

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)



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,)
