






import pickle

import numpy as np
from matplotlib import pyplot as plt






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		Tuple					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		List[Any]					,   __UpperCAmelCase :		Union[str, Any]					,   __UpperCAmelCase :		Tuple					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		str=0.2					,   __UpperCAmelCase :		Union[str, Any]=0.2    )       ->Union[str, Any]:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		int					,   __UpperCAmelCase :		int    )       ->Tuple:




       """simple docstring"""


       a						    = {
           '''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[int]					,   __UpperCAmelCase :		Tuple    )       ->Any:




       """simple docstring"""


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

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







      def  __lowerCAmelCase   ( self :		Optional[int]					,   __UpperCAmelCase :		Optional[int]    )       ->Any:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		List[str]					,   __UpperCAmelCase :		List[str]    )       ->Any:




       """simple docstring"""


       return round(__UpperCAmelCase					,   3    )







      def  __lowerCAmelCase   ( self :		Optional[int]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Union[str, Any]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		int    )       ->Any:




       """simple docstring"""


       a						    = convs[0]
       a						    = convs[1]
       a						    = np.shape(__UpperCAmelCase    )[0]
       # get the data slice of original image data, data_focus
       a						    = []
       for i_focus in range(0					,   size_data - size_conv + 1					,   __UpperCAmelCase    ):
        for j_focus in range(0					,   size_data - size_conv + 1					,   __UpperCAmelCase    ):
         a						    = 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
       a						    = []
       a						    = int((size_data - size_conv) / conv_step + 1    )
       for i_map in range(__UpperCAmelCase    ):
        a						    = []
        for i_focus in range(len(__UpperCAmelCase    )    ):
         a						    = (
             np.sum(np.multiply(data_focus[i_focus]					,   w_convs[i_map]    )    )
             - thre_convs[i_map]
         )
         featuremap.append(self.sig(__UpperCAmelCase    )    )
        a						    = np.asmatrix(__UpperCAmelCase    ).reshape(
            __UpperCAmelCase					,   __UpperCAmelCase    )
        data_featuremap.append(__UpperCAmelCase    )

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







      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		Tuple					,   __UpperCAmelCase :		Union[str, Any]					,   __UpperCAmelCase :		List[Any]="average_pool"    )       ->Optional[Any]:




       """simple docstring"""


       a						    = len(featuremaps[0]    )
       a						    = int(size_map / size_pooling    )
       a						    = []
       for i_map in range(len(__UpperCAmelCase    )    ):
        a						    = featuremaps[i_map]
        a						    = []
        for i_focus in range(0					,   __UpperCAmelCase					,   __UpperCAmelCase    ):
         for j_focus in range(0					,   __UpperCAmelCase					,   __UpperCAmelCase    ):
          a						    = 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    )    )
        a						    = np.asmatrix(__UpperCAmelCase    ).reshape(__UpperCAmelCase					,   __UpperCAmelCase    )
        featuremap_pooled.append(__UpperCAmelCase    )
       return featuremap_pooled







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		List[str]    )       ->Tuple:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		int    )       ->str:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Tuple					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		List[Any]					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		Optional[int]    )       ->List[Any]:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		Tuple					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		Optional[int]					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		Dict=bool    )       ->List[Any]:




       """simple docstring"""


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

         a						    = np.dot(__UpperCAmelCase					,   self.vji.T    ) - self.thre_bpa
         a						    = self.sig(__UpperCAmelCase    )
         a						    = np.dot(__UpperCAmelCase					,   self.wkj.T    ) - self.thre_bpa
         a						    = self.sig(__UpperCAmelCase    )

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

         a						    = pd_i_all / (self.size_poolinga * self.size_poolinga)
         a						    = pd_conva_pooled.T.getA().tolist()
         a						    = 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]    ):
          a						    = self._expand_mat(pd_conva_all[k_conv]    )
          a						    = self.rate_weight * np.dot(__UpperCAmelCase					,   __UpperCAmelCase    )

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

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

       def draw_error():
        a						    = [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 :		int					,   __UpperCAmelCase :		str    )       ->int:




       """simple docstring"""


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

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







      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		str    )       ->Dict:




       """simple docstring"""


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

       return data_conveda, data_pooleda


if __name__ == "__main__":
     pass










import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCAmelCase__							  =						version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 		_a       ( a					:Union[str, Any]      ,				a					:tuple      ,				a					:Path      ,				a					:List[Any]      ,				a					:Union[str, Any]      ,				a					:Optional[int]      ,				a					:List[Any]      ,				a					:Union[str, Any]=False      ,				)				->     Any:
 output_path.parent.mkdir(parents=a      ,				exist_ok=a					)
 # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
 # so we check the torch version for backwards compatibility
 if is_torch_less_than_1_11:
  export(
      a      ,				a      ,				f=output_path.as_posix()      ,				input_names=a      ,				output_names=a      ,				dynamic_axes=a      ,				do_constant_folding=a      ,				use_external_data_format=a      ,				enable_onnx_checker=a      ,				opset_version=a      ,				)
 else:
  export(
      a      ,				a      ,				f=output_path.as_posix()      ,				input_names=a      ,				output_names=a      ,				dynamic_axes=a      ,				do_constant_folding=a      ,				opset_version=a      ,				)
@torch.no_grad()
def 		_a       ( a					:str      ,				a					:str      ,				a					:int      ,				a					:bool = False					)				->     Union[str, Any]:
 a						    = torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  a						    = '''cuda'''
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''					)
 else:
  a						    = '''cpu'''
 a						    = Path(a					)

 # VAE DECODER
 a						    = AutoencoderKL.from_pretrained(model_path + '''/vae'''					)
 a						    = vae_decoder.config.latent_channels
 # forward only through the decoder part
 a						    = vae_decoder.decode
 onnx_export(
     a      ,				model_args=(
         torch.randn(1      ,				a      ,				25      ,				25					).to(device=a      ,				dtype=a					),
         False,
     )      ,				output_path=output_path / '''vae_decoder''' / '''model.onnx'''      ,				ordered_input_names=['''latent_sample''', '''return_dict''']      ,				output_names=['''sample''']      ,				dynamic_axes={
         '''latent_sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
     }      ,				opset=a      ,				)
 del vae_decoder


if __name__ == "__main__":
     UpperCAmelCase__							  =						argparse.ArgumentParser()

     parser.add_argument(
         "--model_path",
         type=str,
         required=True,
         help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
     )

     parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
     parser.add_argument(
         "--opset",
         default=14,
         type=int,
         help="The version of the ONNX operator set to use.",
     )
     parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

     UpperCAmelCase__							  =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")


