

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__snake_case							:     int 						=	"""pcuenq/k-upscaler"""


def 			_UpperCAmelCase							(		a__       ,					a__       ,					*, a__       ,					a__):

			'''simple docstring'''



			a_		:		int				    =				{
			    # 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 			_UpperCAmelCase							(		a__       ,					*, a__       ,					a__):

			'''simple docstring'''



			a_    ,			a_    ,			a_		:		List[Any]				    =				checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3       ,					dim=0)
			a_    ,			a_    ,			a_		:		Tuple				    =				checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3       ,					dim=0)
			a_		:		Union[str, Any]				    =				{
			    # 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 			_UpperCAmelCase							(		a__       ,					*, a__       ,					a__       ,					a__):

			'''simple docstring'''



			a_    ,			a_		:		List[str]				    =				checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2       ,					dim=0)
			a_    ,			a_		:		List[str]				    =				checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2       ,					dim=0)

			a_		:		Tuple				    =				{
			    # 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 			_UpperCAmelCase							(		a__       ,					a__       ,					a__       ,					a__):

			'''simple docstring'''



			a_		:		List[Any]				    =				"""inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
			a_		:		Dict				    =				f'''{block_prefix}.{block_idx}'''

			a_		:		str				    =				{}

			if not hasattr(a__       ,					"""attentions"""):
						a_		:		Union[str, Any]				    =				1  # resnet only
			elif not block.attentions[0].add_self_attention:
						a_		:		Optional[int]				    =				2  # resnet -> cross-attention
			else:
						a_		:		Optional[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}"
						a_		:		Optional[Any]				    =				f'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
						a_		:		Tuple				    =				n * resnet_idx if block_type == """up""" else n * resnet_idx + 1
						a_		:		Optional[Any]				    =				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):
									a_		:		str				    =				f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
									a_		:		List[Any]				    =				n * attention_idx + 1 if block_type == """up""" else n * attention_idx + 2
									a_		:		int				    =				f'''{block_prefix}.{idx}'''
									a_		:		Tuple				    =				f'''{block_prefix}.{idx }'''
									a_		:		List[Any]				    =				1 if not attention.add_self_attention else 2
									a_		:		List[str]				    =				(
									    n * attention_idx + cross_attention_index
									    if block_type == """up"""
									    else n * attention_idx + cross_attention_index + 1
									)
									a_		:		Tuple				    =				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 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			a_		:		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(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 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		List[str]				    =				original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
			a_		:		List[Any]				    =				original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

			a_		:		Optional[int]				    =				original_config["""channels"""]

			assert (
			    len(set(original_config["""depths"""])) == 1
			), "UNet2DConditionModel currently do not support blocks with different number of layers"
			a_		:		List[Any]				    =				original_config["""depths"""][0]

			a_		:		int				    =				original_config["""mapping_cond_dim"""]
			a_		:		Any				    =				original_config["""cross_cond_dim"""]

			a_		:		int				    =				[]
			a_		:		Optional[Any]				    =				[]
			for s, c in zip(original_config["""self_attn_depths"""]       ,					original_config["""cross_attn_depths"""]):
						if s:
									a_		:		Tuple				    =				"""self"""
									a_		:		Any				    =				"""cross""" if c else None
						elif c:
									a_		:		Tuple				    =				"""cross"""
									a_		:		Any				    =				None
						else:
									a_		:		Tuple				    =				None
									a_		:		Optional[Any]				    =				None
						attna_types.append(a__)
						attna_types.append(a__)

			a_		:		int				    =				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=6_4       ,					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 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		Tuple				    =				torch.device("""cuda""" if torch.cuda.is_available() else """cpu""")

			a_		:		Union[str, Any]				    =				huggingface_hub.hf_hub_download(a__       ,					"""config_laion_text_cond_latent_upscaler_2.json""")
			a_		:		Dict				    =				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""")
			a_		:		Optional[Any]				    =				K.config.load_config(open(a__))["""model"""]
			a_		:		Union[str, Any]				    =				unet_model_from_original_config(a__)

			a_		:		str				    =				torch.load(a__       ,					map_location=a__)["""model_ema"""]
			a_		:		List[str]				    =				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							:     Any 						=	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)





def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			return 1 if input_a == 0 else 0


def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			assert not_gate(0) == 1
			assert not_gate(1) == 0


if __name__ == "__main__":
	print(not_gate(0))
	print(not_gate(1))


