


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__lowerCamelCase   	=  '''pcuenq/k-upscaler'''



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	*, __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[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 			_a      (    __UpperCamelCase	,	*, __UpperCamelCase	,	__UpperCamelCase			):
     a_      ,      a_      ,      a_     :  str				      =     checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3	,	dim=0			)
     a_      ,      a_      ,      a_     :  Tuple				      =     checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3	,	dim=0			)
     a_     :  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 			_a      (    __UpperCamelCase	,	*, __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_      ,      a_     :  str				      =     checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2	,	dim=0			)
     a_      ,      a_     :  List[Any]				      =     checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2	,	dim=0			)

     a_     :  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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[int]				      =     """inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
     a_     :  int				      =     F'''{block_prefix}.{block_idx}'''

     a_     :  Tuple				      =     {}

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

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

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

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

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

     return diffusers_checkpoint



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Tuple				      =     {}

     # 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(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	"""down"""			)			)

     # up_blocks
     for up_block_idx, up_block in enumerate(model.up_blocks			):
          diffusers_checkpoint.update(block_to_diffusers_checkpoint(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	"""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      (    __UpperCamelCase			):
     a_     :  Union[str, Any]				      =     original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
     a_     :  List[str]				      =     original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

     a_     :  int				      =     original_config["""channels"""]

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

     a_     :  str				      =     original_config["""mapping_cond_dim"""]
     a_     :  str				      =     original_config["""cross_cond_dim"""]

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

     a_     :  Dict				      =     UNetaDConditionModel(
         in_channels=__UpperCamelCase	,	out_channels=__UpperCamelCase	,	down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""")	,	mid_block_type=__UpperCamelCase	,	up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")	,	block_out_channels=__UpperCamelCase	,	layers_per_block=__UpperCamelCase	,	act_fn="""gelu"""	,	norm_num_groups=__UpperCamelCase	,	cross_attention_dim=__UpperCamelCase	,	attention_head_dim=6_4	,	time_cond_proj_dim=__UpperCamelCase	,	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      (    __UpperCamelCase			):
     a_     :  Tuple				      =     torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""			)

     a_     :  int				      =     huggingface_hub.hf_hub_download(__UpperCamelCase	,	"""config_laion_text_cond_latent_upscaler_2.json"""			)
     a_     :  List[Any]				      =     huggingface_hub.hf_hub_download(
         __UpperCamelCase	,	"""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_     :  Dict				      =     K.config.load_config(open(__UpperCamelCase			)			)["""model"""]
     a_     :  int				      =     unet_model_from_original_config(__UpperCamelCase			)

     a_     :  Optional[Any]				      =     torch.load(__UpperCamelCase	,	map_location=__UpperCamelCase			)["""model_ema"""]
     a_     :  str				      =     unet_to_diffusers_checkpoint(__UpperCamelCase	,	__UpperCamelCase			)

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


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

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

       main(args)








"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       str				,						__lowerCamelCase							:       int = 13      ):
			"""simple docstring"""
			lowerCamelCase__						:						List[str]						      =''''''
			for c in s:
						if "A" <= c <= "Z":
									out += chr(ord('''A'''      ) + (ord(__lowerCamelCase      ) - ord('''A'''      ) + n) % 26      )
						elif "a" <= c <= "z":
									out += chr(ord('''a'''      ) + (ord(__lowerCamelCase      ) - ord('''a'''      ) + n) % 26      )
						else:
									out += c
			return out






def 	snake_case__  (						):
			"""simple docstring"""
			lowerCamelCase__						:						List[str]						      =input('''Enter message: '''      )

			lowerCamelCase__						:						str						      =dencrypt(__lowerCamelCase				,						13      )
			print('''Encryption:'''				,						__lowerCamelCase      )

			lowerCamelCase__						:						Any						      =dencrypt(__lowerCamelCase				,						13      )
			print('''Decryption: '''				,						__lowerCamelCase      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




