






"""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







"""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()