"""simple docstring"""
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowercase__   			=      'pcuenq/k-upscaler'







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	*, _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Dict:
  a__:	Union[str, Any]     				=      {
      # 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  (      _SCREAMING_SNAKE_CASE						,	*, _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__				,       a__				,       a__:	Union[str, Any]     				=      checkpoint[F'{attention_prefix}.qkv_proj.weight'].chunk(3						,	dim=0  )
  a__				,       a__				,       a__:	Tuple     				=      checkpoint[F'{attention_prefix}.qkv_proj.bias'].chunk(3						,	dim=0  )
  a__:	Optional[int]     				=      {
      # 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  (      _SCREAMING_SNAKE_CASE						,	*, _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->int:
  a__				,       a__:	Tuple     				=      checkpoint[F'{attention_prefix}.kv_proj.weight'].chunk(2						,	dim=0  )
  a__				,       a__:	Optional[Any]     				=      checkpoint[F'{attention_prefix}.kv_proj.bias'].chunk(2						,	dim=0  )

  a__:	int     				=      {
      # 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  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Any:
  a__:	Optional[Any]     				=      'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
  a__:	str     				=      F'{block_prefix}.{block_idx}'

  a__:	Dict     				=      {}

  if not hasattr(SCREAMING_SNAKE_CASE__						,	'attentions'  ):
    a__:	str     				=      1  # resnet only
  elif not block.attentions[0].add_self_attention:
    a__:	Optional[Any]     				=      2  # resnet -> cross-attention
  else:
    a__:	Optional[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__:	List[str]     				=      F'{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'
    a__:	List[Any]     				=      n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
    a__:	Union[str, Any]     				=      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  ):
      a__:	str     				=      F'{block_type}_blocks.{block_idx}.attentions.{attention_idx}'
      a__:	Union[str, Any]     				=      n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
      a__:	str     				=      F'{block_prefix}.{idx}'
      a__:	Tuple     				=      F'{block_prefix}.{idx }'
      a__:	Dict     				=      1 if not attention.add_self_attention else 2
      a__:	Dict     				=      (
          n * attention_idx + cross_attention_index
          if block_type == 'up'
          else n * attention_idx + cross_attention_index + 1
      )
      a__:	Union[str, Any]     				=      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 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[Any]:
  a__:	Any     				=      {}

  # 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 		__a  (      _SCREAMING_SNAKE_CASE  )      ->Tuple:
  a__:	Union[str, Any]     				=      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__:	List[str]     				=      original_config['channels']

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

  a__:	Optional[Any]     				=      original_config['mapping_cond_dim']
  a__:	Tuple     				=      original_config['cross_cond_dim']

  a__:	Optional[int]     				=      []
  a__:	int     				=      []
  for s, c in zip(original_config['self_attn_depths']						,	original_config['cross_attn_depths']  ):
    if s:
      a__:	Dict     				=      'self'
      a__:	Union[str, Any]     				=      'cross' if c else None
    elif c:
      a__:	Optional[Any]     				=      'cross'
      a__:	List[Any]     				=      None
    else:
      a__:	int     				=      None
      a__:	Optional[int]     				=      None
    attna_types.append(SCREAMING_SNAKE_CASE__  )
    attna_types.append(SCREAMING_SNAKE_CASE__  )

  a__:	Optional[int]     				=      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=64						,	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 		__a  (      _SCREAMING_SNAKE_CASE  )      ->Optional[int]:
  a__:	str     				=      torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )

  a__:	List[Any]     				=      huggingface_hub.hf_hub_download(SCREAMING_SNAKE_CASE__						,	'config_laion_text_cond_latent_upscaler_2.json'  )
  a__:	Any     				=      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'  )
  a__:	Any     				=      K.config.load_config(open(SCREAMING_SNAKE_CASE__  )  )['model']
  a__:	Any     				=      unet_model_from_original_config(SCREAMING_SNAKE_CASE__  )

  a__:	Union[str, Any]     				=      torch.load(SCREAMING_SNAKE_CASE__						,	map_location=SCREAMING_SNAKE_CASE__  )['model_ema']
  a__:	Any     				=      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__":
 lowercase__   			=      argparse.ArgumentParser()

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

 main(args)






_A									=		{
    0: 'underwear',
    1: 'pants',
    2: 'belt',
    3: 'suit',
    4: 'shoe',
    5: 'socks',
    6: 'shirt',
    7: 'tie',
    8: 'watch',
}

_A									=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_A									=		[0 for x in range(len(graph))]
_A									=		[]

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     while stack:
          __UpperCamelCase       				=stack.pop()
          print(SCREAMING_SNAKE_CASE__     ,	clothes[current_clothing]							)
          order += 1

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     stack.append(SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[str]							):
     for v in range(len(SCREAMING_SNAKE_CASE__							)							):
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)





