"""simple docstring"""




import numpy as np
from matplotlib import pyplot as plt

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Optional[Any]:
							return 1 / (1 + np.exp(-1 * x							))




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE=False    )						->List[str]:
								lowerCAmelCase     		=		units
								lowerCAmelCase     		=		None
								lowerCAmelCase     		=		None
								lowerCAmelCase     		=		activation
								if learning_rate is None:
															lowerCAmelCase     		=		0.3
								lowerCAmelCase     		=		learning_rate
								lowerCAmelCase     		=		is_input_layer


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->List[Any]:
								lowerCAmelCase     		=		np.asmatrix(np.random.normal(0		,      0.5		,      (self.units, back_units)    )    )
								lowerCAmelCase     		=		np.asmatrix(np.random.normal(0		,      0.5		,      self.units    )    ).T
								if self.activation is None:
															lowerCAmelCase     		=		sigmoid


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Union[str, Any]:






								if self.activation == sigmoid:
															lowerCAmelCase     		=		np.dot(self.output		,      (1 - self.output).T    )
															lowerCAmelCase     		=		np.diag(np.diag(__SCREAMING_SNAKE_CASE    )    )
								else:
															lowerCAmelCase     		=		1
								return gradient_activation


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								lowerCAmelCase     		=		xdata
								if self.is_input_layer:
															# input layer
															lowerCAmelCase     		=		xdata
															lowerCAmelCase     		=		xdata
															return xdata
								else:
															lowerCAmelCase     		=		np.dot(self.weight		,      self.xdata    ) - self.bias
															lowerCAmelCase     		=		self.activation(self.wx_plus_b    )
															return self.output




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								lowerCAmelCase     		=		self.cal_gradient()  # i * i 维
								lowerCAmelCase     		=		np.asmatrix(np.dot(gradient.T		,      __SCREAMING_SNAKE_CASE    )    )

								lowerCAmelCase     		=		np.asmatrix(self.xdata    )
								lowerCAmelCase     		=		-1
								lowerCAmelCase     		=		self.weight

								lowerCAmelCase     		=		np.dot(gradient.T		,      self._gradient_weight.T    )
								lowerCAmelCase     		=		gradient * self._gradient_bias
								lowerCAmelCase     		=		np.dot(__SCREAMING_SNAKE_CASE		,      self._gradient_x    ).T
								# upgrade: the Negative gradient direction
								lowerCAmelCase     		=		self.weight - self.learn_rate * self.gradient_weight
								lowerCAmelCase     		=		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        lowercase_  :



	"""simple docstring"""





	def __init__(							self    )						->List[Any]:
								lowerCAmelCase     		=		[]
								lowerCAmelCase     		=		[]
								lowerCAmelCase     		=		plt.figure()
								lowerCAmelCase     		=		self.fig_loss.add_subplot(1		,      1		,      1    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->List[Any]:
								self.layers.append(__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:






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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Dict:






								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       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								lowerCAmelCase     		=		train_round
								lowerCAmelCase     		=		accuracy

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

								lowerCAmelCase     		=		np.shape(__SCREAMING_SNAKE_CASE    )
								for _ in range(__SCREAMING_SNAKE_CASE    ):
															lowerCAmelCase     		=		0
															for row in range(x_shape[0]    ):
																						lowerCAmelCase     		=		np.asmatrix(xdata[row, :]    ).T
																						lowerCAmelCase     		=		np.asmatrix(ydata[row, :]    ).T

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

																						lowerCAmelCase     		=		self.cal_loss(_ydata		,      _xdata    )
																						lowerCAmelCase     		=		all_loss + loss

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

															lowerCAmelCase     		=		all_loss / x_shape[0]
															self.train_mse.append(__SCREAMING_SNAKE_CASE    )

															self.plot_loss()

															if mse < self.accuracy:
																						print('''----达到精度----'''    )
																						return mse
								return None


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Tuple:
								lowerCAmelCase     		=		np.sum(np.power((ydata - ydata_)		,      2    )    )
								lowerCAmelCase     		=		2 * (ydata_ - ydata)
								# vector (shape is the same as _ydata.shape)
								return self.loss, self.loss_gradient




	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:






								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        SCREAMING_SNAKE_CASE_					(						)					->     Optional[int]:
							lowerCAmelCase     		=		np.random.randn(1_0       ,  1_0							)
							lowerCAmelCase     		=		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],
							    ]							)
							lowerCAmelCase     		=		BPNN()
							for i in (1_0, 2_0, 3_0, 2):
														model.add_layer(DenseLayer(_UpperCamelCase							)							)
							model.build()
							model.summary()
							model.train(xdata=_UpperCamelCase       ,  ydata=_UpperCamelCase       ,  train_round=1_0_0       ,  accuracy=0.01							)


if __name__ == "__main__":
	example()




import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


lowercase__      :    str          =     [nn.LayerNorm]

lowercase__      :    str          =     logging.get_logger(__name__)

lowercase__      :    List[Any]          =     version.parse(version.parse(torch.__version__).base_version)

lowercase__      :    Dict          =     parsed_torch_version_base >= version.parse('''2.0''')
lowercase__      :    int          =     parsed_torch_version_base >= version.parse('''1.12''')
lowercase__      :    Optional[int]          =     parsed_torch_version_base >= version.parse('''1.11''')
lowercase__      :    int          =     parsed_torch_version_base < version.parse('''1.11''')

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  self							)					->     Union[str, Any]:
							from torch import _softmax_backward_data

							if is_torch_less_than_1_11:
														return _softmax_backward_data(snake_case__       ,  snake_case__       ,  parent.dim       ,  self							)
							else:
														return _softmax_backward_data(snake_case__       ,  snake_case__       ,  parent.dim       ,  self.dtype							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__ = 0							)					->     nn.Linear:
							lowerCAmelCase     		=		index.to(layer.weight.device							)
							lowerCAmelCase     		=		layer.weight.index_select(snake_case__       ,  snake_case__							).clone().detach()
							if layer.bias is not None:
														if dim == 1:
																					lowerCAmelCase     		=		layer.bias.clone().detach()
														else:
																					lowerCAmelCase     		=		layer.bias[index].clone().detach()
							lowerCAmelCase     		=		list(layer.weight.size()							)
							lowerCAmelCase     		=		len(snake_case__							)
							lowerCAmelCase     		=		nn.Linear(new_size[1]       ,  new_size[0]       ,  bias=layer.bias is not None							).to(layer.weight.device							)
							lowerCAmelCase     		=		False
							new_layer.weight.copy_(W.contiguous()							)
							lowerCAmelCase     		=		True
							if layer.bias is not None:
														lowerCAmelCase     		=		False
														new_layer.bias.copy_(b.contiguous()							)
														lowerCAmelCase     		=		True
							return new_layer




class        lowercase_  (  nn.Module						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Optional[Any]:
								super().__init__()
								lowerCAmelCase     		=		nf
								lowerCAmelCase     		=		nn.Parameter(torch.empty(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )    )
								lowerCAmelCase     		=		nn.Parameter(torch.zeros(__SCREAMING_SNAKE_CASE    )    )
								nn.init.normal_(self.weight		,      std=0.0_2    )




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->List[str]:
								lowerCAmelCase     		=		x.size()[:-1] + (self.nf,)
								lowerCAmelCase     		=		torch.addmm(self.bias		,      x.view(-1		,      x.size(-1    )    )		,      self.weight    )
								lowerCAmelCase     		=		x.view(__SCREAMING_SNAKE_CASE    )
								return x

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__ = 1							)					->     ConvaD:
							lowerCAmelCase     		=		index.to(layer.weight.device							)
							lowerCAmelCase     		=		layer.weight.index_select(snake_case__       ,  snake_case__							).clone().detach()
							if dim == 0:
														lowerCAmelCase     		=		layer.bias.clone().detach()
							else:
														lowerCAmelCase     		=		layer.bias[index].clone().detach()
							lowerCAmelCase     		=		list(layer.weight.size()							)
							lowerCAmelCase     		=		len(snake_case__							)
							lowerCAmelCase     		=		ConvaD(new_size[1]       ,  new_size[0]							).to(layer.weight.device							)
							lowerCAmelCase     		=		False
							new_layer.weight.copy_(W.contiguous()							)
							lowerCAmelCase     		=		True
							lowerCAmelCase     		=		False
							new_layer.bias.copy_(b.contiguous()							)
							lowerCAmelCase     		=		True
							return new_layer

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__ = None							)					->     Union[nn.Linear, ConvaD]:
							if isinstance(snake_case__       ,  nn.Linear							):
														return prune_linear_layer(snake_case__       ,  snake_case__       ,  dim=0 if dim is None else dim							)
							elif isinstance(snake_case__       ,  snake_case__							):
														return prune_convad_layer(snake_case__       ,  snake_case__       ,  dim=1 if dim is None else dim							)
							else:
														raise ValueError(f"Can't prune layer of class {layer.__class__}"							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  *snake_case__							)					->     torch.Tensor:
							assert len(snake_case__							) > 0, f"{input_tensors} has to be a tuple/list of tensors"

							# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
							lowerCAmelCase     		=		len(inspect.signature(snake_case__							).parameters							)
							if num_args_in_forward_chunk_fn != len(snake_case__							):
														raise ValueError(
														    f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(snake_case__							)} input "
														    '''tensors are given'''							)

							if chunk_size > 0:
														lowerCAmelCase     		=		input_tensors[0].shape[chunk_dim]
														for input_tensor in input_tensors:
																					if input_tensor.shape[chunk_dim] != tensor_shape:
																												raise ValueError(
																												    f"All input tenors have to be of the same shape: {tensor_shape}, "
																												    f"found shape {input_tensor.shape[chunk_dim]}"							)

														if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
																					raise ValueError(
																					    f"The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk "
																					    f"size {chunk_size}"							)

														lowerCAmelCase     		=		input_tensors[0].shape[chunk_dim] // chunk_size

														# chunk input tensor into tuples
														lowerCAmelCase     		=		tuple(input_tensor.chunk(snake_case__       ,  dim=snake_case__							) for input_tensor in input_tensors							)
														# apply forward fn to every tuple
														lowerCAmelCase     		=		tuple(forward_fn(*snake_case__							) for input_tensors_chunk in zip(*snake_case__							)							)
														# concatenate output at same dimension
														return torch.cat(snake_case__       ,  dim=snake_case__							)

							return forward_fn(*snake_case__							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__							)					->     Tuple[Set[int], torch.LongTensor]:
							lowerCAmelCase     		=		torch.ones(snake_case__       ,  snake_case__							)
							lowerCAmelCase     		=		set(snake_case__							) - already_pruned_heads  # Convert to set and remove already pruned heads
							for head in heads:
														# Compute how many pruned heads are before the head and move the index accordingly
														lowerCAmelCase     		=		head - sum(1 if h < head else 0 for h in already_pruned_heads							)
														lowerCAmelCase     		=		0
							lowerCAmelCase     		=		mask.view(-1							).contiguous().eq(1							)
							lowerCAmelCase     		=		torch.arange(len(snake_case__							)							)[mask].long()
							return heads, index

def        SCREAMING_SNAKE_CASE_					(						*snake_case__       ,  snake_case__ = None							)					->     Tuple[torch.Tensor, ...]:
							return torch.meshgrid(*snake_case__       ,  indexing=snake_case__							)






def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Tuple[torch.device, int, int]:
							return tensor.device, storage_ptr(snake_case__							), storage_size(snake_case__							)



