




import pickle

import numpy as np
from matplotlib import pyplot as plt







class a :
						"""simple docstring"""

						def __init__( self :     Optional[int]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     List[str]=0.2				,							lowerCamelCase__ :     Optional[int]=0.2				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    bp_numa
													__lowercase      =    bp_numa
													__lowercase      =    bp_numa
													__lowercase      =    conva_get[:2]
													__lowercase      =    conva_get[2]
													__lowercase      =    size_pa
													__lowercase      =    rate_w
													__lowercase      =    rate_t
													__lowercase      =    [
													    np.mat(-1 * np.random.rand(self.conva[0]				,							self.conva[0]				) + 0.5				)
													    for i in range(self.conva[1]				)
													]
													__lowercase      =    np.mat(-1 * np.random.rand(self.num_bpa				,							self.num_bpa				) + 0.5				)
													__lowercase      =    np.mat(-1 * np.random.rand(self.num_bpa				,							self.num_bpa				) + 0.5				)
													__lowercase      =    -2 * np.random.rand(self.conva[1]				) + 1
													__lowercase      =    -2 * np.random.rand(self.num_bpa				) + 1
													__lowercase      =    -2 * np.random.rand(self.num_bpa				) + 1






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     int				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    {
													    '''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(lowerCamelCase__				,							'''wb'''				) as f:
																				pickle.dump(lowerCamelCase__				,							lowerCamelCase__				)

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






						@classmethod
						def     UpperCAmelCase_      ( cls :     Any				,							lowerCamelCase__ :     Union[str, Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													with open(lowerCamelCase__				,							'''rb'''				) as f:
																				__lowercase      =    pickle.load(lowerCamelCase__				)  # noqa: S301

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






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Optional[int]				)    ->						str:
													"""simple docstring"""


													return 1 / (1 + np.exp(-1 * x				))






						def     UpperCAmelCase_      ( self :     str				,							lowerCamelCase__ :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


													return round(lowerCamelCase__				,							3				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Any				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     Union[str, Any]				)    ->						str:
													"""simple docstring"""


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

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






						def     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Dict="average_pool"				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    len(featuremaps[0]				)
													__lowercase      =    int(size_map / size_pooling				)
													__lowercase      =    []
													for i_map in range(len(lowerCamelCase__				)				):
																				__lowercase      =    featuremaps[i_map]
																				__lowercase      =    []
																				for i_focus in range(0				,							lowerCamelCase__				,							lowerCamelCase__				):
																											for j_focus in range(0				,							lowerCamelCase__				,							lowerCamelCase__				):
																																		__lowercase      =    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(lowerCamelCase__				)				)
																																		elif pooling_type == "max_pooling":
																																									# max pooling
																																									map_pooled.append(np.max(lowerCamelCase__				)				)
																				__lowercase      =    np.asmatrix(lowerCamelCase__				).reshape(lowerCamelCase__				,							lowerCamelCase__				)
																				featuremap_pooled.append(lowerCamelCase__				)
													return featuremap_pooled






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     str				)    ->						int:
													"""simple docstring"""


													__lowercase      =    []
													for i in range(len(lowerCamelCase__				)				):
																				__lowercase      =    np.shape(data[i]				)
																				__lowercase      =    data[i].reshape(1				,							shapes[0] * shapes[1]				)
																				__lowercase      =    data_listed.getA().tolist()[0]
																				data_expanded.extend(lowerCamelCase__				)
													__lowercase      =    np.asarray(lowerCamelCase__				)
													return data_expanded






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     List[str]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    np.asarray(lowerCamelCase__				)
													__lowercase      =    np.shape(lowerCamelCase__				)
													__lowercase      =    data_mat.reshape(1				,							shapes[0] * shapes[1]				)
													return data_expanded






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     Union[str, Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    []
													__lowercase      =    0
													for i_map in range(lowerCamelCase__				):
																				__lowercase      =    np.ones((size_map, size_map)				)
																				for i in range(0				,							lowerCamelCase__				,							lowerCamelCase__				):
																											for j in range(0				,							lowerCamelCase__				,							lowerCamelCase__				):
																																		__lowercase      =    pd_pool[
																																		    i_pool
																																		]
																																		__lowercase      =    i_pool + 1
																				__lowercase      =    np.multiply(
																				    lowerCamelCase__				,							np.multiply(out_map[i_map]				,							(1 - out_map[i_map])				)				)
																				pd_all.append(lowerCamelCase__				)
													return pd_all






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     Any				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     str=bool				)    ->						List[str]:
													"""simple docstring"""


													print('''----------------------Start Training-------------------------'''				)
													print((''' - - Shape: Train_Data  ''', np.shape(lowerCamelCase__				))				)
													print((''' - - Shape: Teach_Data  ''', np.shape(lowerCamelCase__				))				)
													__lowercase      =    0
													__lowercase      =    []
													__lowercase      =    10_000
													while rp < n_repeat and mse >= error_accuracy:
																				__lowercase      =    0
																				print(f'-------------Learning Time {rp}--------------'				)
																				for p in range(len(lowerCamelCase__				)				):
																											# print('------------Learning Image: %d--------------'%p)
																											__lowercase      =    np.asmatrix(datas_train[p]				)
																											__lowercase      =    np.asarray(datas_teach[p]				)
																											__lowercase     ,     __lowercase      =    self.convolute(
																											    lowerCamelCase__				,							self.conva				,							self.w_conva				,							self.thre_conva				,							conv_step=self.step_conva				,							)
																											__lowercase      =    self.pooling(lowerCamelCase__				,							self.size_poolinga				)
																											__lowercase      =    np.shape(lowerCamelCase__				)
																											__lowercase      =    self._expand(lowerCamelCase__				)
																											__lowercase      =    data_bp_input

																											__lowercase      =    np.dot(lowerCamelCase__				,							self.vji.T				) - self.thre_bpa
																											__lowercase      =    self.sig(lowerCamelCase__				)
																											__lowercase      =    np.dot(lowerCamelCase__				,							self.wkj.T				) - self.thre_bpa
																											__lowercase      =    self.sig(lowerCamelCase__				)

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

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

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

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

													def draw_error():
																				__lowercase      =    [error_accuracy for i in range(int(n_repeat * 1.2				)				)]
																				plt.plot(lowerCamelCase__				,							'''+-'''				)
																				plt.plot(lowerCamelCase__				,							'''r--'''				)
																				plt.xlabel('''Learning Times'''				)
																				plt.ylabel('''All_mse'''				)
																				plt.grid(lowerCamelCase__				,							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 :     Optional[int]				,							lowerCamelCase__ :     Optional[int]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    []
													print('''-------------------Start Testing-------------------------'''				)
													print((''' - - Shape: Test_Data  ''', np.shape(lowerCamelCase__				))				)
													for p in range(len(lowerCamelCase__				)				):
																				__lowercase      =    np.asmatrix(datas_test[p]				)
																				__lowercase     ,     __lowercase      =    self.convolute(
																				    lowerCamelCase__				,							self.conva				,							self.w_conva				,							self.thre_conva				,							conv_step=self.step_conva				,							)
																				__lowercase      =    self.pooling(lowerCamelCase__				,							self.size_poolinga				)
																				__lowercase      =    self._expand(lowerCamelCase__				)

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






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     Optional[Any]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    np.asmatrix(lowerCamelCase__				)
													__lowercase     ,     __lowercase      =    self.convolute(
													    lowerCamelCase__				,							self.conva				,							self.w_conva				,							self.thre_conva				,							conv_step=self.step_conva				,							)
													__lowercase      =    self.pooling(lowerCamelCase__				,							self.size_poolinga				)

													return data_conveda, data_pooleda


if __name__ == "__main__":
			pass












import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[Any]       	=		(DEISMultistepScheduler,)
						UpperCamelCase_	:       Any       	=		(('num_inference_steps', 25),)






						def     UpperCAmelCase_      ( self :     List[str]				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    {
													    '''num_train_timesteps''': 1_000,
													    '''beta_start''': 0.0_0_0_1,
													    '''beta_end''': 0.0_2,
													    '''beta_schedule''': '''linear''',
													    '''solver_order''': 2,
													}

													config.update(**lowerCamelCase__				)
													return config






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     Union[str, Any]=0				,							**lowerCamelCase__ :     Dict				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)
													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)
													__lowercase      =    self.dummy_sample
													__lowercase      =    0.1 * sample
													__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

													for scheduler_class in self.scheduler_classes:
																				__lowercase      =    self.get_scheduler_config(**lowerCamelCase__				)
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)
																				scheduler.set_timesteps(lowerCamelCase__				)
																				# copy over dummy past residuals
																				__lowercase      =    dummy_past_residuals[: scheduler.config.solver_order]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCamelCase__				)
																											__lowercase      =    scheduler_class.from_pretrained(lowerCamelCase__				)
																											new_scheduler.set_timesteps(lowerCamelCase__				)
																											# copy over dummy past residuals
																											__lowercase      =    dummy_past_residuals[: new_scheduler.config.solver_order]

																				__lowercase     ,     __lowercase      =    sample, sample
																				for t in range(lowerCamelCase__				,							time_step + scheduler.config.solver_order + 1				):
																											__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample
																											__lowercase      =    new_scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample

																											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"






						def     UpperCAmelCase_      ( self :     Any				)    ->						Tuple:
													"""simple docstring"""


													pass






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Any=0				,							**lowerCamelCase__ :     Any				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)
													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)
													__lowercase      =    self.dummy_sample
													__lowercase      =    0.1 * sample
													__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

													for scheduler_class in self.scheduler_classes:
																				__lowercase      =    self.get_scheduler_config()
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)
																				scheduler.set_timesteps(lowerCamelCase__				)

																				# copy over dummy past residuals (must be after setting timesteps)
																				__lowercase      =    dummy_past_residuals[: scheduler.config.solver_order]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCamelCase__				)
																											__lowercase      =    scheduler_class.from_pretrained(lowerCamelCase__				)
																											# copy over dummy past residuals
																											new_scheduler.set_timesteps(lowerCamelCase__				)

																											# copy over dummy past residual (must be after setting timesteps)
																											__lowercase      =    dummy_past_residuals[: new_scheduler.config.solver_order]

																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample
																				__lowercase      =    new_scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample

																				assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"






						def     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     Optional[int]=None				,							**lowerCamelCase__ :     Dict				)    ->						Optional[Any]:
													"""simple docstring"""


													if scheduler is None:
																				__lowercase      =    self.scheduler_classes[0]
																				__lowercase      =    self.get_scheduler_config(**lowerCamelCase__				)
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config(**lowerCamelCase__				)
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    10
													__lowercase      =    self.dummy_model()
													__lowercase      =    self.dummy_sample_deter
													scheduler.set_timesteps(lowerCamelCase__				)

													for i, t in enumerate(scheduler.timesteps				):
																				__lowercase      =    model(lowerCamelCase__				,							lowerCamelCase__				)
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				).prev_sample

													return sample






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)

													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)

													for scheduler_class in self.scheduler_classes:
																				__lowercase      =    self.get_scheduler_config()
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)

																				__lowercase      =    self.dummy_sample
																				__lowercase      =    0.1 * sample

																				if num_inference_steps is not None and hasattr(lowerCamelCase__				,							'''set_timesteps'''				):
																											scheduler.set_timesteps(lowerCamelCase__				)
																				elif num_inference_steps is not None and not hasattr(lowerCamelCase__				,							'''set_timesteps'''				):
																											__lowercase      =    num_inference_steps

																				# copy over dummy past residuals (must be done after set_timesteps)
																				__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																				__lowercase      =    dummy_past_residuals[: scheduler.config.solver_order]

																				__lowercase      =    scheduler.timesteps[5]
																				__lowercase      =    scheduler.timesteps[6]

																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample

																				self.assertEqual(output_a.shape				,							sample.shape				)
																				self.assertEqual(output_a.shape				,							output_a.shape				)






						def     UpperCAmelCase_      ( self :     str				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    DEISMultistepScheduler(**self.get_scheduler_config()				)
													__lowercase      =    self.full_loop(scheduler=lowerCamelCase__				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3

													__lowercase      =    DPMSolverSinglestepScheduler.from_config(scheduler.config				)
													__lowercase      =    DPMSolverMultistepScheduler.from_config(scheduler.config				)
													__lowercase      =    UniPCMultistepScheduler.from_config(scheduler.config				)
													__lowercase      =    DEISMultistepScheduler.from_config(scheduler.config				)

													__lowercase      =    self.full_loop(scheduler=lowerCamelCase__				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3






						def     UpperCAmelCase_      ( self :     int				)    ->						List[Any]:
													"""simple docstring"""


													for timesteps in [25, 50, 100, 999, 1_000]:
																				self.check_over_configs(num_train_timesteps=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Any:
													"""simple docstring"""


													self.check_over_configs(thresholding=lowerCamelCase__				)
													for order in [1, 2, 3]:
																				for solver_type in ["logrho"]:
																											for threshold in [0.5, 1.0, 2.0]:
																																		for prediction_type in ["epsilon", "sample"]:
																																									self.check_over_configs(
																																									    thresholding=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							sample_max_value=lowerCamelCase__				,							algorithm_type='''deis'''				,							solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Optional[Any]:
													"""simple docstring"""


													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						int:
													"""simple docstring"""


													for algorithm_type in ["deis"]:
																				for solver_type in ["logrho"]:
																											for order in [1, 2, 3]:
																																		for prediction_type in ["epsilon", "sample"]:
																																									self.check_over_configs(
																																									    solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							algorithm_type=lowerCamelCase__				,							)
																																									__lowercase      =    self.full_loop(
																																									    solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							algorithm_type=lowerCamelCase__				,							)
																																									assert not torch.isnan(lowerCamelCase__				).any(), "Samples have nan numbers"






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						List[Any]:
													"""simple docstring"""


													self.check_over_configs(lower_order_final=lowerCamelCase__				)
													self.check_over_configs(lower_order_final=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Dict:
													"""simple docstring"""


													for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																				self.check_over_forward(num_inference_steps=lowerCamelCase__				,							time_step=0				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.full_loop()
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.full_loop(prediction_type='''v_prediction'''				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.0_9_1				) < 1e-3






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config(thresholding=lowerCamelCase__				,							dynamic_thresholding_ratio=0				)
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    10
													__lowercase      =    self.dummy_model()
													__lowercase      =    self.dummy_sample_deter.half()
													scheduler.set_timesteps(lowerCamelCase__				)

													for i, t in enumerate(scheduler.timesteps				):
																				__lowercase      =    model(lowerCamelCase__				,							lowerCamelCase__				)
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				).prev_sample

													assert sample.dtype == torch.floataa






