




'''simple docstring'''





import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a		:  str   			=			"""pcuenq/k-upscaler"""





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  *, UpperCAmelCase_      ,  UpperCAmelCase_	):
				_a							:       Dict       					=			{
				    # 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						(					UpperCAmelCase_      ,  *, UpperCAmelCase_      ,  UpperCAmelCase_	):
				_a							:       Optional[Any]       					=			checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3      ,  dim=0	)
				_a							:       List[str]       					=			checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3      ,  dim=0	)
				_a							:       List[str]       					=			{
				    # 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						(					UpperCAmelCase_      ,  *, UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
				_a							:       Optional[int]       					=			checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2      ,  dim=0	)
				_a							:       Dict       					=			checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2      ,  dim=0	)

				_a							:       List[str]       					=			{
				    # 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						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
				_a							:       List[Any]       					=			"inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
				_a							:       Optional[Any]       					=			F'''{block_prefix}.{block_idx}'''

				_a							:       Dict       					=			{}

				if not hasattr(UpperCAmelCase_      ,  """attentions"""	):
								_a							:       Tuple       					=			1  # resnet only
				elif not block.attentions[0].add_self_attention:
								_a							:       List[str]       					=			2  # resnet -> cross-attention
				else:
								_a							:       Union[str, 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							:       Union[str, Any]       					=			F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
								_a							:       str       					=			n * resnet_idx if block_type == "up" else n * resnet_idx + 1
								_a							:       str       					=			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	):
												_a							:       Union[str, Any]       					=			F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
												_a							:       Any       					=			n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
												_a							:       List[str]       					=			F'''{block_prefix}.{idx}'''
												_a							:       int       					=			F'''{block_prefix}.{idx }'''
												_a							:       List[str]       					=			1 if not attention.add_self_attention else 2
												_a							:       Any       					=			(
												    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(
												        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 UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_	):
				_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(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 UpperCAmelCase						(					UpperCAmelCase_	):
				_a							:       Dict       					=			original_config["input_channels"] + original_config["unet_cond_dim"]
				_a							:       Union[str, Any]       					=			original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

				_a							:       int       					=			original_config["channels"]

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

				_a							:       Union[str, Any]       					=			original_config["mapping_cond_dim"]
				_a							:       Tuple       					=			original_config["cross_cond_dim"]

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

				_a							:       str       					=			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 UpperCAmelCase						(					UpperCAmelCase_	):
				_a							:       Optional[int]       					=			torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""	)

				_a							:       Tuple       					=			huggingface_hub.hf_hub_download(UpperCAmelCase_      ,  """config_laion_text_cond_latent_upscaler_2.json"""	)
				_a							:       Optional[int]       					=			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"""	)
				_a							:       List[str]       					=			K.config.load_config(open(UpperCAmelCase_	)	)["model"]
				_a							:       str       					=			unet_model_from_original_config(UpperCAmelCase_	)

				_a							:       Tuple       					=			torch.load(UpperCAmelCase_      ,  map_location=UpperCAmelCase_	)["model_ema"]
				_a							:       Union[str, Any]       					=			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__":
		a		:  Any   			=			argparse.ArgumentParser()

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

		main(args)








'''simple docstring'''





def UpperCAmelCase						(					UpperCAmelCase_	):
    _a							:       List[Any]       					=			len(UpperCAmelCase_	)
    for cycle_start in range(0      ,  array_len - 1	):
        _a							:       int       					=			array[cycle_start]

        _a							:       int       					=			cycle_start
        for i in range(cycle_start + 1      ,  UpperCAmelCase_	):
            if array[i] < item:
                pos += 1

        if pos == cycle_start:
            continue

        while item == array[pos]:
            pos += 1

        _a					,   _a							:       List[str]       					=			item, array[pos]
        while pos != cycle_start:
            _a							:       Any       					=			cycle_start
            for i in range(cycle_start + 1      ,  UpperCAmelCase_	):
                if array[i] < item:
                    pos += 1

            while item == array[pos]:
                pos += 1

            _a					,   _a							:       List[Any]       					=			item, array[pos]

    return array


if __name__ == "__main__":
  assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


