


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase       )		->List[str]:


 '''simple docstring'''
 snake_case   : Optional[int]	  =      UNetaDModel(**lowerCamelCase__       )
 snake_case   : Optional[int]	  =      checkpoint['''all_modules.0.W'''].data
 snake_case   : Any	  =      checkpoint['''all_modules.0.W'''].data
 snake_case   : str	  =      checkpoint['''all_modules.1.weight'''].data
 snake_case   : Tuple	  =      checkpoint['''all_modules.1.bias'''].data

 snake_case   : Tuple	  =      checkpoint['''all_modules.2.weight'''].data
 snake_case   : str	  =      checkpoint['''all_modules.2.bias'''].data

 snake_case   : int	  =      checkpoint['''all_modules.3.weight'''].data
 snake_case   : List[Any]	  =      checkpoint['''all_modules.3.bias'''].data

 snake_case   : Union[str, Any]	  =      checkpoint[list(checkpoint.keys()       )[-4]].data
 snake_case   : Any	  =      checkpoint[list(checkpoint.keys()       )[-3]].data
 snake_case   : Optional[Any]	  =      checkpoint[list(checkpoint.keys()       )[-2]].data
 snake_case   : int	  =      checkpoint[list(checkpoint.keys()       )[-1]].data

 snake_case   : List[Any]	  =      4

 def set_attention_weights(__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       ):
  snake_case   : List[Any]	  =      old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
  snake_case   : Dict	  =      old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
  snake_case   : int	  =      old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

  snake_case   : Optional[int]	  =      old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
  snake_case   : List[str]	  =      old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
  snake_case   : Any	  =      old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

  snake_case   : Optional[Any]	  =      old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
  snake_case   : Optional[int]	  =      old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

  snake_case   : List[Any]	  =      old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
  snake_case   : List[Any]	  =      old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

 def set_resnet_weights(__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       ):
  snake_case   : Dict	  =      old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
  snake_case   : Optional[int]	  =      old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
  snake_case   : str	  =      old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
  snake_case   : str	  =      old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

  snake_case   : Optional[int]	  =      old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
  snake_case   : List[Any]	  =      old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
  snake_case   : Tuple	  =      old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
  snake_case   : List[str]	  =      old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

  snake_case   : Tuple	  =      old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
  snake_case   : Optional[int]	  =      old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

  if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
   snake_case   : Optional[int]	  =      old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
   snake_case   : Any	  =      old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

 for i, block in enumerate(new_model_architecture.downsample_blocks       ):
  snake_case   : Optional[Any]	  =      hasattr(lowerCamelCase__ ,     '''attentions'''       )
  for j in range(len(block.resnets       )       ):
   set_resnet_weights(block.resnets[j] ,     lowerCamelCase__ ,     lowerCamelCase__       )
   module_index += 1
   if has_attentions:
    set_attention_weights(block.attentions[j] ,     lowerCamelCase__ ,     lowerCamelCase__       )
    module_index += 1

  if hasattr(lowerCamelCase__ ,     '''downsamplers'''       ) and block.downsamplers is not None:
   set_resnet_weights(block.resnet_down ,     lowerCamelCase__ ,     lowerCamelCase__       )
   module_index += 1
   snake_case   : List[str]	  =      checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
   snake_case   : str	  =      checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
   module_index += 1

 set_resnet_weights(new_model_architecture.mid_block.resnets[0] ,     lowerCamelCase__ ,     lowerCamelCase__       )
 module_index += 1
 set_attention_weights(new_model_architecture.mid_block.attentions[0] ,     lowerCamelCase__ ,     lowerCamelCase__       )
 module_index += 1
 set_resnet_weights(new_model_architecture.mid_block.resnets[1] ,     lowerCamelCase__ ,     lowerCamelCase__       )
 module_index += 1

 for i, block in enumerate(new_model_architecture.up_blocks       ):
  snake_case   : int	  =      hasattr(lowerCamelCase__ ,     '''attentions'''       )
  for j in range(len(block.resnets       )       ):
   set_resnet_weights(block.resnets[j] ,     lowerCamelCase__ ,     lowerCamelCase__       )
   module_index += 1
  if has_attentions:
   set_attention_weights(
       block.attentions[0] ,     lowerCamelCase__ ,     lowerCamelCase__       )  # why can there only be a single attention layer for up?
   module_index += 1

  if hasattr(lowerCamelCase__ ,     '''resnet_up'''       ) and block.resnet_up is not None:
   snake_case   : Union[str, Any]	  =      checkpoint[f'''all_modules.{module_index}.weight'''].data
   snake_case   : List[str]	  =      checkpoint[f'''all_modules.{module_index}.bias'''].data
   module_index += 1
   snake_case   : List[str]	  =      checkpoint[f'''all_modules.{module_index}.weight'''].data
   snake_case   : int	  =      checkpoint[f'''all_modules.{module_index}.bias'''].data
   module_index += 1
   set_resnet_weights(block.resnet_up ,     lowerCamelCase__ ,     lowerCamelCase__       )
   module_index += 1

 snake_case   : int	  =      checkpoint[f'''all_modules.{module_index}.weight'''].data
 snake_case   : Optional[Any]	  =      checkpoint[f'''all_modules.{module_index}.bias'''].data
 module_index += 1
 snake_case   : List[Any]	  =      checkpoint[f'''all_modules.{module_index}.weight'''].data
 snake_case   : List[Any]	  =      checkpoint[f'''all_modules.{module_index}.bias'''].data

 return new_model_architecture.state_dict()


if __name__ == "__main__":
    a				:							Dict					=			argparse.ArgumentParser()

    parser.add_argument(
        '--checkpoint_path',
        default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
        type=str,
        required=False,
        help='Path to the checkpoint to convert.',
    )

    parser.add_argument(
        '--config_file',
        default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
        type=str,
        required=False,
        help='The config json file corresponding to the architecture.',
    )

    parser.add_argument(
        '--dump_path',
        default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
        type=str,
        required=False,
        help='Path to the output model.',
    )

    a				:							str					=			parser.parse_args()

    a				:							List[str]					=			torch.load(args.checkpoint_path, map_location='cpu')

    with open(args.config_file) as f:
        a				:							str					=			json.loads(f.read())

    a				:							Dict					=			convert_ncsnpp_checkpoint(
        checkpoint,
        config,
    )

    if "sde" in config:
        del config["sde"]

    a				:							Union[str, Any]					=			UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    try:
        a				:							int					=			ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

        a				:							Tuple					=			ScoreSdeVePipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)
    except:  # noqa: E722
        model.save_pretrained(args.dump_path)










import os
import sys

from . import rsa_key_generator as rkg

a				:							str					=			128
a				:							str					=			256






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase = DEFAULT_BLOCK_SIZE       )		->list[int]:


 '''simple docstring'''
 snake_case   : List[str]	  =      message.encode('''ascii'''       )

 snake_case   : List[Any]	  =      []
 for block_start in range(0 ,     len(__lowerCamelCase       ) ,     __lowerCamelCase       ):
  snake_case   : int	  =      0
  for i in range(__lowerCamelCase ,     min(block_start + block_size ,     len(__lowerCamelCase       )       )       ):
   block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
  block_ints.append(__lowerCamelCase       )
 return block_ints






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase = DEFAULT_BLOCK_SIZE       )		->str:


 '''simple docstring'''
 snake_case   : list[str]	  =      []
 for block_int in block_ints:
  snake_case   : list[str]	  =      []
  for i in range(block_size - 1 ,     -1 ,     -1       ):
   if len(__lowerCamelCase       ) + i < message_length:
    snake_case   : Any	  =      block_int // (BYTE_SIZE**i)
    snake_case   : Optional[Any]	  =      block_int % (BYTE_SIZE**i)
    block_message.insert(0 ,     chr(__lowerCamelCase       )       )
  message.extend(__lowerCamelCase       )
 return "".join(__lowerCamelCase       )






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase = DEFAULT_BLOCK_SIZE       )		->list[int]:


 '''simple docstring'''
 snake_case   : str	  =      []
 snake_case  ,					snake_case   : Optional[Any]	  =      key

 for block in get_blocks_from_text(__lowerCamelCase ,     __lowerCamelCase       ):
  encrypted_blocks.append(pow(__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )       )
 return encrypted_blocks






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase = DEFAULT_BLOCK_SIZE ,     )		->str:


 '''simple docstring'''
 snake_case   : List[Any]	  =      []
 snake_case  ,					snake_case   : List[str]	  =      key
 for block in encrypted_blocks:
  decrypted_blocks.append(pow(__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )       )
 return get_text_from_blocks(__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )






def    _UpperCAmelCase	(   __lowerCamelCase       )		->tuple[int, int, int]:


 '''simple docstring'''
 with open(__lowerCamelCase       ) as fo:
  snake_case   : List[Any]	  =      fo.read()
 snake_case  ,					snake_case  ,					snake_case   : Dict	  =      content.split(''','''       )
 return (int(__lowerCamelCase       ), int(__lowerCamelCase       ), int(__lowerCamelCase       ))






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase = DEFAULT_BLOCK_SIZE ,     )		->str:


 '''simple docstring'''
 snake_case  ,					snake_case  ,					snake_case   : List[Any]	  =      read_key_file(__lowerCamelCase       )
 if key_size < block_size * 8:
  sys.exit(
      '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
      '''requires the block size to be equal to or greater than the key size. '''
      '''Either decrease the block size or use different keys.'''.format(
          block_size * 8 ,     __lowerCamelCase       )       )

 snake_case   : Any	  =      [str(__lowerCamelCase       ) for i in encrypt_message(__lowerCamelCase ,     (n, e) ,     __lowerCamelCase       )]

 snake_case   : List[str]	  =      ''','''.join(__lowerCamelCase       )
 snake_case   : str	  =      f'''{len(__lowerCamelCase       )}_{block_size}_{encrypted_content}'''
 with open(__lowerCamelCase ,     '''w'''       ) as fo:
  fo.write(__lowerCamelCase       )
 return encrypted_content






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase       )		->str:


 '''simple docstring'''
 snake_case  ,					snake_case  ,					snake_case   : str	  =      read_key_file(__lowerCamelCase       )
 with open(__lowerCamelCase       ) as fo:
  snake_case   : Dict	  =      fo.read()
 snake_case  ,					snake_case  ,					snake_case   : Union[str, Any]	  =      content.split('''_'''       )
 snake_case   : str	  =      int(__lowerCamelCase       )
 snake_case   : int	  =      int(__lowerCamelCase       )

 if key_size < block_size * 8:
  sys.exit(
      '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
      '''requires the block size to be equal to or greater than the key size. '''
      '''Did you specify the correct key file and encrypted file?'''.format(
          block_size * 8 ,     __lowerCamelCase       )       )

 snake_case   : str	  =      []
 for block in encrypted_message.split(''','''       ):
  encrypted_blocks.append(int(__lowerCamelCase       )       )

 return decrypt_message(__lowerCamelCase ,     __lowerCamelCase ,     (n, d) ,     __lowerCamelCase       )






def    _UpperCAmelCase	(   )		->None:


 '''simple docstring'''
 snake_case   : List[Any]	  =      '''encrypted_file.txt'''
 snake_case   : Optional[int]	  =      input(R'''Encrypt\Decrypt [e\d]: '''       )

 if response.lower().startswith('''e'''       ):
  snake_case   : Dict	  =      '''encrypt'''
 elif response.lower().startswith('''d'''       ):
  snake_case   : Dict	  =      '''decrypt'''

 if mode == "encrypt":
  if not os.path.exists('''rsa_pubkey.txt'''       ):
   rkg.make_key_files('''rsa''' ,     1024       )

  snake_case   : List[Any]	  =      input('''\nEnter message: '''       )
  snake_case   : Union[str, Any]	  =      '''rsa_pubkey.txt'''
  print(f'''Encrypting and writing to {filename}...'''       )
  snake_case   : List[str]	  =      encrypt_and_write_to_file(__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )

  print('''\nEncrypted text:'''       )
  print(__lowerCamelCase       )

 elif mode == "decrypt":
  snake_case   : str	  =      '''rsa_privkey.txt'''
  print(f'''Reading from {filename} and decrypting...'''       )
  snake_case   : int	  =      read_from_file_and_decrypt(__lowerCamelCase ,     __lowerCamelCase       )
  print('''writing decryption to rsa_decryption.txt...'''       )
  with open('''rsa_decryption.txt''' ,     '''w'''       ) as dec:
   dec.write(__lowerCamelCase       )

  print('''\nDecryption:'''       )
  print(__lowerCamelCase       )


if __name__ == "__main__":
  main()






