


"""simple docstring"""




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowerCamelCase						 =	'''pcuenq/k-upscaler'''




def      a_				(   SCREAMING_SNAKE_CASE__ :				Dict		,      SCREAMING_SNAKE_CASE__ :				str		,      *, SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				int					):
       '''simple docstring'''





       _lowerCamelCase	:		Tuple          ={
           # 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__ :				Tuple		,      *, SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				Union[str, Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		Tuple          =checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3		,      dim=0					)
       _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      a_				(   SCREAMING_SNAKE_CASE__ :				List[str]		,      *, SCREAMING_SNAKE_CASE__ :				Union[str, Any]		,      SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Tuple					):
       '''simple docstring'''





       _lowerCamelCase	:		Dict          =checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2		,      dim=0					)
       _lowerCamelCase	:		str          =checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2		,      dim=0					)

       _lowerCamelCase	:		Optional[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      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Optional[Any]		,      SCREAMING_SNAKE_CASE__ :				Tuple					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          ='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[int]          ={}

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





       _lowerCamelCase	:		Optional[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__ :				int					):
       '''simple docstring'''





       _lowerCamelCase	:		str          =original_config['input_channels'] + original_config['unet_cond_dim']
       _lowerCamelCase	:		Tuple          =original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

       _lowerCamelCase	:		str          =original_config['channels']

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

       _lowerCamelCase	:		str          =original_config['mapping_cond_dim']
       _lowerCamelCase	:		Union[str, Any]          =original_config['cross_cond_dim']

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

       _lowerCamelCase	:		str          =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__ :				str					):
       '''simple docstring'''





       _lowerCamelCase	:		Dict          =torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)

       _lowerCamelCase	:		List[Any]          =huggingface_hub.hf_hub_download(SCREAMING_SNAKE_CASE__		,      'config_laion_text_cond_latent_upscaler_2.json'					)
       _lowerCamelCase	:		Union[str, 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'					)
       _lowerCamelCase	:		Optional[Any]          =K.config.load_config(open(SCREAMING_SNAKE_CASE__					)					)['model']
       _lowerCamelCase	:		Tuple          =unet_model_from_original_config(SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Dict          =torch.load(SCREAMING_SNAKE_CASE__		,      map_location=SCREAMING_SNAKE_CASE__					)['model_ema']
       _lowerCamelCase	:		int          =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__":
 lowerCamelCase						 =	argparse.ArgumentParser()

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

 main(args)






from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar




class A  (       UpperCamelCase_					):






      @abstractmethod
      def __lt__( self						:					T      ,       lowercase_						:					T			) ->						bool:
             """simple docstring"""




             pass






      @abstractmethod
      def __gt__( self						:					T      ,       lowercase_						:					T			) ->						bool:
             """simple docstring"""




             pass






      @abstractmethod
      def __eq__( self						:					T      ,       lowercase_						:					object			) ->						bool:
             """simple docstring"""




             pass




lowerCamelCase						 =	TypeVar('T', bound=Comparable)




class A  (       Generic[T]					):






      def __init__( self						:					Optional[Any]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		list[T]          =[]
             _lowerCamelCase	:		int          =0






      def __repr__( self						:					int			) ->						str:
             """simple docstring"""




             return str(self.h			)






      def 	lowerCamelCase	( self						:					str      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




             if child_idx > 0:
                    return (child_idx - 1) // 2
             return None






      def 	lowerCamelCase	( self						:					Union[str, Any]      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




             _lowerCamelCase	:		Dict          =2 * parent_idx + 1
             if left_child_index < self.heap_size:
                    return left_child_index
             return None






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =2 * parent_idx + 2
             if right_child_index < self.heap_size:
                    return right_child_index
             return None






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					int			) ->						None:
             """simple docstring"""




             if index < self.heap_size:
                    _lowerCamelCase	:		int          =index
                    _lowerCamelCase	:		Any          =self.left_child_idx(lowercase_			)
                    _lowerCamelCase	:		int          =self.right_child_idx(lowercase_			)
                    # check which child is larger than its parent
                    if left_child is not None and self.h[left_child] > self.h[violation]:
                           _lowerCamelCase	:		str          =left_child
                    if right_child is not None and self.h[right_child] > self.h[violation]:
                           _lowerCamelCase	:		str          =right_child
                    # if violation indeed exists
                    if violation != index:
                           # swap to fix the violation
                           _lowerCamelCase     ,					_lowerCamelCase	:		List[str]          =self.h[index], self.h[violation]
                           # fix the subsequent violation recursively if any
                           self.max_heapify(lowercase_			)






      def 	lowerCamelCase	( self						:					int      ,       lowercase_						:					Iterable[T]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =list(lowercase_			)
             _lowerCamelCase	:		Dict          =len(self.h			)
             if self.heap_size > 1:
                    # max_heapify from right to left but exclude leaves (last level)
                    for i in range(self.heap_size // 2 - 1      ,       -1      ,       -1			):
                           self.max_heapify(lowercase_			)






      def 	lowerCamelCase	( self						:					Dict			) ->						T:
             """simple docstring"""




             if self.heap_size >= 2:
                    _lowerCamelCase	:		Optional[int]          =self.h[0]
                    _lowerCamelCase	:		Optional[int]          =self.h.pop(-1			)
                    self.heap_size -= 1
                    self.max_heapify(0			)
                    return me
             elif self.heap_size == 1:
                    self.heap_size -= 1
                    return self.h.pop(-1			)
             else:
                    raise Exception('Empty heap'			)






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					T			) ->						None:
             """simple docstring"""




             self.h.append(lowercase_			)
             _lowerCamelCase	:		List[Any]          =(self.heap_size - 1) // 2
             self.heap_size += 1
             while idx >= 0:
                    self.max_heapify(lowercase_			)
                    _lowerCamelCase	:		Tuple          =(idx - 1) // 2






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =self.heap_size
             for j in range(size - 1      ,       0      ,       -1			):
                    _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =self.h[j], self.h[0]
                    self.heap_size -= 1
                    self.max_heapify(0			)
             _lowerCamelCase	:		Union[str, Any]          =size


if __name__ == "__main__":
 import doctest

 # run doc test
 doctest.testmod()

 # demo
 for unsorted in [
     [0],
     [2],
     [3, 5],
     [5, 3],
     [5, 5],
     [0, 0, 0, 0],
     [1, 1, 1, 1],
     [2, 2, 3, 5],
     [0, 2, 2, 3, 5],
     [2, 5, 3, 0, 2, 3, 0, 3],
     [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
     [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
     [-45, -2, -5],
 ]:
  print(F"""unsorted array: {unsorted}""")

  lowerCamelCase						 =	Heap()
  heap.build_max_heap(unsorted)
  print(F"""after build heap: {heap}""")

  print(F"""max value: {heap.extract_max()}""")
  print(F"""after max value removed: {heap}""")

  heap.insert(1_00)
  print(F"""after new value 100 inserted: {heap}""")

  heap.heap_sort()
  print(F"""heap-sorted array: {heap}\n""")


