




import pickle

import numpy as np
from matplotlib import pyplot as plt


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	List[Any]=0.2					, UpperCAmelCase_       :	Union[str, Any]=0.2      ):
       SCREAMING_SNAKE_CASE       :      int							=    bp_numa
       SCREAMING_SNAKE_CASE       :      Any							=    bp_numa
       SCREAMING_SNAKE_CASE       :      List[str]							=    bp_numa
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    conva_get[:2]
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    conva_get[2]
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    size_pa
       SCREAMING_SNAKE_CASE       :      List[Any]							=    rate_w
       SCREAMING_SNAKE_CASE       :      Any							=    rate_t
       SCREAMING_SNAKE_CASE       :      List[str]							=    [
           np.mat(-1 * np.random.rand(self.conva[0]					, self.conva[0]      ) + 0.5      )
           for i in range(self.conva[1]      )
       ]
       SCREAMING_SNAKE_CASE       :      Dict							=    np.mat(-1 * np.random.rand(self.num_bpa					, self.num_bpa      ) + 0.5      )
       SCREAMING_SNAKE_CASE       :      str							=    np.mat(-1 * np.random.rand(self.num_bpa					, self.num_bpa      ) + 0.5      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    -2 * np.random.rand(self.conva[1]      ) + 1
       SCREAMING_SNAKE_CASE       :      List[Any]							=    -2 * np.random.rand(self.num_bpa      ) + 1
       SCREAMING_SNAKE_CASE       :      Tuple							=    -2 * np.random.rand(self.num_bpa      ) + 1


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	Any      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    {
           "num_bp1": self.num_bpa,
           "num_bp2": self.num_bpa,
           "num_bp3": self.num_bpa,
           "conv1": self.conva,
           "step_conv1": self.step_conva,
           "size_pooling1": self.size_poolinga,
           "rate_weight": self.rate_weight,
           "rate_thre": self.rate_thre,
           "w_conv1": self.w_conva,
           "wkj": self.wkj,
           "vji": self.vji,
           "thre_conv1": self.thre_conva,
           "thre_bp2": self.thre_bpa,
           "thre_bp3": self.thre_bpa,
       }
       with open(_SCREAMING_SNAKE_CASE					, "wb"      ) as f:
         pickle.dump(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      )

       print(f'''Model saved： {save_path}'''      )


     @classmethod
     def 				_A       (				cls       :	List[str]					, UpperCAmelCase_       :	Optional[Any]      ):






       with open(_SCREAMING_SNAKE_CASE					, "rb"      ) as f:
         SCREAMING_SNAKE_CASE       :      Any							=    pickle.load(_SCREAMING_SNAKE_CASE      )  # noqa: S301

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    model_dic.get("conv1"      )
       conv_get.append(model_dic.get("step_conv1"      )      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model_dic.get("size_pooling1"      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    model_dic.get("num_bp1"      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model_dic.get("num_bp2"      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model_dic.get("num_bp3"      )
       SCREAMING_SNAKE_CASE       :      Dict							=    model_dic.get("rate_weight"      )
       SCREAMING_SNAKE_CASE       :      Any							=    model_dic.get("rate_thre"      )
       # create model instance
       SCREAMING_SNAKE_CASE       :      List[str]							=    CNN(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      )
       # modify model parameter
       SCREAMING_SNAKE_CASE       :      Dict							=    model_dic.get("w_conv1"      )
       SCREAMING_SNAKE_CASE       :      Dict							=    model_dic.get("wkj"      )
       SCREAMING_SNAKE_CASE       :      str							=    model_dic.get("vji"      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    model_dic.get("thre_conv1"      )
       SCREAMING_SNAKE_CASE       :      Dict							=    model_dic.get("thre_bp2"      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    model_dic.get("thre_bp3"      )
       return conv_ins


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Optional[int]      ):
       return 1 / (1 + np.exp(-1 * x      ))


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	str      ):
       return round(_SCREAMING_SNAKE_CASE					, 3      )


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    convs[0]
       SCREAMING_SNAKE_CASE       :      Dict							=    convs[1]
       SCREAMING_SNAKE_CASE       :      int							=    np.shape(_SCREAMING_SNAKE_CASE      )[0]
       # get the data slice of original image data, data_focus
       SCREAMING_SNAKE_CASE       :      Tuple							=    []
       for i_focus in range(0					, size_data - size_conv + 1					, _SCREAMING_SNAKE_CASE      ):
         for j_focus in range(0					, size_data - size_conv + 1					, _SCREAMING_SNAKE_CASE      ):
           SCREAMING_SNAKE_CASE       :      List[str]							=    data[
               i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
           ]
           data_focus.append(_SCREAMING_SNAKE_CASE      )
        # calculate the feature map of every single kernel, and saved as list of matrix
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    []
       SCREAMING_SNAKE_CASE       :      str							=    int((size_data - size_conv) / conv_step + 1      )
       for i_map in range(_SCREAMING_SNAKE_CASE      ):
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    []
         for i_focus in range(len(_SCREAMING_SNAKE_CASE      )      ):
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    (
               np.sum(np.multiply(data_focus[i_focus]					, w_convs[i_map]      )      )
               - thre_convs[i_map]
           )
           featuremap.append(self.sig(_SCREAMING_SNAKE_CASE      )      )
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    np.asmatrix(_SCREAMING_SNAKE_CASE      ).reshape(
             _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      )
         data_featuremap.append(_SCREAMING_SNAKE_CASE      )

       # expanding the data slice to One dimenssion
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
       for each_focus in data_focus:
         focusa_list.extend(self.Expand_Mat(_SCREAMING_SNAKE_CASE      )      )
       SCREAMING_SNAKE_CASE       :      str							=    np.asarray(_SCREAMING_SNAKE_CASE      )
       return focus_list, data_featuremap


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Dict="average_pool"      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    len(featuremaps[0]      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    int(size_map / size_pooling      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    []
       for i_map in range(len(_SCREAMING_SNAKE_CASE      )      ):
         SCREAMING_SNAKE_CASE       :      int							=    featuremaps[i_map]
         SCREAMING_SNAKE_CASE       :      str							=    []
         for i_focus in range(0					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      ):
           for j_focus in range(0					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      ):
             SCREAMING_SNAKE_CASE       :      Tuple							=    feature_map[
                 i_focus : i_focus + size_pooling,
                 j_focus : j_focus + size_pooling,
             ]
             if pooling_type == "average_pool":
               # average pooling
               map_pooled.append(np.average(_SCREAMING_SNAKE_CASE      )      )
             elif pooling_type == "max_pooling":
               # max pooling
               map_pooled.append(np.max(_SCREAMING_SNAKE_CASE      )      )
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    np.asmatrix(_SCREAMING_SNAKE_CASE      ).reshape(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      )
         featuremap_pooled.append(_SCREAMING_SNAKE_CASE      )
       return featuremap_pooled


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    []
       for i in range(len(_SCREAMING_SNAKE_CASE      )      ):
         SCREAMING_SNAKE_CASE       :      str							=    np.shape(data[i]      )
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    data[i].reshape(1					, shapes[0] * shapes[1]      )
         SCREAMING_SNAKE_CASE       :      int							=    data_listed.getA().tolist()[0]
         data_expanded.extend(_SCREAMING_SNAKE_CASE      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    np.asarray(_SCREAMING_SNAKE_CASE      )
       return data_expanded


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	int      ):
       SCREAMING_SNAKE_CASE       :      Any							=    np.asarray(_SCREAMING_SNAKE_CASE      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    np.shape(_SCREAMING_SNAKE_CASE      )
       SCREAMING_SNAKE_CASE       :      Dict							=    data_mat.reshape(1					, shapes[0] * shapes[1]      )
       return data_expanded


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    []
       SCREAMING_SNAKE_CASE       :      int							=    0
       for i_map in range(_SCREAMING_SNAKE_CASE      ):
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    np.ones((size_map, size_map)      )
         for i in range(0					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      ):
           for j in range(0					, _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      ):
             SCREAMING_SNAKE_CASE       :      str							=    pd_pool[
                 i_pool
             ]
             SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    i_pool + 1
         SCREAMING_SNAKE_CASE       :      Any							=    np.multiply(
             _SCREAMING_SNAKE_CASE					, np.multiply(out_map[i_map]					, (1 - out_map[i_map])      )      )
         pd_all.append(_SCREAMING_SNAKE_CASE      )
       return pd_all


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	List[str]=bool      ):
       print("----------------------Start Training-------------------------"      )
       print((" - - Shape: Train_Data  ", np.shape(_SCREAMING_SNAKE_CASE      ))      )
       print((" - - Shape: Teach_Data  ", np.shape(_SCREAMING_SNAKE_CASE      ))      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    0
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    []
       SCREAMING_SNAKE_CASE       :      int							=    1_0000
       while rp < n_repeat and mse >= error_accuracy:
         SCREAMING_SNAKE_CASE       :      int							=    0
         print(f'''-------------Learning Time {rp}--------------'''      )
         for p in range(len(_SCREAMING_SNAKE_CASE      )      ):
           # print('------------Learning Image: %d--------------'%p)
           SCREAMING_SNAKE_CASE       :      Dict							=    np.asmatrix(datas_train[p]      )
           SCREAMING_SNAKE_CASE       :      Tuple							=    np.asarray(datas_teach[p]      )
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.convolute(
               _SCREAMING_SNAKE_CASE					, self.conva					, self.w_conva					, self.thre_conva					, conv_step=self.step_conva					, )
           SCREAMING_SNAKE_CASE       :      Tuple							=    self.pooling(_SCREAMING_SNAKE_CASE					, self.size_poolinga      )
           SCREAMING_SNAKE_CASE       :      str							=    np.shape(_SCREAMING_SNAKE_CASE      )
           SCREAMING_SNAKE_CASE       :      Any							=    self._expand(_SCREAMING_SNAKE_CASE      )
           SCREAMING_SNAKE_CASE       :      Dict							=    data_bp_input

           SCREAMING_SNAKE_CASE       :      Optional[int]							=    np.dot(_SCREAMING_SNAKE_CASE					, self.vji.T      ) - self.thre_bpa
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.sig(_SCREAMING_SNAKE_CASE      )
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    np.dot(_SCREAMING_SNAKE_CASE					, self.wkj.T      ) - self.thre_bpa
           SCREAMING_SNAKE_CASE       :      Tuple							=    self.sig(_SCREAMING_SNAKE_CASE      )

           # --------------Model Leaning ------------------------
           # calculate error and gradient---------------
           SCREAMING_SNAKE_CASE       :      Any							=    np.multiply(
               (data_teach - bp_outa)					, np.multiply(_SCREAMING_SNAKE_CASE					, (1 - bp_outa)      )      )
           SCREAMING_SNAKE_CASE       :      Tuple							=    np.multiply(
               np.dot(_SCREAMING_SNAKE_CASE					, self.wkj      )					, np.multiply(_SCREAMING_SNAKE_CASE					, (1 - bp_outa)      )      )
           SCREAMING_SNAKE_CASE       :      Any							=    np.dot(_SCREAMING_SNAKE_CASE					, self.vji      )

           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    pd_i_all / (self.size_poolinga * self.size_poolinga)
           SCREAMING_SNAKE_CASE       :      List[str]							=    pd_conva_pooled.T.getA().tolist()
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    self._calculate_gradient_from_pool(
               _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, shape_featuremapa[0]					, shape_featuremapa[1]					, self.size_poolinga					, )
           # weight and threshold learning process---------
           # convolution layer
           for k_conv in range(self.conva[1]      ):
             SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self._expand_mat(pd_conva_all[k_conv]      )
             SCREAMING_SNAKE_CASE       :      Tuple							=    self.rate_weight * np.dot(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE      )

             SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.w_conva[k_conv] + delta_w.reshape(
                 (self.conva[0], self.conva[0])      )

             SCREAMING_SNAKE_CASE       :      Any							=    (
                 self.thre_conva[k_conv]
                 - np.sum(pd_conva_all[k_conv]      ) * self.rate_thre
             )
           # all connected layer
           SCREAMING_SNAKE_CASE       :      Tuple							=    self.wkj + pd_k_all.T * bp_outa * self.rate_weight
           SCREAMING_SNAKE_CASE       :      List[str]							=    self.vji + pd_j_all.T * bp_outa * self.rate_weight
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.thre_bpa - pd_k_all * self.rate_thre
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.thre_bpa - pd_j_all * self.rate_thre
           # calculate the sum error of all single image
           SCREAMING_SNAKE_CASE       :      Tuple							=    np.sum(abs(data_teach - bp_outa      )      )
           error_count += errors
           # print('   ----Teach      ',data_teach)
           # print('   ----BP_output  ',bp_out3)
         SCREAMING_SNAKE_CASE       :      Any							=    rp + 1
         SCREAMING_SNAKE_CASE       :      str							=    error_count / patterns
         all_mse.append(_SCREAMING_SNAKE_CASE      )

       def draw_error():
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    [error_accuracy for i in range(int(n_repeat * 1.2      )      )]
         plt.plot(_SCREAMING_SNAKE_CASE					, "+-"      )
         plt.plot(_SCREAMING_SNAKE_CASE					, "r--"      )
         plt.xlabel("Learning Times"      )
         plt.ylabel("All_mse"      )
         plt.grid(_SCREAMING_SNAKE_CASE					, alpha=0.5      )
         plt.show()

       print("------------------Training Complished---------------------"      )
       print((" - - Training epoch: ", rp, f'''     - - Mse: {mse:.6f}''')      )
       if draw_e:
         draw_error()
       return mse


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    []
       print("-------------------Start Testing-------------------------"      )
       print((" - - Shape: Test_Data  ", np.shape(_SCREAMING_SNAKE_CASE      ))      )
       for p in range(len(_SCREAMING_SNAKE_CASE      )      ):
         SCREAMING_SNAKE_CASE       :      Dict							=    np.asmatrix(datas_test[p]      )
         SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[Any]							=    self.convolute(
             _SCREAMING_SNAKE_CASE					, self.conva					, self.w_conva					, self.thre_conva					, conv_step=self.step_conva					, )
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.pooling(_SCREAMING_SNAKE_CASE					, self.size_poolinga      )
         SCREAMING_SNAKE_CASE       :      str							=    self._expand(_SCREAMING_SNAKE_CASE      )

         SCREAMING_SNAKE_CASE       :      int							=    data_bp_input
         SCREAMING_SNAKE_CASE       :      str							=    bp_outa * self.vji.T - self.thre_bpa
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.sig(_SCREAMING_SNAKE_CASE      )
         SCREAMING_SNAKE_CASE       :      str							=    bp_outa * self.wkj.T - self.thre_bpa
         SCREAMING_SNAKE_CASE       :      int							=    self.sig(_SCREAMING_SNAKE_CASE      )
         produce_out.extend(bp_outa.getA().tolist()      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [list(map(self.do_round					, _SCREAMING_SNAKE_CASE      )      ) for each in produce_out]
       return np.asarray(_SCREAMING_SNAKE_CASE      )






     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      str							=    np.asmatrix(_SCREAMING_SNAKE_CASE      )
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Any							=    self.convolute(
           _SCREAMING_SNAKE_CASE					, self.conva					, self.w_conva					, self.thre_conva					, conv_step=self.step_conva					, )
       SCREAMING_SNAKE_CASE       :      Dict							=    self.pooling(_SCREAMING_SNAKE_CASE					, self.size_poolinga      )

       return data_conveda, data_pooleda


if __name__ == "__main__":
   pass














# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		torch.FloatTensor
     UpperCamelCase_							:		Optional[torch.FloatTensor]			=	None





def   lowerCamelCase__			(     lowercase						,     lowercase=0.999						,     lowercase="cosine"						,     ):

  """simple docstring"""






  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase				):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowercase				):
      return math.exp(t * -12.0				)

  else:
    raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
  for i in range(lowercase				):
    SCREAMING_SNAKE_CASE       :      str							=    i / num_diffusion_timesteps
    SCREAMING_SNAKE_CASE       :      List[str]							=    (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowercase				) / alpha_bar_fn(lowercase				)						,     lowercase				)				)
  return torch.tensor(lowercase						,     dtype=torch.floataa				)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[Any]			=	1


     @register_to_config
     def __init__(				self       :	Union[str, Any]					, UpperCAmelCase_       :	int = 1000					, UpperCAmelCase_       :	float = 0.0_001					, UpperCAmelCase_       :	float = 0.02					, UpperCAmelCase_       :	str = "linear"					, UpperCAmelCase_       :	Optional[Union[np.ndarray, List[float]]] = None					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	int = 0					, UpperCAmelCase_       :	str = "epsilon"					, UpperCAmelCase_       :	float = 1.0					, **UpperCAmelCase_       :	Dict					, ):
       if kwargs.get("set_alpha_to_one"					, UpperCAmelCase_      ) is not None:
         SCREAMING_SNAKE_CASE       :      int							=    (
             "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
         )
         deprecate("set_alpha_to_one"					, "1.0.0"					, UpperCAmelCase_					, standard_warn=UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      int							=    kwargs["set_alpha_to_one"]
       if trained_betas is not None:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.tensor(UpperCAmelCase_					, dtype=torch.floataa      )
       elif beta_schedule == "linear":
         SCREAMING_SNAKE_CASE       :      List[str]							=    torch.linspace(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, dtype=torch.floataa      )
       elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (
             torch.linspace(beta_start**0.5					, beta_end**0.5					, UpperCAmelCase_					, dtype=torch.floataa      ) ** 2
         )
       elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         SCREAMING_SNAKE_CASE       :      int							=    betas_for_alpha_bar(UpperCAmelCase_      )
       else:
         raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    1.0 - self.betas
       SCREAMING_SNAKE_CASE       :      Any							=    torch.cumprod(self.alphas					, dim=0      )

       # At every step in inverted ddim, we are looking into the next alphas_cumprod
       # For the final step, there is no next alphas_cumprod, and the index is out of bounds
       # `set_alpha_to_zero` decides whether we set this parameter simply to zero
       # in this case, self.step() just output the predicted noise
       # or whether we use the final alpha of the "non-previous" one.
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.tensor(0.0      ) if set_alpha_to_zero else self.alphas_cumprod[-1]

       # standard deviation of the initial noise distribution
       SCREAMING_SNAKE_CASE       :      List[str]							=    1.0

       # setable values
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    None
       SCREAMING_SNAKE_CASE       :      List[str]							=    torch.from_numpy(np.arange(0					, UpperCAmelCase_      ).copy().astype(np.intaa      )      )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	Optional[int] = None      ):
       return sample


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Union[str, torch.device] = None      ):

       if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
             f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
             f''' maximal {self.config.num_train_timesteps} timesteps.'''      )

       SCREAMING_SNAKE_CASE       :      Dict							=    num_inference_steps
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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
       SCREAMING_SNAKE_CASE       :      str							=    (np.arange(0					, UpperCAmelCase_      ) * step_ratio).round().copy().astype(np.intaa      )
       SCREAMING_SNAKE_CASE       :      str							=    torch.from_numpy(UpperCAmelCase_      ).to(UpperCAmelCase_      )
       self.timesteps += self.config.steps_offset


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	float = 0.0					, UpperCAmelCase_       :	bool = False					, UpperCAmelCase_       :	Optional[torch.FloatTensor] = None					, UpperCAmelCase_       :	bool = True					, ):
       # 1. get previous step value (=t+1)
       SCREAMING_SNAKE_CASE       :      Tuple							=    timestep + self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       # change original implementation to exactly match noise levels for analogous forward process
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.alphas_cumprod[timestep]
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    (
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       SCREAMING_SNAKE_CASE       :      Tuple							=    1 - alpha_prod_t

       # 3. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       if self.config.prediction_type == "epsilon":
         SCREAMING_SNAKE_CASE       :      Any							=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         SCREAMING_SNAKE_CASE       :      List[Any]							=    model_output
       elif self.config.prediction_type == "sample":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model_output
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         SCREAMING_SNAKE_CASE       :      Tuple							=    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
       else:
         raise ValueError(
             f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
             " `v_prediction`"      )

       # 4. Clip or threshold "predicted x_0"
       if self.config.clip_sample:
         SCREAMING_SNAKE_CASE       :      List[str]							=    pred_original_sample.clamp(
             -self.config.clip_sample_range					, self.config.clip_sample_range      )

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       SCREAMING_SNAKE_CASE       :      Dict							=    (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

       # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       SCREAMING_SNAKE_CASE       :      str							=    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if not return_dict:
         return (prev_sample, pred_original_sample)
       return DDIMSchedulerOutput(prev_sample=UpperCAmelCase_					, pred_original_sample=UpperCAmelCase_      )






     def __len__(				self       :	Dict      ):
       return self.config.num_train_timesteps






