






"""simple docstring"""







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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =42







class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case							):





							"""simple docstring"""





							@register_to_config
							def __init__(   self:     Any				,		__A:     int = 65536				,		__A:     Optional[int] = None				,		__A:     int = 2				,		__A:     int = 2				,		__A:     int = 0				,		__A:     str = "fourier"				,		__A:     bool = True				,		__A:     bool = False				,		__A:     float = 0.0				,		__A:     Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,		__A:     Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,		__A:     Tuple[str] = "UNetMidBlock1D"				,		__A:     str = None				,		__A:     Tuple[int] = (32, 32, 64)				,		__A:     str = None				,		__A:     int = 8				,		__A:     int = 1				,		__A:     bool = False				,		):

												'''simple docstring'''

												super().__init__()
												a__          =  sample_size

												# time
												if time_embedding_type == "fourier":
																	a__          =  GaussianFourierProjection(
																	    embedding_size=8				,		set_W_to_weight=__A				,		log=__A				,		flip_sin_to_cos=__A    )
																	a__          =  2 * block_out_channels[0]
												elif time_embedding_type == "positional":
																	a__          =  Timesteps(
																	    block_out_channels[0]				,		flip_sin_to_cos=__A				,		downscale_freq_shift=__A    )
																	a__          =  block_out_channels[0]

												if use_timestep_embedding:
																	a__          =  block_out_channels[0] * 4
																	a__          =  TimestepEmbedding(
																	    in_channels=__A				,		time_embed_dim=__A				,		act_fn=__A				,		out_dim=block_out_channels[0]				,		)

												a__          =  nn.ModuleList([]    )
												a__          =  None
												a__          =  nn.ModuleList([]    )
												a__          =  None

												# down
												a__          =  in_channels
												for i, down_block_type in enumerate(__A    ):
																	a__          =  output_channel
																	a__          =  block_out_channels[i]

																	if i == 0:
																						input_channel += extra_in_channels

																	a__          =  i == len(__A    ) - 1

																	a__          =  get_down_block(
																	    __A				,		num_layers=__A				,		in_channels=__A				,		out_channels=__A				,		temb_channels=block_out_channels[0]				,		add_downsample=not is_final_block or downsample_each_block				,		)
																	self.down_blocks.append(__A    )

												# mid
												a__          =  get_mid_block(
												    __A				,		in_channels=block_out_channels[-1]				,		mid_channels=block_out_channels[-1]				,		out_channels=block_out_channels[-1]				,		embed_dim=block_out_channels[0]				,		num_layers=__A				,		add_downsample=__A				,		)

												# up
												a__          =  list(reversed(__A    )    )
												a__          =  reversed_block_out_channels[0]
												if out_block_type is None:
																	a__          =  out_channels
												else:
																	a__          =  block_out_channels[0]

												for i, up_block_type in enumerate(__A    ):
																	a__          =  output_channel
																	a__          =  (
																	    reversed_block_out_channels[i + 1] if i < len(__A    ) - 1 else final_upsample_channels
																	)

																	a__          =  i == len(__A    ) - 1

																	a__          =  get_up_block(
																	    __A				,		num_layers=__A				,		in_channels=__A				,		out_channels=__A				,		temb_channels=block_out_channels[0]				,		add_upsample=not is_final_block				,		)
																	self.up_blocks.append(__A    )
																	a__          =  output_channel

												# out
												a__          =  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,		32    )
												a__          =  get_out_block(
												    out_block_type=__A				,		num_groups_out=__A				,		embed_dim=block_out_channels[0]				,		out_channels=__A				,		act_fn=__A				,		fc_dim=block_out_channels[-1] // 4				,		)





							def  lowercase		(   self:     Optional[Any]				,		__A:     torch.FloatTensor				,		__A:     Union[torch.Tensor, float, int]				,		__A:     bool = True				,		):

												'''simple docstring'''

												a__          =  timestep
												if not torch.is_tensor(__A    ):
																	a__          =  torch.tensor([timesteps]				,		dtype=torch.long				,		device=sample.device    )
												elif torch.is_tensor(__A    ) and len(timesteps.shape    ) == 0:
																	a__          =  timesteps[None].to(sample.device    )

												a__          =  self.time_proj(__A    )
												if self.config.use_timestep_embedding:
																	a__          =  self.time_mlp(__A    )
												else:
																	a__          =  timestep_embed[..., None]
																	a__          =  timestep_embed.repeat([1, 1, sample.shape[2]]    ).to(sample.dtype    )
																	a__          =  timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])    )

												# 2. down
												a__          =  ()
												for downsample_block in self.down_blocks:
																	a__       ,a__          =  downsample_block(hidden_states=__A				,		temb=__A    )
																	down_block_res_samples += res_samples

												# 3. mid
												if self.mid_block:
																	a__          =  self.mid_block(__A				,		__A    )

												# 4. up
												for i, upsample_block in enumerate(self.up_blocks    ):
																	a__          =  down_block_res_samples[-1:]
																	a__          =  down_block_res_samples[:-1]
																	a__          =  upsample_block(__A				,		res_hidden_states_tuple=__A				,		temb=__A    )

												# 5. post-process
												if self.out_block:
																	a__          =  self.out_block(__A				,		__A    )

												if not return_dict:
																	return (sample,)

												return UNetaDOutput(sample=__A    )










"""simple docstring"""







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  OmegaConf.load(lowerCamelCase_)
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')['''model''']
					a__          =  list(state_dict.keys())

					# extract state_dict for VQVAE
					a__          =  {}
					a__          =  '''first_stage_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

    # extract state_dict for UNetLDM
					a__          =  {}
					a__          =  '''model.diffusion_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

					a__          =  config.model.params.first_stage_config.params
					a__          =  config.model.params.unet_config.params

					a__          =  VQModel(**lowerCamelCase_).eval()
					vqvae.load_state_dict(lowerCamelCase_)

					a__          =  UNetLDMModel(**lowerCamelCase_).eval()
					unet.load_state_dict(lowerCamelCase_)

					a__          =  DDIMScheduler(
					    timesteps=config.model.params.timesteps ,     beta_schedule='''scaled_linear''' ,     beta_start=config.model.params.linear_start ,     beta_end=config.model.params.linear_end ,     clip_sample=lowerCamelCase_ ,     )

					a__          =  LDMPipeline(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					pipeline.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
	__a       :	Tuple             = argparse.ArgumentParser()
	parser.add_argument('--checkpoint_path', type=str, required=True)
	parser.add_argument('--config_path', type=str, required=True)
	parser.add_argument('--output_path', type=str, required=True)
	__a       :	Optional[int]             = parser.parse_args()

	convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


