
import pickle

import numpy as np
from matplotlib import pyplot as plt


class        lowercase      :



		def __init__(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_=0.2       ,   A_=0.2 )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  bp_numa
				UpperCamelCase						=  bp_numa
				UpperCamelCase						=  bp_numa
				UpperCamelCase						=  conva_get[:2]
				UpperCamelCase						=  conva_get[2]
				UpperCamelCase						=  size_pa
				UpperCamelCase						=  rate_w
				UpperCamelCase						=  rate_t
				UpperCamelCase						=  [
				    np.mat(-1 * np.random.rand(self.conva[0]       ,   self.conva[0] ) + 0.5 )
				    for i in range(self.conva[1] )
				]
				UpperCamelCase						=  np.mat(-1 * np.random.rand(self.num_bpa       ,   self.num_bpa ) + 0.5 )
				UpperCamelCase						=  np.mat(-1 * np.random.rand(self.num_bpa       ,   self.num_bpa ) + 0.5 )
				UpperCamelCase						=  -2 * np.random.rand(self.conva[1] ) + 1
				UpperCamelCase						=  -2 * np.random.rand(self.num_bpa ) + 1
				UpperCamelCase						=  -2 * np.random.rand(self.num_bpa ) + 1



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				# save model dict with pickle
				UpperCamelCase						=  {
				    '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(_lowercase       ,   'wb' ) as f:
						pickle.dump(_lowercase       ,   _lowercase )

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



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ )						->      int:
				"""simple docstring"""
				# read saved model
				with open(_lowercase       ,   'rb' ) as f:
						UpperCamelCase						=  pickle.load(_lowercase )  # noqa: S301

				UpperCamelCase						=  model_dic.get('conv1' )
				conv_get.append(model_dic.get('step_conv1' ) )
				UpperCamelCase						=  model_dic.get('size_pooling1' )
				UpperCamelCase						=  model_dic.get('num_bp1' )
				UpperCamelCase						=  model_dic.get('num_bp2' )
				UpperCamelCase						=  model_dic.get('num_bp3' )
				UpperCamelCase						=  model_dic.get('rate_weight' )
				UpperCamelCase						=  model_dic.get('rate_thre' )
				# create model instance
				UpperCamelCase						=  CNN(_lowercase       ,   _lowercase       ,   _lowercase       ,   _lowercase       ,   _lowercase       ,   _lowercase       ,   _lowercase )
				# modify model parameter
				UpperCamelCase						=  model_dic.get('w_conv1' )
				UpperCamelCase						=  model_dic.get('wkj' )
				UpperCamelCase						=  model_dic.get('vji' )
				UpperCamelCase						=  model_dic.get('thre_conv1' )
				UpperCamelCase						=  model_dic.get('thre_bp2' )
				UpperCamelCase						=  model_dic.get('thre_bp3' )
				return conv_ins



		def __UpperCamelCase				(				self       ,   A_ )						->      List[str]:
				"""simple docstring"""
				return 1 / (1 + np.exp(-1 * x ))



		def __UpperCamelCase				(				self       ,   A_ )						->      Any:
				"""simple docstring"""
				return round(_lowercase       ,   3 )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_ )						->      List[str]:
				"""simple docstring"""
				# convolution process
				UpperCamelCase						=  convs[0]
				UpperCamelCase						=  convs[1]
				UpperCamelCase						=  np.shape(_lowercase )[0]
				# get the data slice of original image data, data_focus
				UpperCamelCase						=  []
				for i_focus in range(0       ,   size_data - size_conv + 1       ,   _lowercase ):
						for j_focus in range(0       ,   size_data - size_conv + 1       ,   _lowercase ):
								UpperCamelCase						=  data[
								    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
								]
								data_focus.append(_lowercase )
        # calculate the feature map of every single kernel, and saved as list of matrix
				UpperCamelCase						=  []
				UpperCamelCase						=  int((size_data - size_conv) / conv_step + 1 )
				for i_map in range(_lowercase ):
						UpperCamelCase						=  []
						for i_focus in range(len(_lowercase ) ):
								UpperCamelCase						=  (
								    np.sum(np.multiply(data_focus[i_focus]       ,   w_convs[i_map] ) )
								    - thre_convs[i_map]
								)
								featuremap.append(self.sig(_lowercase ) )
						UpperCamelCase						=  np.asmatrix(_lowercase ).reshape(
						    _lowercase       ,   _lowercase )
						data_featuremap.append(_lowercase )

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



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_="average_pool" )						->      int:
				"""simple docstring"""
				# pooling process
				UpperCamelCase						=  len(featuremaps[0] )
				UpperCamelCase						=  int(size_map / size_pooling )
				UpperCamelCase						=  []
				for i_map in range(len(_lowercase ) ):
						UpperCamelCase						=  featuremaps[i_map]
						UpperCamelCase						=  []
						for i_focus in range(0       ,   _lowercase       ,   _lowercase ):
								for j_focus in range(0       ,   _lowercase       ,   _lowercase ):
										UpperCamelCase						=  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(_lowercase ) )
										elif pooling_type == "max_pooling":
												# max pooling
												map_pooled.append(np.max(_lowercase ) )
						UpperCamelCase						=  np.asmatrix(_lowercase ).reshape(_lowercase       ,   _lowercase )
						featuremap_pooled.append(_lowercase )
				return featuremap_pooled



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				# expanding three dimension data to one dimension list
				UpperCamelCase						=  []
				for i in range(len(_lowercase ) ):
						UpperCamelCase						=  np.shape(data[i] )
						UpperCamelCase						=  data[i].reshape(1       ,   shapes[0] * shapes[1] )
						UpperCamelCase						=  data_listed.getA().tolist()[0]
						data_expanded.extend(_lowercase )
				UpperCamelCase						=  np.asarray(_lowercase )
				return data_expanded



		def __UpperCamelCase				(				self       ,   A_ )						->      Any:
				"""simple docstring"""
				# expanding matrix to one dimension list
				UpperCamelCase						=  np.asarray(_lowercase )
				UpperCamelCase						=  np.shape(_lowercase )
				UpperCamelCase						=  data_mat.reshape(1       ,   shapes[0] * shapes[1] )
				return data_expanded



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  []
				UpperCamelCase						=  0
				for i_map in range(_lowercase ):
						UpperCamelCase						=  np.ones((size_map, size_map) )
						for i in range(0       ,   _lowercase       ,   _lowercase ):
								for j in range(0       ,   _lowercase       ,   _lowercase ):
										UpperCamelCase						=  pd_pool[
										    i_pool
										]
										UpperCamelCase						=  i_pool + 1
						UpperCamelCase						=  np.multiply(
						    _lowercase       ,   np.multiply(out_map[i_map]       ,   (1 - out_map[i_map]) ) )
						pd_all.append(_lowercase )
				return pd_all



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_=bool )						->      Tuple:
				"""simple docstring"""
				# model traning
				print('----------------------Start Training-------------------------' )
				print((' - - Shape: Train_Data  ', np.shape(_lowercase )) )
				print((' - - Shape: Teach_Data  ', np.shape(_lowercase )) )
				UpperCamelCase						=  0
				UpperCamelCase						=  []
				UpperCamelCase						=  10_000
				while rp < n_repeat and mse >= error_accuracy:
						UpperCamelCase						=  0
						print(F'''-------------Learning Time {rp}--------------''' )
						for p in range(len(_lowercase ) ):
								# print('------------Learning Image: %d--------------'%p)
								UpperCamelCase						=  np.asmatrix(datas_train[p] )
								UpperCamelCase						=  np.asarray(datas_teach[p] )
								UpperCamelCase			,						UpperCamelCase						=  self.convolute(
								    _lowercase       ,   self.conva       ,   self.w_conva       ,   self.thre_conva       ,   conv_step=self.step_conva       ,   )
								UpperCamelCase						=  self.pooling(_lowercase       ,   self.size_poolinga )
								UpperCamelCase						=  np.shape(_lowercase )
								UpperCamelCase						=  self._expand(_lowercase )
								UpperCamelCase						=  data_bp_input

								UpperCamelCase						=  np.dot(_lowercase       ,   self.vji.T ) - self.thre_bpa
								UpperCamelCase						=  self.sig(_lowercase )
								UpperCamelCase						=  np.dot(_lowercase       ,   self.wkj.T ) - self.thre_bpa
								UpperCamelCase						=  self.sig(_lowercase )

								# --------------Model Leaning ------------------------
								# calculate error and gradient---------------
								UpperCamelCase						=  np.multiply(
								    (data_teach - bp_outa)       ,   np.multiply(_lowercase       ,   (1 - bp_outa) ) )
								UpperCamelCase						=  np.multiply(
								    np.dot(_lowercase       ,   self.wkj )       ,   np.multiply(_lowercase       ,   (1 - bp_outa) ) )
								UpperCamelCase						=  np.dot(_lowercase       ,   self.vji )

								UpperCamelCase						=  pd_i_all / (self.size_poolinga * self.size_poolinga)
								UpperCamelCase						=  pd_conva_pooled.T.getA().tolist()
								UpperCamelCase						=  self._calculate_gradient_from_pool(
								    _lowercase       ,   _lowercase       ,   shape_featuremapa[0]       ,   shape_featuremapa[1]       ,   self.size_poolinga       ,   )
								# weight and threshold learning process---------
								# convolution layer
								for k_conv in range(self.conva[1] ):
										UpperCamelCase						=  self._expand_mat(pd_conva_all[k_conv] )
										UpperCamelCase						=  self.rate_weight * np.dot(_lowercase       ,   _lowercase )

										UpperCamelCase						=  self.w_conva[k_conv] + delta_w.reshape(
										    (self.conva[0], self.conva[0]) )

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

				def draw_error():
						UpperCamelCase						=  [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
						plt.plot(_lowercase       ,   '+-' )
						plt.plot(_lowercase       ,   'r--' )
						plt.xlabel('Learning Times' )
						plt.ylabel('All_mse' )
						plt.grid(_lowercase       ,   alpha=0.5 )
						plt.show()

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



		def __UpperCamelCase				(				self       ,   A_ )						->      List[str]:
				"""simple docstring"""
				# model predict
				UpperCamelCase						=  []
				print('-------------------Start Testing-------------------------' )
				print((' - - Shape: Test_Data  ', np.shape(_lowercase )) )
				for p in range(len(_lowercase ) ):
						UpperCamelCase						=  np.asmatrix(datas_test[p] )
						UpperCamelCase			,						UpperCamelCase						=  self.convolute(
						    _lowercase       ,   self.conva       ,   self.w_conva       ,   self.thre_conva       ,   conv_step=self.step_conva       ,   )
						UpperCamelCase						=  self.pooling(_lowercase       ,   self.size_poolinga )
						UpperCamelCase						=  self._expand(_lowercase )

						UpperCamelCase						=  data_bp_input
						UpperCamelCase						=  bp_outa * self.vji.T - self.thre_bpa
						UpperCamelCase						=  self.sig(_lowercase )
						UpperCamelCase						=  bp_outa * self.wkj.T - self.thre_bpa
						UpperCamelCase						=  self.sig(_lowercase )
						produce_out.extend(bp_outa.getA().tolist() )
				UpperCamelCase						=  [list(map(self.do_round       ,   _lowercase ) ) for each in produce_out]
				return np.asarray(_lowercase )



		def __UpperCamelCase				(				self       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				# return the data of image after convoluting process so we can check it out
				UpperCamelCase						=  np.asmatrix(_lowercase )
				UpperCamelCase			,						UpperCamelCase						=  self.convolute(
				    _lowercase       ,   self.conva       ,   self.w_conva       ,   self.thre_conva       ,   conv_step=self.step_conva       ,   )
				UpperCamelCase						=  self.pooling(_lowercase       ,   self.size_poolinga )

				return data_conveda, data_pooleda


if __name__ == "__main__":
					pass

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       (DDPMScheduler,)



		def __UpperCamelCase				(				self       ,   **A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  {
				    'num_train_timesteps': 1_000,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'variance_type': 'fixed_small',
				    'clip_sample': True,
				}

				config.update(**A_ )
				return config



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				for timesteps in [1, 5, 100, 1_000]:
						self.check_over_configs(num_train_timesteps=A_ )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,   [0.002, 0.02, 0.2, 2] ):
						self.check_over_configs(beta_start=A_       ,   beta_end=A_ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				for schedule in ["linear", "squaredcos_cap_v2"]:
						self.check_over_configs(beta_schedule=A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				for variance in ["fixed_small", "fixed_large", "other"]:
						self.check_over_configs(variance_type=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				for clip_sample in [True, False]:
						self.check_over_configs(clip_sample=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				self.check_over_configs(thresholding=A_ )
				for threshold in [0.5, 1.0, 2.0]:
						for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(
								    thresholding=A_       ,   prediction_type=A_       ,   sample_max_value=A_       ,   )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				for prediction_type in ["epsilon", "sample", "v_prediction"]:
						self.check_over_configs(prediction_type=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				for t in [0, 500, 999]:
						self.check_over_forward(time_step=A_ )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

				UpperCamelCase						=  self.dummy_model()
				UpperCamelCase						=  self.dummy_sample_deter
				UpperCamelCase						=  torch.manual_seed(0 )

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

						# 2. predict previous mean of sample x_t-1
						UpperCamelCase						=  scheduler.step(A_       ,   A_       ,   A_       ,   generator=A_ ).prev_sample

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 258.9606 ) < 1e-2
				assert abs(result_mean.item() - 0.3372 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config(prediction_type='v_prediction' )
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

				UpperCamelCase						=  self.dummy_model()
				UpperCamelCase						=  self.dummy_sample_deter
				UpperCamelCase						=  torch.manual_seed(0 )

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

						# 2. predict previous mean of sample x_t-1
						UpperCamelCase						=  scheduler.step(A_       ,   A_       ,   A_       ,   generator=A_ ).prev_sample

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 202.0296 ) < 1e-2
				assert abs(result_mean.item() - 0.2631 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=A_ )

				UpperCamelCase						=  scheduler.timesteps

				for i, timestep in enumerate(A_ ):
						if i == len(A_ ) - 1:
								UpperCamelCase						=  -1
						else:
								UpperCamelCase						=  timesteps[i + 1]

						UpperCamelCase						=  scheduler.previous_timestep(A_ )
						UpperCamelCase						=  prev_t.item()

						self.assertEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 51, 0]

				with self.assertRaises(A_       ,   msg='`custom_timesteps` must be in descending order.' ):
						scheduler.set_timesteps(timesteps=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]
				UpperCamelCase						=  len(A_ )

				with self.assertRaises(A_       ,   msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
						scheduler.set_timesteps(num_inference_steps=A_       ,   timesteps=A_ )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    A_       ,   msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'       ,   ):
						scheduler.set_timesteps(timesteps=A_ )
