



import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCAmelCase_          =				"pcuenq/k-upscaler"
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Optional[Any]  ,					_SCREAMING_SNAKE_CASE :						List[Any]  ,					*, _SCREAMING_SNAKE_CASE :						int  ,					_SCREAMING_SNAKE_CASE :						Dict					)->str:
      _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 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						str  ,					*, _SCREAMING_SNAKE_CASE :						Optional[Any]  ,					_SCREAMING_SNAKE_CASE :						Union[str, Any]					)->Union[str, Any]:
      _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 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						int  ,					*, _SCREAMING_SNAKE_CASE :						List[Any]  ,					_SCREAMING_SNAKE_CASE :						int  ,					_SCREAMING_SNAKE_CASE :						int					)->List[str]:
      _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 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Tuple  ,					_SCREAMING_SNAKE_CASE :						Union[str, Any]  ,					_SCREAMING_SNAKE_CASE :						Dict  ,					_SCREAMING_SNAKE_CASE :						Tuple					)->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(_SCREAMING_SNAKE_CASE  ,					'''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(
                    _SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					diffusers_resnet_prefix=_SCREAMING_SNAKE_CASE  ,					resnet_prefix=_SCREAMING_SNAKE_CASE					)					)

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

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

      return diffusers_checkpoint
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Optional[Any]  ,					_SCREAMING_SNAKE_CASE :						Optional[int]					)->Union[str, Any]:
      _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(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					'''down'''					)					)

      # up_blocks
      for up_block_idx, up_block in enumerate(model.up_blocks					):
            diffusers_checkpoint.update(block_to_diffusers_checkpoint(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					'''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__		(					_SCREAMING_SNAKE_CASE :						List[str]					)->str:
      _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(_SCREAMING_SNAKE_CASE					)
            attna_types.append(_SCREAMING_SNAKE_CASE					)

      _lowerCAmelCase			      =	UNetaDConditionModel(
          in_channels=_SCREAMING_SNAKE_CASE  ,					out_channels=_SCREAMING_SNAKE_CASE  ,					down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''')  ,					mid_block_type=_SCREAMING_SNAKE_CASE  ,					up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')  ,					block_out_channels=_SCREAMING_SNAKE_CASE  ,					layers_per_block=_SCREAMING_SNAKE_CASE  ,					act_fn='''gelu'''  ,					norm_num_groups=_SCREAMING_SNAKE_CASE  ,					cross_attention_dim=_SCREAMING_SNAKE_CASE  ,					attention_head_dim=6_4  ,					time_cond_proj_dim=_SCREAMING_SNAKE_CASE  ,					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__		(					_SCREAMING_SNAKE_CASE :						Optional[int]					)->Dict:
      _lowerCAmelCase			      =	torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''					)

      _lowerCAmelCase			      =	huggingface_hub.hf_hub_download(_SCREAMING_SNAKE_CASE  ,					'''config_laion_text_cond_latent_upscaler_2.json'''					)
      _lowerCAmelCase			      =	huggingface_hub.hf_hub_download(
          _SCREAMING_SNAKE_CASE  ,					'''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(_SCREAMING_SNAKE_CASE					)					)['''model''']
      _lowerCAmelCase			      =	unet_model_from_original_config(_SCREAMING_SNAKE_CASE					)

      _lowerCAmelCase			      =	torch.load(_SCREAMING_SNAKE_CASE  ,					map_location=_SCREAMING_SNAKE_CASE					)['''model_ema''']
      _lowerCAmelCase			      =	unet_to_diffusers_checkpoint(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					)

      model.load_state_dict(_SCREAMING_SNAKE_CASE  ,					strict=_SCREAMING_SNAKE_CASE					)
      model.save_pretrained(args.dump_path					)
      print(f'''saving converted unet model in {args.dump_path}'''					)


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

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

    main(args)




def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						float  ,					_SCREAMING_SNAKE_CASE :						float					)->float:
      _lowerCAmelCase			      =	float()
      try:
            _lowerCAmelCase			      =	mass * acceleration
      except Exception:
            return -0.0
      return force


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # demo
    UpperCAmelCase_          =				12.5
    UpperCAmelCase_          =				1_0
    UpperCAmelCase_          =				newtons_second_law_of_motion(mass, acceleration)
    print("The force is ", force, "N")