

'''simple docstring'''





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__:  Optional[Any]		       =   checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3  ,dim=0			)
	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__:  Optional[int]		       =   checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2  ,dim=0			)
	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)


import numpy as np



def  _A  (			SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  List[Any]		       =   np.exp(SCREAMING_SNAKE_CASE			)

	# Add up the all the exponentials
	UpperCAmelCase__:  str		       =   np.sum(SCREAMING_SNAKE_CASE			)

	# Divide every exponent by the sum of all exponents
	UpperCAmelCase__:  Tuple		       =   exponent_vector / sum_of_exponents

	return softmax_vector


if __name__ == "__main__":
					print(softmax((0,)))