
'''simple docstring'''







import numpy as np
from matplotlib import pyplot as plt



def       _a		(						_SCREAMING_SNAKE_CASE		: Any	):
						return 1 / (1 + np.exp(-1 * x	))


class       lowerCAmelCase			:




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=False   ):
								_SCREAMING_SNAKE_CASE					=			units
								_SCREAMING_SNAKE_CASE					=			None
								_SCREAMING_SNAKE_CASE					=			None
								_SCREAMING_SNAKE_CASE					=			activation
								if learning_rate is None:
														_SCREAMING_SNAKE_CASE					=			0.3
								_SCREAMING_SNAKE_CASE					=			learning_rate
								_SCREAMING_SNAKE_CASE					=			is_input_layer




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			np.asmatrix(np.random.normal(0       ,    0.5       ,    (self.units, back_units)   )   )
								_SCREAMING_SNAKE_CASE					=			np.asmatrix(np.random.normal(0       ,    0.5       ,    self.units   )   ).T
								if self.activation is None:
														_SCREAMING_SNAKE_CASE					=			sigmoid




		def      lowercase							(					self   ):
								# activation function may be sigmoid or linear
								if self.activation == sigmoid:
														_SCREAMING_SNAKE_CASE					=			np.dot(self.output       ,    (1 - self.output).T   )
														_SCREAMING_SNAKE_CASE					=			np.diag(np.diag(UpperCamelCase   )   )
								else:
														_SCREAMING_SNAKE_CASE					=			1
								return gradient_activation




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			xdata
								if self.is_input_layer:
														# input layer
														_SCREAMING_SNAKE_CASE					=			xdata
														_SCREAMING_SNAKE_CASE					=			xdata
														return xdata
								else:
														_SCREAMING_SNAKE_CASE					=			np.dot(self.weight       ,    self.xdata   ) - self.bias
														_SCREAMING_SNAKE_CASE					=			self.activation(self.wx_plus_b   )
														return self.output




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			self.cal_gradient()  # i * i 维
								_SCREAMING_SNAKE_CASE					=			np.asmatrix(np.dot(gradient.T       ,    UpperCamelCase   )   )

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

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




		def __init__(					self   ):
								_SCREAMING_SNAKE_CASE					=			[]
								_SCREAMING_SNAKE_CASE					=			[]
								_SCREAMING_SNAKE_CASE					=			plt.figure()
								_SCREAMING_SNAKE_CASE					=			self.fig_loss.add_subplot(1       ,    1       ,    1   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								self.layers.append(UpperCamelCase   )




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




		def      lowercase							(					self   ):
								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       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			train_round
								_SCREAMING_SNAKE_CASE					=			accuracy

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

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

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

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

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

														_SCREAMING_SNAKE_CASE					=			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      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			np.sum(np.power((ydata - ydata_)       ,    2   )   )
								_SCREAMING_SNAKE_CASE					=			2 * (ydata_ - ydata)
								# vector (shape is the same as _ydata.shape)
								return self.loss, self.loss_gradient




		def      lowercase							(					self   ):
								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		(						):
						_SCREAMING_SNAKE_CASE					=			np.random.randn(10						,      10	)
						_SCREAMING_SNAKE_CASE					=			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],
						    ]	)
						_SCREAMING_SNAKE_CASE					=			BPNN()
						for i in (10, 20, 30, 2):
												model.add_layer(DenseLayer(_SCREAMING_SNAKE_CASE	)	)
						model.build()
						model.summary()
						model.train(xdata=_SCREAMING_SNAKE_CASE						,      ydata=_SCREAMING_SNAKE_CASE						,      train_round=100						,      accuracy=0.01	)


if __name__ == "__main__":
						example()

'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=self.device   )

								# set step values
								self.scheduler.set_timesteps(UpperCamelCase   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE					=			self.unet(UpperCamelCase       ,    UpperCamelCase   ).sample

														# 2. compute previous image: x_t -> x_t-1
														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase   ).prev_sample

								_SCREAMING_SNAKE_CASE					=			(image / 2 + 0.5).clamp(0       ,    1   )
								_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )