





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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

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

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



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


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

lowercase :			Dict     		=    [
    # (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 :			int     		=    F"""encoder.down_blocks.{i}.resnets.{j}."""
        lowercase :			Tuple     		=    F"""encoder.down.{i}.block.{j}."""
        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

    if i < 3:
        lowercase :			List[str]     		=    F"""down_blocks.{i}.downsamplers.0."""
        lowercase :			Optional[Any]     		=    F"""down.{i}.downsample."""
        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

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


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



def A_      (						A__							)    ->						Optional[Any]:
       return w.reshape(*w.shape						, 1						, 1							)



def A_      (						A__							)    ->						str:
       a__		:      Optional[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:
                     a__		:      Union[str, Any]					=							v.replace(snake_case__						, snake_case__							)
              a__		:      int					=							v
       for k, v in mapping.items():
              if "attentions" in k:
                     for sd_part, hf_part in vae_conversion_map_attn:
                            a__		:      str					=							v.replace(snake_case__						, snake_case__							)
                     a__		:      List[str]					=							v
       a__		:      str					=							{v: vae_state_dict[k] for k, v in mapping.items()}
       a__		:      str					=							['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'							)
                            a__		:      Any					=							reshape_weight_for_sd(snake_case__							)
       return new_state_dict


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


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

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



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

              a__		:      Any					=							textenc_pattern.sub(lambda A__							: protected[re.escape(m.group(0							)							)]						, snake_case__							)
              a__		:      Dict					=							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'							)
              a__		:      Tuple					=							textenc_pattern.sub(lambda A__							: protected[re.escape(m.group(0							)							)]						, snake_case__							)
              a__		:      Optional[Any]					=							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'							)
              a__		:      Optional[Any]					=							textenc_pattern.sub(lambda A__							: protected[re.escape(m.group(0							)							)]						, snake_case__							)
              a__		:      Dict					=							torch.cat(snake_case__							)

       return new_state_dict



def A_      (						A__							)    ->						List[str]:
       return text_enc_dict


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

    if osp.exists(vae_path):
        lowercase :			List[str]     		=    load_file(vae_path, device="""cpu""")
    else:
        lowercase :			Optional[Any]     		=    osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
        lowercase :			Any     		=    torch.load(vae_path, map_location="""cpu""")

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

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

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

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

    if args.use_safetensors:
        save_file(state_dict, args.checkpoint_path)
    else:
        lowercase :			Union[str, Any]     		=    {"""state_dict""": state_dict}
        torch.save(state_dict, args.checkpoint_path)




from __future__ import annotations

__a         =   8.988E9  # units = N * m^s * C^-2




def 							a		(				snake_case__:   float		,  snake_case__:   float		,  snake_case__:   float		,  snake_case__:   float   ):


				'''simple docstring'''
				lowercase_	    =   abs(chargea * chargea   )

				if (force, chargea, chargea, distance).count(0   ) != 1:
								raise ValueError('''One and only one argument must be 0'''   )
				if distance < 0:
								raise ValueError('''Distance cannot be negative'''   )
				if force == 0:
								lowercase_	    =   COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								lowercase_	    =   abs(snake_case__   ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								lowercase_	    =   abs(snake_case__   ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								lowercase_	    =   (COULOMBS_CONSTANT * charge_product / abs(snake_case__   )) ** 0.5
								return {"distance": distance}
				raise ValueError('''Exactly one argument must be 0'''   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




