

'''simple docstring'''





import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel







def 	lowerCamelCase__			(   __A						:Any  ,__A						:Tuple=1		):


       """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 	lowerCamelCase__			(   __A						:Dict  ,__A						:Optional[int]=0		):


       """simple docstring"""




       __snake_case					  =       []
       for old_item in old_list:
              __snake_case					  =       old_item
              __snake_case					  =       new_item.replace("""block."""  ,"""resnets."""		)
              __snake_case					  =       new_item.replace("""conv_shorcut"""  ,"""conv1"""		)
              __snake_case					  =       new_item.replace("""in_shortcut"""  ,"""conv_shortcut"""		)
              __snake_case					  =       new_item.replace("""temb_proj"""  ,"""time_emb_proj"""		)

              __snake_case					  =       shave_segments(_A  ,n_shave_prefix_segments=_A		)

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

       return mapping







def 	lowerCamelCase__			(   __A						:Union[str, Any]  ,__A						:Tuple=0  ,__A						:Any=False		):


       """simple docstring"""




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

              # In `model.mid`, the layer is called `attn`.
              if not in_mid:
                     __snake_case					  =       new_item.replace("""attn"""  ,"""attentions"""		)
              __snake_case					  =       new_item.replace(""".k."""  ,""".key."""		)
              __snake_case					  =       new_item.replace(""".v."""  ,""".value."""		)
              __snake_case					  =       new_item.replace(""".q."""  ,""".query."""		)

              __snake_case					  =       new_item.replace("""proj_out"""  ,"""proj_attn"""		)
              __snake_case					  =       new_item.replace("""norm"""  ,"""group_norm"""		)

              __snake_case					  =       shave_segments(_A  ,n_shave_prefix_segments=_A		)
              mapping.append({"""old""": old_item, """new""": new_item}		)

       return mapping







def 	lowerCamelCase__			(   __A						:List[str]  ,__A						:Any  ,__A						:Tuple  ,__A						:Optional[Any]=None  ,__A						:Tuple=None  ,__A						:List[str]=None		):


       """simple docstring"""




       assert isinstance(_A  ,_A		), "Paths should be a list of dicts containing 'old' and 'new' keys."

       if attention_paths_to_split is not None:
              if config is None:
                     raise ValueError("""Please specify the config if setting \'attention_paths_to_split\' to \'True\'."""		)

              for path, path_map in attention_paths_to_split.items():
                     __snake_case					  =       old_checkpoint[path]
                     __snake_case					  =       old_tensor.shape[0] // 3

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

                     __snake_case					  =       old_tensor.shape[0] // config.get("""num_head_channels"""  ,1		) // 3

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

                     __snake_case					  =       query.reshape(_A		).squeeze()
                     __snake_case					  =       key.reshape(_A		).squeeze()
                     __snake_case					  =       value.reshape(_A		).squeeze()

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

              if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                     continue

              __snake_case					  =       new_path.replace("""down."""  ,"""down_blocks."""		)
              __snake_case					  =       new_path.replace("""up."""  ,"""up_blocks."""		)

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

              if "attentions" in new_path:
                     __snake_case					  =       old_checkpoint[path["""old"""]].squeeze()
              else:
                     __snake_case					  =       old_checkpoint[path["""old"""]]







def 	lowerCamelCase__			(   __A						:Optional[int]  ,__A						:Optional[int]		):


       """simple docstring"""




       __snake_case					  =       {}

       __snake_case					  =       checkpoint["""temb.dense.0.weight"""]
       __snake_case					  =       checkpoint["""temb.dense.0.bias"""]
       __snake_case					  =       checkpoint["""temb.dense.1.weight"""]
       __snake_case					  =       checkpoint["""temb.dense.1.bias"""]

       __snake_case					  =       checkpoint["""norm_out.weight"""]
       __snake_case					  =       checkpoint["""norm_out.bias"""]

       __snake_case					  =       checkpoint["""conv_in.weight"""]
       __snake_case					  =       checkpoint["""conv_in.bias"""]
       __snake_case					  =       checkpoint["""conv_out.weight"""]
       __snake_case					  =       checkpoint["""conv_out.bias"""]

       __snake_case					  =       len({""".""".join(layer.split("""."""		)[:2]		) for layer in checkpoint if """down""" in layer}		)
       __snake_case					  =       {
           layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(_A		)
       }

       __snake_case					  =       len({""".""".join(layer.split("""."""		)[:2]		) for layer in checkpoint if """up""" in layer}		)
       __snake_case					  =       {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(_A		)}

       for i in range(_A		):
              __snake_case					  =       (i - 1) // (config["""layers_per_block"""] + 1)

              if any("""downsample""" in layer for layer in down_blocks[i]		):
                     __snake_case					  =       checkpoint[
                         F'down.{i}.downsample.op.weight'
                     ]
                     __snake_case					  =       checkpoint[F'down.{i}.downsample.op.bias']
              #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
              #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

              if any("""block""" in layer for layer in down_blocks[i]		):
                     __snake_case					  =       len(
                         {""".""".join(shave_segments(_A  ,2		).split("""."""		)[:2]		) for layer in down_blocks[i] if """block""" in layer}		)
                     __snake_case					  =       {
                         layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
                         for layer_id in range(_A		)
                     }

                     if num_blocks > 0:
                            for j in range(config["""layers_per_block"""]		):
                                   __snake_case					  =       renew_resnet_paths(blocks[j]		)
                                   assign_to_checkpoint(_A  ,_A  ,_A		)

              if any("""attn""" in layer for layer in down_blocks[i]		):
                     __snake_case					  =       len(
                         {""".""".join(shave_segments(_A  ,2		).split("""."""		)[:2]		) for layer in down_blocks[i] if """attn""" in layer}		)
                     __snake_case					  =       {
                         layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
                         for layer_id in range(_A		)
                     }

                     if num_attn > 0:
                            for j in range(config["""layers_per_block"""]		):
                                   __snake_case					  =       renew_attention_paths(attns[j]		)
                                   assign_to_checkpoint(_A  ,_A  ,_A  ,config=_A		)

       __snake_case					  =       [key for key in checkpoint if """mid.block_1""" in key]
       __snake_case					  =       [key for key in checkpoint if """mid.block_2""" in key]
       __snake_case					  =       [key for key in checkpoint if """mid.attn_1""" in key]

       # Mid new 2
       __snake_case					  =       renew_resnet_paths(_A		)
       assign_to_checkpoint(
           _A  ,_A  ,_A  ,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]  ,)

       __snake_case					  =       renew_resnet_paths(_A		)
       assign_to_checkpoint(
           _A  ,_A  ,_A  ,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]  ,)

       __snake_case					  =       renew_attention_paths(_A  ,in_mid=_A		)
       assign_to_checkpoint(
           _A  ,_A  ,_A  ,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]  ,)

       for i in range(_A		):
              __snake_case					  =       num_up_blocks - 1 - i

              if any("""upsample""" in layer for layer in up_blocks[i]		):
                     __snake_case					  =       checkpoint[
                         F'up.{i}.upsample.conv.weight'
                     ]
                     __snake_case					  =       checkpoint[F'up.{i}.upsample.conv.bias']

              if any("""block""" in layer for layer in up_blocks[i]		):
                     __snake_case					  =       len(
                         {""".""".join(shave_segments(_A  ,2		).split("""."""		)[:2]		) for layer in up_blocks[i] if """block""" in layer}		)
                     __snake_case					  =       {
                         layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(_A		)
                     }

                     if num_blocks > 0:
                            for j in range(config["""layers_per_block"""] + 1		):
                                   __snake_case					  =       {"""old""": F'up_blocks.{i}', """new""": F'up_blocks.{block_id}'}
                                   __snake_case					  =       renew_resnet_paths(blocks[j]		)
                                   assign_to_checkpoint(_A  ,_A  ,_A  ,additional_replacements=[replace_indices]		)

              if any("""attn""" in layer for layer in up_blocks[i]		):
                     __snake_case					  =       len(
                         {""".""".join(shave_segments(_A  ,2		).split("""."""		)[:2]		) for layer in up_blocks[i] if """attn""" in layer}		)
                     __snake_case					  =       {
                         layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(_A		)
                     }

                     if num_attn > 0:
                            for j in range(config["""layers_per_block"""] + 1		):
                                   __snake_case					  =       {"""old""": F'up_blocks.{i}', """new""": F'up_blocks.{block_id}'}
                                   __snake_case					  =       renew_attention_paths(attns[j]		)
                                   assign_to_checkpoint(_A  ,_A  ,_A  ,additional_replacements=[replace_indices]		)

       __snake_case					  =       {k.replace("""mid_new_2"""  ,"""mid_block"""		): v for k, v in new_checkpoint.items()}
       return new_checkpoint







def 	lowerCamelCase__			(   __A						:Union[str, Any]  ,__A						:List[Any]		):


       """simple docstring"""




       __snake_case					  =       {}

       __snake_case					  =       checkpoint["""encoder.norm_out.weight"""]
       __snake_case					  =       checkpoint["""encoder.norm_out.bias"""]

       __snake_case					  =       checkpoint["""encoder.conv_in.weight"""]
       __snake_case					  =       checkpoint["""encoder.conv_in.bias"""]
       __snake_case					  =       checkpoint["""encoder.conv_out.weight"""]
       __snake_case					  =       checkpoint["""encoder.conv_out.bias"""]

       __snake_case					  =       checkpoint["""decoder.norm_out.weight"""]
       __snake_case					  =       checkpoint["""decoder.norm_out.bias"""]

       __snake_case					  =       checkpoint["""decoder.conv_in.weight"""]
       __snake_case					  =       checkpoint["""decoder.conv_in.bias"""]
       __snake_case					  =       checkpoint["""decoder.conv_out.weight"""]
       __snake_case					  =       checkpoint["""decoder.conv_out.bias"""]

       __snake_case					  =       len({""".""".join(layer.split("""."""		)[:3]		) for layer in checkpoint if """down""" in layer}		)
       __snake_case					  =       {
           layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(_A		)
       }

       __snake_case					  =       len({""".""".join(layer.split("""."""		)[:3]		) for layer in checkpoint if """up""" in layer}		)
       __snake_case					  =       {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(_A		)}

       for i in range(_A		):
              __snake_case					  =       (i - 1) // (config["""layers_per_block"""] + 1)

              if any("""downsample""" in layer for layer in down_blocks[i]		):
                     __snake_case					  =       checkpoint[
                         F'encoder.down.{i}.downsample.conv.weight'
                     ]
                     __snake_case					  =       checkpoint[
                         F'encoder.down.{i}.downsample.conv.bias'
                     ]

              if any("""block""" in layer for layer in down_blocks[i]		):
                     __snake_case					  =       len(
                         {""".""".join(shave_segments(_A  ,3		).split("""."""		)[:3]		) for layer in down_blocks[i] if """block""" in layer}		)
                     __snake_case					  =       {
                         layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
                         for layer_id in range(_A		)
                     }

                     if num_blocks > 0:
                            for j in range(config["""layers_per_block"""]		):
                                   __snake_case					  =       renew_resnet_paths(blocks[j]		)
                                   assign_to_checkpoint(_A  ,_A  ,_A		)

              if any("""attn""" in layer for layer in down_blocks[i]		):
                     __snake_case					  =       len(
                         {""".""".join(shave_segments(_A  ,3		).split("""."""		)[:3]		) for layer in down_blocks[i] if """attn""" in layer}		)
                     __snake_case					  =       {
                         layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
                         for layer_id in range(_A		)
                     }

                     if num_attn > 0:
                            for j in range(config["""layers_per_block"""]		):
                                   __snake_case					  =       renew_attention_paths(attns[j]		)
                                   assign_to_checkpoint(_A  ,_A  ,_A  ,config=_A		)

       __snake_case					  =       [key for key in checkpoint if """mid.block_1""" in key]
       __snake_case					  =       [key for key in checkpoint if """mid.block_2""" in key]
       __snake_case					  =       [key for key in checkpoint if """mid.attn_1""" in key]

       # Mid new 2
       __snake_case					  =       renew_resnet_paths(_A		)
       assign_to_checkpoint(
           _A  ,_A  ,_A  ,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]  ,)

       __snake_case					  =       renew_resnet_paths(_A		)
       assign_to_checkpoint(
           _A  ,_A  ,_A  ,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]  ,)

       __snake_case					  =       renew_attention_paths(_A  ,in_mid=_A		)
       assign_to_checkpoint(
           _A  ,_A  ,_A  ,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]  ,)

       for i in range(_A		):
              __snake_case					  =       num_up_blocks - 1 - i

              if any("""upsample""" in layer for layer in up_blocks[i]		):
                     __snake_case					  =       checkpoint[
                         F'decoder.up.{i}.upsample.conv.weight'
                     ]
                     __snake_case					  =       checkpoint[
                         F'decoder.up.{i}.upsample.conv.bias'
                     ]

              if any("""block""" in layer for layer in up_blocks[i]		):
                     __snake_case					  =       len(
                         {""".""".join(shave_segments(_A  ,3		).split("""."""		)[:3]		) for layer in up_blocks[i] if """block""" in layer}		)
                     __snake_case					  =       {
                         layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(_A		)
                     }

                     if num_blocks > 0:
                            for j in range(config["""layers_per_block"""] + 1		):
                                   __snake_case					  =       {"""old""": F'up_blocks.{i}', """new""": F'up_blocks.{block_id}'}
                                   __snake_case					  =       renew_resnet_paths(blocks[j]		)
                                   assign_to_checkpoint(_A  ,_A  ,_A  ,additional_replacements=[replace_indices]		)

              if any("""attn""" in layer for layer in up_blocks[i]		):
                     __snake_case					  =       len(
                         {""".""".join(shave_segments(_A  ,3		).split("""."""		)[:3]		) for layer in up_blocks[i] if """attn""" in layer}		)
                     __snake_case					  =       {
                         layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(_A		)
                     }

                     if num_attn > 0:
                            for j in range(config["""layers_per_block"""] + 1		):
                                   __snake_case					  =       {"""old""": F'up_blocks.{i}', """new""": F'up_blocks.{block_id}'}
                                   __snake_case					  =       renew_attention_paths(attns[j]		)
                                   assign_to_checkpoint(_A  ,_A  ,_A  ,additional_replacements=[replace_indices]		)

       __snake_case					  =       {k.replace("""mid_new_2"""  ,"""mid_block"""		): v for k, v in new_checkpoint.items()}
       __snake_case					  =       checkpoint["""quant_conv.weight"""]
       __snake_case					  =       checkpoint["""quant_conv.bias"""]
       if "quantize.embedding.weight" in checkpoint:
              __snake_case					  =       checkpoint["""quantize.embedding.weight"""]
       __snake_case					  =       checkpoint["""post_quant_conv.weight"""]
       __snake_case					  =       checkpoint["""post_quant_conv.bias"""]

       return new_checkpoint


if __name__ == "__main__":
    UpperCamelCase__		   =    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.''')

    UpperCamelCase__		   =    parser.parse_args()
    UpperCamelCase__		   =    torch.load(args.checkpoint_path)

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

    # unet case
    UpperCamelCase__		   =    {key.split('''.''')[0] for key in checkpoint.keys()}
    if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
        UpperCamelCase__		   =    convert_vq_autoenc_checkpoint(checkpoint, config)
    else:
        UpperCamelCase__		   =    convert_ddpm_checkpoint(checkpoint, config)

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

    if config["_class_name"] == "VQModel":
        UpperCamelCase__		   =    VQModel(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    elif config["_class_name"] == "AutoencoderKL":
        UpperCamelCase__		   =    AutoencoderKL(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    else:
        UpperCamelCase__		   =    UNetaDModel(**config)
        model.load_state_dict(converted_checkpoint)

        UpperCamelCase__		   =    DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

        UpperCamelCase__		   =    DDPMPipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)




from __future__ import annotations

class 						__snake_case						:
       """simple docstring"""







       def __init__(		self			,   _UpperCamelCase      )			->       None:






              """simple docstring"""


              __snake_case					  =       value
              __snake_case					  =       None
              __snake_case					  =       None






class 						__snake_case						:
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       42


       def __init__(		self      )			->       None:






              """simple docstring"""


              __snake_case					  =       0


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase      )			->       None:






              """simple docstring"""


              if node is None:
                     return

              if path_sum == self.target:
                     self.paths += 1

              if node.left:
                     self.depth_first_search(node.left			,   path_sum + node.left.value      )
              if node.right:
                     self.depth_first_search(node.right			,   path_sum + node.right.value      )






       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase = None      )			->       int:






              """simple docstring"""


              if node is None:
                     return 0
              if target is not None:
                     __snake_case					  =       target

              self.depth_first_search(_UpperCamelCase			,   node.value      )
              self.path_sum(node.left      )
              self.path_sum(node.right      )

              return self.paths


if __name__ == "__main__":
    import doctest

    doctest.testmod()

