




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowercase_     :    Optional[Any]      =			'pcuenq/k-upscaler'


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   *, snake_case_     ,   snake_case_						):

						'''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 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   *, snake_case_     ,   snake_case_						):

						'''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 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   *, snake_case_     ,   snake_case_     ,   snake_case_						):

						'''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 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_						):

						'''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(snake_case_     ,   "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(
												        snake_case_     ,   snake_case_     ,   diffusers_resnet_prefix=snake_case_     ,   resnet_prefix=snake_case_						)						)

						if hasattr(snake_case_     ,   "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(
																		        snake_case_     ,   diffusers_attention_prefix=snake_case_     ,   diffusers_attention_index=2     ,   attention_prefix=snake_case_     ,   )						)

																		if attention.add_self_attention is True:
																								diffusers_checkpoint.update(
																								    self_attn_to_diffusers_checkpoint(
																								        snake_case_     ,   diffusers_attention_prefix=snake_case_     ,   attention_prefix=snake_case_     ,   )						)

						return diffusers_checkpoint


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''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(snake_case_     ,   snake_case_     ,   snake_case_     ,   "down"						)						)

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks						):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(snake_case_     ,   snake_case_     ,   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 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''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(snake_case_						)
												attna_types.append(snake_case_						)

						_UpperCAmelCase													=					UNetaDConditionModel(
						    in_channels=snake_case_     ,   out_channels=snake_case_     ,   down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")     ,   mid_block_type=snake_case_     ,   up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")     ,   block_out_channels=snake_case_     ,   layers_per_block=snake_case_     ,   act_fn="gelu"     ,   norm_num_groups=snake_case_     ,   cross_attention_dim=snake_case_     ,   attention_head_dim=64     ,   time_cond_proj_dim=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 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


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

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

						_UpperCAmelCase													=					torch.load(snake_case_     ,   map_location=snake_case_						)["model_ema"]
						_UpperCAmelCase													=					unet_to_diffusers_checkpoint(snake_case_     ,   snake_case_						)

						model.load_state_dict(snake_case_     ,   strict=snake_case_						)
						model.save_pretrained(args.dump_path						)
						print(f"""saving converted unet model in {args.dump_path}"""						)


if __name__ == "__main__":
				lowercase_     :    int      =			argparse.ArgumentParser()

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

				main(args)










from __future__ import annotations


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					{}
						for index, val in enumerate(snake_case_						):
												_UpperCAmelCase													=					target - val
												if compl in chk_map:
																		return [chk_map[compl], index]
												_UpperCAmelCase													=					index
						return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(f"""{two_sum([2, 7, 11, 15], 9) = }""")




