import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


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




def 				_snake_case     (       lowerCAmelCase							:       Any       ,			lowerCAmelCase							:       int       ,			*, lowerCAmelCase							:       Optional[Any]       ,			lowerCAmelCase							:       Optional[Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[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 				_snake_case     (       lowerCAmelCase							:       List[str]       ,			*, lowerCAmelCase							:       Union[str, Any]       ,			lowerCAmelCase							:       Optional[int]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		str    				=   checkpoint[f'{attention_prefix}.qkv_proj.weight'].chunk(3       ,			dim=0     )
		SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		List[Any]    				=   checkpoint[f'{attention_prefix}.qkv_proj.bias'].chunk(3       ,			dim=0     )
		SCREAMING_SNAKE_CASE_  :		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 				_snake_case     (       lowerCAmelCase							:       Tuple       ,			*, lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       Union[str, Any]       ,			lowerCAmelCase							:       Any     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		List[Any]    				=   checkpoint[f'{attention_prefix}.kv_proj.weight'].chunk(2       ,			dim=0     )
		SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		List[Any]    				=   checkpoint[f'{attention_prefix}.kv_proj.bias'].chunk(2       ,			dim=0     )

		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   {
		    # 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 				_snake_case     (       lowerCAmelCase							:       Any       ,			lowerCAmelCase							:       Union[str, Any]       ,			lowerCAmelCase							:       Tuple       ,			lowerCAmelCase							:       List[str]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple    				=   "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   f'{block_prefix}.{block_idx}'

		SCREAMING_SNAKE_CASE_  :		Any    				=   {}

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







		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   {}

		# 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 				_snake_case     (       lowerCAmelCase							:       Optional[Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   original_config["input_channels"] + original_config["unet_cond_dim"]
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

		SCREAMING_SNAKE_CASE_  :		Tuple    				=   original_config["channels"]

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

		SCREAMING_SNAKE_CASE_  :		Tuple    				=   original_config["mapping_cond_dim"]
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   original_config["cross_cond_dim"]

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

		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   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=6_4       ,			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 				_snake_case     (       lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int    				=   torch.device("cuda" if torch.cuda.is_available() else "cpu"     )

		SCREAMING_SNAKE_CASE_  :		str    				=   huggingface_hub.hf_hub_download(lowerCAmelCase       ,			"config_laion_text_cond_latent_upscaler_2.json"     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   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"     )
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   K.config.load_config(open(lowerCAmelCase     )     )["model"]
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   unet_model_from_original_config(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   torch.load(lowerCAmelCase       ,			map_location=lowerCAmelCase     )["model_ema"]
		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   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 :     Optional[int]          =  argparse.ArgumentParser()

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

		main(args)

from __future__ import annotations






class 		a__						:

					def __init__(   self				:	Union[str, Any],_A				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   value
							SCREAMING_SNAKE_CASE_  :		Node | None    				=   None
							SCREAMING_SNAKE_CASE_  :		Node | None    				=   None







class 		a__						:
					A     	=							42

					def __init__(   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   0

					def     __UpperCamelCase       (   self				:	List[Any],_A				:	Node | None,_A				:	int   ):







							"""simple docstring"""






							if node is None:
									return

							if path_sum == self.target:
									self.paths += 1

							if node.left:
									self.depth_first_search(node.left,path_sum + node.left.value   )
							if node.right:
									self.depth_first_search(node.right,path_sum + node.right.value   )






					def     __UpperCamelCase       (   self				:	List[str],_A				:	Node | None,_A				:	int | None = None   ):







							"""simple docstring"""






							if node is None:
									return 0
							if target is not None:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   target

							self.depth_first_search(_A,node.value   )
							self.path_sum(node.left   )
							self.path_sum(node.right   )

							return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()
