
import argparse

import torch

from diffusers import UNetaDConditionModel




def 			A_			(					lowercase						, lowercase						, lowercase						, lowercase=None						, lowercase=None						, lowercase=None      )     ->							List[str]:


     """simple docstring"""







     assert isinstance(lowercase						, lowercase      ), "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_						:    Dict    					=  old_checkpoint[path]
               UpperCAmelCase_						:    int    					=  old_tensor.shape[0] // 3

               UpperCAmelCase_						:    List[Any]    					=  (-1, channels) if len(old_tensor.shape      ) == 3 else (-1)

               UpperCAmelCase_						:    Optional[Any]    					=  old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

               UpperCAmelCase_						:    List[str]    					=  query.reshape(lowercase      )
               UpperCAmelCase_						:    Optional[int]    					=  key.reshape(lowercase      )
               UpperCAmelCase_						:    Union[str, Any]    					=  value.reshape(lowercase      )

     for path in paths:
          UpperCAmelCase_						:    Tuple    					=  path["""new"""]

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

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

        # proj_attn.weight has to be converted from conv 1D to linear
          UpperCAmelCase_						:    Union[str, Any]    					=  old_checkpoint[path["""old"""]]
          UpperCAmelCase_						:    Tuple    					=  ["""proj_attn.weight"""]
          UpperCAmelCase_						:    Optional[int]    					=  ["""proj_out.weight""", """proj_in.weight"""]
          if any(k in new_path for k in names      ):
               UpperCAmelCase_						:    str    					=  weight[:, :, 0]
          elif any(k in new_path for k in names_a      ) and len(weight.shape      ) > 2 and ".attentions." not in new_path:
               UpperCAmelCase_						:    Any    					=  weight[:, :, 0]
          else:
               UpperCAmelCase_						:    List[str]    					=  weight




def 			A_			(					lowercase						, lowercase=0      )     ->							Dict:


     """simple docstring"""







     UpperCAmelCase_						:    Dict    					=  []
     for old_item in old_list:
          UpperCAmelCase_						:    Tuple    					=  old_item

          #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
          #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

          #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
          #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

          #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

     return mapping




def 			A_			(					lowercase						, lowercase=1      )     ->							Optional[Any]:


     """simple docstring"""







     if n_shave_prefix_segments >= 0:
          return ".".join(path.split("""."""      )[n_shave_prefix_segments:]      )
     else:
          return ".".join(path.split("""."""      )[:n_shave_prefix_segments]      )




def 			A_			(					lowercase						, lowercase=0      )     ->							Optional[int]:


     """simple docstring"""







     UpperCAmelCase_						:    Optional[Any]    					=  []
     for old_item in old_list:
          mapping.append({"""old""": old_item, """new""": old_item}      )

     return mapping




def 			A_			(					lowercase						, lowercase=0      )     ->							int:


     """simple docstring"""







     UpperCAmelCase_						:    Optional[int]    					=  []
     for old_item in old_list:
          UpperCAmelCase_						:    Union[str, Any]    					=  old_item.replace("""in_layers.0"""						, """norm1"""      )
          UpperCAmelCase_						:    Union[str, Any]    					=  new_item.replace("""in_layers.2"""						, """conv1"""      )

          UpperCAmelCase_						:    Union[str, Any]    					=  new_item.replace("""out_layers.0"""						, """norm2"""      )
          UpperCAmelCase_						:    Union[str, Any]    					=  new_item.replace("""out_layers.3"""						, """conv2"""      )

          UpperCAmelCase_						:    Optional[Any]    					=  new_item.replace("""emb_layers.1"""						, """time_emb_proj"""      )
          UpperCAmelCase_						:    List[Any]    					=  new_item.replace("""skip_connection"""						, """conv_shortcut"""      )

          UpperCAmelCase_						:    Any    					=  shave_segments(lowercase						, n_shave_prefix_segments=lowercase      )

          if "temopral_conv" not in old_item:
               mapping.append({"""old""": old_item, """new""": new_item}      )

     return mapping




def 			A_			(					lowercase						, lowercase						, lowercase=None						, lowercase=False      )     ->							Any:


     """simple docstring"""







     UpperCAmelCase_						:    List[str]    					=  {}
     UpperCAmelCase_						:    Tuple    					=  list(checkpoint.keys()      )

     UpperCAmelCase_						:    int    					=  """model.diffusion_model."""

     # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
     if sum(k.startswith("""model_ema"""      ) for k in keys      ) > 100 and extract_ema:
          print(f'''Checkpoint {path} has both EMA and non-EMA weights.'''      )
          print(
              """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
              """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""      )
          for key in keys:
               if key.startswith("""model.diffusion_model"""      ):
                    UpperCAmelCase_						:    List[str]    					=  """model_ema.""" + """""".join(key.split("""."""      )[1:]      )
                    UpperCAmelCase_						:    Any    					=  checkpoint.pop(lowercase      )
     else:
          if sum(k.startswith("""model_ema"""      ) for k in keys      ) > 100:
               print(
                   """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
                   """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""      )

          for key in keys:
               UpperCAmelCase_						:    Any    					=  checkpoint.pop(lowercase      )

     UpperCAmelCase_						:    List[str]    					=  {}

     UpperCAmelCase_						:    Optional[Any]    					=  unet_state_dict["""time_embed.0.weight"""]
     UpperCAmelCase_						:    Any    					=  unet_state_dict["""time_embed.0.bias"""]
     UpperCAmelCase_						:    List[Any]    					=  unet_state_dict["""time_embed.2.weight"""]
     UpperCAmelCase_						:    List[str]    					=  unet_state_dict["""time_embed.2.bias"""]

     if config["class_embed_type"] is None:
          # No parameters to port
          ...
     elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
          UpperCAmelCase_						:    Any    					=  unet_state_dict["""label_emb.0.0.weight"""]
          UpperCAmelCase_						:    Dict    					=  unet_state_dict["""label_emb.0.0.bias"""]
          UpperCAmelCase_						:    int    					=  unet_state_dict["""label_emb.0.2.weight"""]
          UpperCAmelCase_						:    List[Any]    					=  unet_state_dict["""label_emb.0.2.bias"""]
     else:
          raise NotImplementedError(f'''Not implemented `class_embed_type`: {config['class_embed_type']}'''      )

     UpperCAmelCase_						:    Union[str, Any]    					=  unet_state_dict["""input_blocks.0.0.weight"""]
     UpperCAmelCase_						:    str    					=  unet_state_dict["""input_blocks.0.0.bias"""]

     UpperCAmelCase_						:    List[Any]    					=  [v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""      )]
     UpperCAmelCase_						:    Any    					=  renew_attention_paths(lowercase      )
     UpperCAmelCase_						:    int    					=  {"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
     assign_to_checkpoint(lowercase						, lowercase						, lowercase						, additional_replacements=[meta_path]						, config=lowercase      )

     UpperCAmelCase_						:    Dict    					=  unet_state_dict["""out.0.weight"""]
     UpperCAmelCase_						:    Any    					=  unet_state_dict["""out.0.bias"""]
     UpperCAmelCase_						:    int    					=  unet_state_dict["""out.2.weight"""]
     UpperCAmelCase_						:    Optional[Any]    					=  unet_state_dict["""out.2.bias"""]

     # Retrieves the keys for the input blocks only
     UpperCAmelCase_						:    Union[str, Any]    					=  len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """input_blocks""" in layer}      )
     UpperCAmelCase_						:    Optional[int]    					=  {
         layer_id: [key for key in unet_state_dict if f'''input_blocks.{layer_id}''' in key]
         for layer_id in range(lowercase      )
     }

     # Retrieves the keys for the middle blocks only
     UpperCAmelCase_						:    List[str]    					=  len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """middle_block""" in layer}      )
     UpperCAmelCase_						:    str    					=  {
         layer_id: [key for key in unet_state_dict if f'''middle_block.{layer_id}''' in key]
         for layer_id in range(lowercase      )
     }

     # Retrieves the keys for the output blocks only
     UpperCAmelCase_						:    Optional[int]    					=  len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """output_blocks""" in layer}      )
     UpperCAmelCase_						:    List[Any]    					=  {
         layer_id: [key for key in unet_state_dict if f'''output_blocks.{layer_id}''' in key]
         for layer_id in range(lowercase      )
     }

     for i in range(1						, lowercase      ):
          UpperCAmelCase_						:    str    					=  (i - 1) // (config["""layers_per_block"""] + 1)
          UpperCAmelCase_						:    Optional[Any]    					=  (i - 1) % (config["""layers_per_block"""] + 1)

          UpperCAmelCase_						:    Dict    					=  [
              key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key and f'''input_blocks.{i}.0.op''' not in key
          ]
          UpperCAmelCase_						:    Any    					=  [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
          UpperCAmelCase_						:    Dict    					=  [key for key in input_blocks[i] if f'''input_blocks.{i}.2''' in key]

          if f'''input_blocks.{i}.op.weight''' in unet_state_dict:
               UpperCAmelCase_						:    List[Any]    					=  unet_state_dict.pop(
                   f'''input_blocks.{i}.op.weight'''      )
               UpperCAmelCase_						:    List[str]    					=  unet_state_dict.pop(
                   f'''input_blocks.{i}.op.bias'''      )

          UpperCAmelCase_						:    Union[str, Any]    					=  renew_resnet_paths(lowercase      )
          UpperCAmelCase_						:    Optional[int]    					=  {"""old""": f'''input_blocks.{i}.0''', """new""": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
          assign_to_checkpoint(
              lowercase						, lowercase						, lowercase						, additional_replacements=[meta_path]						, config=lowercase      )

          UpperCAmelCase_						:    str    					=  [key for key in resnets if """temopral_conv""" in key]
          UpperCAmelCase_						:    int    					=  renew_temp_conv_paths(lowercase      )
          UpperCAmelCase_						:    Optional[int]    					=  {
              """old""": f'''input_blocks.{i}.0.temopral_conv''',
              """new""": f'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
          }
          assign_to_checkpoint(
              lowercase						, lowercase						, lowercase						, additional_replacements=[meta_path]						, config=lowercase      )

          if len(lowercase      ):
               UpperCAmelCase_						:    Optional[Any]    					=  renew_attention_paths(lowercase      )
               UpperCAmelCase_						:    List[str]    					=  {"""old""": f'''input_blocks.{i}.1''', """new""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
               assign_to_checkpoint(
                   lowercase						, lowercase						, lowercase						, additional_replacements=[meta_path]						, config=lowercase      )

          if len(lowercase      ):
               UpperCAmelCase_						:    Optional[Any]    					=  renew_attention_paths(lowercase      )
               UpperCAmelCase_						:    Optional[int]    					=  {
                   """old""": f'''input_blocks.{i}.2''',
                   """new""": f'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
               }
               assign_to_checkpoint(
                   lowercase						, lowercase						, lowercase						, additional_replacements=[meta_path]						, config=lowercase      )

     UpperCAmelCase_						:    List[str]    					=  middle_blocks[0]
     UpperCAmelCase_						:    int    					=  [key for key in resnet_a if """temopral_conv""" in key]
     UpperCAmelCase_						:    int    					=  middle_blocks[1]
     UpperCAmelCase_						:    int    					=  middle_blocks[2]
     UpperCAmelCase_						:    List[Any]    					=  middle_blocks[3]
     UpperCAmelCase_						:    Union[str, Any]    					=  [key for key in resnet_a if """temopral_conv""" in key]

     UpperCAmelCase_						:    Union[str, Any]    					=  renew_resnet_paths(lowercase      )
     UpperCAmelCase_						:    List[Any]    					=  {"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
     assign_to_checkpoint(
         lowercase						, lowercase						, lowercase						, config=lowercase						, additional_replacements=[meta_path]      )

     UpperCAmelCase_						:    Optional[Any]    					=  renew_temp_conv_paths(lowercase      )
     UpperCAmelCase_						:    Optional[int]    					=  {"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
     assign_to_checkpoint(
         lowercase						, lowercase						, lowercase						, config=lowercase						, additional_replacements=[meta_path]      )

     UpperCAmelCase_						:    List[Any]    					=  renew_resnet_paths(lowercase      )
     UpperCAmelCase_						:    Tuple    					=  {"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
     assign_to_checkpoint(
         lowercase						, lowercase						, lowercase						, config=lowercase						, additional_replacements=[meta_path]      )

     UpperCAmelCase_						:    Optional[int]    					=  renew_temp_conv_paths(lowercase      )
     UpperCAmelCase_						:    Union[str, Any]    					=  {"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
     assign_to_checkpoint(
         lowercase						, lowercase						, lowercase						, config=lowercase						, additional_replacements=[meta_path]      )

     UpperCAmelCase_						:    Dict    					=  renew_attention_paths(lowercase      )
     UpperCAmelCase_						:    List[str]    					=  {"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
     assign_to_checkpoint(
         lowercase						, lowercase						, lowercase						, additional_replacements=[meta_path]						, config=lowercase      )

     UpperCAmelCase_						:    Tuple    					=  renew_attention_paths(lowercase      )
     UpperCAmelCase_						:    Union[str, Any]    					=  {"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
     assign_to_checkpoint(
         lowercase						, lowercase						, lowercase						, additional_replacements=[meta_path]						, config=lowercase      )

     for i in range(lowercase      ):
          UpperCAmelCase_						:    List[Any]    					=  i // (config["""layers_per_block"""] + 1)
          UpperCAmelCase_						:    List[Any]    					=  i % (config["""layers_per_block"""] + 1)
          UpperCAmelCase_						:    Optional[Any]    					=  [shave_segments(lowercase						, 2      ) for name in output_blocks[i]]
          UpperCAmelCase_						:    List[str]    					=  {}

          for layer in output_block_layers:
               UpperCAmelCase_						:    Dict    					=  layer.split("""."""      )[0], shave_segments(lowercase						, 1      )
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(lowercase      )
               else:
                    UpperCAmelCase_						:    Optional[Any]    					=  [layer_name]

          if len(lowercase      ) > 1:
               UpperCAmelCase_						:    Any    					=  [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
               UpperCAmelCase_						:    List[str]    					=  [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
               UpperCAmelCase_						:    List[Any]    					=  [key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

               UpperCAmelCase_						:    int    					=  renew_resnet_paths(lowercase      )
               UpperCAmelCase_						:    str    					=  renew_resnet_paths(lowercase      )

               UpperCAmelCase_						:    int    					=  {"""old""": f'''output_blocks.{i}.0''', """new""": f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
               assign_to_checkpoint(
                   lowercase						, lowercase						, lowercase						, additional_replacements=[meta_path]						, config=lowercase      )

               UpperCAmelCase_						:    Optional[int]    					=  [key for key in resnets if """temopral_conv""" in key]
               UpperCAmelCase_						:    List[Any]    					=  renew_temp_conv_paths(lowercase      )
               UpperCAmelCase_						:    Any    					=  {
                   """old""": f'''output_blocks.{i}.0.temopral_conv''',
                   """new""": f'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
               }
               assign_to_checkpoint(
                   lowercase						, lowercase						, lowercase						, additional_replacements=[meta_path]						, config=lowercase      )

               UpperCAmelCase_						:    Optional[int]    					=  {k: sorted(lowercase      ) for k, v in output_block_list.items()}
               if ["conv.bias", "conv.weight"] in output_block_list.values():
                    UpperCAmelCase_						:    str    					=  list(output_block_list.values()      ).index(["""conv.bias""", """conv.weight"""]      )
                    UpperCAmelCase_						:    Tuple    					=  unet_state_dict[
                        f'''output_blocks.{i}.{index}.conv.weight'''
                    ]
                    UpperCAmelCase_						:    int    					=  unet_state_dict[
                        f'''output_blocks.{i}.{index}.conv.bias'''
                    ]

                    # Clear attentions as they have been attributed above.
                    if len(lowercase      ) == 2:
                         UpperCAmelCase_						:    List[str]    					=  []

               if len(lowercase      ):
                    UpperCAmelCase_						:    Any    					=  renew_attention_paths(lowercase      )
                    UpperCAmelCase_						:    List[Any]    					=  {
                        """old""": f'''output_blocks.{i}.1''',
                        """new""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                    }
                    assign_to_checkpoint(
                        lowercase						, lowercase						, lowercase						, additional_replacements=[meta_path]						, config=lowercase      )

               if len(lowercase      ):
                    UpperCAmelCase_						:    List[str]    					=  renew_attention_paths(lowercase      )
                    UpperCAmelCase_						:    List[Any]    					=  {
                        """old""": f'''output_blocks.{i}.2''',
                        """new""": f'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                    }
                    assign_to_checkpoint(
                        lowercase						, lowercase						, lowercase						, additional_replacements=[meta_path]						, config=lowercase      )
          else:
               UpperCAmelCase_						:    List[str]    					=  renew_resnet_paths(lowercase						, n_shave_prefix_segments=1      )
               for path in resnet_0_paths:
                    UpperCAmelCase_						:    int    					=  """.""".join(["""output_blocks""", str(lowercase      ), path["""old"""]]      )
                    UpperCAmelCase_						:    List[Any]    					=  """.""".join(["""up_blocks""", str(lowercase      ), """resnets""", str(lowercase      ), path["""new"""]]      )
                    UpperCAmelCase_						:    Tuple    					=  unet_state_dict[old_path]

               UpperCAmelCase_						:    List[Any]    					=  [l for l in output_block_layers if """temopral_conv""" in l]
               for path in temopral_conv_paths:
                    UpperCAmelCase_						:    Any    					=  path.split("""temopral_conv."""      )[-1]
                    UpperCAmelCase_						:    Tuple    					=  """.""".join(["""output_blocks""", str(lowercase      ), str(lowercase      ), """temopral_conv""", pruned_path]      )
                    UpperCAmelCase_						:    Union[str, Any]    					=  """.""".join(["""up_blocks""", str(lowercase      ), """temp_convs""", str(lowercase      ), pruned_path]      )
                    UpperCAmelCase_						:    List[Any]    					=  unet_state_dict[old_path]

     return new_checkpoint


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

       parser.add_argument(
           "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
       )
       parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
       lowercase_						    =					parser.parse_args()

       lowercase_						    =					torch.load(args.checkpoint_path, map_location="cpu")
       lowercase_						    =					UNetaDConditionModel()

       lowercase_						    =					convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

       lowercase_						    =					set(unet.state_dict().keys()) - set(converted_ckpt.keys())
       lowercase_						    =					set(converted_ckpt.keys()) - set(unet.state_dict().keys())

       assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

       # load state_dict
       unet.load_state_dict(converted_ckpt)

       unet.save_pretrained(args.dump_path)

       # -- finish converting the unet --



"""simple docstring"""







def 			A_			(					lowercase = 99_8001      )     ->							int:


     """simple docstring"""







     for number in range(n - 1						, 9999						, -1      ):
          UpperCAmelCase_						:    Union[str, Any]    					=  str(lowercase      )

          # checks whether 'str_number' is a palindrome.
          if str_number == str_number[::-1]:
               UpperCAmelCase_						:    List[str]    					=  999

               # if 'number' is a product of two 3-digit numbers
               # then number is the answer otherwise fetch next number.
               while divisor != 99:
                    if (number % divisor == 0) and (len(str(number // divisor      )      ) == 3.0):
                         return number
                    divisor -= 1
     raise ValueError("""That number is larger than our acceptable range."""      )


if __name__ == "__main__":
       print(f"""{solution() = }""")

