code: 
  
  
  
  
  
  
  """simple docstring"""
  import math
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  
  
  snake_case   :   Tuple    		=		logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
  class 					UpperCamelCase__   (							_a):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   42
      __UpperCAmelCase											=   None
  
  
  def        A	(							__snake_case:   int   ,    __snake_case:   Optional[int]=0.999   ,    __snake_case:   Optional[Any]="cosine"   ,    )						->		Tuple:
  
         """simple docstring"""
  
  
  
  
  
         if alpha_transform_type == "cosine":
  
                def alpha_bar_fn(__snake_case:   List[str]     ):
                       return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2
  
         elif alpha_transform_type == "exp":
  
                def alpha_bar_fn(__snake_case:   List[str]     ):
                       return math.exp(t * -12.0     )
  
         else:
                raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )
  
         __magic_name__										=						[]
         for i in range(__snake_case     ):
                __magic_name__										=						i / num_diffusion_timesteps
                __magic_name__										=						(i + 1) / num_diffusion_timesteps
                betas.append(min(1 - alpha_bar_fn(__snake_case     ) / alpha_bar_fn(__snake_case     )   ,    __snake_case     )     )
         return torch.tensor(__snake_case   ,    dtype=torch.floataa     )
  
  
  
  
  
  
  class 					UpperCamelCase__   (							_a       ,  _a):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   [e.name for e in KarrasDiffusionSchedulers]
      __UpperCAmelCase											=   1
  
  
  
  
  
  
  
      @register_to_config
      def __init__(		self   :  Optional[Any]							,						UpperCamelCase_   :  Any = 1_0_0_0							,						UpperCamelCase_   :  Any = 0.0_001							,						UpperCamelCase_   :  Dict = 0.02							,						UpperCamelCase_   :  List[str] = "linear"							,						UpperCamelCase_   :  Tuple = None							,						UpperCamelCase_   :  Optional[int] = "epsilon"							,						UpperCamelCase_   :  Any = "linspace"							,						UpperCamelCase_   :  str = 0							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if trained_betas is not None:
                    __magic_name__										=						torch.tensor(lowerCamelCase__							,						dtype=torch.floataa   )
             elif beta_schedule == "linear":
                    __magic_name__										=						torch.linspace(lowerCamelCase__							,						lowerCamelCase__							,						lowerCamelCase__							,						dtype=torch.floataa   )
             elif beta_schedule == "scaled_linear":
                    # this schedule is very specific to the latent diffusion model.
                    __magic_name__										=						(
                        torch.linspace(beta_start**0.5							,						beta_end**0.5							,						lowerCamelCase__							,						dtype=torch.floataa   ) ** 2
                    )
             elif beta_schedule == "squaredcos_cap_v2":
                    # Glide cosine schedule
                    __magic_name__										=						betas_for_alpha_bar(lowerCamelCase__   )
             else:
                    raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""   )
  
             __magic_name__										=						1.0 - self.betas
             __magic_name__										=						torch.cumprod(self.alphas							,						dim=0   )
  
             __magic_name__										=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
             __magic_name__										=						np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
             __magic_name__										=						torch.from_numpy(lowerCamelCase__   )
  
             # setable values
             __magic_name__										=						None
             __magic_name__										=						np.linspace(0							,						num_train_timesteps - 1							,						lowerCamelCase__							,						dtype=lowerCamelCase__   )[::-1].copy()
             __magic_name__										=						torch.from_numpy(lowerCamelCase__   )
             __magic_name__										=						False
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.config.timestep_spacing in ["linspace", "trailing"]:
                    return self.sigmas.max()
  
             return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
  
  
  
  
      def 							a__ (		self   :  int							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if isinstance(lowerCamelCase__							,						torch.Tensor   ):
                    __magic_name__										=						timestep.to(self.timesteps.device   )
             __magic_name__										=						(self.timesteps == timestep).nonzero().item()
             __magic_name__										=						self.sigmas[step_index]
             __magic_name__										=						sample / ((sigma**2 + 1) ** 0.5)
             __magic_name__										=						True
             return sample
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Union[str, Any] = None   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						num_inference_steps
  
             # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
             if self.config.timestep_spacing == "linspace":
                    __magic_name__										=						np.linspace(0							,						self.config.num_train_timesteps - 1							,						lowerCamelCase__							,						dtype=lowerCamelCase__   )[
                        ::-1
                    ].copy()
             elif self.config.timestep_spacing == "leading":
                    __magic_name__										=						self.config.num_train_timesteps // self.num_inference_steps
                    # creates integer timesteps by multiplying by ratio
                    # casting to int to avoid issues when num_inference_step is power of 3
                    __magic_name__										=						(np.arange(0							,						lowerCamelCase__   ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__   )
                    timesteps += self.config.steps_offset
             elif self.config.timestep_spacing == "trailing":
                    __magic_name__										=						self.config.num_train_timesteps / self.num_inference_steps
                    # creates integer timesteps by multiplying by ratio
                    # casting to int to avoid issues when num_inference_step is power of 3
                    __magic_name__										=						(np.arange(self.config.num_train_timesteps							,						0							,						-step_ratio   )).round().copy().astype(lowerCamelCase__   )
                    timesteps -= 1
             else:
                    raise ValueError(
                        f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""   )
  
             __magic_name__										=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
             __magic_name__										=						np.interp(lowerCamelCase__							,						np.arange(0							,						len(lowerCamelCase__   )   )							,						lowerCamelCase__   )
             __magic_name__										=						np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
             __magic_name__										=						torch.from_numpy(lowerCamelCase__   ).to(device=lowerCamelCase__   )
             if str(lowerCamelCase__   ).startswith('mps'   ):
                    # mps does not support float64
                    __magic_name__										=						torch.from_numpy(lowerCamelCase__   ).to(lowerCamelCase__							,						dtype=torch.floataa   )
             else:
                    __magic_name__										=						torch.from_numpy(lowerCamelCase__   ).to(device=lowerCamelCase__   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[Any] = None							,						UpperCamelCase_   :  Optional[Any] = True							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if (
                 isinstance(lowerCamelCase__							,						lowerCamelCase__   )
                 or isinstance(lowerCamelCase__							,						torch.IntTensor   )
                 or isinstance(lowerCamelCase__							,						torch.LongTensor   )
             ):
                    raise ValueError(
                        (
                            'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                            ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                            ' one of the `scheduler.timesteps` as a timestep.'
                        )							,						)
  
             if not self.is_scale_input_called:
                    logger.warning(
                        'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                        'See `StableDiffusionPipeline` for a usage example.'   )
  
             if isinstance(lowerCamelCase__							,						torch.Tensor   ):
                    __magic_name__										=						timestep.to(self.timesteps.device   )
  
             __magic_name__										=						(self.timesteps == timestep).nonzero().item()
             __magic_name__										=						self.sigmas[step_index]
  
             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                    __magic_name__										=						sample - sigma * model_output
             elif self.config.prediction_type == "v_prediction":
                    # * c_out + input * c_skip
                    __magic_name__										=						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
             elif self.config.prediction_type == "sample":
                    raise NotImplementedError('prediction_type not implemented yet: sample'   )
             else:
                    raise ValueError(
                        f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""   )
  
             __magic_name__										=						self.sigmas[step_index]
             __magic_name__										=						self.sigmas[step_index + 1]
             __magic_name__										=						(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
             __magic_name__										=						(sigma_to**2 - sigma_up**2) ** 0.5
  
             # 2. Convert to an ODE derivative
             __magic_name__										=						(sample - pred_original_sample) / sigma
  
             __magic_name__										=						sigma_down - sigma
  
             __magic_name__										=						sample + derivative * dt
  
             __magic_name__										=						model_output.device
             __magic_name__										=						randn_tensor(model_output.shape							,						dtype=model_output.dtype							,						device=lowerCamelCase__							,						generator=lowerCamelCase__   )
  
             __magic_name__										=						prev_sample + noise * sigma_up
  
             if not return_dict:
                    return (prev_sample,)
  
             return EulerAncestralDiscreteSchedulerOutput(
                 prev_sample=lowerCamelCase__							,						pred_original_sample=lowerCamelCase__   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Dict							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[Any]							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.sigmas.to(device=original_samples.device							,						dtype=original_samples.dtype   )
             if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__   ):
                    # mps does not support float64
                    __magic_name__										=						self.timesteps.to(original_samples.device							,						dtype=torch.floataa   )
                    __magic_name__										=						timesteps.to(original_samples.device							,						dtype=torch.floataa   )
             else:
                    __magic_name__										=						self.timesteps.to(original_samples.device   )
                    __magic_name__										=						timesteps.to(original_samples.device   )
  
             __magic_name__										=						[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
             __magic_name__										=						sigmas[step_indices].flatten()
             while len(sigma.shape   ) < len(original_samples.shape   ):
                    __magic_name__										=						sigma.unsqueeze(-1   )
  
             __magic_name__										=						original_samples + noise * sigma
             return noisy_samples
  
  
  
  
  
  
  
      def __len__(		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return self.config.num_train_timesteps
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()