code: 
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None    )				->			str:
       assert isinstance(_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE    ), "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__    :     Union[str, Any]        =						old_checkpoint[path]
                 UpperCamelCase__    :     List[str]        =						old_tensor.shape[0] // 3
  
                 UpperCamelCase__    :     Dict        =						(-1, channels) if len(old_tensor.shape    ) == 3 else (-1)
  
                 UpperCamelCase__    :     List[str]        =						old_tensor.shape[0] // config['num_head_channels'] // 3
  
                 UpperCamelCase__    :     str        =						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]    )
                 UpperCamelCase__    :     Dict        =						old_tensor.split(channels // num_heads      ,					dim=1    )
  
                 UpperCamelCase__    :     Optional[int]        =						query.reshape(_SCREAMING_SNAKE_CASE    )
                 UpperCamelCase__    :     Tuple        =						key.reshape(_SCREAMING_SNAKE_CASE    )
                 UpperCamelCase__    :     List[str]        =						value.reshape(_SCREAMING_SNAKE_CASE    )
  
       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__    :     int        =						new_path.replace(replacement["old"]      ,					replacement["new"]    )
  
          # proj_attn.weight has to be converted from conv 1D to linear
            UpperCamelCase__    :     Any        =						old_checkpoint[path['old']]
            UpperCamelCase__    :     Tuple        =						['proj_attn.weight']
            UpperCamelCase__    :     Tuple        =						['proj_out.weight', 'proj_in.weight']
            if any(k in new_path for k in names    ):
                 UpperCamelCase__    :     Tuple        =						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__    :     int        =						weight[:, :, 0]
            else:
                 UpperCamelCase__    :     Union[str, Any]        =						weight
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase=0    )				->			str:
       UpperCamelCase__    :     Tuple        =						[]
       for old_item in old_list:
            UpperCamelCase__    :     Dict        =						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 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase=1    )				->			Any:
       if n_shave_prefix_segments >= 0:
            return ".".join(path.split("."    )[n_shave_prefix_segments:]    )
       else:
            return ".".join(path.split("."    )[:n_shave_prefix_segments]    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase=0    )				->			str:
       UpperCamelCase__    :     Optional[Any]        =						[]
       for old_item in old_list:
            mapping.append({"old": old_item, "new": old_item}    )
  
       return mapping
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase=0    )				->			Any:
       UpperCamelCase__    :     Tuple        =						[]
       for old_item in old_list:
            UpperCamelCase__    :     Optional[int]        =						old_item.replace("in_layers.0"      ,					"norm1"    )
            UpperCamelCase__    :     Optional[Any]        =						new_item.replace("in_layers.2"      ,					"conv1"    )
  
            UpperCamelCase__    :     str        =						new_item.replace("out_layers.0"      ,					"norm2"    )
            UpperCamelCase__    :     str        =						new_item.replace("out_layers.3"      ,					"conv2"    )
  
            UpperCamelCase__    :     List[Any]        =						new_item.replace("emb_layers.1"      ,					"time_emb_proj"    )
            UpperCamelCase__    :     List[Any]        =						new_item.replace("skip_connection"      ,					"conv_shortcut"    )
  
            UpperCamelCase__    :     int        =						shave_segments(_SCREAMING_SNAKE_CASE      ,					n_shave_prefix_segments=_SCREAMING_SNAKE_CASE    )
  
            if "temopral_conv" not in old_item:
                 mapping.append({"old": old_item, "new": new_item}    )
  
       return mapping
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=False    )				->			Tuple:
       UpperCamelCase__    :     Dict        =						{}
       UpperCamelCase__    :     Tuple        =						list(checkpoint.keys()    )
  
       UpperCamelCase__    :     Optional[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__    :     int        =						'model_ema.' + ''.join(key.split("."    )[1:]    )
                      UpperCamelCase__    :     Optional[int]        =						checkpoint.pop(_SCREAMING_SNAKE_CASE    )
       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__    :     Optional[Any]        =						checkpoint.pop(_SCREAMING_SNAKE_CASE    )
  
       UpperCamelCase__    :     Union[str, Any]        =						{}
  
       UpperCamelCase__    :     Optional[Any]        =						unet_state_dict['time_embed.0.weight']
       UpperCamelCase__    :     Union[str, Any]        =						unet_state_dict['time_embed.0.bias']
       UpperCamelCase__    :     Any        =						unet_state_dict['time_embed.2.weight']
       UpperCamelCase__    :     Dict        =						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__    :     List[Any]        =						unet_state_dict['label_emb.0.0.weight']
            UpperCamelCase__    :     Union[str, Any]        =						unet_state_dict['label_emb.0.0.bias']
            UpperCamelCase__    :     Optional[Any]        =						unet_state_dict['label_emb.0.2.weight']
            UpperCamelCase__    :     Union[str, Any]        =						unet_state_dict['label_emb.0.2.bias']
       else:
            raise NotImplementedError(f'Not implemented `class_embed_type`: {config["class_embed_type"]}'    )
  
       UpperCamelCase__    :     str        =						unet_state_dict['input_blocks.0.0.weight']
       UpperCamelCase__    :     Tuple        =						unet_state_dict['input_blocks.0.0.bias']
  
       UpperCamelCase__    :     Optional[int]        =						[v for v in unet_state_dict if v.startswith("input_blocks.0.1"    )]
       UpperCamelCase__    :     List[str]        =						renew_attention_paths(_SCREAMING_SNAKE_CASE    )
       UpperCamelCase__    :     List[str]        =						{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
       assign_to_checkpoint(_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]      ,					config=_SCREAMING_SNAKE_CASE    )
  
       UpperCamelCase__    :     Any        =						unet_state_dict['out.0.weight']
       UpperCamelCase__    :     Union[str, Any]        =						unet_state_dict['out.0.bias']
       UpperCamelCase__    :     Optional[int]        =						unet_state_dict['out.2.weight']
       UpperCamelCase__    :     Union[str, Any]        =						unet_state_dict['out.2.bias']
  
       # Retrieves the keys for the input blocks only
       UpperCamelCase__    :     Optional[Any]        =						len({".".join(layer.split("."    )[:2]    ) for layer in unet_state_dict if "input_blocks" in layer}    )
       UpperCamelCase__    :     Dict        =						{
           layer_id: [key for key in unet_state_dict if f'input_blocks.{layer_id}' in key]
           for layer_id in range(_SCREAMING_SNAKE_CASE    )
       }
  
       # Retrieves the keys for the middle blocks only
       UpperCamelCase__    :     Dict        =						len({".".join(layer.split("."    )[:2]    ) for layer in unet_state_dict if "middle_block" in layer}    )
       UpperCamelCase__    :     Optional[Any]        =						{
           layer_id: [key for key in unet_state_dict if f'middle_block.{layer_id}' in key]
           for layer_id in range(_SCREAMING_SNAKE_CASE    )
       }
  
       # Retrieves the keys for the output blocks only
       UpperCamelCase__    :     Optional[Any]        =						len({".".join(layer.split("."    )[:2]    ) for layer in unet_state_dict if "output_blocks" in layer}    )
       UpperCamelCase__    :     Dict        =						{
           layer_id: [key for key in unet_state_dict if f'output_blocks.{layer_id}' in key]
           for layer_id in range(_SCREAMING_SNAKE_CASE    )
       }
  
       for i in range(1      ,					_SCREAMING_SNAKE_CASE    ):
            UpperCamelCase__    :     List[str]        =						(i - 1) // (config['layers_per_block'] + 1)
            UpperCamelCase__    :     Tuple        =						(i - 1) % (config['layers_per_block'] + 1)
  
            UpperCamelCase__    :     Optional[Any]        =						[
                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__    :     List[str]        =						[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
            UpperCamelCase__    :     Tuple        =						[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__    :     Optional[Any]        =						unet_state_dict.pop(
                     f'input_blocks.{i}.op.weight'    )
                 UpperCamelCase__    :     Optional[int]        =						unet_state_dict.pop(
                     f'input_blocks.{i}.op.bias'    )
  
            UpperCamelCase__    :     str        =						renew_resnet_paths(_SCREAMING_SNAKE_CASE    )
            UpperCamelCase__    :     Tuple        =						{'old': f'input_blocks.{i}.0', 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
            assign_to_checkpoint(
                _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]      ,					config=_SCREAMING_SNAKE_CASE    )
  
            UpperCamelCase__    :     Optional[int]        =						[key for key in resnets if 'temopral_conv' in key]
            UpperCamelCase__    :     Tuple        =						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE    )
            UpperCamelCase__    :     List[Any]        =						{
                'old': f'input_blocks.{i}.0.temopral_conv',
                'new': f'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
            }
            assign_to_checkpoint(
                _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]      ,					config=_SCREAMING_SNAKE_CASE    )
  
            if len(_SCREAMING_SNAKE_CASE    ):
                 UpperCamelCase__    :     List[Any]        =						renew_attention_paths(_SCREAMING_SNAKE_CASE    )
                 UpperCamelCase__    :     Optional[Any]        =						{'old': f'input_blocks.{i}.1', 'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
                 assign_to_checkpoint(
                     _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]      ,					config=_SCREAMING_SNAKE_CASE    )
  
            if len(_SCREAMING_SNAKE_CASE    ):
                 UpperCamelCase__    :     List[str]        =						renew_attention_paths(_SCREAMING_SNAKE_CASE    )
                 UpperCamelCase__    :     str        =						{
                     'old': f'input_blocks.{i}.2',
                     'new': f'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
                 }
                 assign_to_checkpoint(
                     _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]      ,					config=_SCREAMING_SNAKE_CASE    )
  
       UpperCamelCase__    :     Union[str, Any]        =						middle_blocks[0]
       UpperCamelCase__    :     str        =						[key for key in resnet_a if 'temopral_conv' in key]
       UpperCamelCase__    :     Dict        =						middle_blocks[1]
       UpperCamelCase__    :     str        =						middle_blocks[2]
       UpperCamelCase__    :     List[str]        =						middle_blocks[3]
       UpperCamelCase__    :     Any        =						[key for key in resnet_a if 'temopral_conv' in key]
  
       UpperCamelCase__    :     Optional[int]        =						renew_resnet_paths(_SCREAMING_SNAKE_CASE    )
       UpperCamelCase__    :     List[Any]        =						{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
       assign_to_checkpoint(
           _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					config=_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]    )
  
       UpperCamelCase__    :     Optional[int]        =						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE    )
       UpperCamelCase__    :     List[Any]        =						{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
       assign_to_checkpoint(
           _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					config=_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]    )
  
       UpperCamelCase__    :     Optional[int]        =						renew_resnet_paths(_SCREAMING_SNAKE_CASE    )
       UpperCamelCase__    :     Dict        =						{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
       assign_to_checkpoint(
           _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					config=_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]    )
  
       UpperCamelCase__    :     Dict        =						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE    )
       UpperCamelCase__    :     Optional[int]        =						{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
       assign_to_checkpoint(
           _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					config=_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]    )
  
       UpperCamelCase__    :     List[Any]        =						renew_attention_paths(_SCREAMING_SNAKE_CASE    )
       UpperCamelCase__    :     int        =						{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
       assign_to_checkpoint(
           _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]      ,					config=_SCREAMING_SNAKE_CASE    )
  
       UpperCamelCase__    :     str        =						renew_attention_paths(_SCREAMING_SNAKE_CASE    )
       UpperCamelCase__    :     Optional[Any]        =						{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
       assign_to_checkpoint(
           _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]      ,					config=_SCREAMING_SNAKE_CASE    )
  
       for i in range(_SCREAMING_SNAKE_CASE    ):
            UpperCamelCase__    :     List[str]        =						i // (config['layers_per_block'] + 1)
            UpperCamelCase__    :     Optional[int]        =						i % (config['layers_per_block'] + 1)
            UpperCamelCase__    :     Any        =						[shave_segments(_SCREAMING_SNAKE_CASE      ,					2    ) for name in output_blocks[i]]
            UpperCamelCase__    :     Optional[Any]        =						{}
  
            for layer in output_block_layers:
                 UpperCamelCase__    :     Optional[int]        =						layer.split("."    )[0], shave_segments(_SCREAMING_SNAKE_CASE      ,					1    )
                 if layer_id in output_block_list:
                      output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE    )
                 else:
                      UpperCamelCase__    :     int        =						[layer_name]
  
            if len(_SCREAMING_SNAKE_CASE    ) > 1:
                 UpperCamelCase__    :     Any        =						[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
                 UpperCamelCase__    :     List[Any]        =						[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
                 UpperCamelCase__    :     Optional[Any]        =						[key for key in output_blocks[i] if f'output_blocks.{i}.2' in key]
  
                 UpperCamelCase__    :     Optional[int]        =						renew_resnet_paths(_SCREAMING_SNAKE_CASE    )
                 UpperCamelCase__    :     List[Any]        =						renew_resnet_paths(_SCREAMING_SNAKE_CASE    )
  
                 UpperCamelCase__    :     Dict        =						{'old': f'output_blocks.{i}.0', 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
                 assign_to_checkpoint(
                     _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]      ,					config=_SCREAMING_SNAKE_CASE    )
  
                 UpperCamelCase__    :     Dict        =						[key for key in resnets if 'temopral_conv' in key]
                 UpperCamelCase__    :     Tuple        =						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE    )
                 UpperCamelCase__    :     List[str]        =						{
                     'old': f'output_blocks.{i}.0.temopral_conv',
                     'new': f'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
                 }
                 assign_to_checkpoint(
                     _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]      ,					config=_SCREAMING_SNAKE_CASE    )
  
                 UpperCamelCase__    :     List[str]        =						{k: sorted(_SCREAMING_SNAKE_CASE    ) for k, v in output_block_list.items()}
                 if ["conv.bias", "conv.weight"] in output_block_list.values():
                      UpperCamelCase__    :     Tuple        =						list(output_block_list.values()    ).index(["conv.bias", "conv.weight"]    )
                      UpperCamelCase__    :     str        =						unet_state_dict[
                          f'output_blocks.{i}.{index}.conv.weight'
                      ]
                      UpperCamelCase__    :     Tuple        =						unet_state_dict[
                          f'output_blocks.{i}.{index}.conv.bias'
                      ]
  
                      # Clear attentions as they have been attributed above.
                      if len(_SCREAMING_SNAKE_CASE    ) == 2:
                           UpperCamelCase__    :     Optional[Any]        =						[]
  
                 if len(_SCREAMING_SNAKE_CASE    ):
                      UpperCamelCase__    :     Tuple        =						renew_attention_paths(_SCREAMING_SNAKE_CASE    )
                      UpperCamelCase__    :     int        =						{
                          'old': f'output_blocks.{i}.1',
                          'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                      }
                      assign_to_checkpoint(
                          _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]      ,					config=_SCREAMING_SNAKE_CASE    )
  
                 if len(_SCREAMING_SNAKE_CASE    ):
                      UpperCamelCase__    :     Optional[Any]        =						renew_attention_paths(_SCREAMING_SNAKE_CASE    )
                      UpperCamelCase__    :     Optional[Any]        =						{
                          'old': f'output_blocks.{i}.2',
                          'new': f'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
                      }
                      assign_to_checkpoint(
                          _SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					_SCREAMING_SNAKE_CASE      ,					additional_replacements=[meta_path]      ,					config=_SCREAMING_SNAKE_CASE    )
            else:
                 UpperCamelCase__    :     int        =						renew_resnet_paths(_SCREAMING_SNAKE_CASE      ,					n_shave_prefix_segments=1    )
                 for path in resnet_0_paths:
                      UpperCamelCase__    :     str        =						'.'.join(["output_blocks", str(_SCREAMING_SNAKE_CASE    ), path["old"]]    )
                      UpperCamelCase__    :     Dict        =						'.'.join(["up_blocks", str(_SCREAMING_SNAKE_CASE    ), "resnets", str(_SCREAMING_SNAKE_CASE    ), path["new"]]    )
                      UpperCamelCase__    :     Optional[int]        =						unet_state_dict[old_path]
  
                 UpperCamelCase__    :     Union[str, 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__    :     int        =						'.'.join(["output_blocks", str(_SCREAMING_SNAKE_CASE    ), str(_SCREAMING_SNAKE_CASE    ), "temopral_conv", pruned_path]    )
                      UpperCamelCase__    :     str        =						'.'.join(["up_blocks", str(_SCREAMING_SNAKE_CASE    ), "temp_convs", str(_SCREAMING_SNAKE_CASE    ), pruned_path]    )
                      UpperCamelCase__    :     Optional[Any]        =						unet_state_dict[old_path]
  
       return new_checkpoint
  
  
  if __name__ == "__main__":
   lowerCamelCase			:							Optional[int]       =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.''')
   lowerCamelCase			:							str       =parser.parse_args()
  
   lowerCamelCase			:							Dict       =torch.load(args.checkpoint_path, map_location='''cpu''')
   lowerCamelCase			:							Union[str, Any]       =UNetaDConditionModel()
  
   lowerCamelCase			:							Optional[Any]       =convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
   lowerCamelCase			:							Optional[int]       =set(unet.state_dict().keys()) - set(converted_ckpt.keys())
   lowerCamelCase			:							List[str]       =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 --
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import numpy as np  # noqa: E402
  
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...schedulers.scheduling_utils import SchedulerMixin
  
  
  try:
   import librosa  # noqa: E402
  
   lowerCamelCase			:							List[str]       =True
   lowerCamelCase			:							Dict       =''''''
  except Exception as e:
   lowerCamelCase			:							str       =False
   lowerCamelCase			:							List[Any]       =(
       F"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
   )
  
  
  from PIL import Image  # noqa: E402
  
  
  
  
  
  class     __a						(					A__ ,     A__ ):
   _lowerCAmelCase : Optional[Any] 				=				'''mel_config.json'''
  
  
  
  
  
  
  
   @register_to_config
   def __init__(	self      :     Any							,     SCREAMING_SNAKE_CASE      :     int = 2_56							,     SCREAMING_SNAKE_CASE      :     int = 2_56							,     SCREAMING_SNAKE_CASE      :     int = 2_20_50							,     SCREAMING_SNAKE_CASE      :     int = 20_48							,     SCREAMING_SNAKE_CASE      :     int = 5_12							,     SCREAMING_SNAKE_CASE      :     int = 80							,     SCREAMING_SNAKE_CASE      :     int = 32							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						hop_length
        UpperCamelCase__    :     Optional[Any]        =						sample_rate
        UpperCamelCase__    :     Dict        =						n_fft
        UpperCamelCase__    :     str        =						top_db
        UpperCamelCase__    :     Optional[Any]        =						n_iter
        self.set_resolution(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     str        =						None
  
        if not _librosa_can_be_imported:
             raise ValueError(_import_error		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     int							,     SCREAMING_SNAKE_CASE      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Dict        =						x_res
        UpperCamelCase__    :     Dict        =						y_res
        UpperCamelCase__    :     Dict        =						self.y_res
        UpperCamelCase__    :     Any        =						self.x_res * self.hop_length - 1
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     str							,     SCREAMING_SNAKE_CASE      :     str = None							,     SCREAMING_SNAKE_CASE      :     np.ndarray = None		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if audio_file is not None:
             UpperCamelCase__       ,  UpperCamelCase__    :     int        =						librosa.load(SCREAMING_SNAKE_CASE							,     mono=SCREAMING_SNAKE_CASE							,     sr=self.sr		)
        else:
             UpperCamelCase__    :     List[Any]        =						raw_audio
  
        # Pad with silence if necessary.
        if len(self.audio		) < self.x_res * self.hop_length:
             UpperCamelCase__    :     List[Any]        =						np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio		),)		)]		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return len(self.audio		) // self.slice_size
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     int							,     SCREAMING_SNAKE_CASE      :     int = 0		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return self.sr
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[int]        =						librosa.feature.melspectrogram(
            y=self.get_audio_slice(SCREAMING_SNAKE_CASE		)							,     sr=self.sr							,     n_fft=self.n_fft							,     hop_length=self.hop_length							,     n_mels=self.n_mels		)
        UpperCamelCase__    :     Tuple        =						librosa.power_to_db(SCREAMING_SNAKE_CASE							,     ref=np.max							,     top_db=self.top_db		)
        UpperCamelCase__    :     Optional[Any]        =						(((log_S + self.top_db) * 2_55 / self.top_db).clip(0							,     2_55		) + 0.5).astype(np.uinta		)
        UpperCamelCase__    :     Optional[Any]        =						Image.fromarray(SCREAMING_SNAKE_CASE		)
        return image
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]							,     SCREAMING_SNAKE_CASE      :     Image.Image		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						np.frombuffer(image.tobytes()							,     dtype="uint8"		).reshape((image.height, image.width)		)
        UpperCamelCase__    :     str        =						bytedata.astype("float"		) * self.top_db / 2_55 - self.top_db
        UpperCamelCase__    :     Any        =						librosa.db_to_power(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Tuple        =						librosa.feature.inverse.mel_to_audio(
            SCREAMING_SNAKE_CASE							,     sr=self.sr							,     n_fft=self.n_fft							,     hop_length=self.hop_length							,     n_iter=self.n_iter		)
        return audio