


'''simple docstring'''


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_UpperCamelCase          =     """pcuenq/k-upscaler"""


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				*, SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					List[Any]						     =       {
         # norm1
         f"{diffusers_resnet_prefix}.norm1.linear.weight": checkpoint[f"{resnet_prefix}.main.0.mapper.weight"],
         f"{diffusers_resnet_prefix}.norm1.linear.bias": checkpoint[f"{resnet_prefix}.main.0.mapper.bias"],
         # conv1
         f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.main.2.weight"],
         f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.main.2.bias"],
         # norm2
         f"{diffusers_resnet_prefix}.norm2.linear.weight": checkpoint[f"{resnet_prefix}.main.4.mapper.weight"],
         f"{diffusers_resnet_prefix}.norm2.linear.bias": checkpoint[f"{resnet_prefix}.main.4.mapper.bias"],
         # conv2
         f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.main.6.weight"],
         f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.main.6.bias"],
     }

     if resnet.conv_shortcut is not None:
          rv.update(
              {
                  f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.skip.weight"],
              }       )

     return rv


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				*, SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A     ,__A     ,__A  :					int						     =       checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3					,				dim=0       )
     __A     ,__A     ,__A  :					Union[str, Any]						     =       checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3					,				dim=0       )
     __A  :					Union[str, Any]						     =       {
         # norm
         f"{diffusers_attention_prefix}.norm1.linear.weight": checkpoint[f"{attention_prefix}.norm_in.mapper.weight"],
         f"{diffusers_attention_prefix}.norm1.linear.bias": checkpoint[f"{attention_prefix}.norm_in.mapper.bias"],
         # to_q
         f"{diffusers_attention_prefix}.attn1.to_q.weight": weight_q.squeeze(-1       ).squeeze(-1       ),
         f"{diffusers_attention_prefix}.attn1.to_q.bias": bias_q,
         # to_k
         f"{diffusers_attention_prefix}.attn1.to_k.weight": weight_k.squeeze(-1       ).squeeze(-1       ),
         f"{diffusers_attention_prefix}.attn1.to_k.bias": bias_k,
         # to_v
         f"{diffusers_attention_prefix}.attn1.to_v.weight": weight_v.squeeze(-1       ).squeeze(-1       ),
         f"{diffusers_attention_prefix}.attn1.to_v.bias": bias_v,
         # to_out
         f"{diffusers_attention_prefix}.attn1.to_out.0.weight": checkpoint[f"{attention_prefix}.out_proj.weight"]
         .squeeze(-1       )
         .squeeze(-1       ),
         f"{diffusers_attention_prefix}.attn1.to_out.0.bias": checkpoint[f"{attention_prefix}.out_proj.bias"],
     }

     return rv


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				*, SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A     ,__A  :					str						     =       checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2					,				dim=0       )
     __A     ,__A  :					Optional[int]						     =       checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2					,				dim=0       )

     __A  :					Optional[Any]						     =       {
         # norm2 (ada groupnorm)
         f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight": checkpoint[
             f"{attention_prefix}.norm_dec.mapper.weight"
         ],
         f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias": checkpoint[
             f"{attention_prefix}.norm_dec.mapper.bias"
         ],
         # layernorm on encoder_hidden_state
         f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight": checkpoint[
             f"{attention_prefix}.norm_enc.weight"
         ],
         f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias": checkpoint[
             f"{attention_prefix}.norm_enc.bias"
         ],
         # to_q
         f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight": checkpoint[
             f"{attention_prefix}.q_proj.weight"
         ]
         .squeeze(-1       )
         .squeeze(-1       ),
         f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias": checkpoint[
             f"{attention_prefix}.q_proj.bias"
         ],
         # to_k
         f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight": weight_k.squeeze(-1       ).squeeze(-1       ),
         f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias": bias_k,
         # to_v
         f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight": weight_v.squeeze(-1       ).squeeze(-1       ),
         f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias": bias_v,
         # to_out
         f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight": checkpoint[
             f"{attention_prefix}.out_proj.weight"
         ]
         .squeeze(-1       )
         .squeeze(-1       ),
         f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias": checkpoint[
             f"{attention_prefix}.out_proj.bias"
         ],
     }

     return rv


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Optional[int]						     =       "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
     __A  :					Union[str, Any]						     =       f"{block_prefix}.{block_idx}"

     __A  :					Tuple						     =       {}

     if not hasattr(SCREAMING_SNAKE_CASE					,				"attentions"       ):
          __A  :					Any						     =       1  # resnet only
     elif not block.attentions[0].add_self_attention:
          __A  :					Optional[int]						     =       2  # resnet -> cross-attention
     else:
          __A  :					List[Any]						     =       3  # resnet -> self-attention -> cross-attention)

     for resnet_idx, resnet in enumerate(block.resnets       ):
          # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
          __A  :					str						     =       f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
          __A  :					int						     =       n * resnet_idx if block_type == "up" else n * resnet_idx + 1
          __A  :					int						     =       f"{block_prefix}.{idx}" if block_type == "up" else f"{block_prefix}.{idx}"

          diffusers_checkpoint.update(
              resnet_to_diffusers_checkpoint(
                  SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				diffusers_resnet_prefix=SCREAMING_SNAKE_CASE					,				resnet_prefix=SCREAMING_SNAKE_CASE       )       )

     if hasattr(SCREAMING_SNAKE_CASE					,				"attentions"       ):
          for attention_idx, attention in enumerate(block.attentions       ):
               __A  :					str						     =       f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
               __A  :					Tuple						     =       n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
               __A  :					Dict						     =       f"{block_prefix}.{idx}"
               __A  :					Optional[int]						     =       f"{block_prefix}.{idx }"
               __A  :					List[Any]						     =       1 if not attention.add_self_attention else 2
               __A  :					Tuple						     =       (
                   n * attention_idx + cross_attention_index
                   if block_type == "up"
                   else n * attention_idx + cross_attention_index + 1
               )
               __A  :					Optional[int]						     =       f"{block_prefix}.{idx }"

               diffusers_checkpoint.update(
                   cross_attn_to_diffusers_checkpoint(
                       SCREAMING_SNAKE_CASE					,				diffusers_attention_prefix=SCREAMING_SNAKE_CASE					,				diffusers_attention_index=2					,				attention_prefix=SCREAMING_SNAKE_CASE					,				)       )

               if attention.add_self_attention is True:
                    diffusers_checkpoint.update(
                        self_attn_to_diffusers_checkpoint(
                            SCREAMING_SNAKE_CASE					,				diffusers_attention_prefix=SCREAMING_SNAKE_CASE					,				attention_prefix=SCREAMING_SNAKE_CASE					,				)       )

     return diffusers_checkpoint


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Optional[int]						     =       {}

     # pre-processing
     diffusers_checkpoint.update(
         {
             "conv_in.weight": checkpoint["inner_model.proj_in.weight"],
             "conv_in.bias": checkpoint["inner_model.proj_in.bias"],
         }       )

     # timestep and class embedding
     diffusers_checkpoint.update(
         {
             "time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1       ),
             "time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"],
             "time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"],
             "time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"],
             "time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"],
             "time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"],
         }       )

     # down_blocks
     for down_block_idx, down_block in enumerate(model.down_blocks       ):
          diffusers_checkpoint.update(block_to_diffusers_checkpoint(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				"down"       )       )

     # up_blocks
     for up_block_idx, up_block in enumerate(model.up_blocks       ):
          diffusers_checkpoint.update(block_to_diffusers_checkpoint(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				"up"       )       )

     # post-processing
     diffusers_checkpoint.update(
         {
             "conv_out.weight": checkpoint["inner_model.proj_out.weight"],
             "conv_out.bias": checkpoint["inner_model.proj_out.bias"],
         }       )

     return diffusers_checkpoint


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					List[Any]						     =       original_config["input_channels"] + original_config["unet_cond_dim"]
     __A  :					Any						     =       original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

     __A  :					Any						     =       original_config["channels"]

     assert (
         len(set(original_config["depths"]       )       ) == 1
     ), "UNet2DConditionModel currently do not support blocks with different number of layers"
     __A  :					List[str]						     =       original_config["depths"][0]

     __A  :					Union[str, Any]						     =       original_config["mapping_cond_dim"]
     __A  :					int						     =       original_config["cross_cond_dim"]

     __A  :					Union[str, Any]						     =       []
     __A  :					Dict						     =       []
     for s, c in zip(original_config["self_attn_depths"]					,				original_config["cross_attn_depths"]       ):
          if s:
               __A  :					Tuple						     =       "self"
               __A  :					Tuple						     =       "cross" if c else None
          elif c:
               __A  :					Dict						     =       "cross"
               __A  :					int						     =       None
          else:
               __A  :					List[Any]						     =       None
               __A  :					Any						     =       None
          attna_types.append(SCREAMING_SNAKE_CASE       )
          attna_types.append(SCREAMING_SNAKE_CASE       )

     __A  :					Tuple						     =       UNetaDConditionModel(
         in_channels=SCREAMING_SNAKE_CASE					,				out_channels=SCREAMING_SNAKE_CASE					,				down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")					,				mid_block_type=SCREAMING_SNAKE_CASE					,				up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")					,				block_out_channels=SCREAMING_SNAKE_CASE					,				layers_per_block=SCREAMING_SNAKE_CASE					,				act_fn="gelu"					,				norm_num_groups=SCREAMING_SNAKE_CASE					,				cross_attention_dim=SCREAMING_SNAKE_CASE					,				attention_head_dim=64					,				time_cond_proj_dim=SCREAMING_SNAKE_CASE					,				resnet_time_scale_shift="scale_shift"					,				time_embedding_type="fourier"					,				timestep_post_act="gelu"					,				conv_in_kernel=1					,				conv_out_kernel=1					,				)

     return unet


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Any						     =       torch.device("cuda" if torch.cuda.is_available() else "cpu"       )

     __A  :					Any						     =       huggingface_hub.hf_hub_download(SCREAMING_SNAKE_CASE					,				"config_laion_text_cond_latent_upscaler_2.json"       )
     __A  :					Optional[Any]						     =       huggingface_hub.hf_hub_download(
         SCREAMING_SNAKE_CASE					,				"laion_text_cond_latent_upscaler_2_1_00470000_slim.pth"       )
     print(f"loading original model configuration from {orig_config_path}"       )
     print(f"loading original model checkpoint from {orig_weights_path}"       )

     print("converting to diffusers unet"       )
     __A  :					List[Any]						     =       K.config.load_config(open(SCREAMING_SNAKE_CASE       )       )["model"]
     __A  :					str						     =       unet_model_from_original_config(SCREAMING_SNAKE_CASE       )

     __A  :					Optional[int]						     =       torch.load(SCREAMING_SNAKE_CASE					,				map_location=SCREAMING_SNAKE_CASE       )["model_ema"]
     __A  :					Any						     =       unet_to_diffusers_checkpoint(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       )

     model.load_state_dict(SCREAMING_SNAKE_CASE					,				strict=SCREAMING_SNAKE_CASE       )
     model.save_pretrained(args.dump_path       )
     print(f"saving converted unet model in {args.dump_path}"       )


if __name__ == "__main__":
      _UpperCamelCase          =     argparse.ArgumentParser()

      parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      _UpperCamelCase          =     parser.parse_args()

      main(args)





'''simple docstring'''


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__()

          self.register_modules(vae=lowerCamelCase ,    text_encoder=lowerCamelCase ,    tokenizer=lowerCamelCase ,    unet=lowerCamelCase ,    scheduler=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          with torch.no_grad():
               __A  :					Optional[Any]						     =       self.vae.encode(tfms.ToTensor()(lowerCamelCase		).unsqueeze(0		).to(self.device		) * 2 - 1		)
               __A  :					int						     =       0.1_8215 * latent.latent_dist.sample()
          return latent


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       (1 / 0.1_8215) * latent
          with torch.no_grad():
               __A  :					List[Any]						     =       self.vae.decode(lowerCamelCase		).sample
          __A  :					int						     =       (img / 2 + 0.5).clamp(0 ,    1		)
          __A  :					List[str]						     =       img.detach().cpu().permute(0 ,    2 ,    3 ,    1		).numpy()
          __A  :					List[str]						     =       (img * 255).round().astype("uint8"		)
          return Image.fromarray(img[0]		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					str						     =       self.tokenizer(
              lowerCamelCase ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					str						     =       self.text_encoder(text_input.input_ids.to(self.device		)		)[0]

          __A  :					List[str]						     =       self.tokenizer(
              "" ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					Union[str, Any]						     =       self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

          return torch.cat([uncond_embedding, text_embedding]		)


     def __call__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = 0.3 ,    lowerCamelCase = 0.6 ,    lowerCamelCase = 0.5 ,    lowerCamelCase = 42 ,    lowerCamelCase = 50 ,    lowerCamelCase = 7.5 ,    ):


          '''simple docstring'''





          __A  :					Optional[int]						     =       steps - int(kmin * steps		)
          __A  :					Union[str, Any]						     =       steps - int(kmax * steps		)

          __A  :					Any						     =       self.prep_text(lowerCamelCase		)

          self.scheduler.set_timesteps(lowerCamelCase		)

          __A     ,__A  :					List[str]						     =       img.size
          __A  :					str						     =       self.encode(lowerCamelCase		)

          torch.manual_seed(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8) ,    ).to(self.device		)

          __A  :					Dict						     =       self.scheduler.add_noise(
              lowerCamelCase ,    lowerCamelCase ,    timesteps=self.scheduler.timesteps[tmax] ,    )

          __A  :					str						     =       torch.cat([latents] * 2		)

          __A  :					Optional[int]						     =       self.scheduler.scale_model_input(lowerCamelCase ,    self.scheduler.timesteps[tmax]		)

          with torch.no_grad():
               __A  :					Union[str, Any]						     =       self.unet(
                   lowerCamelCase ,    self.scheduler.timesteps[tmax] ,    encoder_hidden_states=lowerCamelCase ,    ).sample

          __A     ,__A  :					int						     =       pred.chunk(2		)
          __A  :					int						     =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

          __A  :					Tuple						     =       self.scheduler.step(lowerCamelCase ,    self.scheduler.timesteps[tmax] ,    lowerCamelCase		).prev_sample

          for i, t in enumerate(tqdm(self.scheduler.timesteps		)		):
               if i > tmax:
                    if i < tmin:  # layout generation phase
                         __A  :					List[Any]						     =       self.scheduler.add_noise(
                             lowerCamelCase ,    lowerCamelCase ,    timesteps=lowerCamelCase ,    )

                         __A  :					Tuple						     =       (mix_factor * latents) + (
                             1 - mix_factor
                         ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                         __A  :					Any						     =       torch.cat([input] * 2		)

                    else:  # content generation phase
                         __A  :					Any						     =       torch.cat([latents] * 2		)

                    __A  :					Dict						     =       self.scheduler.scale_model_input(lowerCamelCase ,    lowerCamelCase		)

                    with torch.no_grad():
                         __A  :					Dict						     =       self.unet(
                             lowerCamelCase ,    lowerCamelCase ,    encoder_hidden_states=lowerCamelCase ,    ).sample

                    __A     ,__A  :					str						     =       pred.chunk(2		)
                    __A  :					Optional[Any]						     =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

                    __A  :					Any						     =       self.scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		).prev_sample

          return self.decode(lowerCamelCase		)

