






"""simple docstring"""



import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_snake_case       :      Optional[Any]       =					"pcuenq/k-upscaler"


def 						lowerCamelCase_				(  __A  :		List[Any]			,	__A  :		Optional[int]			,	*, __A  :		int			,	__A  :		Union[str, Any]			):
					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_				(  __A  :		Dict			,	*, __A  :		Union[str, Any]			,	__A  :		Union[str, Any]			):
					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_				(  __A  :		List[str]			,	*, __A  :		Optional[Any]			,	__A  :		List[str]			,	__A  :		Optional[int]			):
					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_				(  __A  :		Tuple			,	__A  :		Union[str, Any]			,	__A  :		List[str]			,	__A  :		List[str]			):
					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(__A			,	'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(
										        __A			,	__A			,	diffusers_resnet_prefix=__A			,	resnet_prefix=__A			)			)

					if hasattr(__A			,	'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(
															        __A			,	diffusers_attention_prefix=__A			,	diffusers_attention_index=2			,	attention_prefix=__A			,	)			)

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

					return diffusers_checkpoint


def 						lowerCamelCase_				(  __A  :		str			,	__A  :		Dict			):
					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(__A			,	__A			,	__A			,	'down'			)			)

					# up_blocks
					for up_block_idx, up_block in enumerate(model.up_blocks			):
										diffusers_checkpoint.update(block_to_diffusers_checkpoint(__A			,	__A			,	__A			,	'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_				(  __A  :		Optional[int]			):
					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(__A			)
										attna_types.append(__A			)

					lowerCamelCase__				=UNetaDConditionModel(
					    in_channels=__A			,	out_channels=__A			,	down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')			,	mid_block_type=__A			,	up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')			,	block_out_channels=__A			,	layers_per_block=__A			,	act_fn='gelu'			,	norm_num_groups=__A			,	cross_attention_dim=__A			,	attention_head_dim=64			,	time_cond_proj_dim=__A			,	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_				(  __A  :		int			):
					lowerCamelCase__				=torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)

					lowerCamelCase__				=huggingface_hub.hf_hub_download(__A			,	'config_laion_text_cond_latent_upscaler_2.json'			)
					lowerCamelCase__				=huggingface_hub.hf_hub_download(
					    __A			,	'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(__A			)			)['model']
					lowerCamelCase__				=unet_model_from_original_config(__A			)

					lowerCamelCase__				=torch.load(__A			,	map_location=__A			)['model_ema']
					lowerCamelCase__				=unet_to_diffusers_checkpoint(__A			,	__A			)

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


if __name__ == "__main__":
						_snake_case       :      Optional[int]       =					argparse.ArgumentParser()

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

						main(args)







"""simple docstring"""



import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_snake_case       :      Optional[Any]       =					"pcuenq/k-upscaler"




def 						lowerCamelCase_				(  __A  :		List[Any]			,	__A  :		Optional[int]			,	*, __A  :		int			,	__A  :		Union[str, Any]			):
					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_				(  __A  :		Dict			,	*, __A  :		Union[str, Any]			,	__A  :		Union[str, Any]			):
					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_				(  __A  :		List[str]			,	*, __A  :		Optional[Any]			,	__A  :		List[str]			,	__A  :		Optional[int]			):
					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_				(  __A  :		Tuple			,	__A  :		Union[str, Any]			,	__A  :		List[str]			,	__A  :		List[str]			):
					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(__A			,	'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(
										        __A			,	__A			,	diffusers_resnet_prefix=__A			,	resnet_prefix=__A			)			)

					if hasattr(__A			,	'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(
															        __A			,	diffusers_attention_prefix=__A			,	diffusers_attention_index=2			,	attention_prefix=__A			,	)			)

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

					return diffusers_checkpoint




def 						lowerCamelCase_				(  __A  :		str			,	__A  :		Dict			):
					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(__A			,	__A			,	__A			,	'down'			)			)

					# up_blocks
					for up_block_idx, up_block in enumerate(model.up_blocks			):
										diffusers_checkpoint.update(block_to_diffusers_checkpoint(__A			,	__A			,	__A			,	'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_				(  __A  :		Optional[int]			):
					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(__A			)
										attna_types.append(__A			)

					lowerCamelCase__				=UNetaDConditionModel(
					    in_channels=__A			,	out_channels=__A			,	down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')			,	mid_block_type=__A			,	up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')			,	block_out_channels=__A			,	layers_per_block=__A			,	act_fn='gelu'			,	norm_num_groups=__A			,	cross_attention_dim=__A			,	attention_head_dim=64			,	time_cond_proj_dim=__A			,	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_				(  __A  :		int			):
					lowerCamelCase__				=torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)

					lowerCamelCase__				=huggingface_hub.hf_hub_download(__A			,	'config_laion_text_cond_latent_upscaler_2.json'			)
					lowerCamelCase__				=huggingface_hub.hf_hub_download(
					    __A			,	'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(__A			)			)['model']
					lowerCamelCase__				=unet_model_from_original_config(__A			)

					lowerCamelCase__				=torch.load(__A			,	map_location=__A			)['model_ema']
					lowerCamelCase__				=unet_to_diffusers_checkpoint(__A			,	__A			)

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


if __name__ == "__main__":
						_snake_case       :      Optional[int]       =					argparse.ArgumentParser()

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

						main(args)