





import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCAmelCase__  :	Dict     ='''pcuenq/k-upscaler'''





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					*, _UpperCAmelCase	,					_UpperCAmelCase )  ->     Tuple:
				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    _lowercase    (  _UpperCAmelCase	,					*, _UpperCAmelCase	,					_UpperCAmelCase )  ->     int:
				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    _lowercase    (  _UpperCAmelCase	,					*, _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Any:
				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    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Union[str, Any]:
				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(_UpperCAmelCase	,					"""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(
								        _UpperCAmelCase	,					_UpperCAmelCase	,					diffusers_resnet_prefix=_UpperCAmelCase	,					resnet_prefix=_UpperCAmelCase ) )

				if hasattr(_UpperCAmelCase	,					"""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(
												        _UpperCAmelCase	,					diffusers_attention_prefix=_UpperCAmelCase	,					diffusers_attention_index=2	,					attention_prefix=_UpperCAmelCase	,					) )

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

				return diffusers_checkpoint





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase )  ->     str:
				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(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					"""down""" ) )

				# up_blocks
				for up_block_idx, up_block in enumerate(model.up_blocks ):
								diffusers_checkpoint.update(block_to_diffusers_checkpoint(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					"""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    _lowercase    (  _UpperCAmelCase )  ->     Tuple:
				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(_UpperCAmelCase )
								attna_types.append(_UpperCAmelCase )

				lowerCamelCase		       =UNetaDConditionModel(
				    in_channels=_UpperCAmelCase	,					out_channels=_UpperCAmelCase	,					down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""")	,					mid_block_type=_UpperCAmelCase	,					up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")	,					block_out_channels=_UpperCAmelCase	,					layers_per_block=_UpperCAmelCase	,					act_fn="""gelu"""	,					norm_num_groups=_UpperCAmelCase	,					cross_attention_dim=_UpperCAmelCase	,					attention_head_dim=64	,					time_cond_proj_dim=_UpperCAmelCase	,					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    _lowercase    (  _UpperCAmelCase )  ->     Union[str, Any]:
				lowerCamelCase		       =torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )

				lowerCamelCase		       =huggingface_hub.hf_hub_download(_UpperCAmelCase	,					"""config_laion_text_cond_latent_upscaler_2.json""" )
				lowerCamelCase		       =huggingface_hub.hf_hub_download(
				    _UpperCAmelCase	,					"""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(_UpperCAmelCase ) )["""model"""]
				lowerCamelCase		       =unet_model_from_original_config(_UpperCAmelCase )

				lowerCamelCase		       =torch.load(_UpperCAmelCase	,					map_location=_UpperCAmelCase )["""model_ema"""]
				lowerCamelCase		       =unet_to_diffusers_checkpoint(_UpperCAmelCase	,					_UpperCAmelCase )

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


if __name__ == "__main__":
							UpperCAmelCase__  :	Tuple     =argparse.ArgumentParser()

							parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
							UpperCAmelCase__  :	Any     =parser.parse_args()

							main(args)












def    _lowercase    (  _UpperCAmelCase = 99_80_01 )  ->     int:

				# fetches the next number
				for number in range(n - 1	,					99_99	,					-1 ):
								lowerCamelCase		       =str(_UpperCAmelCase )

								# checks whether 'str_number' is a palindrome.
								if str_number == str_number[::-1]:
												lowerCamelCase		       =9_99

												# if 'number' is a product of two 3-digit numbers
												# then number is the answer otherwise fetch next number.
												while divisor != 99:
																if (number % divisor == 0) and (len(str(number // divisor ) ) == 3.0):
																				return number
																divisor -= 1
				raise ValueError("""That number is larger than our acceptable range.""" )


if __name__ == "__main__":
							print(F"{solution() = }")





