


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCAmelCase__       				=							'pcuenq/k-upscaler'
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						*, __snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 _lowercase											={
     # 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     UpperCAmelCase_ (							__snake_case					,						*, __snake_case					,						__snake_case						)					->      Any:







 """simple docstring"""


 _lowercase				,   _lowercase				,   _lowercase											=checkpoint[F"{attention_prefix}.qkv_proj.weight"].chunk(3					,						dim=0						)
 _lowercase				,   _lowercase				,   _lowercase											=checkpoint[F"{attention_prefix}.qkv_proj.bias"].chunk(3					,						dim=0						)
 _lowercase											={
     # 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     UpperCAmelCase_ (							__snake_case					,						*, __snake_case					,						__snake_case					,						__snake_case						)					->      Optional[Any]:







 """simple docstring"""


 _lowercase				,   _lowercase											=checkpoint[F"{attention_prefix}.kv_proj.weight"].chunk(2					,						dim=0						)
 _lowercase				,   _lowercase											=checkpoint[F"{attention_prefix}.kv_proj.bias"].chunk(2					,						dim=0						)

 _lowercase											={
     # 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     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      Union[str, Any]:







 """simple docstring"""


 _lowercase											='''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
 _lowercase											=F"{block_prefix}.{block_idx}"

 _lowercase											={}

 if not hasattr(lowerCamelCase_					,						'''attentions'''						):
  _lowercase											=1  # resnet only
 elif not block.attentions[0].add_self_attention:
  _lowercase											=2  # resnet -> cross-attention
 else:
  _lowercase											=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}"
  _lowercase											=F"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
  _lowercase											=n * resnet_idx if block_type == '''up''' else n * resnet_idx + 1
  _lowercase											=F"{block_prefix}.{idx}" if block_type == '''up''' else F"{block_prefix}.{idx}"

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

 if hasattr(lowerCamelCase_					,						'''attentions'''						):
  for attention_idx, attention in enumerate(block.attentions						):
   _lowercase											=F"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
   _lowercase											=n * attention_idx + 1 if block_type == '''up''' else n * attention_idx + 2
   _lowercase											=F"{block_prefix}.{idx}"
   _lowercase											=F"{block_prefix}.{idx }"
   _lowercase											=1 if not attention.add_self_attention else 2
   _lowercase											=(
       n * attention_idx + cross_attention_index
       if block_type == '''up'''
       else n * attention_idx + cross_attention_index + 1
   )
   _lowercase											=F"{block_prefix}.{idx }"

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

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

 return diffusers_checkpoint
def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 _lowercase											={}

 # 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(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						'''down'''						)						)

 # up_blocks
 for up_block_idx, up_block in enumerate(model.up_blocks						):
  diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						'''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     UpperCAmelCase_ (							__snake_case						)					->      Any:







 """simple docstring"""


 _lowercase											=original_config['''input_channels'''] + original_config['''unet_cond_dim''']
 _lowercase											=original_config['''input_channels'''] + (1 if original_config['''has_variance'''] else 0)

 _lowercase											=original_config['''channels''']

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

 _lowercase											=original_config['''mapping_cond_dim''']
 _lowercase											=original_config['''cross_cond_dim''']

 _lowercase											=[]
 _lowercase											=[]
 for s, c in zip(original_config['''self_attn_depths''']					,						original_config['''cross_attn_depths''']						):
  if s:
   _lowercase											='''self'''
   _lowercase											='''cross''' if c else None
  elif c:
   _lowercase											='''cross'''
   _lowercase											=None
  else:
   _lowercase											=None
   _lowercase											=None
  attna_types.append(lowerCamelCase_						)
  attna_types.append(lowerCamelCase_						)

 _lowercase											=UNetaDConditionModel(
     in_channels=lowerCamelCase_					,						out_channels=lowerCamelCase_					,						down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''')					,						mid_block_type=lowerCamelCase_					,						up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')					,						block_out_channels=lowerCamelCase_					,						layers_per_block=lowerCamelCase_					,						act_fn='''gelu'''					,						norm_num_groups=lowerCamelCase_					,						cross_attention_dim=lowerCamelCase_					,						attention_head_dim=64					,						time_cond_proj_dim=lowerCamelCase_					,						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     UpperCAmelCase_ (							__snake_case						)					->      Optional[int]:







 """simple docstring"""


 _lowercase											=torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''						)

 _lowercase											=huggingface_hub.hf_hub_download(lowerCamelCase_					,						'''config_laion_text_cond_latent_upscaler_2.json'''						)
 _lowercase											=huggingface_hub.hf_hub_download(
     lowerCamelCase_					,						'''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'''						)
 _lowercase											=K.config.load_config(open(lowerCamelCase_						)						)['''model''']
 _lowercase											=unet_model_from_original_config(lowerCamelCase_						)

 _lowercase											=torch.load(lowerCamelCase_					,						map_location=lowerCamelCase_						)['''model_ema''']
 _lowercase											=unet_to_diffusers_checkpoint(lowerCamelCase_					,						lowerCamelCase_						)

 model.load_state_dict(lowerCamelCase_					,						strict=lowerCamelCase_						)
 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)





def     UpperCAmelCase_ (							__snake_case						)					->      str:







 """simple docstring"""


 return " ".join(sorted(set(sentence.split()						)						)						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

