code: 
  
  import argparse
  
  import huggingface_hub
  import k_diffusion as K
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  lowerCamelCase    =      '''pcuenq/k-upscaler'''
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Tuple			,   _A   :				Union[str, Any]			,   *, _A   :				Any			,   _A   :				Tuple							):
  
  
  
  
  
    '''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     UpperCAmelCase__       (    _A   :				str			,   *, _A   :				int			,   _A   :				Tuple							):
  
  
  
  
  
    '''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     UpperCAmelCase__       (    _A   :				int			,   *, _A   :				Any			,   _A   :				Optional[int]			,   _A   :				Any							):
  
  
  
  
  
    '''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     UpperCAmelCase__       (    _A   :				Any			,   _A   :				Any			,   _A   :				Optional[Any]			,   _A   :				List[Any]							):
  
  
  
  
  
    '''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(_A			,   '''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(
              _A			,   _A			,   diffusers_resnet_prefix=_A			,   resnet_prefix=_A							)							)
  
    if hasattr(_A			,   '''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(
                _A			,   diffusers_attention_prefix=_A			,   diffusers_attention_index=2			,   attention_prefix=_A			,   )							)
  
        if attention.add_self_attention is True:
          diffusers_checkpoint.update(
              self_attn_to_diffusers_checkpoint(
                  _A			,   diffusers_attention_prefix=_A			,   attention_prefix=_A			,   )							)
  
    return diffusers_checkpoint
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				str			,   _A   :				List[str]							):
  
  
  
  
  
    '''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(_A			,   _A			,   _A			,   '''down'''							)							)
  
    # up_blocks
    for up_block_idx, up_block in enumerate(model.up_blocks							):
      diffusers_checkpoint.update(block_to_diffusers_checkpoint(_A			,   _A			,   _A			,   '''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     UpperCAmelCase__       (    _A   :				List[Any]							):
  
  
  
  
  
    '''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(_A							)
      attna_types.append(_A							)
  
    a__						=UNetaDConditionModel(
        in_channels=_A			,   out_channels=_A			,   down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''')			,   mid_block_type=_A			,   up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')			,   block_out_channels=_A			,   layers_per_block=_A			,   act_fn='''gelu'''			,   norm_num_groups=_A			,   cross_attention_dim=_A			,   attention_head_dim=64			,   time_cond_proj_dim=_A			,   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     UpperCAmelCase__       (    _A   :				List[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''							)
  
    a__						=huggingface_hub.hf_hub_download(_A			,   '''config_laion_text_cond_latent_upscaler_2.json'''							)
    a__						=huggingface_hub.hf_hub_download(
        _A			,   '''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(_A							)							)["""model"""]
    a__						=unet_model_from_original_config(_A							)
  
    a__						=torch.load(_A			,   map_location=_A							)["""model_ema"""]
    a__						=unet_to_diffusers_checkpoint(_A			,   _A							)
  
    model.load_state_dict(_A			,   strict=_A							)
    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)
  
style_context: 
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
         from .tokenization_xglm import XGLMTokenizer
  else:
         lowerCamelCase     =      None
  
  
  lowerCamelCase     =      logging.get_logger(__name__)
  
  lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  lowerCamelCase     =      {
      '''vocab_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
      },
      '''tokenizer_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
      },
  }
  
  lowerCamelCase     =      {
      '''facebook/xglm-564M''': 2048,
  }
  
  
  
  
  
  
  
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
     lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
     lowerCamelCase__				:			Any												=     XGLMTokenizer
  
  
  
     def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=7
       a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
  
       a__						=kwargs.get('''additional_special_tokens''',					[] )
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]
  
       super().__init__(
           lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)
  
       a__						=vocab_file
       a__						=False if not self.vocab_file else True
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
       a__						=[self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=[self.sep_token_id]
  
       if token_ids_a is None:
         return len(sep + token_ids_a ) * [0]
       return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.''' )
  
       if not os.path.isdir(lowercase_ ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
         return
       a__						=os.path.join(
           lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
  
       if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
         copyfile(self.vocab_file,					lowercase_ )
  
       return (out_vocab_file,)
  