




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCamelCase__            =     """pcuenq/k-upscaler"""







def       _a (		SCREAMING_SNAKE_CASE_				:							Union[str, Any] , SCREAMING_SNAKE_CASE_				:							Union[str, Any] , *, SCREAMING_SNAKE_CASE_				:							List[str] , SCREAMING_SNAKE_CASE_				:							str	):
					__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       _a (		SCREAMING_SNAKE_CASE_				:							Any , *, SCREAMING_SNAKE_CASE_				:							Optional[Any] , SCREAMING_SNAKE_CASE_				:							Dict	):
					__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       _a (		SCREAMING_SNAKE_CASE_				:							Any , *, SCREAMING_SNAKE_CASE_				:							List[Any] , SCREAMING_SNAKE_CASE_				:							List[Any] , SCREAMING_SNAKE_CASE_				:							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       _a (		SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							List[str] , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int	):
					__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(SCREAMING_SNAKE_CASE_ , "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(
										        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	):
															__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(
															        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_				:							Dict , SCREAMING_SNAKE_CASE_				:							int	):
					__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(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_				:							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(SCREAMING_SNAKE_CASE_	)
										attna_types.append(SCREAMING_SNAKE_CASE_	)

					__lowerCAmelCase							  =			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=64 , 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_				:							Optional[Any]	):
					__lowerCAmelCase							  =			torch.device("cuda" if torch.cuda.is_available() else "cpu"	)

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

					__lowerCAmelCase							  =			torch.load(SCREAMING_SNAKE_CASE_ , map_location=SCREAMING_SNAKE_CASE_	)["model_ema"]
					__lowerCAmelCase							  =			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__":
					UpperCamelCase__            =     argparse.ArgumentParser()

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

					main(args)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__            =     {"""configuration_vit_hybrid""": ["""VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTHybridConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     [
					    """VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ViTHybridForImageClassification""",
					    """ViTHybridModel""",
					    """ViTHybridPreTrainedModel""",
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""ViTHybridImageProcessor"""]


if TYPE_CHECKING:
					from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_hybrid import (
										    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTHybridForImageClassification,
										    ViTHybridModel,
										    ViTHybridPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
					import sys

					UpperCamelCase__            =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




