
import pickle

import numpy as np
from matplotlib import pyplot as plt


class   __lowerCAmelCase		:



      """simple docstring"""







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




      def 						snake_case_	(					self						:				Optional[Any] ,				_snake_case						:				int ):
          # save model dict with pickle
          __lowercase						:	Dict					=				{
              "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 						snake_case_	(					cls						:				Tuple ,				_snake_case						:				Dict ):
          # read saved model
          with open(__lowerCamelCase ,				'''rb''' ) as f:
              __lowercase						:	Any					=				pickle.load(__lowerCamelCase )  # noqa: S301

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




      def 						snake_case_	(					self						:				Tuple ,				_snake_case						:				Any ):
          return 1 / (1 + np.exp(-1 * x ))




      def 						snake_case_	(					self						:				Dict ,				_snake_case						:				Union[str, Any] ):
          return round(__lowerCamelCase ,				3 )




      def 						snake_case_	(					self						:				Optional[Any] ,				_snake_case						:				List[str] ,				_snake_case						:				int ,				_snake_case						:				List[str] ,				_snake_case						:				Optional[int] ,				_snake_case						:				Optional[int] ):
          # convolution process
          __lowercase						:	Tuple					=				convs[0]
          __lowercase						:	Union[str, Any]					=				convs[1]
          __lowercase						:	List[Any]					=				np.shape(__lowerCamelCase )[0]
          # get the data slice of original image data, data_focus
          __lowercase						:	Tuple					=				[]
          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						:	Optional[int]					=				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						:	Optional[Any]					=				[]
          __lowercase						:	Optional[int]					=				int((size_data - size_conv) / conv_step + 1 )
          for i_map in range(__lowerCamelCase ):
              __lowercase						:	Optional[int]					=				[]
              for i_focus in range(len(__lowerCamelCase ) ):
                  __lowercase						:	Any					=				(
                      np.sum(np.multiply(data_focus[i_focus] ,				w_convs[i_map] ) )
                      - thre_convs[i_map]
                  )
                  featuremap.append(self.sig(__lowerCamelCase ) )
              __lowercase						:	Optional[Any]					=				np.asmatrix(__lowerCamelCase ).reshape(
                  __lowerCamelCase ,				__lowerCamelCase )
              data_featuremap.append(__lowerCamelCase )

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




      def 						snake_case_	(					self						:				Optional[Any] ,				_snake_case						:				Optional[Any] ,				_snake_case						:				Optional[int] ,				_snake_case						:				Tuple="average_pool" ):
          # pooling process
          __lowercase						:	Optional[Any]					=				len(featuremaps[0] )
          __lowercase						:	str					=				int(size_map / size_pooling )
          __lowercase						:	Optional[int]					=				[]
          for i_map in range(len(__lowerCamelCase ) ):
              __lowercase						:	int					=				featuremaps[i_map]
              __lowercase						:	Optional[int]					=				[]
              for i_focus in range(0 ,				__lowerCamelCase ,				__lowerCamelCase ):
                  for j_focus in range(0 ,				__lowerCamelCase ,				__lowerCamelCase ):
                      __lowercase						:	str					=				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						:	Tuple					=				np.asmatrix(__lowerCamelCase ).reshape(__lowerCamelCase ,				__lowerCamelCase )
              featuremap_pooled.append(__lowerCamelCase )
          return featuremap_pooled




      def 						snake_case_	(					self						:				int ,				_snake_case						:				str ):
          # expanding three dimension data to one dimension list
          __lowercase						:	Tuple					=				[]
          for i in range(len(__lowerCamelCase ) ):
              __lowercase						:	Union[str, Any]					=				np.shape(data[i] )
              __lowercase						:	List[Any]					=				data[i].reshape(1 ,				shapes[0] * shapes[1] )
              __lowercase						:	Optional[Any]					=				data_listed.getA().tolist()[0]
              data_expanded.extend(__lowerCamelCase )
          __lowercase						:	Optional[Any]					=				np.asarray(__lowerCamelCase )
          return data_expanded




      def 						snake_case_	(					self						:				str ,				_snake_case						:				Optional[Any] ):
          # expanding matrix to one dimension list
          __lowercase						:	List[Any]					=				np.asarray(__lowerCamelCase )
          __lowercase						:	Union[str, Any]					=				np.shape(__lowerCamelCase )
          __lowercase						:	Dict					=				data_mat.reshape(1 ,				shapes[0] * shapes[1] )
          return data_expanded




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




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

                  __lowercase						:	int					=				np.dot(__lowerCamelCase ,				self.vji.T ) - self.thre_bpa
                  __lowercase						:	List[Any]					=				self.sig(__lowerCamelCase )
                  __lowercase						:	Union[str, Any]					=				np.dot(__lowerCamelCase ,				self.wkj.T ) - self.thre_bpa
                  __lowercase						:	List[str]					=				self.sig(__lowerCamelCase )

                  # --------------Model Leaning ------------------------
                  # calculate error and gradient---------------
                  __lowercase						:	int					=				np.multiply(
                      (data_teach - bp_outa) ,				np.multiply(__lowerCamelCase ,				(1 - bp_outa) ) )
                  __lowercase						:	Optional[Any]					=				np.multiply(
                      np.dot(__lowerCamelCase ,				self.wkj ) ,				np.multiply(__lowerCamelCase ,				(1 - bp_outa) ) )
                  __lowercase						:	Union[str, Any]					=				np.dot(__lowerCamelCase ,				self.vji )

                  __lowercase						:	Any					=				pd_i_all / (self.size_poolinga * self.size_poolinga)
                  __lowercase						:	Dict					=				pd_conva_pooled.T.getA().tolist()
                  __lowercase						:	Optional[Any]					=				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						:	int					=				self._expand_mat(pd_conva_all[k_conv] )
                      __lowercase						:	Optional[int]					=				self.rate_weight * np.dot(__lowerCamelCase ,				__lowerCamelCase )

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

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

          def draw_error():
              __lowercase						:	Optional[int]					=				[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 						snake_case_	(					self						:				Tuple ,				_snake_case						:				Any ):
          # model predict
          __lowercase						:	Union[str, Any]					=				[]
          print('''-------------------Start Testing-------------------------''' )
          print((''' - - Shape: Test_Data  ''', np.shape(__lowerCamelCase )) )
          for p in range(len(__lowerCamelCase ) ):
              __lowercase						:	int					=				np.asmatrix(datas_test[p] )
              __lowercase						:	List[Any]					=				self.convolute(
                  __lowerCamelCase ,				self.conva ,				self.w_conva ,				self.thre_conva ,				conv_step=self.step_conva ,				)
              __lowercase						:	str					=				self.pooling(__lowerCamelCase ,				self.size_poolinga )
              __lowercase						:	Optional[int]					=				self._expand(__lowerCamelCase )

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




      def 						snake_case_	(					self						:				Dict ,				_snake_case						:				Tuple ):
          # return the data of image after convoluting process so we can check it out
          __lowercase						:	Optional[int]					=				np.asmatrix(__lowerCamelCase )
          __lowercase						:	Tuple					=				self.convolute(
              __lowerCamelCase ,				self.conva ,				self.w_conva ,				self.thre_conva ,				conv_step=self.step_conva ,				)
          __lowercase						:	Union[str, Any]					=				self.pooling(__lowerCamelCase ,				self.size_poolinga )

          return data_conveda, data_pooleda


if __name__ == "__main__":
 pass






import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class      lowerCAmelCase__       (				a     ,    a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VQModel
     __SCREAMING_SNAKE_CASE  			= "sample"


     @property
     def  _lowerCamelCase (						self ,						__lowerCamelCase=(3_2, 3_2))			->				Optional[Any]:
            _A      :       Optional[int]				    = 4
            _A      :       Tuple				    = 3

            _A      :       List[Any]				    = floats_tensor((batch_size, num_channels) + sizes).to(__lowerCamelCase)

            return {"sample": image}


     @property
     def  _lowerCamelCase (						self)			->				int:
            return (3, 3_2, 3_2)


     @property
     def  _lowerCamelCase (						self)			->				List[Any]:
            return (3, 3_2, 3_2)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       List[Any]				    = {
                "block_out_channels": [3_2, 6_4],
                "in_channels": 3,
                "out_channels": 3,
                "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                "latent_channels": 3,
            }
            _A      :       int				    = self.dummy_input
            return init_dict, inputs_dict


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            pass


     def  _lowerCamelCase (						self)			->				Any:
            pass


     def  _lowerCamelCase (						self)			->				Any:
            _A    ,			_A      :       List[Any]				    = VQModel.from_pretrained("fusing/vqgan-dummy" ,						output_loading_info=__lowerCamelCase)
            self.assertIsNotNone(__lowerCamelCase)
            self.assertEqual(len(loading_info["missing_keys"]) ,						0)

            model.to(__lowerCamelCase)
            _A      :       str				    = model(**self.dummy_input)

            assert image is not None, "Make sure output is not None"


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Optional[Any]				    = VQModel.from_pretrained("fusing/vqgan-dummy")
            model.to(__lowerCamelCase).eval()

            torch.manual_seed(0)
            if torch.cuda.is_available():
                   torch.cuda.manual_seed_all(0)

            _A      :       Tuple				    = torch.randn(1 ,						model.config.in_channels ,						model.config.sample_size ,						model.config.sample_size)
            _A      :       Optional[int]				    = image.to(__lowerCamelCase)
            with torch.no_grad():
                   _A      :       List[str]				    = model(__lowerCamelCase).sample

            _A      :       int				    = output[0, -1, -3:, -3:].flatten().cpu()
            # fmt: off
            _A      :       Optional[Any]				    = torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3])
            # fmt: on
            self.assertTrue(torch.allclose(__lowerCamelCase ,						__lowerCamelCase ,						atol=1e-3))
