






'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=0.2    ,	_SCREAMING_SNAKE_CASE=0.2   )						->   Dict:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      {
            """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(_SCREAMING_SNAKE_CASE    ,	"""wb"""   ) as f:
              pickle.dump(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

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

  @classmethod
  def 						A__		(   cls    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        with open(_SCREAMING_SNAKE_CASE    ,	"""rb"""   ) as f:
              UpperCamelCase       						=      pickle.load(_SCREAMING_SNAKE_CASE   )  # noqa: S301

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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        return round(_SCREAMING_SNAKE_CASE    ,	3   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






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

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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE="average_pool"   )						->   Union[str, Any]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=bool   )						->   Union[str, Any]:



        """simple docstring"""






        print("""----------------------Start Training-------------------------"""   )
        print((""" - - Shape: Train_Data  """, np.shape(_SCREAMING_SNAKE_CASE   ))   )
        print((""" - - Shape: Teach_Data  """, np.shape(_SCREAMING_SNAKE_CASE   ))   )
        UpperCamelCase       						=      0
        UpperCamelCase       						=      []
        UpperCamelCase       						=      10000
        while rp < n_repeat and mse >= error_accuracy:
              UpperCamelCase       						=      0
              print(F"-------------Learning Time {rp}--------------"   )
              for p in range(len(_SCREAMING_SNAKE_CASE   )   ):
                    # print('------------Learning Image: %d--------------'%p)
                    UpperCamelCase       						=      np.asmatrix(datas_train[p]   )
                    UpperCamelCase       						=      np.asarray(datas_teach[p]   )
                    UpperCamelCase						,UpperCamelCase       						=      self.convolute(
                        _SCREAMING_SNAKE_CASE    ,	self.conva    ,	self.w_conva    ,	self.thre_conva    ,	conv_step=self.step_conva    ,	)
                    UpperCamelCase       						=      self.pooling(_SCREAMING_SNAKE_CASE    ,	self.size_poolinga   )
                    UpperCamelCase       						=      np.shape(_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      self._expand(_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      data_bp_input

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

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

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

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

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

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

        print("""------------------Training Complished---------------------"""   )
        print((""" - - Training epoch: """, rp, F"     - - Mse: {mse:.6f}")   )
        if draw_e:
              draw_error()
        return mse

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






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

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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






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

        return data_conveda, data_pooleda


if __name__ == "__main__":
     pass








'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=1					)->				Tuple:
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split("""."""					)[n_shave_prefix_segments:]					)
      else:
            return ".".join(path.split("""."""					)[:n_shave_prefix_segments]					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				Dict:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item.replace("""in_layers.0"""	,		"""norm1"""					)
            UpperCamelCase       						=      new_item.replace("""in_layers.2"""	,		"""conv1"""					)

            UpperCamelCase       						=      new_item.replace("""out_layers.0"""	,		"""norm2"""					)
            UpperCamelCase       						=      new_item.replace("""out_layers.3"""	,		"""conv2"""					)

            UpperCamelCase       						=      new_item.replace("""emb_layers.1"""	,		"""time_emb_proj"""					)
            UpperCamelCase       						=      new_item.replace("""skip_connection"""	,		"""conv_shortcut"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

            mapping.append({"""old""": old_item, """new""": new_item}					)

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				List[str]:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item

            UpperCamelCase       						=      new_item.replace("""norm.weight"""	,		"""group_norm.weight"""					)
            UpperCamelCase       						=      new_item.replace("""norm.bias"""	,		"""group_norm.bias"""					)

            UpperCamelCase       						=      new_item.replace("""proj_out.weight"""	,		"""proj_attn.weight"""					)
            UpperCamelCase       						=      new_item.replace("""proj_out.bias"""	,		"""proj_attn.bias"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

            mapping.append({"""old""": old_item, """new""": new_item}					)

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		__UpperCamelCase=None					)->				str:
      assert isinstance(__UpperCamelCase	,		__UpperCamelCase					), "Paths should be a list of dicts containing 'old' and 'new' keys."

      # Splits the attention layers into three variables.
      if attention_paths_to_split is not None:
            for path, path_map in attention_paths_to_split.items():
                  UpperCamelCase       						=      old_checkpoint[path]
                  UpperCamelCase       						=      old_tensor.shape[0] // 3

                  UpperCamelCase       						=      (-1, channels) if len(old_tensor.shape					) == 3 else (-1)

                  UpperCamelCase       						=      old_tensor.shape[0] // config["""num_head_channels"""] // 3

                  UpperCamelCase       						=      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
                  UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      old_tensor.split(channels // num_heads	,		dim=1					)

                  UpperCamelCase       						=      query.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      key.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      value.reshape(__UpperCamelCase					)

      for path in paths:
            UpperCamelCase       						=      path["""new"""]

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            # Global renaming happens here
            UpperCamelCase       						=      new_path.replace("""middle_block.0"""	,		"""mid_block.resnets.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.1"""	,		"""mid_block.attentions.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.2"""	,		"""mid_block.resnets.1"""					)

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        UpperCamelCase       						=      new_path.replace(replacement["""old"""]	,		replacement["""new"""]					)

        # proj_attn.weight has to be converted from conv 1D to linear
            if "proj_attn.weight" in new_path:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]][:, :, 0]
            else:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]]







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Optional[int]:
      UpperCamelCase       						=      {}

      UpperCamelCase       						=      checkpoint["""time_embed.0.weight"""]
      UpperCamelCase       						=      checkpoint["""time_embed.0.bias"""]
      UpperCamelCase       						=      checkpoint["""time_embed.2.weight"""]
      UpperCamelCase       						=      checkpoint["""time_embed.2.bias"""]

      UpperCamelCase       						=      checkpoint["""input_blocks.0.0.weight"""]
      UpperCamelCase       						=      checkpoint["""input_blocks.0.0.bias"""]

      UpperCamelCase       						=      checkpoint["""out.0.weight"""]
      UpperCamelCase       						=      checkpoint["""out.0.bias"""]
      UpperCamelCase       						=      checkpoint["""out.2.weight"""]
      UpperCamelCase       						=      checkpoint["""out.2.bias"""]

      # Retrieves the keys for the input blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """input_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the middle blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """middle_block""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the output blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """output_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      for i in range(1	,		__UpperCamelCase					):
            UpperCamelCase       						=      (i - 1) // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      (i - 1) % (config["""num_res_blocks"""] + 1)

            UpperCamelCase       						=      [key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
            UpperCamelCase       						=      [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

            if F"input_blocks.{i}.0.op.weight" in checkpoint:
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.weight"
                  ]
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.bias"
                  ]
                  continue

            UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
            UpperCamelCase       						=      {"""old""": F"input_blocks.{i}.0", """new""": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
            UpperCamelCase       						=      {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
            assign_to_checkpoint(
                __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path, resnet_op]	,		config=__UpperCamelCase					)

            if len(__UpperCamelCase					):
                  UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      {
                      """old""": F"input_blocks.{i}.1",
                      """new""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
                  }
                  UpperCamelCase       						=      {
                      F"input_blocks.{i}.1.qkv.bias": {
                          """key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                          """query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                          """value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                      },
                      F"input_blocks.{i}.1.qkv.weight": {
                          """key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                          """query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                          """value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                      },
                  }
                  assign_to_checkpoint(
                      __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase	,		)

      UpperCamelCase       						=      middle_blocks[0]
      UpperCamelCase       						=      middle_blocks[1]
      UpperCamelCase       						=      middle_blocks[2]

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
      UpperCamelCase       						=      {
          """middle_block.1.qkv.bias""": {
              """key""": """mid_block.attentions.0.key.bias""",
              """query""": """mid_block.attentions.0.query.bias""",
              """value""": """mid_block.attentions.0.value.bias""",
          },
          """middle_block.1.qkv.weight""": {
              """key""": """mid_block.attentions.0.key.weight""",
              """query""": """mid_block.attentions.0.query.weight""",
              """value""": """mid_block.attentions.0.value.weight""",
          },
      }
      assign_to_checkpoint(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase					)

      for i in range(__UpperCamelCase					):
            UpperCamelCase       						=      i // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      i % (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      [shave_segments(__UpperCamelCase	,		2					) for name in output_blocks[i]]
            UpperCamelCase       						=      {}

            for layer in output_block_layers:
                  UpperCamelCase						,UpperCamelCase       						=      layer.split("""."""					)[0], shave_segments(__UpperCamelCase	,		1					)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(__UpperCamelCase					)
                  else:
                        UpperCamelCase       						=      [layer_name]

            if len(__UpperCamelCase					) > 1:
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)

                  UpperCamelCase       						=      {"""old""": F"output_blocks.{i}.0", """new""": F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
                  assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		config=__UpperCamelCase					)

                  if ["conv.weight", "conv.bias"] in output_block_list.values():
                        UpperCamelCase       						=      list(output_block_list.values()					).index(["""conv.weight""", """conv.bias"""]					)
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.weight"
                        ]
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.bias"
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(__UpperCamelCase					) == 2:
                              UpperCamelCase       						=      []

                  if len(__UpperCamelCase					):
                        UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                        UpperCamelCase       						=      {
                            """old""": F"output_blocks.{i}.1",
                            """new""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                        }
                        UpperCamelCase       						=      {
                            F"output_blocks.{i}.1.qkv.bias": {
                                """key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                                """query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                                """value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                            },
                            F"output_blocks.{i}.1.qkv.weight": {
                                """key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                                """query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                                """value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                            },
                        }
                        assign_to_checkpoint(
                            __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions					) else None	,		config=__UpperCamelCase	,		)
            else:
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase	,		n_shave_prefix_segments=1					)
                  for path in resnet_0_paths:
                        UpperCamelCase       						=      """.""".join(["""output_blocks""", str(__UpperCamelCase					), path["""old"""]]					)
                        UpperCamelCase       						=      """.""".join(["""up_blocks""", str(__UpperCamelCase					), """resnets""", str(__UpperCamelCase					), path["""new"""]]					)

                        UpperCamelCase       						=      checkpoint[old_path]

      return new_checkpoint


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

     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
     )

     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The config json file corresponding to the architecture.',
     )

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          SCREAMING_SNAKE_CASE__											=     json.loads(f.read())

     SCREAMING_SNAKE_CASE__											=     convert_ldm_checkpoint(checkpoint, config)

     if "ldm" in config:
          del config["ldm"]

     SCREAMING_SNAKE_CASE__											=     UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          SCREAMING_SNAKE_CASE__											=     DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
          SCREAMING_SNAKE_CASE__											=     VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

          SCREAMING_SNAKE_CASE__											=     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)
