

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__UpperCAmelCase     					=  'pcuenq/k-upscaler'







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*, SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       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 						_snake_case		(     SCREAMING_SNAKE_CASE		,				*, SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   List[Any]:

     """simple docstring"""







     _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase :       Tuple	   =     checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3		,				dim=0					)
     _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase :       Dict	   =     checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3		,				dim=0					)
     _lowerCAmelCase :       List[str]	   =     {
         # 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 						_snake_case		(     SCREAMING_SNAKE_CASE		,				*, SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   List[Any]:

     """simple docstring"""







     _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2		,				dim=0					)
     _lowerCAmelCase   ,      _lowerCAmelCase :       Dict	   =     checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2		,				dim=0					)

     _lowerCAmelCase :       str	   =     {
         # 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 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   str:

     """simple docstring"""







     _lowerCAmelCase :       Optional[int]	   =     "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 :       int	   =     {}

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

     """simple docstring"""







     _lowerCAmelCase :       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(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 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[Any]:

     """simple docstring"""







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

     _lowerCAmelCase :       str	   =     original_config["channels"]

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

     _lowerCAmelCase :       Any	   =     original_config["mapping_cond_dim"]
     _lowerCAmelCase :       Tuple	   =     original_config["cross_cond_dim"]

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

     _lowerCAmelCase :       List[Any]	   =     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 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Dict	   =     torch.device("cuda" if torch.cuda.is_available() else "cpu"					)

     _lowerCAmelCase :       Optional[int]	   =     huggingface_hub.hf_hub_download(SCREAMING_SNAKE_CASE		,				"config_laion_text_cond_latent_upscaler_2.json"					)
     _lowerCAmelCase :       str	   =     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"					)
     _lowerCAmelCase :       str	   =     K.config.load_config(open(SCREAMING_SNAKE_CASE					)					)["model"]
     _lowerCAmelCase :       Any	   =     unet_model_from_original_config(SCREAMING_SNAKE_CASE					)

     _lowerCAmelCase :       Dict	   =     torch.load(SCREAMING_SNAKE_CASE		,				map_location=SCREAMING_SNAKE_CASE					)["model_ema"]
     _lowerCAmelCase :       int	   =     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)









def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[int]:

     """simple docstring"""







     if power < 0:
          return -1
     base %= mod
     _lowerCAmelCase :       Tuple	   =     1

     while power > 0:
          if power & 1:
               _lowerCAmelCase :       Optional[Any]	   =     (result * base) % mod
          _lowerCAmelCase :       int	   =     power >> 1
          _lowerCAmelCase :       Optional[int]	   =     (base * base) % mod

     return result







def 						_snake_case		(     )							->   Optional[int]:

     """simple docstring"""







     print(modular_exponential(3		,				200		,				13					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  main()






