






"""simple docstring"""




from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


UpperCamelCase_     =logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class 				_a		(   _lowerCAmelCase       ):
				UpperCamelCase			=					42




class 				_a		(   _lowerCAmelCase			,       _lowerCAmelCase       ):
				UpperCamelCase			=					1






				@register_to_config
				def __init__(				self	:					Union[str, Any],				lowerCAmelCase__	:					int = 4_0,				lowerCAmelCase__	:					float = 0.002,				lowerCAmelCase__	:					float = 80.0,				lowerCAmelCase__	:					float = 0.5,				lowerCAmelCase__	:					float = 1.0,				lowerCAmelCase__	:					float = 7.0,				lowerCAmelCase__	:					bool = True,				)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					sigma_max

										_UpperCamelCase    :      Dict						     =					np.linspace(0,				1,				lowerCAmelCase__							)
										_UpperCamelCase    :      int						     =					self._convert_to_karras(lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					self.sigma_to_t(lowerCAmelCase__							)

										# setable values
										_UpperCamelCase    :      Union[str, Any]						     =					None
										_UpperCamelCase    :      List[Any]						     =					torch.from_numpy(lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					torch.from_numpy(lowerCAmelCase__							)
										_UpperCamelCase    :      List[Any]						     =					False
										_UpperCamelCase    :      int						     =					False






				def        snake_case			(				self	:					Optional[int],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					Tuple=None							)			->   List[Any]:




										'''simple docstring'''



										if schedule_timesteps is None:
																_UpperCamelCase    :      Optional[int]						     =					self.timesteps

										_UpperCamelCase    :      int						     =					(schedule_timesteps == timestep).nonzero()
										return indices.item()






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					Union[float, torch.FloatTensor]							)			->   torch.FloatTensor:




										'''simple docstring'''



										if isinstance(lowerCAmelCase__,				torch.Tensor							):
																_UpperCamelCase    :      Optional[int]						     =					timestep.to(self.timesteps.device							)
										_UpperCamelCase    :      List[str]						     =					self.index_for_timestep(lowerCAmelCase__							)
										_UpperCamelCase    :      Optional[Any]						     =					self.sigmas[step_idx]

										_UpperCamelCase    :      str						     =					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

										_UpperCamelCase    :      int						     =					True
										return sample






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					Union[float, np.ndarray]							)			->   str:




										'''simple docstring'''



										if not isinstance(lowerCAmelCase__,				np.ndarray							):
																_UpperCamelCase    :      Optional[int]						     =					np.array(lowerCAmelCase__,				dtype=np.floataa							)

										_UpperCamelCase    :      List[str]						     =					1_0_0_0 * 0.25 * np.log(sigmas + 1e-4_4							)

										return timesteps






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					Optional[int] = None,				lowerCAmelCase__	:					Union[str, torch.device] = None,				lowerCAmelCase__	:					Optional[List[int]] = None,				)			->   Tuple:




										'''simple docstring'''



										if num_inference_steps is None and timesteps is None:
																raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''							)

										if num_inference_steps is not None and timesteps is not None:
																raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''							)

										# Follow DDPMScheduler custom timesteps logic
										if timesteps is not None:
																for i in range(1,				len(lowerCAmelCase__							)							):
																						if timesteps[i] >= timesteps[i - 1]:
																												raise ValueError('''`timesteps` must be in descending order.'''							)

																if timesteps[0] >= self.config.num_train_timesteps:
																						raise ValueError(
																						    f"""`timesteps` must start before `self.config.train_timesteps`:"""
																						    f""" {self.config.num_train_timesteps}."""							)

																_UpperCamelCase    :      int						     =					np.array(lowerCAmelCase__,				dtype=np.intaa							)
																_UpperCamelCase    :      List[Any]						     =					True
										else:
																if num_inference_steps > self.config.num_train_timesteps:
																						raise ValueError(
																						    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																						    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																						    f""" maximal {self.config.num_train_timesteps} timesteps."""							)

																_UpperCamelCase    :      Union[str, Any]						     =					num_inference_steps

																_UpperCamelCase    :      Any						     =					self.config.num_train_timesteps // self.num_inference_steps
																_UpperCamelCase    :      Tuple						     =					(np.arange(0,				lowerCAmelCase__							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
																_UpperCamelCase    :      Union[str, Any]						     =					False

										# Map timesteps to Karras sigmas directly for multistep sampling
										# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
										_UpperCamelCase    :      Optional[Any]						     =					self.config.num_train_timesteps
										_UpperCamelCase    :      Dict						     =					timesteps[::-1].copy()
										_UpperCamelCase    :      Dict						     =					ramp / (num_train_timesteps - 1)
										_UpperCamelCase    :      List[Any]						     =					self._convert_to_karras(lowerCAmelCase__							)
										_UpperCamelCase    :      str						     =					self.sigma_to_t(lowerCAmelCase__							)

										_UpperCamelCase    :      Optional[Any]						     =					np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
										_UpperCamelCase    :      List[str]						     =					torch.from_numpy(lowerCAmelCase__							).to(device=lowerCAmelCase__							)

										if str(lowerCAmelCase__							).startswith('''mps'''							):
																# mps does not support float64
																_UpperCamelCase    :      Union[str, Any]						     =					torch.from_numpy(lowerCAmelCase__							).to(lowerCAmelCase__,				dtype=torch.floataa							)
										else:
																_UpperCamelCase    :      List[str]						     =					torch.from_numpy(lowerCAmelCase__							).to(device=lowerCAmelCase__							)






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					str							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      float						     =					self.config.sigma_min
										_UpperCamelCase    :      float						     =					self.config.sigma_max

										_UpperCamelCase    :      List[str]						     =					self.config.rho
										_UpperCamelCase    :      str						     =					sigma_min ** (1 / rho)
										_UpperCamelCase    :      List[Any]						     =					sigma_max ** (1 / rho)
										_UpperCamelCase    :      Union[str, Any]						     =					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas






				def        snake_case			(				self	:					Tuple,				lowerCAmelCase__	:					str							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					self.config.sigma_data

										_UpperCamelCase    :      Optional[Any]						     =					sigma_data**2 / (sigma**2 + sigma_data**2)
										_UpperCamelCase    :      int						     =					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out






				def        snake_case			(				self	:					str,				lowerCAmelCase__	:					Union[str, Any]							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					self.config.sigma_min
										_UpperCamelCase    :      int						     =					self.config.sigma_data

										_UpperCamelCase    :      Tuple						     =					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
										_UpperCamelCase    :      List[str]						     =					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					Union[float, torch.FloatTensor],				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					Optional[torch.Generator] = None,				lowerCAmelCase__	:					bool = True,				)			->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:




										'''simple docstring'''



										if (
										    isinstance(lowerCAmelCase__,				lowerCAmelCase__							)
										    or isinstance(lowerCAmelCase__,				torch.IntTensor							)
										    or isinstance(lowerCAmelCase__,				torch.LongTensor							)
										):
																raise ValueError(
																    (
																        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
																        f""" `{self.__class__}.step()` is not supported. Make sure to pass"""
																        ''' one of the `scheduler.timesteps` as a timestep.'''
																    ),				)

										if not self.is_scale_input_called:
																logger.warning(
																    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
																    '''See `StableDiffusionPipeline` for a usage example.'''							)

										if isinstance(lowerCAmelCase__,				torch.Tensor							):
																_UpperCamelCase    :      Any						     =					timestep.to(self.timesteps.device							)

										_UpperCamelCase    :      int						     =					self.config.sigma_min
										_UpperCamelCase    :      List[Any]						     =					self.config.sigma_max

										_UpperCamelCase    :      Tuple						     =					self.index_for_timestep(lowerCAmelCase__							)

										# sigma_next corresponds to next_t in original implementation
										_UpperCamelCase    :      Union[str, Any]						     =					self.sigmas[step_index]
										if step_index + 1 < self.config.num_train_timesteps:
																_UpperCamelCase    :      List[Any]						     =					self.sigmas[step_index + 1]
										else:
																# Set sigma_next to sigma_min
																_UpperCamelCase    :      Tuple						     =					self.sigmas[-1]

										# Get scalings for boundary conditions
										_UpperCamelCase    :      List[Any]						     =					self.get_scalings_for_boundary_condition(lowerCAmelCase__							)

										# 1. Denoise model output using boundary conditions
										_UpperCamelCase    :      Any						     =					c_out * model_output + c_skip * sample
										if self.config.clip_denoised:
																_UpperCamelCase    :      List[Any]						     =					denoised.clamp(-1,				1							)

										# 2. Sample z ~ N(0, s_noise^2 * I)
										# Noise is not used for onestep sampling.
										if len(self.timesteps							) > 1:
																_UpperCamelCase    :      Union[str, Any]						     =					randn_tensor(
																    model_output.shape,				dtype=model_output.dtype,				device=model_output.device,				generator=lowerCAmelCase__							)
										else:
																_UpperCamelCase    :      Optional[int]						     =					torch.zeros_like(lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					noise * self.config.s_noise

										_UpperCamelCase    :      List[Any]						     =					sigma_next.clamp(min=lowerCAmelCase__,				max=lowerCAmelCase__							)

										# 3. Return noisy sample
										# tau = sigma_hat, eps = sigma_min
										_UpperCamelCase    :      Optional[int]						     =					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

										if not return_dict:
																return (prev_sample,)

										return CMStochasticIterativeSchedulerOutput(prev_sample=lowerCAmelCase__							)






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					torch.FloatTensor,				)			->   torch.FloatTensor:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					self.sigmas.to(device=original_samples.device,				dtype=original_samples.dtype							)
										if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__							):
																# mps does not support float64
																_UpperCamelCase    :      List[str]						     =					self.timesteps.to(original_samples.device,				dtype=torch.floataa							)
																_UpperCamelCase    :      List[Any]						     =					timesteps.to(original_samples.device,				dtype=torch.floataa							)
										else:
																_UpperCamelCase    :      Dict						     =					self.timesteps.to(original_samples.device							)
																_UpperCamelCase    :      List[str]						     =					timesteps.to(original_samples.device							)

										_UpperCamelCase    :      Any						     =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

										_UpperCamelCase    :      Optional[Any]						     =					sigmas[step_indices].flatten()
										while len(sigma.shape							) < len(original_samples.shape							):
																_UpperCamelCase    :      str						     =					sigma.unsqueeze(-1							)

										_UpperCamelCase    :      str						     =					original_samples + noise * sigma
										return noisy_samples






				def __len__(				self	:					List[Any]							)			->   Any:




										'''simple docstring'''



										return self.config.num_train_timesteps










"""simple docstring"""




import argparse
import json

import torch

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





def  a_       (							_lowercase ,		_lowercase=1     ):
						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('''.'''     )[n_shave_prefix_segments:]     )
						else:
												return ".".join(path.split('''.'''     )[:n_shave_prefix_segments]     )





def  a_       (							_lowercase ,		_lowercase=0     ):
						_UpperCamelCase    :      Dict						     =					[]
						for old_item in old_list:
												_UpperCamelCase    :      Tuple						     =					old_item
												_UpperCamelCase    :      Optional[Any]						     =					new_item.replace('''block.''' ,		'''resnets.'''     )
												_UpperCamelCase    :      int						     =					new_item.replace('''conv_shorcut''' ,		'''conv1'''     )
												_UpperCamelCase    :      List[str]						     =					new_item.replace('''in_shortcut''' ,		'''conv_shortcut'''     )
												_UpperCamelCase    :      Union[str, Any]						     =					new_item.replace('''temb_proj''' ,		'''time_emb_proj'''     )

												_UpperCamelCase    :      List[Any]						     =					shave_segments(_lowercase ,		n_shave_prefix_segments=_lowercase     )

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

						return mapping





def  a_       (							_lowercase ,		_lowercase=0 ,		_lowercase=False     ):
						_UpperCamelCase    :      str						     =					[]
						for old_item in old_list:
												_UpperCamelCase    :      Tuple						     =					old_item

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

												_UpperCamelCase    :      Optional[Any]						     =					new_item.replace('''proj_out''' ,		'''proj_attn'''     )
												_UpperCamelCase    :      Dict						     =					new_item.replace('''norm''' ,		'''group_norm'''     )

												_UpperCamelCase    :      str						     =					shave_segments(_lowercase ,		n_shave_prefix_segments=_lowercase     )
												mapping.append({'''old''': old_item, '''new''': new_item}     )

						return mapping





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase=None ,		_lowercase=None ,		_lowercase=None     ):
						assert isinstance(_lowercase ,		_lowercase     ), "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    :      Union[str, Any]						     =					old_checkpoint[path]
																		_UpperCamelCase    :      Any						     =					old_tensor.shape[0] // 3

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

																		_UpperCamelCase    :      Dict						     =					old_tensor.shape[0] // config.get('''num_head_channels''' ,		1     ) // 3

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

																		_UpperCamelCase    :      Union[str, Any]						     =					query.reshape(_lowercase     ).squeeze()
																		_UpperCamelCase    :      Any						     =					key.reshape(_lowercase     ).squeeze()
																		_UpperCamelCase    :      str						     =					value.reshape(_lowercase     ).squeeze()

						for path in paths:
												_UpperCamelCase    :      List[str]						     =					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    :      Optional[int]						     =					new_path.replace('''up.''' ,		'''up_blocks.'''     )

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

												if "attentions" in new_path:
																		_UpperCamelCase    :      Optional[int]						     =					old_checkpoint[path['''old''']].squeeze()
												else:
																		_UpperCamelCase    :      Union[str, Any]						     =					old_checkpoint[path['''old''']]





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Union[str, Any]						     =					{}

						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.0.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.0.bias''']
						_UpperCamelCase    :      List[Any]						     =					checkpoint['''temb.dense.1.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.1.bias''']

						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''norm_out.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''norm_out.bias''']

						_UpperCamelCase    :      Dict						     =					checkpoint['''conv_in.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''conv_in.bias''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''conv_out.weight''']
						_UpperCamelCase    :      str						     =					checkpoint['''conv_out.bias''']

						_UpperCamelCase    :      Optional[int]						     =					len({'''.'''.join(layer.split('''.'''     )[:2]     ) for layer in checkpoint if '''down''' in layer}     )
						_UpperCamelCase    :      Union[str, Any]						     =					{
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowercase     )
						}

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

						for i in range(_lowercase     ):
												_UpperCamelCase    :      Optional[Any]						     =					(i - 1) // (config['''layers_per_block'''] + 1)

												if any('''downsample''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      Tuple						     =					checkpoint[
																		    F"""down.{i}.downsample.op.weight"""
																		]
																		_UpperCamelCase    :      Union[str, Any]						     =					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    :      Optional[int]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		2     ).split('''.'''     )[:2]     ) for layer in down_blocks[i] if '''block''' in layer}     )
																		_UpperCamelCase    :      Optional[int]						     =					{
																		    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
																		    for layer_id in range(_lowercase     )
																		}

																		if num_blocks > 0:
																								for j in range(config['''layers_per_block''']     ):
																														_UpperCamelCase    :      str						     =					renew_resnet_paths(blocks[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase     )

												if any('''attn''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      Any						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		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(_lowercase     )
																		}

																		if num_attn > 0:
																								for j in range(config['''layers_per_block''']     ):
																														_UpperCamelCase    :      Union[str, Any]						     =					renew_attention_paths(attns[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase ,		config=_lowercase     )

						_UpperCamelCase    :      Dict						     =					[key for key in checkpoint if '''mid.block_1''' in key]
						_UpperCamelCase    :      int						     =					[key for key in checkpoint if '''mid.block_2''' in key]
						_UpperCamelCase    :      Tuple						     =					[key for key in checkpoint if '''mid.attn_1''' in key]

						# Mid new 2
						_UpperCamelCase    :      Tuple						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}] ,		)

						_UpperCamelCase    :      int						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}] ,		)

						_UpperCamelCase    :      str						     =					renew_attention_paths(_lowercase ,		in_mid=_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}] ,		)

						for i in range(_lowercase     ):
												_UpperCamelCase    :      List[str]						     =					num_up_blocks - 1 - i

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

												if any('''block''' in layer for layer in up_blocks[i]     ):
																		_UpperCamelCase    :      Union[str, Any]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		2     ).split('''.'''     )[:2]     ) 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(_lowercase     )
																		}

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

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

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

						_UpperCamelCase    :      int						     =					{k.replace('''mid_new_2''' ,		'''mid_block'''     ): v for k, v in new_checkpoint.items()}
						return new_checkpoint





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      List[Any]						     =					{}

						_UpperCamelCase    :      List[str]						     =					checkpoint['''encoder.norm_out.weight''']
						_UpperCamelCase    :      Tuple						     =					checkpoint['''encoder.norm_out.bias''']

						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''encoder.conv_in.weight''']
						_UpperCamelCase    :      Any						     =					checkpoint['''encoder.conv_in.bias''']
						_UpperCamelCase    :      Optional[int]						     =					checkpoint['''encoder.conv_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''encoder.conv_out.bias''']

						_UpperCamelCase    :      Dict						     =					checkpoint['''decoder.norm_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.norm_out.bias''']

						_UpperCamelCase    :      str						     =					checkpoint['''decoder.conv_in.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.conv_in.bias''']
						_UpperCamelCase    :      List[Any]						     =					checkpoint['''decoder.conv_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.conv_out.bias''']

						_UpperCamelCase    :      Tuple						     =					len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in checkpoint if '''down''' in layer}     )
						_UpperCamelCase    :      Optional[int]						     =					{
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowercase     )
						}

						_UpperCamelCase    :      List[str]						     =					len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in checkpoint if '''up''' in layer}     )
						_UpperCamelCase    :      Tuple						     =					{layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(_lowercase     )}

						for i in range(_lowercase     ):
												_UpperCamelCase    :      List[Any]						     =					(i - 1) // (config['''layers_per_block'''] + 1)

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

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

																		if num_blocks > 0:
																								for j in range(config['''layers_per_block''']     ):
																														_UpperCamelCase    :      Optional[int]						     =					renew_resnet_paths(blocks[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase     )

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

																		if num_attn > 0:
																								for j in range(config['''layers_per_block''']     ):
																														_UpperCamelCase    :      Tuple						     =					renew_attention_paths(attns[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase ,		config=_lowercase     )

						_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    :      int						     =					[key for key in checkpoint if '''mid.attn_1''' in key]

						# Mid new 2
						_UpperCamelCase    :      int						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}] ,		)

						_UpperCamelCase    :      Any						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}] ,		)

						_UpperCamelCase    :      Any						     =					renew_attention_paths(_lowercase ,		in_mid=_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}] ,		)

						for i in range(_lowercase     ):
												_UpperCamelCase    :      Dict						     =					num_up_blocks - 1 - i

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

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

																		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    :      str						     =					renew_resnet_paths(blocks[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[replace_indices]     )

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

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

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



