"""simple docstring"""


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a							='pcuenq/k-upscaler'





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   *, __lowerCAmelCase       ,   __lowerCAmelCase    )							-> str:




						'''simple docstring'''



						lowerCamelCase__ ={
						    # 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_			( __lowerCAmelCase       ,   *, __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Dict:




						'''simple docstring'''



						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ =checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3       ,   dim=0    )
						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ =checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3       ,   dim=0    )
						lowerCamelCase__ ={
						    # 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_			( __lowerCAmelCase       ,   *, __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> str:




						'''simple docstring'''



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

						lowerCamelCase__ ={
						    # 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_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> int:




						'''simple docstring'''



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

						lowerCamelCase__ ={}

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




						'''simple docstring'''



						lowerCamelCase__ ={}

						# 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       lowerCamelCase_			( __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



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

						lowerCamelCase__ =original_config["channels"]

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

						lowerCamelCase__ =original_config["mapping_cond_dim"]
						lowerCamelCase__ =original_config["cross_cond_dim"]

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

						lowerCamelCase__ =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       lowerCamelCase_			( __lowerCAmelCase    )							-> Optional[Any]:




						'''simple docstring'''



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

						lowerCamelCase__ =huggingface_hub.hf_hub_download(__lowerCAmelCase       ,   "config_laion_text_cond_latent_upscaler_2.json"    )
						lowerCamelCase__ =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"    )
						lowerCamelCase__ =K.config.load_config(open(__lowerCAmelCase    )    )["model"]
						lowerCamelCase__ =unet_model_from_original_config(__lowerCAmelCase    )

						lowerCamelCase__ =torch.load(__lowerCAmelCase       ,   map_location=__lowerCAmelCase    )["model_ema"]
						lowerCamelCase__ =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)



"""simple docstring"""


def       lowerCamelCase_			( __lowerCAmelCase    )							-> bool:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												raise ValueError("Input series is not valid, valid series - [2, 4, 8]"    )
						if len(__lowerCAmelCase    ) == 0:
												raise ValueError("Input list must be a non empty list"    )
						if len(__lowerCAmelCase    ) == 1:
												return True
						try:
												lowerCamelCase__ =series[1] / series[0]
												for index in range(len(__lowerCAmelCase    ) - 1    ):
																		if series[index + 1] / series[index] != common_ratio:
																								return False
						except ZeroDivisionError:
												return False
						return True





def       lowerCamelCase_			( __lowerCAmelCase    )							-> float:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												raise ValueError("Input series is not valid, valid series - [2, 4, 8]"    )
						if len(__lowerCAmelCase    ) == 0:
												raise ValueError("Input list must be a non empty list"    )
						lowerCamelCase__ =1
						for value in series:
												answer *= value
						return pow(__lowerCAmelCase       ,   1 / len(__lowerCAmelCase    )    )


if __name__ == "__main__":
	import doctest

	doctest.testmod()


