code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import argparse
  
  import huggingface_hub
  import k_diffusion as K
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  a__				: List[Any]									=   '''pcuenq/k-upscaler'''
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							*, UpperCAmelCase_				,							UpperCAmelCase_       )      ->Optional[int]:
  	snake_case__  					=      {
  	    # 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 (  UpperCAmelCase_				,							*, UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__					,     snake_case__					,     snake_case__  					=      checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3				,							dim=0       )
  	snake_case__					,     snake_case__					,     snake_case__  					=      checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3				,							dim=0       )
  	snake_case__  					=      {
  	    # 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 (  UpperCAmelCase_				,							*, UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Tuple:
  	snake_case__					,     snake_case__  					=      checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2				,							dim=0       )
  	snake_case__					,     snake_case__  					=      checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2				,							dim=0       )
  
  	snake_case__  					=      {
  	    # 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 (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->List[str]:
  	snake_case__  					=      'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
  	snake_case__  					=      f'''{block_prefix}.{block_idx}'''
  
  	snake_case__  					=      {}
  
  	if not hasattr(UpperCAmelCase_				,							'attentions'       ):
  		snake_case__  					=      1  # resnet only
  	elif not block.attentions[0].add_self_attention:
  		snake_case__  					=      2  # resnet -> cross-attention
  	else:
  		snake_case__  					=      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}"
  		snake_case__  					=      f'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
  		snake_case__  					=      n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
  		snake_case__  					=      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       ):
  			snake_case__  					=      f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
  			snake_case__  					=      n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
  			snake_case__  					=      f'''{block_prefix}.{idx}'''
  			snake_case__  					=      f'''{block_prefix}.{idx }'''
  			snake_case__  					=      1 if not attention.add_self_attention else 2
  			snake_case__  					=      (
  			    n * attention_idx + cross_attention_index
  			    if block_type == 'up'
  			    else n * attention_idx + cross_attention_index + 1
  			)
  			snake_case__  					=      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 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Any:
  	snake_case__  					=      {}
  
  	# 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 	__lowerCamelCase (  UpperCAmelCase_       )      ->Optional[int]:
  	snake_case__  					=      original_config['input_channels'] + original_config['unet_cond_dim']
  	snake_case__  					=      original_config['input_channels'] + (1 if original_config['has_variance'] else 0)
  
  	snake_case__  					=      original_config['channels']
  
  	assert (
  	    len(set(original_config['depths']       )       ) == 1
  	), "UNet2DConditionModel currently do not support blocks with different number of layers"
  	snake_case__  					=      original_config['depths'][0]
  
  	snake_case__  					=      original_config['mapping_cond_dim']
  	snake_case__  					=      original_config['cross_cond_dim']
  
  	snake_case__  					=      []
  	snake_case__  					=      []
  	for s, c in zip(original_config['self_attn_depths']				,							original_config['cross_attn_depths']       ):
  		if s:
  			snake_case__  					=      'self'
  			snake_case__  					=      'cross' if c else None
  		elif c:
  			snake_case__  					=      'cross'
  			snake_case__  					=      None
  		else:
  			snake_case__  					=      None
  			snake_case__  					=      None
  		attna_types.append(UpperCAmelCase_       )
  		attna_types.append(UpperCAmelCase_       )
  
  	snake_case__  					=      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=64				,							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 	__lowerCamelCase (  UpperCAmelCase_       )      ->Union[str, Any]:
  	snake_case__  					=      torch.device('cuda' if torch.cuda.is_available() else 'cpu'       )
  
  	snake_case__  					=      huggingface_hub.hf_hub_download(UpperCAmelCase_				,							'config_laion_text_cond_latent_upscaler_2.json'       )
  	snake_case__  					=      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'       )
  	snake_case__  					=      K.config.load_config(open(UpperCAmelCase_       )       )['model']
  	snake_case__  					=      unet_model_from_original_config(UpperCAmelCase_       )
  
  	snake_case__  					=      torch.load(UpperCAmelCase_				,							map_location=UpperCAmelCase_       )['model_ema']
  	snake_case__  					=      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__":
  			a__				: List[str]									=   argparse.ArgumentParser()
  
  			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
  			a__				: int									=   parser.parse_args()
  
  			main(args)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from typing import Callable
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->str:
  
  	def wrapper(UpperCAmelCase_       ):
  		snake_case__  					=      function_with_docstring.__doc__
  		return target_function
  
  	return wrapper
  
  
  
  