






"""simple docstring"""
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


A_			: Optional[Any]				=			"pcuenq/k-upscaler"






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   *, _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			List[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        lowerCamelCase_  (						_lowerCamelCase			,   *, _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__     :			Tuple     				=						checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3			,   dim=0				)
 lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__     :			Dict     				=						checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3			,   dim=0				)
 lowerCamelCase__     :			Any     				=						{
     # 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        lowerCamelCase_  (						_lowerCamelCase			,   *, _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__				,     lowerCamelCase__     :			Any     				=						checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2			,   dim=0				)
 lowerCamelCase__				,     lowerCamelCase__     :			Any     				=						checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2			,   dim=0				)

 lowerCamelCase__     :			List[Any]     				=						{
     # 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        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Any     				=						'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
 lowerCamelCase__     :			int     				=						f'''{block_prefix}.{block_idx}'''

 lowerCamelCase__     :			Optional[Any]     				=						{}

 if not hasattr(_lowerCamelCase			,   'attentions'				):
  lowerCamelCase__     :			Optional[int]     				=						1  # resnet only
 elif not block.attentions[0].add_self_attention:
  lowerCamelCase__     :			str     				=						2  # resnet -> cross-attention
 else:
  lowerCamelCase__     :			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}"
  lowerCamelCase__     :			List[str]     				=						f'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
  lowerCamelCase__     :			int     				=						n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
  lowerCamelCase__     :			Union[str, Any]     				=						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				):
   lowerCamelCase__     :			Union[str, Any]     				=						f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
   lowerCamelCase__     :			Optional[int]     				=						n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
   lowerCamelCase__     :			Optional[int]     				=						f'''{block_prefix}.{idx}'''
   lowerCamelCase__     :			Any     				=						f'''{block_prefix}.{idx }'''
   lowerCamelCase__     :			Dict     				=						1 if not attention.add_self_attention else 2
   lowerCamelCase__     :			Union[str, Any]     				=						(
       n * attention_idx + cross_attention_index
       if block_type == 'up'
       else n * attention_idx + cross_attention_index + 1
   )
   lowerCamelCase__     :			List[str]     				=						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        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Optional[int]     				=						{}

 # 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        lowerCamelCase_  (						_lowerCamelCase				):
 lowerCamelCase__     :			Optional[Any]     				=						original_config['input_channels'] + original_config['unet_cond_dim']
 lowerCamelCase__     :			Optional[int]     				=						original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

 lowerCamelCase__     :			Dict     				=						original_config['channels']

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

 lowerCamelCase__     :			int     				=						original_config['mapping_cond_dim']
 lowerCamelCase__     :			Any     				=						original_config['cross_cond_dim']

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

 lowerCamelCase__     :			List[Any]     				=						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        lowerCamelCase_  (						_lowerCamelCase				):
 lowerCamelCase__     :			Optional[Any]     				=						torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)

 lowerCamelCase__     :			Optional[int]     				=						huggingface_hub.hf_hub_download(_lowerCamelCase			,   'config_laion_text_cond_latent_upscaler_2.json'				)
 lowerCamelCase__     :			Optional[int]     				=						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'				)
 lowerCamelCase__     :			Tuple     				=						K.config.load_config(open(_lowerCamelCase				)				)['model']
 lowerCamelCase__     :			Any     				=						unet_model_from_original_config(_lowerCamelCase				)

 lowerCamelCase__     :			List[str]     				=						torch.load(_lowerCamelCase			,   map_location=_lowerCamelCase				)['model_ema']
 lowerCamelCase__     :			str     				=						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_			: Optional[int]				=			argparse.ArgumentParser()

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

      main(args)









"""simple docstring"""
import os






def        lowerCamelCase_  (						):
 lowerCamelCase__     :			List[Any]     				=						os.path.dirname(os.path.realpath(_lowerCamelCase				)				)
 lowerCamelCase__     :			Any     				=						os.path.join(_lowerCamelCase			,   'triangle.txt'				)

 with open(_lowerCamelCase				) as in_file:
  lowerCamelCase__     :			Dict     				=						[[int(_lowerCamelCase				) for i in line.split()] for line in in_file]

 while len(_lowerCamelCase				) != 1:
  lowerCamelCase__     :			Dict     				=						triangle.pop()
  lowerCamelCase__     :			Optional[Any]     				=						triangle[-1]
  for j in range(len(_lowerCamelCase				) - 1				):
   curr_row[j] += max(last_row[j]			,   last_row[j + 1]				)
 return triangle[0][0]


if __name__ == "__main__":
      print(solution())

