code: 
  
  import argparse
  
  import huggingface_hub
  import k_diffusion as K
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  _lowerCAmelCase     :			str    ="""pcuenq/k-upscaler"""
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,*, SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Dict		       =   {
  	    # 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  (			SCREAMING_SNAKE_CASE  ,*, SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  Optional[Any]		       =   checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3  ,dim=0			)
  	UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  List[str]		       =   checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3  ,dim=0			)
  	UpperCAmelCase__:  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  (			SCREAMING_SNAKE_CASE  ,*, SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__       ,	UpperCAmelCase__:  Optional[int]		       =   checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2  ,dim=0			)
  	UpperCAmelCase__       ,	UpperCAmelCase__:  Dict		       =   checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2  ,dim=0			)
  
  	UpperCAmelCase__:  List[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  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  List[Any]		       =   "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
  	UpperCAmelCase__:  Optional[Any]		       =   f"{block_prefix}.{block_idx}"
  
  	UpperCAmelCase__:  Dict		       =   {}
  
  	if not hasattr(SCREAMING_SNAKE_CASE  ,"attentions"			):
  		UpperCAmelCase__:  Tuple		       =   1  # resnet only
  	elif not block.attentions[0].add_self_attention:
  		UpperCAmelCase__:  List[str]		       =   2  # resnet -> cross-attention
  	else:
  		UpperCAmelCase__:  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}"
  		UpperCAmelCase__:  Union[str, Any]		       =   f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
  		UpperCAmelCase__:  str		       =   n * resnet_idx if block_type == "up" else n * resnet_idx + 1
  		UpperCAmelCase__:  str		       =   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__:  Union[str, Any]		       =   f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
  			UpperCAmelCase__:  Any		       =   n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
  			UpperCAmelCase__:  List[str]		       =   f"{block_prefix}.{idx}"
  			UpperCAmelCase__:  int		       =   f"{block_prefix}.{idx }"
  			UpperCAmelCase__:  List[str]		       =   1 if not attention.add_self_attention else 2
  			UpperCAmelCase__:  Any		       =   (
  			    n * attention_idx + cross_attention_index
  			    if block_type == "up"
  			    else n * attention_idx + cross_attention_index + 1
  			)
  			UpperCAmelCase__:  Tuple		       =   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  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Dict		       =   {}
  
  	# 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  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Dict		       =   original_config["input_channels"] + original_config["unet_cond_dim"]
  	UpperCAmelCase__:  Union[str, Any]		       =   original_config["input_channels"] + (1 if original_config["has_variance"] else 0)
  
  	UpperCAmelCase__:  int		       =   original_config["channels"]
  
  	assert (
  	    len(set(original_config["depths"]			)			) == 1
  	), "UNet2DConditionModel currently do not support blocks with different number of layers"
  	UpperCAmelCase__:  Optional[int]		       =   original_config["depths"][0]
  
  	UpperCAmelCase__:  Union[str, Any]		       =   original_config["mapping_cond_dim"]
  	UpperCAmelCase__:  Tuple		       =   original_config["cross_cond_dim"]
  
  	UpperCAmelCase__:  Tuple		       =   []
  	UpperCAmelCase__:  Optional[int]		       =   []
  	for s, c in zip(original_config["self_attn_depths"]  ,original_config["cross_attn_depths"]			):
  		if s:
  			UpperCAmelCase__:  int		       =   "self"
  			UpperCAmelCase__:  Optional[int]		       =   "cross" if c else None
  		elif c:
  			UpperCAmelCase__:  Tuple		       =   "cross"
  			UpperCAmelCase__:  Any		       =   None
  		else:
  			UpperCAmelCase__:  Optional[int]		       =   None
  			UpperCAmelCase__:  List[str]		       =   None
  		attna_types.append(SCREAMING_SNAKE_CASE			)
  		attna_types.append(SCREAMING_SNAKE_CASE			)
  
  	UpperCAmelCase__:  str		       =   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=6_4  ,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  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Optional[int]		       =   torch.device("cuda" if torch.cuda.is_available() else "cpu"			)
  
  	UpperCAmelCase__:  Tuple		       =   huggingface_hub.hf_hub_download(SCREAMING_SNAKE_CASE  ,"config_laion_text_cond_latent_upscaler_2.json"			)
  	UpperCAmelCase__:  Optional[int]		       =   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__:  List[str]		       =   K.config.load_config(open(SCREAMING_SNAKE_CASE			)			)["model"]
  	UpperCAmelCase__:  str		       =   unet_model_from_original_config(SCREAMING_SNAKE_CASE			)
  
  	UpperCAmelCase__:  Tuple		       =   torch.load(SCREAMING_SNAKE_CASE  ,map_location=SCREAMING_SNAKE_CASE			)["model_ema"]
  	UpperCAmelCase__:  Union[str, Any]		       =   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__":
  					_lowerCAmelCase     :			Any    =argparse.ArgumentParser()
  
  					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
  					_lowerCAmelCase     :			List[str]    =parser.parse_args()
  
  					main(args)
style_context: 
  
  _lowerCAmelCase     :			Optional[int]    ={
      """+""": lambda x, y: x + y,
      """-""": lambda x, y: x - y,
      """*""": lambda x, y: x * y,
      """/""": lambda x, y: x / y,
  }
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	return c.isdigit()
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  str		       =   []
  
  	# iterate over the string in reverse order
  	for c in expression.split()[::-1]:
  		# push operand to stack
  		if is_operand(SCREAMING_SNAKE_CASE			):
  			stack.append(int(SCREAMING_SNAKE_CASE			)			)
  
  		else:
  			# pop values from stack can calculate the result
  			# push the result onto the stack again
  			UpperCAmelCase__:  Any		       =   stack.pop()
  			UpperCAmelCase__:  int		       =   stack.pop()
  			stack.append(calc[c](SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)			)
  
  	return stack.pop()
  
  
  # Driver code
  if __name__ == "__main__":
  					_lowerCAmelCase     :			Optional[int]    ="""+ 9 * 2 6"""
  					print(evaluate(test_expression))
  
  					_lowerCAmelCase     :			Optional[Any]    ="""/ * 10 2 + 4 1 """
  					print(evaluate(test_expression))