



"""simple docstring"""






import numpy as np
from matplotlib import pyplot as plt






def 	snake_case__  (						__lowerCamelCase							:       Optional[Any]      ):
			"""simple docstring"""
			return 1 / (1 + np.exp(-1 * x      ))

class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					def __init__(			self				:    Tuple,    lowerCamelCase				:    int,    lowerCamelCase				:    Optional[Any]=None,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    Union[str, Any]=False	)->   List[Any]:
								lowerCamelCase__						:						Union[str, Any]						      =units
								lowerCamelCase__						:						str						      =None
								lowerCamelCase__						:						int						      =None
								lowerCamelCase__						:						List[Any]						      =activation
								if learning_rate is None:
											lowerCamelCase__						:						Union[str, Any]						      =0.3
								lowerCamelCase__						:						List[str]						      =learning_rate
								lowerCamelCase__						:						List[Any]						      =is_input_layer



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    Optional[int]	)->   Optional[Any]:
								lowerCamelCase__						:						List[Any]						      =np.asmatrix(np.random.normal(0,    0.5,    (self.units, back_units)	)	)
								lowerCamelCase__						:						Union[str, Any]						      =np.asmatrix(np.random.normal(0,    0.5,    self.units	)	).T
								if self.activation is None:
											lowerCamelCase__						:						Dict						      =sigmoid



					def 						snake_case			(			self				:    Tuple	)->   Dict:
								# activation function may be sigmoid or linear
								if self.activation == sigmoid:
											lowerCamelCase__						:						List[Any]						      =np.dot(self.output,    (1 - self.output).T	)
											lowerCamelCase__						:						Union[str, Any]						      =np.diag(np.diag(lowerCamelCase	)	)
								else:
											lowerCamelCase__						:						Union[str, Any]						      =1
								return gradient_activation



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    Dict	)->   Dict:
								lowerCamelCase__						:						Union[str, Any]						      =xdata
								if self.is_input_layer:
											# input layer
											lowerCamelCase__						:						Tuple						      =xdata
											lowerCamelCase__						:						Optional[Any]						      =xdata
											return xdata
								else:
											lowerCamelCase__						:						Union[str, Any]						      =np.dot(self.weight,    self.xdata	) - self.bias
											lowerCamelCase__						:						Any						      =self.activation(self.wx_plus_b	)
											return self.output




					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    Union[str, Any]	)->   int:
								lowerCamelCase__						:						int						      =self.cal_gradient()  # i * i 维
								lowerCamelCase__						:						Optional[Any]						      =np.asmatrix(np.dot(gradient.T,    lowerCamelCase	)	)

								lowerCamelCase__						:						int						      =np.asmatrix(self.xdata	)
								lowerCamelCase__						:						List[str]						      =-1
								lowerCamelCase__						:						List[str]						      =self.weight

								lowerCamelCase__						:						int						      =np.dot(gradient.T,    self._gradient_weight.T	)
								lowerCamelCase__						:						Optional[int]						      =gradient * self._gradient_bias
								lowerCamelCase__						:						Tuple						      =np.dot(lowerCamelCase,    self._gradient_x	).T
								# upgrade: the Negative gradient direction
								lowerCamelCase__						:						Any						      =self.weight - self.learn_rate * self.gradient_weight
								lowerCamelCase__						:						Optional[Any]						      =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 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					def __init__(			self				:    Any	)->   Dict:
								lowerCamelCase__						:						Union[str, Any]						      =[]
								lowerCamelCase__						:						str						      =[]
								lowerCamelCase__						:						int						      =plt.figure()
								lowerCamelCase__						:						Optional[Any]						      =self.fig_loss.add_subplot(1,    1,    1	)



					def 						snake_case			(			self				:    Union[str, Any],    lowerCamelCase				:    str	)->   Tuple:
								self.layers.append(lowerCamelCase	)



					def 						snake_case			(			self				:    Optional[Any]	)->   Any:
								for i, layer in enumerate(self.layers[:]	):
											if i < 1:
														lowerCamelCase__						:						Optional[int]						      =True
											else:
														layer.initializer(self.layers[i - 1].units	)



					def 						snake_case			(			self				:    List[Any]	)->   Tuple:
								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 						snake_case			(			self				:    List[str],    lowerCamelCase				:    Union[str, Any],    lowerCamelCase				:    Dict,    lowerCamelCase				:    List[Any],    lowerCamelCase				:    Optional[Any]	)->   Optional[Any]:
								lowerCamelCase__						:						Dict						      =train_round
								lowerCamelCase__						:						List[Any]						      =accuracy

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

								lowerCamelCase__						:						Optional[int]						      =np.shape(lowerCamelCase	)
								for _ in range(lowerCamelCase	):
											lowerCamelCase__						:						List[str]						      =0
											for row in range(x_shape[0]	):
														lowerCamelCase__						:						str						      =np.asmatrix(xdata[row, :]	).T
														lowerCamelCase__						:						List[str]						      =np.asmatrix(ydata[row, :]	).T

														# forward propagation
														for layer in self.layers:
																	lowerCamelCase__						:						Optional[int]						      =layer.forward_propagation(_xdata	)

														lowerCamelCase__      ,  lowerCamelCase__						:						Tuple						      =self.cal_loss(_ydata,    _xdata	)
														lowerCamelCase__						:						Dict						      =all_loss + loss

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

											lowerCamelCase__						:						List[Any]						      =all_loss / x_shape[0]
											self.train_mse.append(lowerCamelCase	)

											self.plot_loss()

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



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    Any	)->   Tuple:
								lowerCamelCase__						:						Union[str, Any]						      =np.sum(np.power((ydata - ydata_),    2	)	)
								lowerCamelCase__						:						Union[str, Any]						      =2 * (ydata_ - ydata)
								# vector (shape is the same as _ydata.shape)
								return self.loss, self.loss_gradient




					def 						snake_case			(			self				:    Tuple	)->   str:
								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 	snake_case__  (						):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =np.random.randn(10				,						10      )
			lowerCamelCase__						:						List[str]						      =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__						:						Tuple						      =BPNN()
			for i in (10, 20, 30, 2):
						model.add_layer(DenseLayer(__lowerCamelCase      )      )
			model.build()
			model.summary()
			model.train(xdata=__lowerCamelCase				,						ydata=__lowerCamelCase				,						train_round=100				,						accuracy=0.01      )


if __name__ == "__main__":
		example()








from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__UpperCamelCase							:							Any    							=					logging.get_logger(__name__)  # pylint: disable=invalid-name

class    __magic_name__						(  __lowerCAmelCase):
     A:		Dict								=   ["safety_checker", "feature_extractor"]


     def __init__(   self				:  List[str]				,						lowerCamelCase__				:  AutoencoderKL				,						lowerCamelCase__				:  CLIPTextModel				,						lowerCamelCase__				:  CLIPTokenizer				,						lowerCamelCase__				:  UNetaDConditionModel				,						lowerCamelCase__				:  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				,						lowerCamelCase__				:  StableDiffusionSafetyChecker				,						lowerCamelCase__				:  CLIPImageProcessor				,						lowerCamelCase__				:  bool = True				,						)	->   Optional[Any]:





      '''simple docstring'''






      super().__init__()
      if hasattr(scheduler.config				,						'''steps_offset'''      ) and scheduler.config.steps_offset != 1:
       UpperCamelCase__       :     int 							= (
           F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
           F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
           '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
           ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
           ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
           ''' file'''
       )
       deprecate('''steps_offset!=1'''				,						'''1.0.0'''				,						lowerCamelCase__				,						standard_warn=lowerCamelCase__      )
       UpperCamelCase__       :     int 							= dict(scheduler.config      )
       UpperCamelCase__       :     List[str] 							= 1
       UpperCamelCase__       :     List[str] 							= FrozenDict(lowerCamelCase__      )

      self.register_modules(
          vae=lowerCamelCase__				,						text_encoder=lowerCamelCase__				,						tokenizer=lowerCamelCase__				,						unet=lowerCamelCase__				,						scheduler=lowerCamelCase__				,						safety_checker=lowerCamelCase__				,						feature_extractor=lowerCamelCase__				,						)
      self.register_to_config(requires_safety_checker=lowerCamelCase__      )


     @property
     def   UpperCAmelCase__						(   self				:  str      )	->   Dict[str, Any]:





      '''simple docstring'''






      return {k: getattr(self				,						lowerCamelCase__      ) for k in self.config.keys() if not k.startswith('''_'''      )}


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  Optional[Union[str, int]] = "auto"      )	->   str:





      '''simple docstring'''






      if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       UpperCamelCase__       :     Dict 							= self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(lowerCamelCase__      )


     def   UpperCAmelCase__						(   self				:  int      )	->   Tuple:





      '''simple docstring'''






      self.enable_attention_slicing(lowerCamelCase__      )


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Optional[Any]				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  float = 0.8				,						lowerCamelCase__				:  Optional[int] = 50				,						lowerCamelCase__				:  Optional[float] = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  Optional[float] = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						)	->   Optional[Any]:





      '''simple docstring'''






      return StableDiffusionInpaintPipelineLegacy(**self.components      )(
          prompt=lowerCamelCase__				,						image=lowerCamelCase__				,						mask_image=lowerCamelCase__				,						strength=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						)


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Any				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  float = 0.8				,						lowerCamelCase__				:  Optional[int] = 50				,						lowerCamelCase__				:  Optional[float] = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  Optional[float] = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						**lowerCamelCase__				:  str				,						)	->   str:





      '''simple docstring'''






      return StableDiffusionImgaImgPipeline(**self.components      )(
          prompt=lowerCamelCase__				,						image=lowerCamelCase__				,						strength=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						callback_steps=lowerCamelCase__				,						)


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  int = 512				,						lowerCamelCase__				:  int = 512				,						lowerCamelCase__				:  int = 50				,						lowerCamelCase__				:  float = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  float = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[torch.FloatTensor] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						)	->   Optional[Any]:





      '''simple docstring'''






      return StableDiffusionPipeline(**self.components      )(
          prompt=lowerCamelCase__				,						height=lowerCamelCase__				,						width=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						latents=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						callback_steps=lowerCamelCase__				,						)


