



"""simple docstring"""






from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather






@overload
def 	snake_case__  (						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       None      ):
			"""simple docstring"""
			...






@overload
def 	snake_case__  (						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor      ):
			"""simple docstring"""
			...






def 	snake_case__  (						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       List[str]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Union[str, Any]						      =aatype == rc.restype_order['''G''']
			lowerCamelCase__						:						Any						      =rc.atom_order['''CA''']
			lowerCamelCase__						:						Any						      =rc.atom_order['''CB''']
			lowerCamelCase__						:						Optional[int]						      =torch.where(
			    is_gly[..., None].expand(*((-1,) * len(is_gly.shape      ))				,						3      )				,						all_atom_positions[..., ca_idx, :]				,						all_atom_positions[..., cb_idx, :]				,						)

			if all_atom_masks is not None:
						lowerCamelCase__						:						Union[str, Any]						      =torch.where(
						    __lowerCamelCase				,						all_atom_masks[..., ca_idx]				,						all_atom_masks[..., cb_idx]				,						)
						return pseudo_beta, pseudo_beta_mask
			else:
						return pseudo_beta






def 	snake_case__  (						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       Dict[str, torch.Tensor]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Any						      =batched_gather(
			    __lowerCamelCase				,						batch['''residx_atom37_to_atom14''']				,						dim=-2				,						no_batch_dims=len(atomaa.shape[:-2]      )				,						)

			lowerCamelCase__						:						Union[str, Any]						      =atomaa_data * batch['''atom37_atom_exists'''][..., None]

			return atomaa_data






def 	snake_case__  (						__lowerCamelCase							:       Dict[str, torch.Tensor]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Optional[int]						      =template_feats['''template_aatype''']
			lowerCamelCase__						:						List[str]						      =template_feats['''template_torsion_angles_sin_cos''']
			lowerCamelCase__						:						Optional[int]						      =template_feats['''template_alt_torsion_angles_sin_cos''']
			lowerCamelCase__						:						Optional[Any]						      =template_feats['''template_torsion_angles_mask''']
			lowerCamelCase__						:						Union[str, Any]						      =torch.cat(
			    [
			        nn.functional.one_hot(__lowerCamelCase				,						22      ),
			        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]				,						14      ),
			        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]				,						14      ),
			        torsion_angles_mask,
			    ]				,						dim=-1				,						)

			return template_angle_feat






def 	snake_case__  (						__lowerCamelCase							:       Dict[str, torch.Tensor]				,						__lowerCamelCase							:       torch.types.Number				,						__lowerCamelCase							:       torch.types.Number				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       bool = False				,						__lowerCamelCase							:       float = 1e-2_0				,						__lowerCamelCase							:       float = 1e8				,						):
			"""simple docstring"""
			lowerCamelCase__						:						Union[str, Any]						      =batch['''template_pseudo_beta_mask''']
			lowerCamelCase__						:						Any						      =template_mask[..., None] * template_mask[..., None, :]

			# Compute distogram (this seems to differ slightly from Alg. 5)
			lowerCamelCase__						:						str						      =batch['''template_pseudo_beta''']
			lowerCamelCase__						:						Optional[Any]						      =torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2				,						dim=-1				,						keepdim=__lowerCamelCase      )
			lowerCamelCase__						:						Any						      =torch.linspace(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase				,						device=tpb.device      ) ** 2
			lowerCamelCase__						:						List[Any]						      =torch.cat([lower[1:], lower.new_tensor([inf]      )]				,						dim=-1      )
			lowerCamelCase__						:						List[Any]						      =((dgram > lower) * (dgram < upper)).type(dgram.dtype      )

			lowerCamelCase__						:						List[str]						      =[dgram, template_mask_ad[..., None]]

			lowerCamelCase__						:						torch.LongTensor						      =nn.functional.one_hot(
			    batch['''template_aatype''']				,						rc.restype_num + 2				,						)

			lowerCamelCase__						:						Union[str, Any]						      =batch['''template_aatype'''].shape[-1]
			to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]				,						__lowerCamelCase				,						-1				,						-1      )      )
			to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]				,						-1				,						__lowerCamelCase				,						-1      )      )

			lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						Tuple						      =[rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
			lowerCamelCase__						:						Dict						      =Rigid.make_transform_from_reference(
			    n_xyz=batch['''template_all_atom_positions'''][..., n, :]				,						ca_xyz=batch['''template_all_atom_positions'''][..., ca, :]				,						c_xyz=batch['''template_all_atom_positions'''][..., c, :]				,						eps=__lowerCamelCase				,						)
			lowerCamelCase__						:						List[str]						      =rigids.get_trans()[..., None, :, :]
			lowerCamelCase__						:						Any						      =rigids[..., None].invert_apply(__lowerCamelCase      )

			lowerCamelCase__						:						List[str]						      =torch.rsqrt(eps + torch.sum(rigid_vec**2				,						dim=-1      )      )

			lowerCamelCase__						:						str						      =batch['''template_all_atom_mask''']
			lowerCamelCase__						:						Optional[Any]						      =t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
			lowerCamelCase__						:						Dict						      =template_mask[..., None] * template_mask[..., None, :]

			lowerCamelCase__						:						Tuple						      =inv_distance_scalar * template_mask_ad
			lowerCamelCase__						:						Dict						      =rigid_vec * inv_distance_scalar[..., None]

			if not use_unit_vector:
						lowerCamelCase__						:						List[str]						      =unit_vector * 0.0

			to_concat.extend(torch.unbind(unit_vector[..., None, :]				,						dim=-1      )      )
			to_concat.append(template_mask_ad[..., None]      )

			lowerCamelCase__						:						int						      =torch.cat(__lowerCamelCase				,						dim=-1      )
			lowerCamelCase__						:						int						      =act * template_mask_ad[..., None]

			return act






def 	snake_case__  (						__lowerCamelCase							:       Dict[str, torch.Tensor]      ):
			"""simple docstring"""
			lowerCamelCase__						:						torch.LongTensor						      =nn.functional.one_hot(batch['''extra_msa''']				,						23      )
			lowerCamelCase__						:						Optional[int]						      =[
			    msa_ahot,
			    batch['''extra_has_deletion'''].unsqueeze(-1      ),
			    batch['''extra_deletion_value'''].unsqueeze(-1      ),
			]
			return torch.cat(__lowerCamelCase				,						dim=-1      )






def 	snake_case__  (						__lowerCamelCase							:       Rigid				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						):
			"""simple docstring"""
			# [*, N, 8, 4, 4]
			lowerCamelCase__						:						str						      =rrgdf[aatype, ...]

			# [*, N, 8] transformations, i.e.
			#   One [*, N, 8, 3, 3] rotation matrix and
			#   One [*, N, 8, 3]    translation matrix
			lowerCamelCase__						:						Tuple						      =r.from_tensor_axa(__lowerCamelCase      )

			lowerCamelCase__						:						Union[str, Any]						      =alpha.new_zeros((*((1,) * len(alpha.shape[:-1]      )), 2)      )
			lowerCamelCase__						:						Tuple						      =1

			# [*, N, 8, 2]
			lowerCamelCase__						:						Optional[Any]						      =torch.cat([bb_rot.expand(*alpha.shape[:-2]				,						-1				,						-1      ), alpha]				,						dim=-2      )

			# [*, N, 8, 3, 3]
			# Produces rotation matrices of the form:
			# [
			#   [1, 0  , 0  ],
			#   [0, a_2,-a_1],
			#   [0, a_1, a_2]
			# ]
			# This follows the original code rather than the supplement, which uses
			# different indices.

			lowerCamelCase__						:						int						      =alpha.new_zeros(default_r.get_rots().get_rot_mats().shape      )
			lowerCamelCase__						:						Any						      =1
			lowerCamelCase__						:						int						      =alpha[..., 1]
			lowerCamelCase__						:						Optional[int]						      =-alpha[..., 0]
			lowerCamelCase__						:						Union[str, Any]						      =alpha

			lowerCamelCase__						:						Optional[int]						      =default_r.compose(Rigid(Rotation(rot_mats=__lowerCamelCase      )				,						__lowerCamelCase      )      )

			lowerCamelCase__						:						Dict						      =all_frames[..., 5]
			lowerCamelCase__						:						int						      =all_frames[..., 6]
			lowerCamelCase__						:						Optional[int]						      =all_frames[..., 7]

			lowerCamelCase__						:						Optional[int]						      =all_frames[..., 4]
			lowerCamelCase__						:						Union[str, Any]						      =chia_frame_to_bb.compose(__lowerCamelCase      )
			lowerCamelCase__						:						Tuple						      =chia_frame_to_bb.compose(__lowerCamelCase      )
			lowerCamelCase__						:						Any						      =chia_frame_to_bb.compose(__lowerCamelCase      )

			lowerCamelCase__						:						Tuple						      =Rigid.cat(
			    [
			        all_frames[..., :5],
			        chia_frame_to_bb.unsqueeze(-1      ),
			        chia_frame_to_bb.unsqueeze(-1      ),
			        chia_frame_to_bb.unsqueeze(-1      ),
			    ]				,						dim=-1				,						)

			lowerCamelCase__						:						Union[str, Any]						      =r[..., None].compose(__lowerCamelCase      )

			return all_frames_to_global






def 	snake_case__  (						__lowerCamelCase							:       Rigid				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						__lowerCamelCase							:       torch.Tensor				,						):
			"""simple docstring"""
			# [*, N, 14]
			lowerCamelCase__						:						Union[str, Any]						      =group_idx[aatype, ...]

			# [*, N, 14, 8]
			lowerCamelCase__						:						torch.LongTensor						      =nn.functional.one_hot(
			    __lowerCamelCase				,						num_classes=default_frames.shape[-3]				,						)

			# [*, N, 14, 8]
			lowerCamelCase__						:						str						      =r[..., None, :] * group_mask_one_hot

			# [*, N, 14]
			lowerCamelCase__						:						Union[str, Any]						      =t_atoms_to_global.map_tensor_fn(lambda __lowerCamelCase      : torch.sum(__lowerCamelCase				,						dim=-1      )      )

			# [*, N, 14, 1]
			lowerCamelCase__						:						Any						      =atom_mask[aatype, ...].unsqueeze(-1      )

			# [*, N, 14, 3]
			lowerCamelCase__						:						str						      =lit_positions[aatype, ...]
			lowerCamelCase__						:						str						      =t_atoms_to_global.apply(__lowerCamelCase      )
			lowerCamelCase__						:						Any						      =pred_positions * atom_mask

			return pred_positions









'''simple docstring'''
import argparse
import json

import torch

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


def 	lowercase_						( lowerCAmelCase__						:	Tuple      ,				lowerCAmelCase__						:	Optional[Any]=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 	lowercase_						( lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	str=0			):

			"""simple docstring"""





			__UpperCAmelCase :   int    					=  []
			for old_item in old_list:
						__UpperCAmelCase :   str    					=  old_item
						__UpperCAmelCase :   Dict    					=  new_item.replace("""block."""      ,				"""resnets."""			)
						__UpperCAmelCase :   Union[str, Any]    					=  new_item.replace("""conv_shorcut"""      ,				"""conv1"""			)
						__UpperCAmelCase :   List[str]    					=  new_item.replace("""in_shortcut"""      ,				"""conv_shortcut"""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace("""temb_proj"""      ,				"""time_emb_proj"""			)

						__UpperCAmelCase :   List[str]    					=  shave_segments(lowerCAmelCase__      ,				n_shave_prefix_segments=lowerCAmelCase__			)

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

			return mapping


def 	lowercase_						( lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	Any=0      ,				lowerCAmelCase__						:	Optional[Any]=False			):

			"""simple docstring"""





			__UpperCAmelCase :   Any    					=  []
			for old_item in old_list:
						__UpperCAmelCase :   List[Any]    					=  old_item

						# In `model.mid`, the layer is called `attn`.
						if not in_mid:
									__UpperCAmelCase :   List[Any]    					=  new_item.replace("""attn"""      ,				"""attentions"""			)
						__UpperCAmelCase :   str    					=  new_item.replace(""".k."""      ,				""".key."""			)
						__UpperCAmelCase :   Dict    					=  new_item.replace(""".v."""      ,				""".value."""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace(""".q."""      ,				""".query."""			)

						__UpperCAmelCase :   str    					=  new_item.replace("""proj_out"""      ,				"""proj_attn"""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace("""norm"""      ,				"""group_norm"""			)

						__UpperCAmelCase :   List[str]    					=  shave_segments(lowerCAmelCase__      ,				n_shave_prefix_segments=lowerCAmelCase__			)
						mapping.append({"""old""": old_item, """new""": new_item}			)

			return mapping


def 	lowercase_						( lowerCAmelCase__						:	Dict      ,				lowerCAmelCase__						:	Optional[Any]      ,				lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	int=None      ,				lowerCAmelCase__						:	List[str]=None      ,				lowerCAmelCase__						:	int=None			):

			"""simple docstring"""





			assert isinstance(lowerCAmelCase__      ,				lowerCAmelCase__			), "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():
									__UpperCAmelCase :   Optional[int]    					=  old_checkpoint[path]
									__UpperCAmelCase :   Union[str, Any]    					=  old_tensor.shape[0] // 3

									__UpperCAmelCase :   Optional[int]    					=  (-1, channels) if len(old_tensor.shape			) == 3 else (-1)

									__UpperCAmelCase :   Optional[int]    					=  old_tensor.shape[0] // config.get("""num_head_channels"""      ,				1			) // 3

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

									__UpperCAmelCase :   Optional[Any]    					=  query.reshape(lowerCAmelCase__			).squeeze()
									__UpperCAmelCase :   Union[str, Any]    					=  key.reshape(lowerCAmelCase__			).squeeze()
									__UpperCAmelCase :   str    					=  value.reshape(lowerCAmelCase__			).squeeze()

			for path in paths:
						__UpperCAmelCase :   Optional[Any]    					=  path["""new"""]

						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						__UpperCAmelCase :   List[str]    					=  new_path.replace("""down."""      ,				"""down_blocks."""			)
						__UpperCAmelCase :   List[str]    					=  new_path.replace("""up."""      ,				"""up_blocks."""			)

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

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


def 	lowercase_						( lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	Tuple			):

			"""simple docstring"""





			__UpperCAmelCase :   Optional[int]    					=  {}

			__UpperCAmelCase :   Dict    					=  checkpoint["""temb.dense.0.weight"""]
			__UpperCAmelCase :   Tuple    					=  checkpoint["""temb.dense.0.bias"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""temb.dense.1.weight"""]
			__UpperCAmelCase :   Any    					=  checkpoint["""temb.dense.1.bias"""]

			__UpperCAmelCase :   Any    					=  checkpoint["""norm_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""norm_out.bias"""]

			__UpperCAmelCase :   Optional[int]    					=  checkpoint["""conv_in.weight"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""conv_in.bias"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""conv_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""conv_out.bias"""]

			__UpperCAmelCase :   Tuple    					=  len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """down""" in layer}			)
			__UpperCAmelCase :   str    					=  {
			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
			}

			__UpperCAmelCase :   str    					=  len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """up""" in layer}			)
			__UpperCAmelCase :   Union[str, Any]    					=  {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)}

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Any    					=  (i - 1) // (config["""layers_per_block"""] + 1)

						if any("""downsample""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   Dict    					=  checkpoint[
									    f'down.{i}.downsample.op.weight'
									]
									__UpperCAmelCase :   int    					=  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]			):
									__UpperCAmelCase :   str    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in down_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   Tuple    					=  {
									    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   List[str]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__			)

						if any("""attn""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   Tuple    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in down_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   str    					=  {
									    layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

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

			__UpperCAmelCase :   List[Any]    					=  [key for key in checkpoint if """mid.block_1""" in key]
			__UpperCAmelCase :   Union[str, Any]    					=  [key for key in checkpoint if """mid.block_2""" in key]
			__UpperCAmelCase :   Optional[Any]    					=  [key for key in checkpoint if """mid.attn_1""" in key]

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

			__UpperCAmelCase :   Union[str, Any]    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]      ,				)

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

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   List[Any]    					=  num_up_blocks - 1 - i

						if any("""upsample""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   str    					=  checkpoint[
									    f'up.{i}.upsample.conv.weight'
									]
									__UpperCAmelCase :   Optional[int]    					=  checkpoint[f'up.{i}.upsample.conv.bias']

						if any("""block""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   Any    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in up_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   Dict    					=  {
									    layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

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

						if any("""attn""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   List[str]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in up_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   str    					=  {
									    layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   List[str]    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Tuple    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

			__UpperCAmelCase :   Optional[int]    					=  {k.replace("""mid_new_2"""      ,				"""mid_block"""			): v for k, v in new_checkpoint.items()}
			return new_checkpoint


def 	lowercase_						( lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	Optional[Any]			):

			"""simple docstring"""





			__UpperCAmelCase :   Tuple    					=  {}

			__UpperCAmelCase :   str    					=  checkpoint["""encoder.norm_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""encoder.norm_out.bias"""]

			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_in.weight"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_in.bias"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_out.weight"""]
			__UpperCAmelCase :   int    					=  checkpoint["""encoder.conv_out.bias"""]

			__UpperCAmelCase :   str    					=  checkpoint["""decoder.norm_out.weight"""]
			__UpperCAmelCase :   Optional[int]    					=  checkpoint["""decoder.norm_out.bias"""]

			__UpperCAmelCase :   Dict    					=  checkpoint["""decoder.conv_in.weight"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""decoder.conv_in.bias"""]
			__UpperCAmelCase :   Tuple    					=  checkpoint["""decoder.conv_out.weight"""]
			__UpperCAmelCase :   str    					=  checkpoint["""decoder.conv_out.bias"""]

			__UpperCAmelCase :   str    					=  len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """down""" in layer}			)
			__UpperCAmelCase :   Tuple    					=  {
			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
			}

			__UpperCAmelCase :   Tuple    					=  len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """up""" in layer}			)
			__UpperCAmelCase :   List[Any]    					=  {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)}

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Any    					=  (i - 1) // (config["""layers_per_block"""] + 1)

						if any("""downsample""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   Optional[Any]    					=  checkpoint[
									    f'encoder.down.{i}.downsample.conv.weight'
									]
									__UpperCAmelCase :   Union[str, Any]    					=  checkpoint[
									    f'encoder.down.{i}.downsample.conv.bias'
									]

						if any("""block""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   List[Any]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   str    					=  {
									    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   List[Any]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__			)

						if any("""attn""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   List[str]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   Dict    					=  {
									    layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   Optional[int]    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				config=lowerCAmelCase__			)

			__UpperCAmelCase :   Optional[Any]    					=  [key for key in checkpoint if """mid.block_1""" in key]
			__UpperCAmelCase :   List[Any]    					=  [key for key in checkpoint if """mid.block_2""" in key]
			__UpperCAmelCase :   Dict    					=  [key for key in checkpoint if """mid.attn_1""" in key]

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

			__UpperCAmelCase :   Optional[Any]    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]      ,				)

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

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Union[str, Any]    					=  num_up_blocks - 1 - i

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

						if any("""block""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   List[str]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   Union[str, Any]    					=  {
									    layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   List[str]    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Optional[int]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

						if any("""attn""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   int    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   List[str]    					=  {
									    layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   List[str]    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Union[str, Any]    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

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




