





import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCAmelCase_       : Optional[Any]							=    """pcuenq/k-upscaler"""






def 				_lowerCAmelCase			(	_a :       Union[str, Any]       , _a :       int       , *, _a :       Dict       , _a :       int      )						->      Optional[Any]:
     lowerCAmelCase_       :							Optional[int]			 =		{
         # 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 				_lowerCAmelCase			(	_a :       Optional[int]       , *, _a :       Dict       , _a :       Any      )						->      List[Any]:
     lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_       :							Dict			 =		checkpoint[F'{attention_prefix}.qkv_proj.weight'].chunk(3       , dim=0      )
     lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_       :							List[str]			 =		checkpoint[F'{attention_prefix}.qkv_proj.bias'].chunk(3       , dim=0      )
     lowerCAmelCase_       :							int			 =		{
         # 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 				_lowerCAmelCase			(	_a :       Optional[int]       , *, _a :       Optional[int]       , _a :       Optional[Any]       , _a :       List[str]      )						->      int:
     lowerCAmelCase_ ,	lowerCAmelCase_       :							List[str]			 =		checkpoint[F'{attention_prefix}.kv_proj.weight'].chunk(2       , dim=0      )
     lowerCAmelCase_ ,	lowerCAmelCase_       :							List[Any]			 =		checkpoint[F'{attention_prefix}.kv_proj.bias'].chunk(2       , dim=0      )

     lowerCAmelCase_       :							Optional[int]			 =		{
         # 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 				_lowerCAmelCase			(	_a :       Dict       , _a :       Any       , _a :       str       , _a :       int      )						->      Any:
     lowerCAmelCase_       :							Union[str, Any]			 =		"""inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
     lowerCAmelCase_       :							Tuple			 =		F'{block_prefix}.{block_idx}'

     lowerCAmelCase_       :							List[Any]			 =		{}

     if not hasattr(_a       , """attentions"""      ):
          lowerCAmelCase_       :							Union[str, Any]			 =		1  # resnet only
     elif not block.attentions[0].add_self_attention:
          lowerCAmelCase_       :							Optional[int]			 =		2  # resnet -> cross-attention
     else:
          lowerCAmelCase_       :							Dict			 =		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}"
          lowerCAmelCase_       :							Dict			 =		F'{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'
          lowerCAmelCase_       :							Tuple			 =		n * resnet_idx if block_type == """up""" else n * resnet_idx + 1
          lowerCAmelCase_       :							str			 =		F'{block_prefix}.{idx}' if block_type == """up""" else F'{block_prefix}.{idx}'

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

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

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

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

     return diffusers_checkpoint






def 				_lowerCAmelCase			(	_a :       List[str]       , _a :       int      )						->      Tuple:
     lowerCAmelCase_       :							Optional[Any]			 =		{}

     # 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(_a       , _a       , _a       , """down"""      )      )

     # up_blocks
     for up_block_idx, up_block in enumerate(model.up_blocks      ):
          diffusers_checkpoint.update(block_to_diffusers_checkpoint(_a       , _a       , _a       , """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 				_lowerCAmelCase			(	_a :       str      )						->      Optional[Any]:
     lowerCAmelCase_       :							Optional[Any]			 =		original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
     lowerCAmelCase_       :							List[str]			 =		original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

     lowerCAmelCase_       :							List[str]			 =		original_config["""channels"""]

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

     lowerCAmelCase_       :							Union[str, Any]			 =		original_config["""mapping_cond_dim"""]
     lowerCAmelCase_       :							Dict			 =		original_config["""cross_cond_dim"""]

     lowerCAmelCase_       :							Any			 =		[]
     lowerCAmelCase_       :							Tuple			 =		[]
     for s, c in zip(original_config["""self_attn_depths"""]       , original_config["""cross_attn_depths"""]      ):
          if s:
               lowerCAmelCase_       :							Optional[int]			 =		"""self"""
               lowerCAmelCase_       :							Optional[int]			 =		"""cross""" if c else None
          elif c:
               lowerCAmelCase_       :							Optional[int]			 =		"""cross"""
               lowerCAmelCase_       :							List[Any]			 =		None
          else:
               lowerCAmelCase_       :							Any			 =		None
               lowerCAmelCase_       :							List[Any]			 =		None
          attna_types.append(_a      )
          attna_types.append(_a      )

     lowerCAmelCase_       :							str			 =		UNetaDConditionModel(
         in_channels=_a       , out_channels=_a       , down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""")       , mid_block_type=_a       , up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")       , block_out_channels=_a       , layers_per_block=_a       , act_fn="""gelu"""       , norm_num_groups=_a       , cross_attention_dim=_a       , attention_head_dim=64       , time_cond_proj_dim=_a       , 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 				_lowerCAmelCase			(	_a :       Any      )						->      Union[str, Any]:
     lowerCAmelCase_       :							Any			 =		torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )

     lowerCAmelCase_       :							Any			 =		huggingface_hub.hf_hub_download(_a       , """config_laion_text_cond_latent_upscaler_2.json"""      )
     lowerCAmelCase_       :							Tuple			 =		huggingface_hub.hf_hub_download(
         _a       , """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"""      )
     lowerCAmelCase_       :							Dict			 =		K.config.load_config(open(_a      )      )["""model"""]
     lowerCAmelCase_       :							Dict			 =		unet_model_from_original_config(_a      )

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

     model.load_state_dict(_a       , strict=_a      )
     model.save_pretrained(args.dump_path      )
     print(F'saving converted unet model in {args.dump_path}'      )


if __name__ == "__main__":
      UpperCAmelCase_       : List[str]							=    argparse.ArgumentParser()

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

      main(args)







def 				_lowerCAmelCase			(	_a :       str      )						->      int:
     lowerCAmelCase_       :							Dict			 =		0
     for plain_chr in plain_text:
          lowerCAmelCase_       :							List[Any]			 =		(
              ord(_a      ) + (hash_value << 6) + (hash_value << 16) - hash_value
          )
     return hash_value
