



'''simple docstring'''





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

SCREAMING_SNAKE_CASE__          =  [
    # (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"),
]

SCREAMING_SNAKE_CASE__          =  [
    # (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"),
]

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

SCREAMING_SNAKE_CASE__          =  "mid_block.attentions.0."
SCREAMING_SNAKE_CASE__          =  "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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




def 							lowerCamelCase	(					_snake_case	:    Tuple							):





    '''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(__lowerCAmelCase  ,__lowerCAmelCase							)
            lowercase__									=    v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            lowercase__									=    v.replace(__lowerCAmelCase  ,__lowerCAmelCase							)
        lowercase__									=    v
    lowercase__									=    {v: unet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


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

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

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

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


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




def 							lowerCamelCase	(					_snake_case	:    Any							):





    '''simple docstring'''


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




def 							lowerCamelCase	(					_snake_case	:    int							):





    '''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(__lowerCAmelCase  ,__lowerCAmelCase							)
        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(__lowerCAmelCase  ,__lowerCAmelCase							)
            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(__lowerCAmelCase							)
    return new_state_dict


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


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

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




def 							lowerCamelCase	(					_snake_case	:    str							):





    '''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 _snake_case							: protected[re.escape(m.group(0							)							)]  ,__lowerCAmelCase							)
        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 _snake_case							: protected[re.escape(m.group(0							)							)]  ,__lowerCAmelCase							)
        lowercase__									=    torch.cat(__lowerCAmelCase							)

    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 _snake_case							: protected[re.escape(m.group(0							)							)]  ,__lowerCAmelCase							)
        lowercase__									=    torch.cat(__lowerCAmelCase							)

    return new_state_dict




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]							):





    '''simple docstring'''


    return text_enc_dict


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

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

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

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

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

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

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

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












UpperCamelCase    							=      8.3_144_598


def 				__lowerCamelCase     (							__lowerCAmelCase							:					float					,							__lowerCAmelCase							:					float							)     ->  float:
   if temperature < 0:
      raise Exception("""Temperature cannot be less than 0 K"""							)
   if molar_mass <= 0:
      raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""							)
   else:
      return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
       import doctest

       # run doctest
       doctest.testmod()

       # example
       UpperCamelCase    							=      300
       UpperCamelCase    							=      28
       UpperCamelCase    							=      rms_speed_of_molecule(temperature, molar_mass)
       print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")



