





'''simple docstring'''

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowercase_					= [
    # (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'''),
]

lowercase_					= [
    # (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'''),
]

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

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

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





def lowerCamelCase						(     __lowerCamelCase :    Any    )						->Optional[Any]:
    # buyer beware: this is a *brittle* function,
    # and correct output requires that all of these pieces interact in
    # the exact order in which I have arranged them.
    _SCREAMING_SNAKE_CASE									=			{k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        _SCREAMING_SNAKE_CASE									=			sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                _SCREAMING_SNAKE_CASE									=			v.replace(_UpperCAmelCase					,       _UpperCAmelCase    )
            _SCREAMING_SNAKE_CASE									=			v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            _SCREAMING_SNAKE_CASE									=			v.replace(_UpperCAmelCase					,       _UpperCAmelCase    )
        _SCREAMING_SNAKE_CASE									=			v
    _SCREAMING_SNAKE_CASE									=			{v: unet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


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

lowercase_					= [
    # (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):
            lowercase_					= f"""encoder.down_blocks.{i}.resnets.{j}."""
            lowercase_					= f"""encoder.down.{i}.block.{j}."""
            vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

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


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





def lowerCamelCase						(     __lowerCamelCase :    Dict    )						->List[Any]:
    # convert HF linear weights to SD conv2d weights
    return w.reshape(*w.shape					,       1					,       1    )





def lowerCamelCase						(     __lowerCamelCase :    str    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			{k: k for k in vae_state_dict.keys()}
    for k, v in mapping.items():
        for sd_part, hf_part in vae_conversion_map:
            _SCREAMING_SNAKE_CASE									=			v.replace(_UpperCAmelCase					,       _UpperCAmelCase    )
        _SCREAMING_SNAKE_CASE									=			v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                _SCREAMING_SNAKE_CASE									=			v.replace(_UpperCAmelCase					,       _UpperCAmelCase    )
            _SCREAMING_SNAKE_CASE									=			v
    _SCREAMING_SNAKE_CASE									=			{v: vae_state_dict[k] for k, v in mapping.items()}
    _SCREAMING_SNAKE_CASE									=			["""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'    )
                _SCREAMING_SNAKE_CASE									=			reshape_weight_for_sd(_UpperCAmelCase    )
    return new_state_dict


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


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

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





def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]    )						->Any:
    _SCREAMING_SNAKE_CASE									=			{}
    _SCREAMING_SNAKE_CASE									=			{}
    _SCREAMING_SNAKE_CASE									=			{}
    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"""    )
        ):
            _SCREAMING_SNAKE_CASE									=			k[: -len(""".q_proj.weight"""    )]
            _SCREAMING_SNAKE_CASE									=			k[-len("""q_proj.weight"""    )]
            if k_pre not in capture_qkv_weight:
                _SCREAMING_SNAKE_CASE									=			[None, None, None]
            _SCREAMING_SNAKE_CASE									=			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"""    )
        ):
            _SCREAMING_SNAKE_CASE									=			k[: -len(""".q_proj.bias"""    )]
            _SCREAMING_SNAKE_CASE									=			k[-len("""q_proj.bias"""    )]
            if k_pre not in capture_qkv_bias:
                _SCREAMING_SNAKE_CASE									=			[None, None, None]
            _SCREAMING_SNAKE_CASE									=			v
            continue

        _SCREAMING_SNAKE_CASE									=			textenc_pattern.sub(lambda __lowerCamelCase    : protected[re.escape(m.group(0    )    )]					,       _UpperCAmelCase    )
        _SCREAMING_SNAKE_CASE									=			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"""    )
        _SCREAMING_SNAKE_CASE									=			textenc_pattern.sub(lambda __lowerCamelCase    : protected[re.escape(m.group(0    )    )]					,       _UpperCAmelCase    )
        _SCREAMING_SNAKE_CASE									=			torch.cat(_UpperCAmelCase    )

    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"""    )
        _SCREAMING_SNAKE_CASE									=			textenc_pattern.sub(lambda __lowerCamelCase    : protected[re.escape(m.group(0    )    )]					,       _UpperCAmelCase    )
        _SCREAMING_SNAKE_CASE									=			torch.cat(_UpperCAmelCase    )

    return new_state_dict





def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]    )						->Union[str, Any]:
    return text_enc_dict


if __name__ == "__main__":
      lowercase_					= 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."""
      )

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

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

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

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

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

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

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








import os

try:
 from .build_directory_md import good_file_paths
except ImportError:
 from build_directory_md import good_file_paths  # type: ignore

__snake_case						:Optional[Any]      		=     list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__snake_case						:Any      		=     [file for file in filepaths if file != file.lower()]
if upper_files:
 print(f'{len(upper_files)} files contain uppercase characters:')
 print('''\n'''.join(upper_files) + '''\n''')

__snake_case						:Tuple      		=     [file for file in filepaths if ''' ''' in file]
if space_files:
 print(f'{len(space_files)} files contain space characters:')
 print('''\n'''.join(space_files) + '''\n''')

__snake_case						:Optional[int]      		=     [file for file in filepaths if '''-''' in file]
if hyphen_files:
 print(f'{len(hyphen_files)} files contain hyphen characters:')
 print('''\n'''.join(hyphen_files) + '''\n''')

__snake_case						:Optional[int]      		=     [file for file in filepaths if os.sep not in file]
if nodir_files:
 print(f'{len(nodir_files)} files are not in a directory:')
 print('''\n'''.join(nodir_files) + '''\n''')

__snake_case						:int      		=     len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
 import sys

 sys.exit(bad_files)





