and save_folder is not None:
                    with open(os.path.join(UpperCamelCase_							,						'index.json'   )   ) as f:
                           __magic_name__										=						json.load(UpperCamelCase_   )
             __magic_name__										=						{} if index is None else index
             __magic_name__										=						list(self.state_dict.keys()   )
             self.all_keys.extend([key for key in self.index if key not in self.all_keys]   )
             __magic_name__										=						device
  
  
  
  
  
  
  
      def __getitem__(		self   :  Dict							,						UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             if key in self.state_dict:
                    return self.state_dict[key]
             __magic_name__										=						self.index[key]
             if weight_info.get('safetensors_file'   ) is not None:
                    if not is_safetensors_available():
                           raise ImportError('These offloaded weights require the use of safetensors: `pip install safetensors`.'   )
  
                    from safetensors import safe_open
  
                    __magic_name__										=						'cpu' if self.device is None else self.device
                    with safe_open(weight_info['safetensors_file']							,						framework='pt'							,						device=UpperCamelCase_   ) as f:
                           __magic_name__										=						f.get_tensor(weight_info.get('weight_name'							,						UpperCamelCase_   )   )
  
                    if "dtype" in weight_info:
                           return tensor.to(getattr(UpperCamelCase_							,						weight_info['dtype']   )   )
                    else:
                           return tensor
  
             __magic_name__										=						os.path.join(self.save_folder							,						f"""{key}.dat"""   )
             return load_offloaded_weight(UpperCamelCase_							,						UpperCamelCase_   )
  
  
  
  
  
  
  
      def __iter__(		self   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             return iter(self.all_keys   )
  
  
  
  
  
  
  
      def __len__(		self   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             return len(self.all_keys   )
  
  
  def        A	(							__snake_case:   Dict[str, torch.Tensor]   ,    __snake_case:   List[str]     )						->		Tuple:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						{}
         for module_name in submodule_names:
                # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
                # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
                result.update(
                    {
                        key: param
                        for key, param in state_dict.items()
                        if key == module_name or key.startswith(module_name + '.'     )
                    }     )
         return result
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()