




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCAmelCase__						:       Optional[int]							=    'pcuenq/k-upscaler'


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     *, UpperCamelCase					,     UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase: str						     =    {
      # 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 		_lowerCamelCase				(					UpperCamelCase					,     *, UpperCamelCase					,     UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  _lowercase: Any						     =    checkpoint[F'{attention_prefix}.qkv_proj.weight'].chunk(3					,     dim=0							)
  _lowercase: List[Any]						     =    checkpoint[F'{attention_prefix}.qkv_proj.bias'].chunk(3					,     dim=0							)
  _lowercase: Optional[int]						     =    {
      # 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 		_lowerCamelCase				(					UpperCamelCase					,     *, UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: int						     =    checkpoint[F'{attention_prefix}.kv_proj.weight'].chunk(2					,     dim=0							)
  _lowercase: Optional[Any]						     =    checkpoint[F'{attention_prefix}.kv_proj.bias'].chunk(2					,     dim=0							)

  _lowercase: Optional[int]						     =    {
      # 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 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Dict:





  """simple docstring"""





  _lowercase: str						     =    'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
  _lowercase: int						     =    F'{block_prefix}.{block_idx}'

  _lowercase: str						     =    {}

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

    diffusers_checkpoint.update(
        resnet_to_diffusers_checkpoint(
            UpperCamelCase					,     UpperCamelCase					,     diffusers_resnet_prefix=UpperCamelCase					,     resnet_prefix=UpperCamelCase							)							)

  if hasattr(UpperCamelCase					,     'attentions'							):
    for attention_idx, attention in enumerate(block.attentions							):
      _lowercase: Union[str, Any]						     =    F'{block_type}_blocks.{block_idx}.attentions.{attention_idx}'
      _lowercase: Optional[int]						     =    n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
      _lowercase: Union[str, Any]						     =    F'{block_prefix}.{idx}'
      _lowercase: Dict						     =    F'{block_prefix}.{idx }'
      _lowercase: List[str]						     =    1 if not attention.add_self_attention else 2
      _lowercase: int						     =    (
          n * attention_idx + cross_attention_index
          if block_type == 'up'
          else n * attention_idx + cross_attention_index + 1
      )
      _lowercase: Tuple						     =    F'{block_prefix}.{idx }'

      diffusers_checkpoint.update(
          cross_attn_to_diffusers_checkpoint(
              UpperCamelCase					,     diffusers_attention_prefix=UpperCamelCase					,     diffusers_attention_index=2					,     attention_prefix=UpperCamelCase					,     )							)

      if attention.add_self_attention is True:
        diffusers_checkpoint.update(
            self_attn_to_diffusers_checkpoint(
                UpperCamelCase					,     diffusers_attention_prefix=UpperCamelCase					,     attention_prefix=UpperCamelCase					,     )							)

  return diffusers_checkpoint


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: Dict						     =    {}

  # 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(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     'down'							)							)

  # up_blocks
  for up_block_idx, up_block in enumerate(model.up_blocks							):
    diffusers_checkpoint.update(block_to_diffusers_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     '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 		_lowerCamelCase				(					UpperCamelCase							)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: List[Any]						     =    original_config['input_channels'] + original_config['unet_cond_dim']
  _lowercase: Dict						     =    original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

  _lowercase: Optional[int]						     =    original_config['channels']

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

  _lowercase: Optional[int]						     =    original_config['mapping_cond_dim']
  _lowercase: Union[str, Any]						     =    original_config['cross_cond_dim']

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

  _lowercase: Tuple						     =    UNetaDConditionModel(
      in_channels=UpperCamelCase					,     out_channels=UpperCamelCase					,     down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')					,     mid_block_type=UpperCamelCase					,     up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')					,     block_out_channels=UpperCamelCase					,     layers_per_block=UpperCamelCase					,     act_fn='gelu'					,     norm_num_groups=UpperCamelCase					,     cross_attention_dim=UpperCamelCase					,     attention_head_dim=64					,     time_cond_proj_dim=UpperCamelCase					,     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 		_lowerCamelCase				(					UpperCamelCase							)			->		Optional[int]:





  """simple docstring"""





  _lowercase: List[Any]						     =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

  _lowercase: Tuple						     =    huggingface_hub.hf_hub_download(UpperCamelCase					,     'config_laion_text_cond_latent_upscaler_2.json'							)
  _lowercase: Any						     =    huggingface_hub.hf_hub_download(
      UpperCamelCase					,     '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'							)
  _lowercase: List[Any]						     =    K.config.load_config(open(UpperCamelCase							)							)['model']
  _lowercase: Any						     =    unet_model_from_original_config(UpperCamelCase							)

  _lowercase: Optional[Any]						     =    torch.load(UpperCamelCase					,     map_location=UpperCamelCase							)['model_ema']
  _lowercase: Optional[int]						     =    unet_to_diffusers_checkpoint(UpperCamelCase					,     UpperCamelCase							)

  model.load_state_dict(UpperCamelCase					,     strict=UpperCamelCase							)
  model.save_pretrained(args.dump_path							)
  print(F'saving converted unet model in {args.dump_path}'							)


if __name__ == "__main__":
   UpperCAmelCase__						:       str							=    argparse.ArgumentParser()

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

   main(args)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__						:       Union[str, Any]							=    {'configuration_bert_generation': ['BertGenerationConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       str							=    ['BertGenerationTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       Tuple							=    [
       'BertGenerationDecoder',
       'BertGenerationEncoder',
       'BertGenerationPreTrainedModel',
       'load_tf_weights_in_bert_generation',
   ]


if TYPE_CHECKING:
   from .configuration_bert_generation import BertGenerationConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bert_generation import BertGenerationTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bert_generation import (
          BertGenerationDecoder,
          BertGenerationEncoder,
          BertGenerationPreTrainedModel,
          load_tf_weights_in_bert_generation,
      )

else:
   import sys

   UpperCAmelCase__						:       List[Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
