




"""simple docstring"""


import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

A_      =    [
    # (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'''),
]

A_      =    [
    # (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'''),
]

A_      =    []
# 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
          A_      =    F'''down_blocks.{i}.resnets.{j}.'''
          A_      =    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
               A_      =    F'''down_blocks.{i}.attentions.{j}.'''
               A_      =    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
          A_      =    F'''up_blocks.{i}.resnets.{j}.'''
          A_      =    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
               A_      =    F'''up_blocks.{i}.attentions.{j}.'''
               A_      =    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
          A_      =    F'''down_blocks.{i}.downsamplers.0.conv.'''
          A_      =    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
          A_      =    F'''up_blocks.{i}.upsamplers.0.'''
          A_      =    F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
          unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

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

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


def  UpperCAmelCase__	(snake_case__							: Tuple   ):
  """simple docstring"""

  _snake_case							:		int								=  {k: k for k in unet_state_dict.keys()}
  for sd_name, hf_name in unet_conversion_map:
    _snake_case							:		str								=  sd_name
  for k, v in mapping.items():
    if "resnets" in k:
      for sd_part, hf_part in unet_conversion_map_resnet:
        _snake_case							:		Optional[Any]								=  v.replace(snake_case__		,       snake_case__   )
      _snake_case							:		Tuple								=  v
  for k, v in mapping.items():
    for sd_part, hf_part in unet_conversion_map_layer:
      _snake_case							:		int								=  v.replace(snake_case__		,       snake_case__   )
    _snake_case							:		str								=  v
  _snake_case							:		Optional[Any]								=  {v: unet_state_dict[k] for k, v in mapping.items()}
  return new_state_dict


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

A_      =    [
    # (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):
          A_      =    F'''encoder.down_blocks.{i}.resnets.{j}.'''
          A_      =    F'''encoder.down.{i}.block.{j}.'''
          vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

          A_      =    F'''up_blocks.{i}.upsamplers.0.'''
          A_      =    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):
          A_      =    F'''decoder.up_blocks.{i}.resnets.{j}.'''
          A_      =    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):
     A_      =    F'''mid_block.resnets.{i}.'''
     A_      =    F'''mid.block_{i+1}.'''
     vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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


def  UpperCAmelCase__	(snake_case__							: Tuple   ):
  """simple docstring"""

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


def  UpperCAmelCase__	(snake_case__							: str   ):
  """simple docstring"""

  _snake_case							:		int								=  {k: k for k in vae_state_dict.keys()}
  for k, v in mapping.items():
    for sd_part, hf_part in vae_conversion_map:
      _snake_case							:		Tuple								=  v.replace(snake_case__		,       snake_case__   )
    _snake_case							:		Optional[int]								=  v
  for k, v in mapping.items():
    if "attentions" in k:
      for sd_part, hf_part in vae_conversion_map_attn:
        _snake_case							:		int								=  v.replace(snake_case__		,       snake_case__   )
      _snake_case							:		str								=  v
  _snake_case							:		int								=  {v: vae_state_dict[k] for k, v in mapping.items()}
  _snake_case							:		List[Any]								=  ["""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"   )
        _snake_case							:		Tuple								=  reshape_weight_for_sd(snake_case__   )
  return new_state_dict


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


A_      =    [
    # (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'''),
]
A_      =    {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A_      =    re.compile('''|'''.join(protected.keys()))

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


def  UpperCAmelCase__	(snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		int								=  {}
  _snake_case							:		List[str]								=  {}
  _snake_case							:		Dict								=  {}
  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"""   )
    ):
      _snake_case							:		List[Any]								=  k[: -len(""".q_proj.weight"""   )]
      _snake_case							:		Dict								=  k[-len("""q_proj.weight"""   )]
      if k_pre not in capture_qkv_weight:
        _snake_case							:		Any								=  [None, None, None]
      _snake_case							:		int								=  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"""   )
    ):
      _snake_case							:		Optional[int]								=  k[: -len(""".q_proj.bias"""   )]
      _snake_case							:		Union[str, Any]								=  k[-len("""q_proj.bias"""   )]
      if k_pre not in capture_qkv_bias:
        _snake_case							:		Optional[Any]								=  [None, None, None]
      _snake_case							:		List[str]								=  v
      continue

    _snake_case							:		Optional[Any]								=  textenc_pattern.sub(lambda snake_case__   : protected[re.escape(m.group(0   )   )]		,       snake_case__   )
    _snake_case							:		Any								=  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"""   )
    _snake_case							:		str								=  textenc_pattern.sub(lambda snake_case__   : protected[re.escape(m.group(0   )   )]		,       snake_case__   )
    _snake_case							:		int								=  torch.cat(snake_case__   )

  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"""   )
    _snake_case							:		Any								=  textenc_pattern.sub(lambda snake_case__   : protected[re.escape(m.group(0   )   )]		,       snake_case__   )
    _snake_case							:		Tuple								=  torch.cat(snake_case__   )

  return new_state_dict


def  UpperCAmelCase__	(snake_case__							: Dict   ):
  """simple docstring"""

  return text_enc_dict


if __name__ == "__main__":
     A_      =    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.'''
     )

     A_      =    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
     A_      =    osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
     A_      =    osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
     A_      =    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):
          A_      =    load_file(unet_path, device='''cpu''')
     else:
          A_      =    osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
          A_      =    torch.load(unet_path, map_location='''cpu''')

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

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

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

     # Convert the VAE model
     A_      =    convert_vae_state_dict(vae_state_dict)
     A_      =    {'''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
     A_      =    '''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
          A_      =    {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
          A_      =    convert_text_enc_state_dict_vaa(text_enc_dict)
          A_      =    {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
     else:
          A_      =    convert_text_enc_state_dict(text_enc_dict)
          A_      =    {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

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

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








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 10_00   ):
  """simple docstring"""

  _snake_case		, _snake_case							:		List[Any]								=  1, 1
  _snake_case							:		str								=  []
  for i in range(1		,       n + 1   ):
    _snake_case							:		Any								=  prev_numerator + 2 * prev_denominator
    _snake_case							:		Optional[Any]								=  prev_numerator + prev_denominator
    if len(str(snake_case__   )   ) > len(str(snake_case__   )   ):
      result.append(snake_case__   )
    _snake_case							:		int								=  numerator
    _snake_case							:		Any								=  denominator

  return len(snake_case__   )


if __name__ == "__main__":
     print(F'''{solution() = }''')


