
import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

UpperCAmelCase__		:     Union[str, 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'),
]

UpperCAmelCase__		:     List[str]     			=      [
    # (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__		:     List[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[str]     			=      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__		:     int     			=      F"""down_blocks.{i}.attentions.{j}."""
         UpperCAmelCase__		:     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
      UpperCAmelCase__		:     Optional[int]     			=      F"""up_blocks.{i}.resnets.{j}."""
      UpperCAmelCase__		:     Union[str, Any]     			=      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__		:     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
      UpperCAmelCase__		:     Optional[Any]     			=      F"""down_blocks.{i}.downsamplers.0.conv."""
      UpperCAmelCase__		:     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__		:     Dict     			=      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__		:     str     			=      '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__		:     List[str]     			=      F"""mid_block.resnets.{j}."""
   UpperCAmelCase__		:     str     			=      F"""middle_block.{2*j}."""
   unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def   lowerCamelCase__							(					a      )      ->   List[str]:
   # 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.
   _A:  Any					=       {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:  Any					=       v.replace(a		,		a      )
         _A:  Dict					=       v
   for k, v in mapping.items():
      for sd_part, hf_part in unet_conversion_map_layer:
         _A:  int					=       v.replace(a		,		a      )
      _A:  Any					=       v
   _A:  Optional[Any]					=       {v: unet_state_dict[k] for k, v in mapping.items()}
   return new_state_dict


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

UpperCAmelCase__		:     List[str]     			=      [
    # (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__		:     str     			=      F"""encoder.down_blocks.{i}.resnets.{j}."""
      UpperCAmelCase__		:     Tuple     			=      F"""encoder.down.{i}.block.{j}."""
      vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

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


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






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






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


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


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

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






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

      _A:  str					=       textenc_pattern.sub(lambda a      : protected[re.escape(m.group(0      )      )]		,		a      )
      _A:  List[str]					=       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:  Union[str, Any]					=       textenc_pattern.sub(lambda a      : protected[re.escape(m.group(0      )      )]		,		a      )
      _A:  List[str]					=       torch.cat(a      )

   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:  Tuple					=       textenc_pattern.sub(lambda a      : protected[re.escape(m.group(0      )      )]		,		a      )
      _A:  int					=       torch.cat(a      )

   return new_state_dict






def   lowerCamelCase__							(					a      )      ->   int:
   return text_enc_dict


if __name__ == "__main__":
   UpperCAmelCase__		:     Dict     			=      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__		:     List[str]     			=      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__		:     Tuple     			=      load_file(unet_path, device='cpu')
   else:
      UpperCAmelCase__		:     Any     			=      osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
      UpperCAmelCase__		:     int     			=      torch.load(unet_path, map_location='cpu')

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

   if osp.exists(text_enc_path):
      UpperCAmelCase__		:     Union[str, Any]     			=      load_file(text_enc_path, device='cpu')
   else:
      UpperCAmelCase__		:     str     			=      osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
      UpperCAmelCase__		:     Union[str, Any]     			=      torch.load(text_enc_path, map_location='cpu')

   # Convert the UNet model
   UpperCAmelCase__		:     Optional[Any]     			=      convert_unet_state_dict(unet_state_dict)
   UpperCAmelCase__		:     List[str]     			=      {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

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

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

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





import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCAmelCase__		:     Any     			=      3






def   lowerCamelCase__							(					a      )      ->   int:
   print('''Generating primitive root of p'''      )
   while True:
      _A:  Union[str, Any]					=       random.randrange(3		,		a      )
      if pow(a		,		2		,		a      ) == 1:
         continue
      if pow(a		,		a		,		a      ) == 1:
         continue
      return g






def   lowerCamelCase__							(					a      )      ->   tuple[tuple[int, int, int, int], tuple[int, int]]:
   print('''Generating prime p...'''      )
   _A:  Dict					=       rabin_miller.generate_large_prime(a      )  # select large prime number.
   _A:  Any					=       primitive_root(a      )  # one primitive root on modulo p.
   _A:  Optional[Any]					=       random.randrange(3		,		a      )  # private_key -> have to be greater than 2 for safety.
   _A:  Dict					=       cryptomath.find_mod_inverse(pow(a		,		a		,		a      )		,		a      )

   _A:  Union[str, Any]					=       (key_size, e_a, e_a, p)
   _A:  Union[str, Any]					=       (key_size, d)

   return public_key, private_key






def   lowerCamelCase__							(					a		,		a      )      ->   None:
   if os.path.exists(f"""{name}_pubkey.txt"""      ) or os.path.exists(f"""{name}_privkey.txt"""      ):
      print('''\nWARNING:'''      )
      print(
          f"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
          '''Use a different name or delete these files and re-run this program.'''      )
      sys.exit()

   _A			,  _A:  Any					=       generate_key(a      )
   print(f"""\nWriting public key to file {name}_pubkey.txt..."""      )
   with open(f"""{name}_pubkey.txt"""		,		'''w'''      ) as fo:
      fo.write(f"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""      )

   print(f"""Writing private key to file {name}_privkey.txt..."""      )
   with open(f"""{name}_privkey.txt"""		,		'''w'''      ) as fo:
      fo.write(f"""{private_key[0]},{private_key[1]}"""      )






def   lowerCamelCase__							(					)      ->   None:
   print('''Making key files...'''      )
   make_key_files('''elgamal'''		,		20_48      )
   print('''Key files generation successful'''      )


if __name__ == "__main__":
   main()



