

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple=1      )							->      List[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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Any=0      )							->      int:





    '''simple docstring'''






    A__          =   []
    for old_item in old_list:
        A__          =   old_item.replace('in_layers.0'    ,			'norm1'      )
        A__          =   new_item.replace('in_layers.2'    ,			'conv1'      )

        A__          =   new_item.replace('out_layers.0'    ,			'norm2'      )
        A__          =   new_item.replace('out_layers.3'    ,			'conv2'      )

        A__          =   new_item.replace('emb_layers.1'    ,			'time_emb_proj'      )
        A__          =   new_item.replace('skip_connection'    ,			'conv_shortcut'      )

        A__          =   shave_segments(UpperCamelCase__    ,			n_shave_prefix_segments=UpperCamelCase__      )

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

    return mapping
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	List[str]=0      )							->      Tuple:





    '''simple docstring'''






    A__          =   []
    for old_item in old_list:
        A__          =   old_item

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

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

        A__          =   shave_segments(UpperCamelCase__    ,			n_shave_prefix_segments=UpperCamelCase__      )

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

    return mapping
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=None    ,			SCREAMING_SNAKE_CASE__			:	List[str]=None    ,			SCREAMING_SNAKE_CASE__			:	Tuple=None      )							->      Dict:





    '''simple docstring'''






    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():
            A__          =   old_checkpoint[path]
            A__          =   old_tensor.shape[0] // 3

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

            A__          =   old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

            A__          =   query.reshape(UpperCamelCase__      )
            A__          =   key.reshape(UpperCamelCase__      )
            A__          =   value.reshape(UpperCamelCase__      )

    for path in paths:
        A__          =   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
        A__          =   new_path.replace('middle_block.0'    ,			'mid_block.resnets.0'      )
        A__          =   new_path.replace('middle_block.1'    ,			'mid_block.attentions.0'      )
        A__          =   new_path.replace('middle_block.2'    ,			'mid_block.resnets.1'      )

        if additional_replacements is not None:
            for replacement in additional_replacements:
                A__          =   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:
            A__          =   old_checkpoint[path['''old''']][:, :, 0]
        else:
            A__          =   old_checkpoint[path['''old''']]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Union[str, Any]:





    '''simple docstring'''






    A__          =   {}

    A__          =   checkpoint['''time_embed.0.weight''']
    A__          =   checkpoint['''time_embed.0.bias''']
    A__          =   checkpoint['''time_embed.2.weight''']
    A__          =   checkpoint['''time_embed.2.bias''']

    A__          =   checkpoint['''input_blocks.0.0.weight''']
    A__          =   checkpoint['''input_blocks.0.0.bias''']

    A__          =   checkpoint['''out.0.weight''']
    A__          =   checkpoint['''out.0.bias''']
    A__          =   checkpoint['''out.2.weight''']
    A__          =   checkpoint['''out.2.bias''']

    # Retrieves the keys for the input blocks only
    A__          =   len({'.'.join(layer.split('.'      )[:2]      ) for layer in checkpoint if 'input_blocks' in layer}      )
    A__          =   {
        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
    A__          =   len({'.'.join(layer.split('.'      )[:2]      ) for layer in checkpoint if 'middle_block' in layer}      )
    A__          =   {
        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
    A__          =   len({'.'.join(layer.split('.'      )[:2]      ) for layer in checkpoint if 'output_blocks' in layer}      )
    A__          =   {
        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__      ):
        A__          =   (i - 1) // (config['''num_res_blocks'''] + 1)
        A__          =   (i - 1) % (config['''num_res_blocks'''] + 1)

        A__          =   [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
        A__          =   [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:
            A__          =   checkpoint[
                f'input_blocks.{i}.0.op.weight'
            ]
            A__          =   checkpoint[
                f'input_blocks.{i}.0.op.bias'
            ]
            continue

        A__          =   renew_resnet_paths(UpperCamelCase__      )
        A__          =   {'''old''': f'input_blocks.{i}.0', '''new''': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
        A__          =   {'''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__      ):
            A__          =   renew_attention_paths(UpperCamelCase__      )
            A__          =   {
                '''old''': f'input_blocks.{i}.1',
                '''new''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
            }
            A__          =   {
                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__    ,			)

    A__          =   middle_blocks[0]
    A__          =   middle_blocks[1]
    A__          =   middle_blocks[2]

    A__          =   renew_resnet_paths(UpperCamelCase__      )
    assign_to_checkpoint(UpperCamelCase__    ,			UpperCamelCase__    ,			UpperCamelCase__    ,			config=UpperCamelCase__      )

    A__          =   renew_resnet_paths(UpperCamelCase__      )
    assign_to_checkpoint(UpperCamelCase__    ,			UpperCamelCase__    ,			UpperCamelCase__    ,			config=UpperCamelCase__      )

    A__          =   renew_attention_paths(UpperCamelCase__      )
    A__          =   {
        '''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__      ):
        A__          =   i // (config['''num_res_blocks'''] + 1)
        A__          =   i % (config['''num_res_blocks'''] + 1)
        A__          =   [shave_segments(UpperCamelCase__    ,			2      ) for name in output_blocks[i]]
        A__          =   {}

        for layer in output_block_layers:
            A__          =   layer.split('.'      )[0], shave_segments(UpperCamelCase__    ,			1      )
            if layer_id in output_block_list:
                output_block_list[layer_id].append(UpperCamelCase__      )
            else:
                A__          =   [layer_name]

        if len(UpperCamelCase__      ) > 1:
            A__          =   [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
            A__          =   [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

            A__          =   renew_resnet_paths(UpperCamelCase__      )
            A__          =   renew_resnet_paths(UpperCamelCase__      )

            A__          =   {'''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():
                A__          =   list(output_block_list.values()      ).index(['conv.weight', 'conv.bias']      )
                A__          =   checkpoint[
                    f'output_blocks.{i}.{index}.conv.weight'
                ]
                A__          =   checkpoint[
                    f'output_blocks.{i}.{index}.conv.bias'
                ]

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

            if len(UpperCamelCase__      ):
                A__          =   renew_attention_paths(UpperCamelCase__      )
                A__          =   {
                    '''old''': f'output_blocks.{i}.1',
                    '''new''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                }
                A__          =   {
                    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:
            A__          =   renew_resnet_paths(UpperCamelCase__    ,			n_shave_prefix_segments=1      )
            for path in resnet_0_paths:
                A__          =   '''.'''.join(['output_blocks', str(UpperCamelCase__      ), path['old']]      )
                A__          =   '''.'''.join(['up_blocks', str(UpperCamelCase__      ), 'resnets', str(UpperCamelCase__      ), path['new']]      )

                A__          =   checkpoint[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(
         "--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.")

     lowercase_       		=  parser.parse_args()

     lowercase_       		=  torch.load(args.checkpoint_path)

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

     lowercase_       		=  convert_ldm_checkpoint(checkpoint, config)

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

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

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

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






"""simple docstring"""
def  lowerCamelCase_						(UpperCamelCase__		:		int							):
    if not isinstance(UpperCamelCase__     ,       UpperCamelCase__							) or number < 0:
        raise ValueError('''Input must be a non-negative integer'''							)

    _UpperCAmelCase							:  str											=    0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()


