






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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

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

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


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


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

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

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

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


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


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


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


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


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

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


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

      __UpperCamelCase	:			Optional[int]					=					textenc_pattern.sub(lambda __lowerCAmelCase							: protected[re.escape(m.group(0							)							)]					,							__lowerCAmelCase							)
      __UpperCamelCase	:			List[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"""							)
      __UpperCamelCase	:			str					=					textenc_pattern.sub(lambda __lowerCAmelCase							: protected[re.escape(m.group(0							)							)]					,							__lowerCAmelCase							)
      __UpperCamelCase	:			Tuple					=					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"""							)
      __UpperCamelCase	:			Dict					=					textenc_pattern.sub(lambda __lowerCAmelCase							: protected[re.escape(m.group(0							)							)]					,							__lowerCAmelCase							)
      __UpperCamelCase	:			List[str]					=					torch.cat(__lowerCAmelCase							)

   return new_state_dict


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[Any]							)     ->  List[Any]:
   return text_enc_dict


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

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

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

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

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

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

       if args.use_safetensors:
              save_file(state_dict, args.checkpoint_path)
       else:
              UpperCamelCase    							=      {'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""")



