





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

_UpperCAmelCase       : List[str]			     =       [
    # (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"""),
]

_UpperCAmelCase       : Optional[int]			     =       [
    # (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"""),
]

_UpperCAmelCase       : Union[str, Any]			     =       []
# 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
        _UpperCAmelCase       : List[Any]			     =       F"""down_blocks.{i}.resnets.{j}."""
        _UpperCAmelCase       : Optional[int]			     =       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
            _UpperCAmelCase       : Tuple			     =       F"""down_blocks.{i}.attentions.{j}."""
            _UpperCAmelCase       : Dict			     =       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
        _UpperCAmelCase       : str			     =       F"""up_blocks.{i}.resnets.{j}."""
        _UpperCAmelCase       : Dict			     =       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
            _UpperCAmelCase       : str			     =       F"""up_blocks.{i}.attentions.{j}."""
            _UpperCAmelCase       : List[str]			     =       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
        _UpperCAmelCase       : Union[str, Any]			     =       F"""down_blocks.{i}.downsamplers.0.conv."""
        _UpperCAmelCase       : List[str]			     =       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
        _UpperCAmelCase       : int			     =       F"""up_blocks.{i}.upsamplers.0."""
        _UpperCAmelCase       : List[str]			     =       F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_UpperCAmelCase       : Optional[int]			     =       """mid_block.attentions.0."""
_UpperCAmelCase       : Union[str, Any]			     =       """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
    _UpperCAmelCase       : Optional[int]			     =       F"""mid_block.resnets.{j}."""
    _UpperCAmelCase       : Optional[Any]			     =       F"""middle_block.{2*j}."""
    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


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


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

_UpperCAmelCase       : Any			     =       [
    # (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):
        _UpperCAmelCase       : Dict			     =       F"""encoder.down_blocks.{i}.resnets.{j}."""
        _UpperCAmelCase       : List[Any]			     =       F"""encoder.down.{i}.block.{j}."""
        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

    if i < 3:
        _UpperCAmelCase       : Dict			     =       F"""down_blocks.{i}.downsamplers.0."""
        _UpperCAmelCase       : Optional[int]			     =       F"""down.{i}.downsample."""
        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

        _UpperCAmelCase       : List[Any]			     =       F"""up_blocks.{i}.upsamplers.0."""
        _UpperCAmelCase       : int			     =       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):
        _UpperCAmelCase       : Union[str, Any]			     =       F"""decoder.up_blocks.{i}.resnets.{j}."""
        _UpperCAmelCase       : Dict			     =       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):
    _UpperCAmelCase       : Dict			     =       F"""mid_block.resnets.{i}."""
    _UpperCAmelCase       : List[Any]			     =       F"""mid.block_{i+1}."""
    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_UpperCAmelCase       : List[str]			     =       [
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Dict:
    # convert HF linear weights to SD conv2d weights
    return w.reshape(*w.shape		,			1		,			1				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Dict:
    lowerCamelCase__					:   Union[str, Any]      				=	{k: k for k in vae_state_dict.keys()}
    for k, v in mapping.items():
        for sd_part, hf_part in vae_conversion_map:
            lowerCamelCase__					:   List[str]      				=	v.replace(_UpperCAmelCase		,			_UpperCAmelCase				)
        lowerCamelCase__					:   List[Any]      				=	v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                lowerCamelCase__					:   str      				=	v.replace(_UpperCAmelCase		,			_UpperCAmelCase				)
            lowerCamelCase__					:   str      				=	v
    lowerCamelCase__					:   Optional[int]      				=	{v: vae_state_dict[k] for k, v in mapping.items()}
    lowerCamelCase__					:   int      				=	['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"""				)
                lowerCamelCase__					:   Any      				=	reshape_weight_for_sd(_UpperCAmelCase				)
    return new_state_dict


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


_UpperCAmelCase       : Optional[int]			     =       [
    # (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"""),
]
_UpperCAmelCase       : List[Any]			     =       {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_UpperCAmelCase       : str			     =       re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_UpperCAmelCase       : Union[str, Any]			     =       {"""q""": 0, """k""": 1, """v""": 2}


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Optional[int]:
    lowerCamelCase__					:   Any      				=	{}
    lowerCamelCase__					:   List[str]      				=	{}
    lowerCamelCase__					:   int      				=	{}
    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'				)
        ):
            lowerCamelCase__					:   List[Any]      				=	k[: -len('.q_proj.weight'				)]
            lowerCamelCase__					:   Dict      				=	k[-len('q_proj.weight'				)]
            if k_pre not in capture_qkv_weight:
                lowerCamelCase__					:   Optional[int]      				=	[None, None, None]
            lowerCamelCase__					:   List[str]      				=	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'				)
        ):
            lowerCamelCase__					:   Tuple      				=	k[: -len('.q_proj.bias'				)]
            lowerCamelCase__					:   List[str]      				=	k[-len('q_proj.bias'				)]
            if k_pre not in capture_qkv_bias:
                lowerCamelCase__					:   Union[str, Any]      				=	[None, None, None]
            lowerCamelCase__					:   Optional[Any]      				=	v
            continue

        lowerCamelCase__					:   Optional[int]      				=	textenc_pattern.sub(lambda _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]		,			_UpperCAmelCase				)
        lowerCamelCase__					:   Union[str, 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'				)
        lowerCamelCase__					:   int      				=	textenc_pattern.sub(lambda _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]		,			_UpperCAmelCase				)
        lowerCamelCase__					:   str      				=	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'				)
        lowerCamelCase__					:   Union[str, Any]      				=	textenc_pattern.sub(lambda _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]		,			_UpperCAmelCase				)
        lowerCamelCase__					:   int      				=	torch.cat(_UpperCAmelCase				)

    return new_state_dict


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  List[str]:
    return text_enc_dict


if __name__ == "__main__":
    _UpperCAmelCase       : Optional[Any]			     =       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."""
    )

    _UpperCAmelCase       : Dict			     =       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
    _UpperCAmelCase       : Union[str, Any]			     =       osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
    _UpperCAmelCase       : List[str]			     =       osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
    _UpperCAmelCase       : Union[str, Any]			     =       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):
        _UpperCAmelCase       : Union[str, Any]			     =       load_file(unet_path, device="""cpu""")
    else:
        _UpperCAmelCase       : Optional[Any]			     =       osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
        _UpperCAmelCase       : List[Any]			     =       torch.load(unet_path, map_location="""cpu""")

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

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

    # Convert the UNet model
    _UpperCAmelCase       : Any			     =       convert_unet_state_dict(unet_state_dict)
    _UpperCAmelCase       : Tuple			     =       {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

    # Convert the VAE model
    _UpperCAmelCase       : List[str]			     =       convert_vae_state_dict(vae_state_dict)
    _UpperCAmelCase       : Optional[int]			     =       {"""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
    _UpperCAmelCase       : Union[str, Any]			     =       """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
        _UpperCAmelCase       : Dict			     =       {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
        _UpperCAmelCase       : int			     =       convert_text_enc_state_dict_vaa(text_enc_dict)
        _UpperCAmelCase       : int			     =       {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
    else:
        _UpperCAmelCase       : List[Any]			     =       convert_text_enc_state_dict(text_enc_dict)
        _UpperCAmelCase       : str			     =       {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

    # Put together new checkpoint
    _UpperCAmelCase       : List[str]			     =       {**unet_state_dict, **vae_state_dict, **text_enc_dict}
    if args.half:
        _UpperCAmelCase       : Union[str, Any]			     =       {k: v.half() for k, v in state_dict.items()}

    if args.use_safetensors:
        save_file(state_dict, args.checkpoint_path)
    else:
        _UpperCAmelCase       : int			     =       {"""state_dict""": state_dict}
        torch.save(state_dict, args.checkpoint_path)












from pathlib import Path

import fire


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    lowerCamelCase__					:   Union[str, Any]      				=	Path(_UpperCAmelCase				)
    lowerCamelCase__					:   int      				=	Path(_UpperCAmelCase				)
    dest_dir.mkdir(exist_ok=_UpperCAmelCase				)
    for path in src_dir.iterdir():
        lowerCamelCase__					:   Tuple      				=	[x.rstrip() for x in list(path.open().readlines()				)][:n]
        lowerCamelCase__					:   Optional[int]      				=	dest_dir.joinpath(path.name				)
        print(_UpperCAmelCase				)
        dest_path.open('w'				).write('\n'.join(_UpperCAmelCase				)				)


if __name__ == "__main__":
    fire.Fire(minify)





