






import pickle

import numpy as np
from matplotlib import pyplot as plt


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Tuple       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      Dict=0.2       ,	UpperCamelCase:      int=0.2						)		->		Any:
											snake_case__									=     bp_numa
											snake_case__									=     bp_numa
											snake_case__									=     bp_numa
											snake_case__									=     conva_get[:2]
											snake_case__									=     conva_get[2]
											snake_case__									=     size_pa
											snake_case__									=     rate_w
											snake_case__									=     rate_t
											snake_case__									=     [
											    np.mat(-1 * np.random.rand(self.conva[0]       ,	self.conva[0]						) + 0.5						)
											    for i in range(self.conva[1]						)
											]
											snake_case__									=     np.mat(-1 * np.random.rand(self.num_bpa       ,	self.num_bpa						) + 0.5						)
											snake_case__									=     np.mat(-1 * np.random.rand(self.num_bpa       ,	self.num_bpa						) + 0.5						)
											snake_case__									=     -2 * np.random.rand(self.conva[1]						) + 1
											snake_case__									=     -2 * np.random.rand(self.num_bpa						) + 1
											snake_case__									=     -2 * np.random.rand(self.num_bpa						) + 1
							def lowerCAmelCase_ (     self:      Any       ,	UpperCamelCase:      int						)		->		int:
											# save model dict with pickle
											snake_case__									=     {
											    '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(UpperCamelCase       ,	'wb'						) as f:
															pickle.dump(UpperCamelCase       ,	UpperCamelCase						)

											print(F'''Model saved： {save_path}'''						)
							@classmethod
							def lowerCAmelCase_ (     cls:      Optional[Any]       ,	UpperCamelCase:      str						)		->		Tuple:
											# read saved model
											with open(UpperCamelCase       ,	'rb'						) as f:
															snake_case__									=     pickle.load(UpperCamelCase						)  # noqa: S301

											snake_case__									=     model_dic.get('conv1'						)
											conv_get.append(model_dic.get('step_conv1'						)						)
											snake_case__									=     model_dic.get('size_pooling1'						)
											snake_case__									=     model_dic.get('num_bp1'						)
											snake_case__									=     model_dic.get('num_bp2'						)
											snake_case__									=     model_dic.get('num_bp3'						)
											snake_case__									=     model_dic.get('rate_weight'						)
											snake_case__									=     model_dic.get('rate_thre'						)
											# create model instance
											snake_case__									=     CNN(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
											# modify model parameter
											snake_case__									=     model_dic.get('w_conv1'						)
											snake_case__									=     model_dic.get('wkj'						)
											snake_case__									=     model_dic.get('vji'						)
											snake_case__									=     model_dic.get('thre_conv1'						)
											snake_case__									=     model_dic.get('thre_bp2'						)
											snake_case__									=     model_dic.get('thre_bp3'						)
											return conv_ins
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      Optional[int]						)		->		Dict:
											return 1 / (1 + np.exp(-1 * x						))
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      List[Any]						)		->		Tuple:
											return round(UpperCamelCase       ,	3						)
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      Any						)		->		Optional[Any]:
											# convolution process
											snake_case__									=     convs[0]
											snake_case__									=     convs[1]
											snake_case__									=     np.shape(UpperCamelCase						)[0]
											# get the data slice of original image data, data_focus
											snake_case__									=     []
											for i_focus in range(0       ,	size_data - size_conv + 1       ,	UpperCamelCase						):
															for j_focus in range(0       ,	size_data - size_conv + 1       ,	UpperCamelCase						):
																			snake_case__									=     data[
																			    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																			]
																			data_focus.append(UpperCamelCase						)
        # calculate the feature map of every single kernel, and saved as list of matrix
											snake_case__									=     []
											snake_case__									=     int((size_data - size_conv) / conv_step + 1						)
											for i_map in range(UpperCamelCase						):
															snake_case__									=     []
															for i_focus in range(len(UpperCamelCase						)						):
																			snake_case__									=     (
																			    np.sum(np.multiply(data_focus[i_focus]       ,	w_convs[i_map]						)						)
																			    - thre_convs[i_map]
																			)
																			featuremap.append(self.sig(UpperCamelCase						)						)
															snake_case__									=     np.asmatrix(UpperCamelCase						).reshape(
															    UpperCamelCase       ,	UpperCamelCase						)
															data_featuremap.append(UpperCamelCase						)

											# expanding the data slice to One dimenssion
											snake_case__									=     []
											for each_focus in data_focus:
															focusa_list.extend(self.Expand_Mat(UpperCamelCase						)						)
											snake_case__									=     np.asarray(UpperCamelCase						)
											return focus_list, data_featuremap
							def lowerCAmelCase_ (     self:      Optional[Any]       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      Union[str, Any]="average_pool"						)		->		int:
											# pooling process
											snake_case__									=     len(featuremaps[0]						)
											snake_case__									=     int(size_map / size_pooling						)
											snake_case__									=     []
											for i_map in range(len(UpperCamelCase						)						):
															snake_case__									=     featuremaps[i_map]
															snake_case__									=     []
															for i_focus in range(0       ,	UpperCamelCase       ,	UpperCamelCase						):
																			for j_focus in range(0       ,	UpperCamelCase       ,	UpperCamelCase						):
																							snake_case__									=     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(UpperCamelCase						)						)
																							elif pooling_type == "max_pooling":
																											# max pooling
																											map_pooled.append(np.max(UpperCamelCase						)						)
															snake_case__									=     np.asmatrix(UpperCamelCase						).reshape(UpperCamelCase       ,	UpperCamelCase						)
															featuremap_pooled.append(UpperCamelCase						)
											return featuremap_pooled
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      Optional[Any]						)		->		Optional[int]:
											# expanding three dimension data to one dimension list
											snake_case__									=     []
											for i in range(len(UpperCamelCase						)						):
															snake_case__									=     np.shape(data[i]						)
															snake_case__									=     data[i].reshape(1       ,	shapes[0] * shapes[1]						)
															snake_case__									=     data_listed.getA().tolist()[0]
															data_expanded.extend(UpperCamelCase						)
											snake_case__									=     np.asarray(UpperCamelCase						)
											return data_expanded
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Any						)		->		Tuple:
											# expanding matrix to one dimension list
											snake_case__									=     np.asarray(UpperCamelCase						)
											snake_case__									=     np.shape(UpperCamelCase						)
											snake_case__									=     data_mat.reshape(1       ,	shapes[0] * shapes[1]						)
											return data_expanded
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Union[str, Any]						)		->		Optional[int]:
											snake_case__									=     []
											snake_case__									=     0
											for i_map in range(UpperCamelCase						):
															snake_case__									=     np.ones((size_map, size_map)						)
															for i in range(0       ,	UpperCamelCase       ,	UpperCamelCase						):
																			for j in range(0       ,	UpperCamelCase       ,	UpperCamelCase						):
																							snake_case__									=     pd_pool[
																							    i_pool
																							]
																							snake_case__									=     i_pool + 1
															snake_case__									=     np.multiply(
															    UpperCamelCase       ,	np.multiply(out_map[i_map]       ,	(1 - out_map[i_map])						)						)
															pd_all.append(UpperCamelCase						)
											return pd_all
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      Dict=bool						)		->		List[str]:
											# model traning
											print('----------------------Start Training-------------------------'						)
											print((' - - Shape: Train_Data  ', np.shape(UpperCamelCase						))						)
											print((' - - Shape: Teach_Data  ', np.shape(UpperCamelCase						))						)
											snake_case__									=     0
											snake_case__									=     []
											snake_case__									=     1_00_00
											while rp < n_repeat and mse >= error_accuracy:
															snake_case__									=     0
															print(F'''-------------Learning Time {rp}--------------'''						)
															for p in range(len(UpperCamelCase						)						):
																			# print('------------Learning Image: %d--------------'%p)
																			snake_case__									=     np.asmatrix(datas_train[p]						)
																			snake_case__									=     np.asarray(datas_teach[p]						)
																			snake_case__							,						snake_case__									=     self.convolute(
																			    UpperCamelCase       ,	self.conva       ,	self.w_conva       ,	self.thre_conva       ,	conv_step=self.step_conva       ,	)
																			snake_case__									=     self.pooling(UpperCamelCase       ,	self.size_poolinga						)
																			snake_case__									=     np.shape(UpperCamelCase						)
																			snake_case__									=     self._expand(UpperCamelCase						)
																			snake_case__									=     data_bp_input

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

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

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

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

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

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

											print('------------------Training Complished---------------------'						)
											print((' - - Training epoch: ', rp, F'''     - - Mse: {mse:.6f}''')						)
											if draw_e:
															draw_error()
											return mse
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      Optional[int]						)		->		str:
											# model predict
											snake_case__									=     []
											print('-------------------Start Testing-------------------------'						)
											print((' - - Shape: Test_Data  ', np.shape(UpperCamelCase						))						)
											for p in range(len(UpperCamelCase						)						):
															snake_case__									=     np.asmatrix(datas_test[p]						)
															snake_case__							,						snake_case__									=     self.convolute(
															    UpperCamelCase       ,	self.conva       ,	self.w_conva       ,	self.thre_conva       ,	conv_step=self.step_conva       ,	)
															snake_case__									=     self.pooling(UpperCamelCase       ,	self.size_poolinga						)
															snake_case__									=     self._expand(UpperCamelCase						)

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


							def lowerCAmelCase_ (     self:      Any       ,	UpperCamelCase:      List[str]						)		->		Optional[int]:
											# return the data of image after convoluting process so we can check it out
											snake_case__									=     np.asmatrix(UpperCamelCase						)
											snake_case__							,						snake_case__									=     self.convolute(
											    UpperCamelCase       ,	self.conva       ,	self.w_conva       ,	self.thre_conva       ,	conv_step=self.step_conva       ,	)
											snake_case__									=     self.pooling(UpperCamelCase       ,	self.size_poolinga						)

											return data_conveda, data_pooleda


if __name__ == "__main__":
							pass












import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       StableDiffusionSAGPipeline
							_UpperCAmelCase     =       TEXT_TO_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_BATCH_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      int						)		->		Any:
											torch.manual_seed(0						)
											snake_case__									=     UNetaDConditionModel(
											    block_out_channels=(32, 64)       ,	layers_per_block=2       ,	sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,	cross_attention_dim=32       ,	)
											snake_case__									=     DDIMScheduler(
											    beta_start=0.00_085       ,	beta_end=0.012       ,	beta_schedule='scaled_linear'       ,	clip_sample=UpperCamelCase       ,	set_alpha_to_one=UpperCamelCase       ,	)
											torch.manual_seed(0						)
											snake_case__									=     AutoencoderKL(
											    block_out_channels=[32, 64]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,	latent_channels=4       ,	)
											torch.manual_seed(0						)
											snake_case__									=     CLIPTextConfig(
											    bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=32       ,	intermediate_size=37       ,	layer_norm_eps=1e-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=10_00       ,	)
											snake_case__									=     CLIPTextModel(UpperCamelCase						)
											snake_case__									=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

											snake_case__									=     {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Optional[int]=0						)		->		Union[str, Any]:
											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)
											snake_case__									=     {
											    'prompt': '.',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 1.0,
											    'sag_scale': 1.0,
											    'output_type': 'numpy',
											}
											return inputs


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											super().test_inference_batch_single_identical(expected_max_diff=3e-3						)


@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Optional[int]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_ (     self:      Dict						)		->		Tuple:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-2
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-2


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	width=7_68       ,	height=5_12       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'       ,	)

											snake_case__									=     output.images

											assert image.shape == (1, 5_12, 7_68, 3)




