



"""simple docstring"""


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


snake_case_         = """pcuenq/k-upscaler"""

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			*, lowercase_	,			lowercase_		):
						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 				_lowerCAmelCase   (     lowercase_	,			*, lowercase_	,			lowercase_		):
						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 				_lowerCAmelCase   (     lowercase_	,			*, lowercase_	,			lowercase_	,			lowercase_		):
						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 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_	,			lowercase_		):
						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(lowercase_	,			'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(
												        lowercase_	,			lowercase_	,			diffusers_resnet_prefix=lowercase_	,			resnet_prefix=lowercase_		)		)

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

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

						return diffusers_checkpoint

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						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(lowercase_	,			lowercase_	,			lowercase_	,			'down'		)		)

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks		):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowercase_	,			lowercase_	,			lowercase_	,			'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   (     lowercase_		):
						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(lowercase_		)
												attna_types.append(lowercase_		)

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

						UpperCAmelCase    =				huggingface_hub.hf_hub_download(lowercase_	,			'config_laion_text_cond_latent_upscaler_2.json'		)
						UpperCAmelCase    =				huggingface_hub.hf_hub_download(
						    lowercase_	,			'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(lowercase_		)		)['model']
						UpperCAmelCase    =				unet_model_from_original_config(lowercase_		)

						UpperCAmelCase    =				torch.load(lowercase_	,			map_location=lowercase_		)['model_ema']
						UpperCAmelCase    =				unet_to_diffusers_checkpoint(lowercase_	,			lowercase_		)

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


if __name__ == "__main__":
				snake_case_         = argparse.ArgumentParser()

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

				main(args)










"""simple docstring"""


from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

snake_case_         = TypeVar("""T""", bound=bool)







class    A_    (  Generic[T]       ):



						"""simple docstring"""






						def __init__(      self				:str     ,						lowercase_				:T      )       ->    None:
												UpperCAmelCase    =				value
												UpperCAmelCase    =				None
												UpperCAmelCase    =				None






						@property
						def 			UpperCAmelCase__  (      self				:Any      )       ->    T:
												return self._value






						@staticmethod
						def 			UpperCAmelCase__  (      lowercase_				:RandomizedHeapNode[T] | None     ,						lowercase_				:RandomizedHeapNode[T] | None      )       ->    RandomizedHeapNode[T] | None:
												if not roota:
																		return roota

												if not roota:
																		return roota

												if roota.value > roota.value:
																		UpperCAmelCase   ,					UpperCAmelCase    =				roota, roota

												if random.choice([True, False]      ):
																		UpperCAmelCase   ,					UpperCAmelCase    =				roota.right, roota.left

												UpperCAmelCase    =				RandomizedHeapNode.merge(roota.left     ,						lowercase_      )

												return roota







class    A_    (  Generic[T]       ):



						"""simple docstring"""






						def __init__(      self				:Optional[Any]     ,						lowercase_				:Iterable[T] | None = ()      )       ->    None:
												UpperCAmelCase    =				None

												if data:
																		for item in data:
																								self.insert(lowercase_      )






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:T      )       ->    None:
												UpperCAmelCase    =				RandomizedHeapNode.merge(self._root     ,						RandomizedHeapNode(lowercase_      )      )






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    T | None:
												UpperCAmelCase    =				self.top()

												if self._root is None:
																		return None

												UpperCAmelCase    =				RandomizedHeapNode.merge(self._root.left     ,						self._root.right      )

												return result






						def 			UpperCAmelCase__  (      self				:Tuple      )       ->    T:
												if not self._root:
																		raise IndexError('Can\'t get top element for the empty heap.'      )
												return self._root.value






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    None:
												UpperCAmelCase    =				None






						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    list[Any]:
												UpperCAmelCase    =				[]
												while self:
																		result.append(self.pop()      )

												return result






						def __bool__(      self				:Optional[Any]      )       ->    bool:
												return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





