code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  import huggingface_hub
  import k_diffusion as K
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  lowerCamelCase_     :     Any    		=						'''pcuenq/k-upscaler'''
  def _A		(		lowercase ,					lowercase ,					*, lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					={
          # 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		(		lowercase ,					*, lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3 ,					dim=0	)
      a					=checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3 ,					dim=0	)
      a					={
          # 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		(		lowercase ,					*, lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2 ,					dim=0	)
      a					=checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2 ,					dim=0	)
  
      a					={
          # 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		(		lowercase ,					lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					='inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
      a					=f'''{block_prefix}.{block_idx}'''
  
      a					={}
  
      if not hasattr(lowercase ,					'''attentions'''	):
          a					=1  # resnet only
      elif not block.attentions[0].add_self_attention:
          a					=2  # resnet -> cross-attention
      else:
          a					=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					=f'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
          a					=n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
          a					=f'''{block_prefix}.{idx}''' if block_type == 'up' else f'''{block_prefix}.{idx}'''
  
          diffusers_checkpoint.update(
              resnet_to_diffusers_checkpoint(
                  lowercase ,					lowercase ,					diffusers_resnet_prefix=lowercase ,					resnet_prefix=lowercase	)	)
  
      if hasattr(lowercase ,					'''attentions'''	):
          for attention_idx, attention in enumerate(block.attentions	):
              a					=f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
              a					=n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
              a					=f'''{block_prefix}.{idx}'''
              a					=f'''{block_prefix}.{idx }'''
              a					=1 if not attention.add_self_attention else 2
              a					=(
                  n * attention_idx + cross_attention_index
                  if block_type == 'up'
                  else n * attention_idx + cross_attention_index + 1
              )
              a					=f'''{block_prefix}.{idx }'''
  
              diffusers_checkpoint.update(
                  cross_attn_to_diffusers_checkpoint(
                      lowercase ,					diffusers_attention_prefix=lowercase ,					diffusers_attention_index=2 ,					attention_prefix=lowercase ,					)	)
  
              if attention.add_self_attention is True:
                  diffusers_checkpoint.update(
                      self_attn_to_diffusers_checkpoint(
                          lowercase ,					diffusers_attention_prefix=lowercase ,					attention_prefix=lowercase ,					)	)
  
      return diffusers_checkpoint
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					={}
  
      # 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(lowercase ,					lowercase ,					lowercase ,					'''down'''	)	)
  
      # up_blocks
      for up_block_idx, up_block in enumerate(model.up_blocks	):
          diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowercase ,					lowercase ,					lowercase ,					'''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		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=original_config['input_channels'] + original_config['unet_cond_dim']
      a					=original_config['input_channels'] + (1 if original_config['has_variance'] else 0)
  
      a					=original_config['channels']
  
      assert (
          len(set(original_config['''depths''']	)	) == 1
      ), "UNet2DConditionModel currently do not support blocks with different number of layers"
      a					=original_config['depths'][0]
  
      a					=original_config['mapping_cond_dim']
      a					=original_config['cross_cond_dim']
  
      a					=[]
      a					=[]
      for s, c in zip(original_config['''self_attn_depths'''] ,					original_config['''cross_attn_depths''']	):
          if s:
              a					='self'
              a					='cross' if c else None
          elif c:
              a					='cross'
              a					=None
          else:
              a					=None
              a					=None
          attna_types.append(lowercase	)
          attna_types.append(lowercase	)
  
      a					=UNetaDConditionModel(
          in_channels=lowercase ,					out_channels=lowercase ,					down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''') ,					mid_block_type=lowercase ,					up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''') ,					block_out_channels=lowercase ,					layers_per_block=lowercase ,					act_fn='''gelu''' ,					norm_num_groups=lowercase ,					cross_attention_dim=lowercase ,					attention_head_dim=64 ,					time_cond_proj_dim=lowercase ,					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		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''	)
  
      a					=huggingface_hub.hf_hub_download(lowercase ,					'''config_laion_text_cond_latent_upscaler_2.json'''	)
      a					=huggingface_hub.hf_hub_download(
          lowercase ,					'''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					=K.config.load_config(open(lowercase	)	)['model']
      a					=unet_model_from_original_config(lowercase	)
  
      a					=torch.load(lowercase ,					map_location=lowercase	)['model_ema']
      a					=unet_to_diffusers_checkpoint(lowercase ,					lowercase	)
  
      model.load_state_dict(lowercase ,					strict=lowercase	)
      model.save_pretrained(args.dump_path	)
      print(f'''saving converted unet model in {args.dump_path}'''	)
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Any    		=						argparse.ArgumentParser()
  
         parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
         lowerCamelCase_     :     List[Any]    		=						parser.parse_args()
  
         main(args)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  lowerCamelCase_     :     List[str]    		=						logging.getLogger(__name__)
  
  
  
  
  
  
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self		)      ->  Dict:
       a					=False
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A		)      ->  Tuple:
       if not self.initialized:
           a					=RagRetriever(
               __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					index=__A				,					init_retrieval=__A				,					)
           a					=True
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       self.retriever.index.init_index()
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
       # delete the old self.retriever object before assigning the new index
       del self.retriever
       a					=False
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  str:
       a	,   a					=self.retriever._main_retrieve(__A				,					__A		)
       a					=self.retriever.index.get_doc_dicts(__A		)
       return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A=None		)      ->  int:
       if index is not None and index.is_initialized() and len(__A		) > 0:
           raise ValueError(
               '''When using Ray for distributed fine-tuning, '''
               '''you\'ll need to provide the paths instead, '''
               '''as the dataset and the index are loaded '''
               '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''		)
  
       super().__init__(
           __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					index=__A				,					init_retrieval=__A				,					)
  
       a					=retrieval_workers
       a					=question_encoder_tokenizer
       a					=generator_tokenizer
       if len(self.retrieval_workers		) > 0:
           ray.get(
               [
                   worker.create_rag_retriever.remote(__A				,					__A				,					__A				,					__A		)
                   for worker in self.retrieval_workers
               ]		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       logger.info('''initializing retrieval'''		)
  
       if len(self.retrieval_workers		) > 0:
           ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]		)
       else:
           # Non-distributed training. Load index into this same process.
           self.index.init_index()
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  Dict:
       if len(self.retrieval_workers		) > 0:
           # Select a random retrieval actor.
           a					=self.retrieval_workers[random.randint(0				,					len(self.retrieval_workers		) - 1		)]
           a	,   a	,   a					=ray.get(
               random_worker.retrieve.remote(__A				,					__A		)		)
       else:
           a	,   a					=self._main_retrieve(__A				,					__A		)
           a					=self.index.get_doc_dicts(__A		)
       return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=None				,					**__A		)      ->  Tuple:
       return super(__A				,					cls		).get_tokenizers(__A				,					__A				,					**__A		)
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					__A=None				,					**__A		)      ->  Optional[Any]:
       a					=kwargs.pop('''config'''				,					__A		) or RagConfig.from_pretrained(__A				,					**__A		)
       a					=RagTokenizer.from_pretrained(__A				,					config=__A		)
       a					=rag_tokenizer.question_encoder
       a					=rag_tokenizer.generator
  
       if indexed_dataset is not None:
           a					='''custom'''
           a					=CustomHFIndex(config.retrieval_vector_size				,					__A		)
       else:
           a					=cls._build_index(__A		)
  
       return cls(
           __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					retrieval_workers=__A				,					index=__A				,					)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       logger.info('''re-loading the new dataset with embeddings'''		)
       # access from the training loop
  
       ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]		)
  
       # build the index object again
       a					=self._build_index(self.config		)
  
       ray.get(
           [
               worker.create_rag_retriever.remote(
                   self.config				,					self.question_encoder_tokenizer				,					self.generator_tokenizer				,					__A		)
               for worker in self.retrieval_workers
           ]		)