code: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  snake_case   :   str    		=		{
      """configuration_mobilevitv2""": [
          """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """MobileViTV2Config""",
          """MobileViTV2OnnxConfig""",
      ],
  }
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   str    		=		[
       """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MobileViTV2ForImageClassification""",
       """MobileViTV2ForSemanticSegmentation""",
       """MobileViTV2Model""",
       """MobileViTV2PreTrainedModel""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_mobilevitva import (
       MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileViTVaConfig,
       MobileViTVaOnnxConfig,
   )
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )
  
  else:
   import sys
  
   snake_case   :   Tuple    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import argparse
  
  import huggingface_hub
  import k_diffusion as K
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  snake_case   :   Any    		=		"""pcuenq/k-upscaler"""
  
  
  def        A	(							__snake_case:   List[Any]   ,    __snake_case:   Any   ,    *, __snake_case:   Union[str, Any]   ,    __snake_case:   Any     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						{
             # 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	(							__snake_case:   Optional[int]   ,    *, __snake_case:   Dict   ,    __snake_case:   Union[str, Any]     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__							,     __magic_name__							,     __magic_name__										=						checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3   ,    dim=0     )
         __magic_name__							,     __magic_name__							,     __magic_name__										=						checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3   ,    dim=0     )
         __magic_name__										=						{
             # 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	(							__snake_case:   Optional[Any]   ,    *, __snake_case:   List[str]   ,    __snake_case:   Dict   ,    __snake_case:   Union[str, Any]     )						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__							,     __magic_name__										=						checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2   ,    dim=0     )
         __magic_name__							,     __magic_name__										=						checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2   ,    dim=0     )
  
         __magic_name__										=						{
             # 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	(							__snake_case:   Optional[int]   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Dict   ,    __snake_case:   Optional[Any]     )						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
         __magic_name__										=						F"""{block_prefix}.{block_idx}"""
  
         __magic_name__										=						{}
  
         if not hasattr(__snake_case   ,    'attentions'     ):
                __magic_name__										=						1  # resnet only
         elif not block.attentions[0].add_self_attention:
                __magic_name__										=						2  # resnet -> cross-attention
         else:
                __magic_name__										=						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}"
                __magic_name__										=						F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
                __magic_name__										=						n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
                __magic_name__										=						F"""{block_prefix}.{idx}""" if block_type == 'up' else F"""{block_prefix}.{idx}"""
  
                diffusers_checkpoint.update(
                    resnet_to_diffusers_checkpoint(
                        __snake_case   ,    __snake_case   ,    diffusers_resnet_prefix=__snake_case   ,    resnet_prefix=__snake_case     )     )
  
         if hasattr(__snake_case   ,    'attentions'     ):
                for attention_idx, attention in enumerate(block.attentions     ):
                       __magic_name__										=						F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
                       __magic_name__										=						n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
                       __magic_name__										=						F"""{block_prefix}.{idx}"""
                       __magic_name__										=						F"""{block_prefix}.{idx }"""
                       __magic_name__										=						1 if not attention.add_self_attention else 2
                       __magic_name__										=						(
                           n * attention_idx + cross_attention_index
                           if block_type == 'up'
                           else n * attention_idx + cross_attention_index + 1
                       )
                       __magic_name__										=						F"""{block_prefix}.{idx }"""
  
                       diffusers_checkpoint.update(
                           cross_attn_to_diffusers_checkpoint(
                               __snake_case   ,    diffusers_attention_prefix=__snake_case   ,    diffusers_attention_index=2   ,    attention_prefix=__snake_case   ,    )     )
  
                       if attention.add_self_attention is True:
                              diffusers_checkpoint.update(
                                  self_attn_to_diffusers_checkpoint(
                                      __snake_case   ,    diffusers_attention_prefix=__snake_case   ,    attention_prefix=__snake_case   ,    )     )
  
         return diffusers_checkpoint
  
  
  def        A	(							__snake_case:   Optional[int]   ,    __snake_case:   Optional[Any]     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						{}
  
         # 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(__snake_case   ,    __snake_case   ,    __snake_case   ,    'down'     )     )
  
         # up_blocks
         for up_block_idx, up_block in enumerate(model.up_blocks     ):
                diffusers_checkpoint.update(block_to_diffusers_checkpoint(__snake_case   ,    __snake_case   ,    __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        A	(							__snake_case:   List[Any]     )						->		str:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						original_config['input_channels'] + original_config['unet_cond_dim']
         __magic_name__										=						original_config['input_channels'] + (1 if original_config['has_variance'] else 0)
  
         __magic_name__										=						original_config['channels']
  
         assert (
             len(set(original_config['depths']     )     ) == 1
         ), "UNet2DConditionModel currently do not support blocks with different number of layers"
         __magic_name__										=						original_config['depths'][0]
  
         __magic_name__										=						original_config['mapping_cond_dim']
         __magic_name__										=						original_config['cross_cond_dim']
  
         __magic_name__										=						[]
         __magic_name__										=						[]
         for s, c in zip(original_config['self_attn_depths']   ,    original_config['cross_attn_depths']     ):
                if s:
                       __magic_name__										=						'self'
                       __magic_name__										=						'cross' if c else None
                elif c:
                       __magic_name__										=						'cross'
                       __magic_name__										=						None
                else:
                       __magic_name__										=						None
                       __magic_name__										=						None
                attna_types.append(__snake_case     )
                attna_types.append(__snake_case     )
  
         __magic_name__										=						UNetaDConditionModel(
             in_channels=__snake_case   ,    out_channels=__snake_case   ,    down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')   ,    mid_block_type=__snake_case   ,    up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')   ,    block_out_channels=__snake_case   ,    layers_per_block=__snake_case   ,    act_fn='gelu'   ,    norm_num_groups=__snake_case   ,    cross_attention_dim=__snake_case   ,    attention_head_dim=6_4   ,    time_cond_proj_dim=__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        A	(							__snake_case:   List[Any]     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						torch.device('cuda' if torch.cuda.is_available() else 'cpu'     )
  
         __magic_name__										=						huggingface_hub.hf_hub_download(__snake_case   ,    'config_laion_text_cond_latent_upscaler_2.json'     )
         __magic_name__										=						huggingface_hub.hf_hub_download(
             __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'     )
         __magic_name__										=						K.config.load_config(open(__snake_case     )     )['model']
         __magic_name__										=						unet_model_from_original_config(__snake_case     )
  
         __magic_name__										=						torch.load(__snake_case   ,    map_location=__snake_case     )['model_ema']
         __magic_name__										=						unet_to_diffusers_checkpoint(__snake_case   ,    __snake_case     )
  
         model.load_state_dict(__snake_case   ,    strict=__snake_case     )
         model.save_pretrained(args.dump_path     )
         print(F"""saving converted unet model in {args.dump_path}"""     )
  
  
  if __name__ == "__main__":
   snake_case   :   Optional[Any]    		=		argparse.ArgumentParser()
  
   parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
   snake_case   :   str    		=		parser.parse_args()
  
   main(args)