

'''simple docstring'''






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCAmelCase__   		=      [
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

lowerCAmelCase__   		=      [
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

lowerCAmelCase__   		=      []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  lowerCAmelCase__   		=      f'down_blocks.{i}.resnets.{j}.'
  lowerCAmelCase__   		=      f'input_blocks.{3*i + j + 1}.0.'
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   lowerCAmelCase__   		=      f'down_blocks.{i}.attentions.{j}.'
   lowerCAmelCase__   		=      f'input_blocks.{3*i + j + 1}.1.'
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  lowerCAmelCase__   		=      f'up_blocks.{i}.resnets.{j}.'
  lowerCAmelCase__   		=      f'output_blocks.{3*i + j}.0.'
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   lowerCAmelCase__   		=      f'up_blocks.{i}.attentions.{j}.'
   lowerCAmelCase__   		=      f'output_blocks.{3*i + j}.1.'
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  lowerCAmelCase__   		=      f'down_blocks.{i}.downsamplers.0.conv.'
  lowerCAmelCase__   		=      f'input_blocks.{3*(i+1)}.0.op.'
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  lowerCAmelCase__   		=      f'up_blocks.{i}.upsamplers.0.'
  lowerCAmelCase__   		=      f'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCAmelCase__   		=      '''mid_block.attentions.0.'''
lowerCAmelCase__   		=      '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 lowerCAmelCase__   		=      f'mid_block.resnets.{j}.'
 lowerCAmelCase__   		=      f'middle_block.{2*j}.'
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def 						_A	(       A__ ):






 """simple docstring"""



 __lowercase				    =					{k: k for k in unet_state_dict.keys()}
 for sd_name, hf_name in unet_conversion_map:
  __lowercase				    =					sd_name
 for k, v in mapping.items():
  if "resnets" in k:
   for sd_part, hf_part in unet_conversion_map_resnet:
    __lowercase				    =					v.replace(A__	,							A__ )
   __lowercase				    =					v
 for k, v in mapping.items():
  for sd_part, hf_part in unet_conversion_map_layer:
   __lowercase				    =					v.replace(A__	,							A__ )
  __lowercase				    =					v
 __lowercase				    =					{v: unet_state_dict[k] for k, v in mapping.items()}
 return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCAmelCase__   		=      [
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  lowerCAmelCase__   		=      f'encoder.down_blocks.{i}.resnets.{j}.'
  lowerCAmelCase__   		=      f'encoder.down.{i}.block.{j}.'
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  lowerCAmelCase__   		=      f'down_blocks.{i}.downsamplers.0.'
  lowerCAmelCase__   		=      f'down.{i}.downsample.'
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  lowerCAmelCase__   		=      f'up_blocks.{i}.upsamplers.0.'
  lowerCAmelCase__   		=      f'up.{3-i}.upsample.'
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  lowerCAmelCase__   		=      f'decoder.up_blocks.{i}.resnets.{j}.'
  lowerCAmelCase__   		=      f'decoder.up.{3-i}.block.{j}.'
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 lowerCAmelCase__   		=      f'mid_block.resnets.{i}.'
 lowerCAmelCase__   		=      f'mid.block_{i+1}.'
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCAmelCase__   		=      [
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]


def 						_A	(       A__ ):






 """simple docstring"""



 return w.reshape(*w.shape	,							1	,							1 )


def 						_A	(       A__ ):






 """simple docstring"""



 __lowercase				    =					{k: k for k in vae_state_dict.keys()}
 for k, v in mapping.items():
  for sd_part, hf_part in vae_conversion_map:
   __lowercase				    =					v.replace(A__	,							A__ )
  __lowercase				    =					v
 for k, v in mapping.items():
  if "attentions" in k:
   for sd_part, hf_part in vae_conversion_map_attn:
    __lowercase				    =					v.replace(A__	,							A__ )
   __lowercase				    =					v
 __lowercase				    =					{v: vae_state_dict[k] for k, v in mapping.items()}
 __lowercase				    =					['''q''', '''k''', '''v''', '''proj_out''']
 for k, v in new_state_dict.items():
  for weight_name in weights_to_convert:
   if F"mid.attn_1.{weight_name}.weight" in k:
    print(F"Reshaping {k} for SD format" )
    __lowercase				    =					reshape_weight_for_sd(A__ )
 return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCAmelCase__   		=      [
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
lowerCAmelCase__   		=      {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase__   		=      re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCAmelCase__   		=      {'''q''': 0, '''k''': 1, '''v''': 2}


def 						_A	(       A__ ):






 """simple docstring"""



 __lowercase				    =					{}
 __lowercase				    =					{}
 __lowercase				    =					{}
 for k, v in text_enc_dict.items():
  if (
      k.endswith('''.self_attn.q_proj.weight''' )
      or k.endswith('''.self_attn.k_proj.weight''' )
      or k.endswith('''.self_attn.v_proj.weight''' )
  ):
   __lowercase				    =					k[: -len('''.q_proj.weight''' )]
   __lowercase				    =					k[-len('''q_proj.weight''' )]
   if k_pre not in capture_qkv_weight:
    __lowercase				    =					[None, None, None]
   __lowercase				    =					v
   continue

  if (
      k.endswith('''.self_attn.q_proj.bias''' )
      or k.endswith('''.self_attn.k_proj.bias''' )
      or k.endswith('''.self_attn.v_proj.bias''' )
  ):
   __lowercase				    =					k[: -len('''.q_proj.bias''' )]
   __lowercase				    =					k[-len('''q_proj.bias''' )]
   if k_pre not in capture_qkv_bias:
    __lowercase				    =					[None, None, None]
   __lowercase				    =					v
   continue

  __lowercase				    =					textenc_pattern.sub(lambda A__ : protected[re.escape(m.group(0 ) )]	,							A__ )
  __lowercase				    =					v

 for k_pre, tensors in capture_qkv_weight.items():
  if None in tensors:
   raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
  __lowercase				    =					textenc_pattern.sub(lambda A__ : protected[re.escape(m.group(0 ) )]	,							A__ )
  __lowercase				    =					torch.cat(A__ )

 for k_pre, tensors in capture_qkv_bias.items():
  if None in tensors:
   raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
  __lowercase				    =					textenc_pattern.sub(lambda A__ : protected[re.escape(m.group(0 ) )]	,							A__ )
  __lowercase				    =					torch.cat(A__ )

 return new_state_dict


def 						_A	(       A__ ):






 """simple docstring"""



 return text_enc_dict


if __name__ == "__main__":
 lowerCAmelCase__   		=      argparse.ArgumentParser()

 parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
 parser.add_argument(
     '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
 )

 lowerCAmelCase__   		=      parser.parse_args()

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 lowerCAmelCase__   		=      osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
 lowerCAmelCase__   		=      osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
 lowerCAmelCase__   		=      osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  lowerCAmelCase__   		=      load_file(unet_path, device='''cpu''')
 else:
  lowerCAmelCase__   		=      osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
  lowerCAmelCase__   		=      torch.load(unet_path, map_location='''cpu''')

 if osp.exists(vae_path):
  lowerCAmelCase__   		=      load_file(vae_path, device='''cpu''')
 else:
  lowerCAmelCase__   		=      osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
  lowerCAmelCase__   		=      torch.load(vae_path, map_location='''cpu''')

 if osp.exists(text_enc_path):
  lowerCAmelCase__   		=      load_file(text_enc_path, device='''cpu''')
 else:
  lowerCAmelCase__   		=      osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
  lowerCAmelCase__   		=      torch.load(text_enc_path, map_location='''cpu''')

 # Convert the UNet model
 lowerCAmelCase__   		=      convert_unet_state_dict(unet_state_dict)
 lowerCAmelCase__   		=      {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 lowerCAmelCase__   		=      convert_vae_state_dict(vae_state_dict)
 lowerCAmelCase__   		=      {'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 lowerCAmelCase__   		=      '''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  lowerCAmelCase__   		=      {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
  lowerCAmelCase__   		=      convert_text_enc_state_dict_vaa(text_enc_dict)
  lowerCAmelCase__   		=      {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
 else:
  lowerCAmelCase__   		=      convert_text_enc_state_dict(text_enc_dict)
  lowerCAmelCase__   		=      {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 lowerCAmelCase__   		=      {**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  lowerCAmelCase__   		=      {k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  lowerCAmelCase__   		=      {'''state_dict''': state_dict}
  torch.save(state_dict, args.checkpoint_path)





import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()

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

    parser.add_argument(
        '''--txt2img_unclip''',
        default='''kakaobrain/karlo-v1-alpha''',
        type=str,
        required=False,
        help='''The pretrained txt2img unclip.''',
    )

    __snake_case   							=						parser.parse_args()

    __snake_case   							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    __snake_case   							=						CLIPImageProcessor()
    __snake_case   							=						CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

    __snake_case   							=						UnCLIPImageVariationPipeline(
        decoder=txtaimg.decoder,
        text_encoder=txtaimg.text_encoder,
        tokenizer=txtaimg.tokenizer,
        text_proj=txtaimg.text_proj,
        feature_extractor=feature_extractor,
        image_encoder=image_encoder,
        super_res_first=txtaimg.super_res_first,
        super_res_last=txtaimg.super_res_last,
        decoder_scheduler=txtaimg.decoder_scheduler,
        super_res_scheduler=txtaimg.super_res_scheduler,
    )

    imgaimg.save_pretrained(args.dump_path)


