



import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__A				    =						'''pcuenq/k-upscaler'''



def        __a     (    lowerCAmelCase_   :					List[str]			,lowerCAmelCase_   :					int			,*, lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					Optional[int]				)      -> List[str]:






						'''simple docstring'''



						UpperCAmelCase_=   {
						    # 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     (    lowerCAmelCase_   :					List[str]			,*, lowerCAmelCase_   :					str			,lowerCAmelCase_   :					Dict				)      -> str:






						'''simple docstring'''



						UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3			,dim=0				)
						UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3			,dim=0				)
						UpperCAmelCase_=   {
						    # 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     (    lowerCAmelCase_   :					Union[str, Any]			,*, lowerCAmelCase_   :					List[str]			,lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					List[str]				)      -> Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_,   UpperCAmelCase_=   checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2			,dim=0				)
						UpperCAmelCase_,   UpperCAmelCase_=   checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2			,dim=0				)

						UpperCAmelCase_=   {
						    # 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     (    lowerCAmelCase_   :					Optional[Any]			,lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					Any			,lowerCAmelCase_   :					str				)      -> Dict:






						'''simple docstring'''



						UpperCAmelCase_=   """inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
						UpperCAmelCase_=   F"""{block_prefix}.{block_idx}"""

						UpperCAmelCase_=   {}

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






						'''simple docstring'''



						UpperCAmelCase_=   {}

						# 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        __a     (    lowerCAmelCase_   :					Tuple				)      -> Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=   original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
						UpperCAmelCase_=   original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

						UpperCAmelCase_=   original_config["""channels"""]

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

						UpperCAmelCase_=   original_config["""mapping_cond_dim"""]
						UpperCAmelCase_=   original_config["""cross_cond_dim"""]

						UpperCAmelCase_=   []
						UpperCAmelCase_=   []
						for s, c in zip(original_config["""self_attn_depths"""]			,original_config["""cross_attn_depths"""]				):
												if s:
																		UpperCAmelCase_=   """self"""
																		UpperCAmelCase_=   """cross""" if c else None
												elif c:
																		UpperCAmelCase_=   """cross"""
																		UpperCAmelCase_=   None
												else:
																		UpperCAmelCase_=   None
																		UpperCAmelCase_=   None
												attna_types.append(lowerCAmelCase_				)
												attna_types.append(lowerCAmelCase_				)

						UpperCAmelCase_=   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        __a     (    lowerCAmelCase_   :					Optional[int]				)      -> Dict:






						'''simple docstring'''



						UpperCAmelCase_=   torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""				)

						UpperCAmelCase_=   huggingface_hub.hf_hub_download(lowerCAmelCase_			,"""config_laion_text_cond_latent_upscaler_2.json"""				)
						UpperCAmelCase_=   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_=   K.config.load_config(open(lowerCAmelCase_				)				)["""model"""]
						UpperCAmelCase_=   unet_model_from_original_config(lowerCAmelCase_				)

						UpperCAmelCase_=   torch.load(lowerCAmelCase_			,map_location=lowerCAmelCase_				)["""model_ema"""]
						UpperCAmelCase_=   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__":
	__A				    =						argparse.ArgumentParser()

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

	main(args)









def        __a     (    lowerCAmelCase_   :					list				)      -> bool:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""Input series is not valid, valid series - [1, 2/3, 2]"""				)
						if len(lowerCAmelCase_				) == 0:
												raise ValueError("""Input list must be a non empty list"""				)
						if len(lowerCAmelCase_				) == 1 and series[0] != 0:
												return True
						UpperCAmelCase_=   []
						UpperCAmelCase_=   len(lowerCAmelCase_				)
						for i in range(0			,lowerCAmelCase_				):
												if series[i] == 0:
																		raise ValueError("""Input series cannot have 0 as an element"""				)
												rec_series.append(1 / series[i]				)
						UpperCAmelCase_=   rec_series[1] - rec_series[0]
						for index in range(2			,lowerCAmelCase_				):
												if rec_series[index] - rec_series[index - 1] != common_diff:
																		return False
						return True



def        __a     (    lowerCAmelCase_   :					list				)      -> float:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""				)
						if len(lowerCAmelCase_				) == 0:
												raise ValueError("""Input list must be a non empty list"""				)
						UpperCAmelCase_=   0
						for val in series:
												answer += 1 / val
						return len(lowerCAmelCase_				) / answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()




