

"""simple docstring"""






import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowerCamelCase__				:  Any													=  '''pcuenq/k-upscaler'''
def       UpperCamelCase					(			_lowerCAmelCase     :						Any,  _lowerCAmelCase     :						Dict,  *, _lowerCAmelCase     :						str,  _lowerCAmelCase     :						Tuple )				->			Optional[int]:
 _UpperCAmelCase    :							Tuple									=					{
     # 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					(			_lowerCAmelCase     :						str,  *, _lowerCAmelCase     :						List[str],  _lowerCAmelCase     :						Dict )				->			Any:
 _UpperCAmelCase    :							List[Any]									=					checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3,  dim=0 )
 _UpperCAmelCase    :							Any									=					checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3,  dim=0 )
 _UpperCAmelCase    :							List[Any]									=					{
     # 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					(			_lowerCAmelCase     :						List[Any],  *, _lowerCAmelCase     :						Optional[Any],  _lowerCAmelCase     :						str,  _lowerCAmelCase     :						int )				->			List[Any]:
 _UpperCAmelCase    :							Dict									=					checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2,  dim=0 )
 _UpperCAmelCase    :							Any									=					checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2,  dim=0 )

 _UpperCAmelCase    :							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       UpperCamelCase					(			_lowerCAmelCase     :						List[str],  _lowerCAmelCase     :						Dict,  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						Any )				->			Tuple:
 _UpperCAmelCase    :							List[str]									=					'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
 _UpperCAmelCase    :							Union[str, Any]									=					f'''{block_prefix}.{block_idx}'''

 _UpperCAmelCase    :							Dict									=					{}

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

 # 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					(			_lowerCAmelCase     :						Union[str, Any] )				->			Union[str, Any]:
 _UpperCAmelCase    :							List[str]									=					original_config['input_channels'] + original_config['unet_cond_dim']
 _UpperCAmelCase    :							Dict									=					original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

 _UpperCAmelCase    :							Optional[int]									=					original_config['channels']

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

 _UpperCAmelCase    :							str									=					original_config['mapping_cond_dim']
 _UpperCAmelCase    :							Tuple									=					original_config['cross_cond_dim']

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

 _UpperCAmelCase    :							Dict									=					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					(			_lowerCAmelCase     :						List[str] )				->			Optional[Any]:
 _UpperCAmelCase    :							str									=					torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )

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

 _UpperCAmelCase    :							str									=					torch.load(_lowerCAmelCase,  map_location=_lowerCAmelCase )['model_ema']
 _UpperCAmelCase    :							Dict									=					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__":
      lowerCamelCase__				:  Any													=  argparse.ArgumentParser()

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

      main(args)








"""simple docstring"""






import safs






class     _UpperCAmelCase			(		safs.SaFileSystem):
			pass





