




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


snake_case_					:					Union[str, Any]       						=			"pcuenq/k-upscaler"


def   A		(__A  :       Union[str, Any]  ,							__A  :       Union[str, Any]  ,							*, __A  :       List[str]  ,							__A  :       str			)  -> Optional[int]:







     """simple docstring"""


     UpperCAmelCase_		=				{
         # 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   A		(__A  :       Any  ,							*, __A  :       Optional[Any]  ,							__A  :       Dict			)  -> List[str]:







     """simple docstring"""


     UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3  ,							dim=0			)
     UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3  ,							dim=0			)
     UpperCAmelCase_		=				{
         # 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   A		(__A  :       Any  ,							*, __A  :       List[Any]  ,							__A  :       List[Any]  ,							__A  :       int			)  -> Union[str, Any]:







     """simple docstring"""


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

     UpperCAmelCase_		=				{
         # 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   A		(__A  :       int  ,							__A  :       List[str]  ,							__A  :       int  ,							__A  :       int			)  -> str:







     """simple docstring"""


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

     UpperCAmelCase_		=				{}

     if not hasattr(SCREAMING_SNAKE_CASE_  ,							'''attentions'''			):
          UpperCAmelCase_		=				1  # resnet only
     elif not block.attentions[0].add_self_attention:
          UpperCAmelCase_		=				2  # resnet -> cross-attention
     else:
          UpperCAmelCase_		=				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}"
          UpperCAmelCase_		=				F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
          UpperCAmelCase_		=				n * resnet_idx if block_type == '''up''' else n * resnet_idx + 1
          UpperCAmelCase_		=				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			):
               UpperCAmelCase_		=				F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
               UpperCAmelCase_		=				n * attention_idx + 1 if block_type == '''up''' else n * attention_idx + 2
               UpperCAmelCase_		=				F"""{block_prefix}.{idx}"""
               UpperCAmelCase_		=				F"""{block_prefix}.{idx }"""
               UpperCAmelCase_		=				1 if not attention.add_self_attention else 2
               UpperCAmelCase_		=				(
                   n * attention_idx + cross_attention_index
                   if block_type == '''up'''
                   else n * attention_idx + cross_attention_index + 1
               )
               UpperCAmelCase_		=				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   A		(__A  :       Dict  ,							__A  :       int			)  -> List[str]:







     """simple docstring"""


     UpperCAmelCase_		=				{}

     # 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   A		(__A  :       int			)  -> Union[str, Any]:







     """simple docstring"""


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

     UpperCAmelCase_		=				original_config['''channels''']

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

     UpperCAmelCase_		=				original_config['''mapping_cond_dim''']
     UpperCAmelCase_		=				original_config['''cross_cond_dim''']

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

     UpperCAmelCase_		=				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   A		(__A  :       Optional[Any]			)  -> Optional[int]:







     """simple docstring"""


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

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

     UpperCAmelCase_		=				torch.load(SCREAMING_SNAKE_CASE_  ,							map_location=SCREAMING_SNAKE_CASE_			)['''model_ema''']
     UpperCAmelCase_		=				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__":
    snake_case_					:					Dict       						=			argparse.ArgumentParser()

    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
    snake_case_					:					Union[str, Any]       						=			parser.parse_args()

    main(args)












import decimal







def       _a (		):
					__lowerCAmelCase							  =			0
					__lowerCAmelCase							  =			decimal.Context(prec=1_05	)
					for i in range(2 , 1_00	):
										__lowerCAmelCase							  =			decimal.Decimal(SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			number.sqrt(SCREAMING_SNAKE_CASE_	)
										if len(str(SCREAMING_SNAKE_CASE_	)	) > 1:
															answer += int(str(SCREAMING_SNAKE_CASE_	)[0]	)
															__lowerCAmelCase							  =			str(SCREAMING_SNAKE_CASE_	)[2:1_01]
															answer += sum(int(SCREAMING_SNAKE_CASE_	) for x in sqrt_number_str	)
					return answer


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(f'''{solution() = }''')




