






"""simple docstring"""
from __future__ import annotations


def        A	(							__snake_case:   list   ,    __snake_case:   list     )						->		list:

       """simple docstring"""





       __magic_name__										=						[None] * (len(__snake_case     ) + len(__snake_case     ))

       __magic_name__										=						0  # pointer to current index for left Half
       __magic_name__										=						0  # pointer to current index for the right Half
       __magic_name__										=						0  # pointer to current index for the sorted array Half

       while pointera < len(__snake_case     ) and pointera < len(__snake_case     ):
              if left_half[pointera] < right_half[pointera]:
                     __magic_name__										=						left_half[pointera]
                     pointera += 1
                     index += 1
              else:
                     __magic_name__										=						right_half[pointera]
                     pointera += 1
                     index += 1
       while pointera < len(__snake_case     ):
              __magic_name__										=						left_half[pointera]
              pointera += 1
              index += 1

       while pointera < len(__snake_case     ):
              __magic_name__										=						right_half[pointera]
              pointera += 1
              index += 1

       return sorted_array


def        A	(							__snake_case:   list     )						->		list:

       """simple docstring"""





       if len(__snake_case     ) <= 1:
              return array
       # the actual formula to calculate the middle element = left + (right - left) // 2
       # this avoids integer overflow in case of large N
       __magic_name__										=						0 + (len(__snake_case     ) - 0) // 2

       # Split the array into halves till the array length becomes equal to One
       # merge the arrays of single length returned by mergeSort function and
       # pass them into the merge arrays function which merges the array
       __magic_name__										=						array[:middle]
       __magic_name__										=						array[middle:]

       return merge(merge_sort(__snake_case     )   ,    merge_sort(__snake_case     )     )


if __name__ == "__main__":
      import doctest

      doctest.testmod()







"""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)