






"""simple docstring"""




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


A__     :      Dict             =			'pcuenq/k-upscaler'






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       *, lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      {
				    # 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      (						lowerCamelCase_		,       *, lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__			,						lowercase__			,						lowercase__       			=      checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3		,       dim=0    )
				lowercase__			,						lowercase__			,						lowercase__       			=      checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3		,       dim=0    )
				lowercase__       			=      {
				    # 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      (						lowerCamelCase_		,       *, lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__			,						lowercase__       			=      checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2		,       dim=0    )
				lowercase__			,						lowercase__       			=      checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2		,       dim=0    )

				lowercase__       			=      {
				    # 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      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      '''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
				lowercase__       			=      F"""{block_prefix}.{block_idx}"""

				lowercase__       			=      {}

				if not hasattr(lowerCamelCase_		,       '''attentions'''    ):
								lowercase__       			=      1  # resnet only
				elif not block.attentions[0].add_self_attention:
								lowercase__       			=      2  # resnet -> cross-attention
				else:
								lowercase__       			=      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}"
								lowercase__       			=      F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
								lowercase__       			=      n * resnet_idx if block_type == '''up''' else n * resnet_idx + 1
								lowercase__       			=      F"""{block_prefix}.{idx}""" if block_type == '''up''' else F"""{block_prefix}.{idx}"""

								diffusers_checkpoint.update(
								    resnet_to_diffusers_checkpoint(
								        lowerCamelCase_		,       lowerCamelCase_		,       diffusers_resnet_prefix=lowerCamelCase_		,       resnet_prefix=lowerCamelCase_    )    )

				if hasattr(lowerCamelCase_		,       '''attentions'''    ):
								for attention_idx, attention in enumerate(block.attentions    ):
												lowercase__       			=      F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
												lowercase__       			=      n * attention_idx + 1 if block_type == '''up''' else n * attention_idx + 2
												lowercase__       			=      F"""{block_prefix}.{idx}"""
												lowercase__       			=      F"""{block_prefix}.{idx }"""
												lowercase__       			=      1 if not attention.add_self_attention else 2
												lowercase__       			=      (
												    n * attention_idx + cross_attention_index
												    if block_type == '''up'''
												    else n * attention_idx + cross_attention_index + 1
												)
												lowercase__       			=      F"""{block_prefix}.{idx }"""

												diffusers_checkpoint.update(
												    cross_attn_to_diffusers_checkpoint(
												        lowerCamelCase_		,       diffusers_attention_prefix=lowerCamelCase_		,       diffusers_attention_index=2		,       attention_prefix=lowerCamelCase_		,       )    )

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

				return diffusers_checkpoint






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      {}

				# 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(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       '''down'''    )    )

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






				'''simple docstring'''
				lowercase__       			=      original_config['''input_channels'''] + original_config['''unet_cond_dim''']
				lowercase__       			=      original_config['''input_channels'''] + (1 if original_config['''has_variance'''] else 0)

				lowercase__       			=      original_config['''channels''']

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

				lowercase__       			=      original_config['''mapping_cond_dim''']
				lowercase__       			=      original_config['''cross_cond_dim''']

				lowercase__       			=      []
				lowercase__       			=      []
				for s, c in zip(original_config['''self_attn_depths''']		,       original_config['''cross_attn_depths''']    ):
								if s:
												lowercase__       			=      '''self'''
												lowercase__       			=      '''cross''' if c else None
								elif c:
												lowercase__       			=      '''cross'''
												lowercase__       			=      None
								else:
												lowercase__       			=      None
												lowercase__       			=      None
								attna_types.append(lowerCamelCase_    )
								attna_types.append(lowerCamelCase_    )

				lowercase__       			=      UNetaDConditionModel(
				    in_channels=lowerCamelCase_		,       out_channels=lowerCamelCase_		,       down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''')		,       mid_block_type=lowerCamelCase_		,       up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')		,       block_out_channels=lowerCamelCase_		,       layers_per_block=lowerCamelCase_		,       act_fn='''gelu'''		,       norm_num_groups=lowerCamelCase_		,       cross_attention_dim=lowerCamelCase_		,       attention_head_dim=64		,       time_cond_proj_dim=lowerCamelCase_		,       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      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''    )

				lowercase__       			=      huggingface_hub.hf_hub_download(lowerCamelCase_		,       '''config_laion_text_cond_latent_upscaler_2.json'''    )
				lowercase__       			=      huggingface_hub.hf_hub_download(
				    lowerCamelCase_		,       '''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'''    )
				lowercase__       			=      K.config.load_config(open(lowerCamelCase_    )    )['''model''']
				lowercase__       			=      unet_model_from_original_config(lowerCamelCase_    )

				lowercase__       			=      torch.load(lowerCamelCase_		,       map_location=lowerCamelCase_    )['''model_ema''']
				lowercase__       			=      unet_to_diffusers_checkpoint(lowerCamelCase_		,       lowerCamelCase_    )

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


if __name__ == "__main__":
							A__     :      List[str]             =			argparse.ArgumentParser()

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

							main(args)












from collections import defaultdict






def   a      (						lowerCamelCase_ = 5    ):






				'''simple docstring'''
				lowercase__       			=      defaultdict(lowerCamelCase_    )
				lowercase__       			=      0

				while True:
								lowercase__       			=      get_digits(lowerCamelCase_    )
								freqs[digits].append(lowerCamelCase_    )

								if len(freqs[digits]    ) == max_base:
												lowercase__       			=      freqs[digits][0] ** 3
												return base

								num += 1






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return "".join(sorted(str(num**3    )    )    )


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




