





import pickle

import numpy as np
from matplotlib import pyplot as plt





class 					A_					:







       """simple docstring"""





       def __init__(     self						:						str    ,__A						:						Optional[Any]    ,__A						:						Any    ,__A						:						Optional[Any]    ,__A						:						Dict    ,__A						:						Union[str, Any]    ,__A						:						Optional[int]=0.2    ,__A						:						Optional[Any]=0.2	)    ->       Tuple:
         _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						:						Union[str, Any]    ,__A						:						int	)    ->       Any:
         _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(A_    ,'wb'	) as f:
           pickle.dump(A_    ,A_	)

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







       @classmethod
       def  __UpperCAmelCase			(     cls						:						Union[str, Any]    ,__A						:						int	)    ->       List[Any]:




         with open(A_    ,'rb'	) as f:
           _lowercase						    =			pickle.load(A_	)  # 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(A_    ,A_    ,A_    ,A_    ,A_    ,A_    ,A_	)
         # 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[Any]    ,__A						:						str	)    ->       Dict:
         return 1 / (1 + np.exp(-1 * x	))







       def  __UpperCAmelCase			(     self						:						Tuple    ,__A						:						Tuple	)    ->       Tuple:
         return round(A_    ,3	)







       def  __UpperCAmelCase			(     self						:						Dict    ,__A						:						Tuple    ,__A						:						Any    ,__A						:						Union[str, Any]    ,__A						:						Optional[Any]    ,__A						:						Any	)    ->       Dict:
         _lowercase						    =			convs[0]
         _lowercase						    =			convs[1]
         _lowercase						    =			np.shape(A_	)[0]
         # get the data slice of original image data, data_focus
         _lowercase						    =			[]
         for i_focus in range(0    ,size_data - size_conv + 1    ,A_	):
           for j_focus in range(0    ,size_data - size_conv + 1    ,A_	):
             _lowercase						    =			data[
                 i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
             ]
             data_focus.append(A_	)
        # 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(A_	):
           _lowercase						    =			[]
           for i_focus in range(len(A_	)	):
             _lowercase						    =			(
                 np.sum(np.multiply(data_focus[i_focus]    ,w_convs[i_map]	)	)
                 - thre_convs[i_map]
             )
             featuremap.append(self.sig(A_	)	)
           _lowercase						    =			np.asmatrix(A_	).reshape(
               A_    ,A_	)
           data_featuremap.append(A_	)

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







       def  __UpperCAmelCase			(     self						:						List[str]    ,__A						:						List[str]    ,__A						:						Dict    ,__A						:						Any="average_pool"	)    ->       Tuple:
         _lowercase						    =			len(featuremaps[0]	)
         _lowercase						    =			int(size_map / size_pooling	)
         _lowercase						    =			[]
         for i_map in range(len(A_	)	):
           _lowercase						    =			featuremaps[i_map]
           _lowercase						    =			[]
           for i_focus in range(0    ,A_    ,A_	):
             for j_focus in range(0    ,A_    ,A_	):
               _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(A_	)	)
               elif pooling_type == "max_pooling":
                 # max pooling
                 map_pooled.append(np.max(A_	)	)
           _lowercase						    =			np.asmatrix(A_	).reshape(A_    ,A_	)
           featuremap_pooled.append(A_	)
         return featuremap_pooled







       def  __UpperCAmelCase			(     self						:						List[str]    ,__A						:						Optional[Any]	)    ->       Union[str, Any]:
         _lowercase						    =			[]
         for i in range(len(A_	)	):
           _lowercase						    =			np.shape(data[i]	)
           _lowercase						    =			data[i].reshape(1    ,shapes[0] * shapes[1]	)
           _lowercase						    =			data_listed.getA().tolist()[0]
           data_expanded.extend(A_	)
         _lowercase						    =			np.asarray(A_	)
         return data_expanded







       def  __UpperCAmelCase			(     self						:						Any    ,__A						:						Optional[int]	)    ->       Tuple:
         _lowercase						    =			np.asarray(A_	)
         _lowercase						    =			np.shape(A_	)
         _lowercase						    =			data_mat.reshape(1    ,shapes[0] * shapes[1]	)
         return data_expanded







       def  __UpperCAmelCase			(     self						:						int    ,__A						:						str    ,__A						:						int    ,__A						:						Optional[int]    ,__A						:						Any    ,__A						:						str	)    ->       str:
         _lowercase						    =			[]
         _lowercase						    =			0
         for i_map in range(A_	):
           _lowercase						    =			np.ones((size_map, size_map)	)
           for i in range(0    ,A_    ,A_	):
             for j in range(0    ,A_    ,A_	):
               _lowercase						    =			pd_pool[
                   i_pool
               ]
               _lowercase						    =			i_pool + 1
           _lowercase						    =			np.multiply(
               A_    ,np.multiply(out_map[i_map]    ,(1 - out_map[i_map])	)	)
           pd_all.append(A_	)
         return pd_all







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

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

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

               _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(A_	)

         def draw_error():
           _lowercase						    =			[error_accuracy for i in range(int(n_repeat * 1.2	)	)]
           plt.plot(A_    ,'+-'	)
           plt.plot(A_    ,'r--'	)
           plt.xlabel('Learning Times'	)
           plt.ylabel('All_mse'	)
           plt.grid(A_    ,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[Any]    ,__A						:						List[Any]	)    ->       List[Any]:
         _lowercase						    =			[]
         print('-------------------Start Testing-------------------------'	)
         print((' - - Shape: Test_Data  ', np.shape(A_	))	)
         for p in range(len(A_	)	):
           _lowercase						    =			np.asmatrix(datas_test[p]	)
           _lowercase    ,		_lowercase						    =			self.convolute(
               A_    ,self.conva    ,self.w_conva    ,self.thre_conva    ,conv_step=self.step_conva    ,)
           _lowercase						    =			self.pooling(A_    ,self.size_poolinga	)
           _lowercase						    =			self._expand(A_	)

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







       def  __UpperCAmelCase			(     self						:						Tuple    ,__A						:						Union[str, Any]	)    ->       Union[str, Any]:
         _lowercase						    =			np.asmatrix(A_	)
         _lowercase    ,		_lowercase						    =			self.convolute(
             A_    ,self.conva    ,self.w_conva    ,self.thre_conva    ,conv_step=self.step_conva    ,)
         _lowercase						    =			self.pooling(A_    ,self.size_poolinga	)

         return data_conveda, data_pooleda


if __name__ == "__main__":
      pass



import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



@skip_mps
class 	__lowerCamelCase			(_a				,      unittest.TestCase						):
       _lowercase											=     VideoToVideoSDPipeline
       _lowercase											=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""}						) - {"""image""", """width""", """height"""}
       _lowercase											=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""}						) - {"""image"""}
       _lowercase											=     PipelineTesterMixin.required_optional_params - {"""latents"""}
       _lowercase											=     False

       # No `output_type`.
       _lowercase											=     frozenset(
           [
               """num_inference_steps""",
               """generator""",
               """latents""",
               """return_dict""",
               """callback""",
               """callback_steps""",
           ]						)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            torch.manual_seed(0					)
            __UpperCamelCase =			UNetaDConditionModel(
                block_out_channels=(32, 64, 64, 64),layers_per_block=2,sample_size=32,in_channels=4,out_channels=4,down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D'),up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D'),cross_attention_dim=32,attention_head_dim=4,)
            __UpperCamelCase =			DDIMScheduler(
                beta_start=0.0_0_0_8_5,beta_end=0.0_1_2,beta_schedule='scaled_linear',clip_sample=A_,set_alpha_to_one=A_,)
            torch.manual_seed(0					)
            __UpperCamelCase =			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,sample_size=128,)
            torch.manual_seed(0					)
            __UpperCamelCase =			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=1000,hidden_act='gelu',projection_dim=512,)
            __UpperCamelCase =			CLIPTextModel(A_					)
            __UpperCamelCase =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

            __UpperCamelCase =			{
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
            }
            return components







       def 		snake_case_	(			self: Union[str, Any],A_: Any,A_: Any=0					):






            '''simple docstring'''




            __UpperCamelCase =			floats_tensor((1, 3, 3, 32, 32),rng=random.Random(A_					)					).to(A_					)

            if str(A_					).startswith('mps'					):
                 __UpperCamelCase =			torch.manual_seed(A_					)
            else:
                 __UpperCamelCase =			torch.Generator(device=A_					).manual_seed(A_					)
            __UpperCamelCase =			{
                'prompt': 'A painting of a squirrel eating a burger',
                'video': video,
                'generator': generator,
                'num_inference_steps': 2,
                'guidance_scale': 6.0,
                'output_type': 'pt',
            }
            return inputs







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			'cpu'  # ensure determinism for the device-dependent torch.Generator
            __UpperCamelCase =			self.get_dummy_components()
            __UpperCamelCase =			VideoToVideoSDPipeline(**A_					)
            __UpperCamelCase =			sd_pipe.to(A_					)
            sd_pipe.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			self.get_dummy_inputs(A_					)
            __UpperCamelCase =			'np'
            __UpperCamelCase =			sd_pipe(**A_					).frames
            __UpperCamelCase =			frames[0][-3:, -3:, -1]

            assert frames[0].shape == (32, 32, 3)
            __UpperCamelCase =			np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2







       @unittest.skipIf(
           torch_device != 'cuda' or not is_xformers_available(),reason='XFormers attention is only available with CUDA and `xformers` installed',)
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_,expected_max_diff=5E-3					)







       @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'					)
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            pass







       @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'					)
       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            pass







       @unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'					)
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            pass







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            return super().test_progress_bar()



@slow
@skip_mps
class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL',torch_dtype=torch.floataa					)
            pipe.enable_model_cpu_offload()

            # 10 frames
            __UpperCamelCase =			torch.Generator(device='cpu'					).manual_seed(0					)
            __UpperCamelCase =			torch.randn((1, 10, 3, 1024, 576),generator=A_					)
            __UpperCamelCase =			video.to('cuda'					)

            __UpperCamelCase =			'Spiderman is surfing'

            __UpperCamelCase =			pipe(A_,video=A_,generator=A_,num_inference_steps=3,output_type='pt'					).frames

            __UpperCamelCase =			np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]					)
            assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array					).sum() < 1E-2


