

import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
def 					a				(      A__     :       Dict				,       A__     :       Union[str, Any]=1					)   ->				Dict:





      """simple docstring"""



      if n_shave_prefix_segments >= 0:
            return ".".join(path.split('.'					)[n_shave_prefix_segments:]					)
      else:
            return ".".join(path.split('.'					)[:n_shave_prefix_segments]					)
def 					a				(      A__     :       List[Any]				,       A__     :       List[Any]=0					)   ->				Optional[int]:





      """simple docstring"""



      _lowercase     	=[]
      for old_item in old_list:
            _lowercase     	=old_item
            _lowercase     	=new_item.replace('block.'				,       'resnets.'					)
            _lowercase     	=new_item.replace('conv_shorcut'				,       'conv1'					)
            _lowercase     	=new_item.replace('in_shortcut'				,       'conv_shortcut'					)
            _lowercase     	=new_item.replace('temb_proj'				,       'time_emb_proj'					)

            _lowercase     	=shave_segments(A__				,       n_shave_prefix_segments=A__					)

            mapping.append({'old': old_item, 'new': new_item}					)

      return mapping
def 					a				(      A__     :       Any				,       A__     :       Optional[Any]=0				,       A__     :       str=False					)   ->				Optional[int]:





      """simple docstring"""



      _lowercase     	=[]
      for old_item in old_list:
            _lowercase     	=old_item

            # In `model.mid`, the layer is called `attn`.
            if not in_mid:
                  _lowercase     	=new_item.replace('attn'				,       'attentions'					)
            _lowercase     	=new_item.replace('.k.'				,       '.key.'					)
            _lowercase     	=new_item.replace('.v.'				,       '.value.'					)
            _lowercase     	=new_item.replace('.q.'				,       '.query.'					)

            _lowercase     	=new_item.replace('proj_out'				,       'proj_attn'					)
            _lowercase     	=new_item.replace('norm'				,       'group_norm'					)

            _lowercase     	=shave_segments(A__				,       n_shave_prefix_segments=A__					)
            mapping.append({'old': old_item, 'new': new_item}					)

      return mapping
def 					a				(      A__     :       int				,       A__     :       Dict				,       A__     :       Optional[int]				,       A__     :       Dict=None				,       A__     :       List[Any]=None				,       A__     :       Optional[int]=None					)   ->				Optional[int]:





      """simple docstring"""



      assert isinstance(A__				,       A__					), "Paths should be a list of dicts containing 'old' and 'new' keys."

      if attention_paths_to_split is not None:
            if config is None:
                  raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'					)

            for path, path_map in attention_paths_to_split.items():
                  _lowercase     	=old_checkpoint[path]
                  _lowercase     	=old_tensor.shape[0] // 3

                  _lowercase     	=(-1, channels) if len(old_tensor.shape					) == 3 else (-1)

                  _lowercase     	=old_tensor.shape[0] // config.get('num_head_channels'				,       1					) // 3

                  _lowercase     	=old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
                  _lowercase	,	_lowercase	,	_lowercase     	=old_tensor.split(channels // num_heads				,       dim=1					)

                  _lowercase     	=query.reshape(A__					).squeeze()
                  _lowercase     	=key.reshape(A__					).squeeze()
                  _lowercase     	=value.reshape(A__					).squeeze()

      for path in paths:
            _lowercase     	=path['new']

            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            _lowercase     	=new_path.replace('down.'				,       'down_blocks.'					)
            _lowercase     	=new_path.replace('up.'				,       'up_blocks.'					)

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        _lowercase     	=new_path.replace(replacement['old']				,       replacement['new']					)

            if "attentions" in new_path:
                  _lowercase     	=old_checkpoint[path['old']].squeeze()
            else:
                  _lowercase     	=old_checkpoint[path['old']]
def 					a				(      A__     :       List[str]				,       A__     :       Optional[int]					)   ->				List[Any]:





      """simple docstring"""



      _lowercase     	={}

      _lowercase     	=checkpoint['temb.dense.0.weight']
      _lowercase     	=checkpoint['temb.dense.0.bias']
      _lowercase     	=checkpoint['temb.dense.1.weight']
      _lowercase     	=checkpoint['temb.dense.1.bias']

      _lowercase     	=checkpoint['norm_out.weight']
      _lowercase     	=checkpoint['norm_out.bias']

      _lowercase     	=checkpoint['conv_in.weight']
      _lowercase     	=checkpoint['conv_in.bias']
      _lowercase     	=checkpoint['conv_out.weight']
      _lowercase     	=checkpoint['conv_out.bias']

      _lowercase     	=len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'down' in layer}					)
      _lowercase     	={
          layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(A__					)
      }

      _lowercase     	=len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'up' in layer}					)
      _lowercase     	={layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(A__					)}

      for i in range(A__					):
            _lowercase     	=(i - 1) // (config['layers_per_block'] + 1)

            if any('downsample' in layer for layer in down_blocks[i]					):
                  _lowercase     	=checkpoint[
                      F'''down.{i}.downsample.op.weight'''
                  ]
                  _lowercase     	=checkpoint[F'''down.{i}.downsample.op.bias''']
            #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
            #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

            if any('block' in layer for layer in down_blocks[i]					):
                  _lowercase     	=len(
                      {'.'.join(shave_segments(A__				,       2					).split('.'					)[:2]					) for layer in down_blocks[i] if 'block' in layer}					)
                  _lowercase     	={
                      layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
                      for layer_id in range(A__					)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block']					):
                              _lowercase     	=renew_resnet_paths(blocks[j]					)
                              assign_to_checkpoint(A__				,       A__				,       A__					)

            if any('attn' in layer for layer in down_blocks[i]					):
                  _lowercase     	=len(
                      {'.'.join(shave_segments(A__				,       2					).split('.'					)[:2]					) for layer in down_blocks[i] if 'attn' in layer}					)
                  _lowercase     	={
                      layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
                      for layer_id in range(A__					)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block']					):
                              _lowercase     	=renew_attention_paths(attns[j]					)
                              assign_to_checkpoint(A__				,       A__				,       A__				,       config=A__					)

      _lowercase     	=[key for key in checkpoint if 'mid.block_1' in key]
      _lowercase     	=[key for key in checkpoint if 'mid.block_2' in key]
      _lowercase     	=[key for key in checkpoint if 'mid.attn_1' in key]

      # Mid new 2
      _lowercase     	=renew_resnet_paths(A__					)
      assign_to_checkpoint(
          A__				,       A__				,       A__				,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]				,       )

      _lowercase     	=renew_resnet_paths(A__					)
      assign_to_checkpoint(
          A__				,       A__				,       A__				,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]				,       )

      _lowercase     	=renew_attention_paths(A__				,       in_mid=A__					)
      assign_to_checkpoint(
          A__				,       A__				,       A__				,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]				,       )

      for i in range(A__					):
            _lowercase     	=num_up_blocks - 1 - i

            if any('upsample' in layer for layer in up_blocks[i]					):
                  _lowercase     	=checkpoint[
                      F'''up.{i}.upsample.conv.weight'''
                  ]
                  _lowercase     	=checkpoint[F'''up.{i}.upsample.conv.bias''']

            if any('block' in layer for layer in up_blocks[i]					):
                  _lowercase     	=len(
                      {'.'.join(shave_segments(A__				,       2					).split('.'					)[:2]					) for layer in up_blocks[i] if 'block' in layer}					)
                  _lowercase     	={
                      layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(A__					)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block'] + 1					):
                              _lowercase     	={'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
                              _lowercase     	=renew_resnet_paths(blocks[j]					)
                              assign_to_checkpoint(A__				,       A__				,       A__				,       additional_replacements=[replace_indices]					)

            if any('attn' in layer for layer in up_blocks[i]					):
                  _lowercase     	=len(
                      {'.'.join(shave_segments(A__				,       2					).split('.'					)[:2]					) for layer in up_blocks[i] if 'attn' in layer}					)
                  _lowercase     	={
                      layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(A__					)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block'] + 1					):
                              _lowercase     	={'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
                              _lowercase     	=renew_attention_paths(attns[j]					)
                              assign_to_checkpoint(A__				,       A__				,       A__				,       additional_replacements=[replace_indices]					)

      _lowercase     	={k.replace('mid_new_2'				,       'mid_block'					): v for k, v in new_checkpoint.items()}
      return new_checkpoint
def 					a				(      A__     :       List[Any]				,       A__     :       List[Any]					)   ->				Tuple:





      """simple docstring"""



      _lowercase     	={}

      _lowercase     	=checkpoint['encoder.norm_out.weight']
      _lowercase     	=checkpoint['encoder.norm_out.bias']

      _lowercase     	=checkpoint['encoder.conv_in.weight']
      _lowercase     	=checkpoint['encoder.conv_in.bias']
      _lowercase     	=checkpoint['encoder.conv_out.weight']
      _lowercase     	=checkpoint['encoder.conv_out.bias']

      _lowercase     	=checkpoint['decoder.norm_out.weight']
      _lowercase     	=checkpoint['decoder.norm_out.bias']

      _lowercase     	=checkpoint['decoder.conv_in.weight']
      _lowercase     	=checkpoint['decoder.conv_in.bias']
      _lowercase     	=checkpoint['decoder.conv_out.weight']
      _lowercase     	=checkpoint['decoder.conv_out.bias']

      _lowercase     	=len({'.'.join(layer.split('.'					)[:3]					) for layer in checkpoint if 'down' in layer}					)
      _lowercase     	={
          layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(A__					)
      }

      _lowercase     	=len({'.'.join(layer.split('.'					)[:3]					) for layer in checkpoint if 'up' in layer}					)
      _lowercase     	={layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(A__					)}

      for i in range(A__					):
            _lowercase     	=(i - 1) // (config['layers_per_block'] + 1)

            if any('downsample' in layer for layer in down_blocks[i]					):
                  _lowercase     	=checkpoint[
                      F'''encoder.down.{i}.downsample.conv.weight'''
                  ]
                  _lowercase     	=checkpoint[
                      F'''encoder.down.{i}.downsample.conv.bias'''
                  ]

            if any('block' in layer for layer in down_blocks[i]					):
                  _lowercase     	=len(
                      {'.'.join(shave_segments(A__				,       3					).split('.'					)[:3]					) for layer in down_blocks[i] if 'block' in layer}					)
                  _lowercase     	={
                      layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
                      for layer_id in range(A__					)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block']					):
                              _lowercase     	=renew_resnet_paths(blocks[j]					)
                              assign_to_checkpoint(A__				,       A__				,       A__					)

            if any('attn' in layer for layer in down_blocks[i]					):
                  _lowercase     	=len(
                      {'.'.join(shave_segments(A__				,       3					).split('.'					)[:3]					) for layer in down_blocks[i] if 'attn' in layer}					)
                  _lowercase     	={
                      layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
                      for layer_id in range(A__					)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block']					):
                              _lowercase     	=renew_attention_paths(attns[j]					)
                              assign_to_checkpoint(A__				,       A__				,       A__				,       config=A__					)

      _lowercase     	=[key for key in checkpoint if 'mid.block_1' in key]
      _lowercase     	=[key for key in checkpoint if 'mid.block_2' in key]
      _lowercase     	=[key for key in checkpoint if 'mid.attn_1' in key]

      # Mid new 2
      _lowercase     	=renew_resnet_paths(A__					)
      assign_to_checkpoint(
          A__				,       A__				,       A__				,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]				,       )

      _lowercase     	=renew_resnet_paths(A__					)
      assign_to_checkpoint(
          A__				,       A__				,       A__				,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]				,       )

      _lowercase     	=renew_attention_paths(A__				,       in_mid=A__					)
      assign_to_checkpoint(
          A__				,       A__				,       A__				,       additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]				,       )

      for i in range(A__					):
            _lowercase     	=num_up_blocks - 1 - i

            if any('upsample' in layer for layer in up_blocks[i]					):
                  _lowercase     	=checkpoint[
                      F'''decoder.up.{i}.upsample.conv.weight'''
                  ]
                  _lowercase     	=checkpoint[
                      F'''decoder.up.{i}.upsample.conv.bias'''
                  ]

            if any('block' in layer for layer in up_blocks[i]					):
                  _lowercase     	=len(
                      {'.'.join(shave_segments(A__				,       3					).split('.'					)[:3]					) for layer in up_blocks[i] if 'block' in layer}					)
                  _lowercase     	={
                      layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(A__					)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block'] + 1					):
                              _lowercase     	={'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
                              _lowercase     	=renew_resnet_paths(blocks[j]					)
                              assign_to_checkpoint(A__				,       A__				,       A__				,       additional_replacements=[replace_indices]					)

            if any('attn' in layer for layer in up_blocks[i]					):
                  _lowercase     	=len(
                      {'.'.join(shave_segments(A__				,       3					).split('.'					)[:3]					) for layer in up_blocks[i] if 'attn' in layer}					)
                  _lowercase     	={
                      layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(A__					)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block'] + 1					):
                              _lowercase     	={'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
                              _lowercase     	=renew_attention_paths(attns[j]					)
                              assign_to_checkpoint(A__				,       A__				,       A__				,       additional_replacements=[replace_indices]					)

      _lowercase     	={k.replace('mid_new_2'				,       'mid_block'					): v for k, v in new_checkpoint.items()}
      _lowercase     	=checkpoint['quant_conv.weight']
      _lowercase     	=checkpoint['quant_conv.bias']
      if "quantize.embedding.weight" in checkpoint:
            _lowercase     	=checkpoint['quantize.embedding.weight']
      _lowercase     	=checkpoint['post_quant_conv.weight']
      _lowercase     	=checkpoint['post_quant_conv.bias']

      return new_checkpoint


if __name__ == "__main__":
  lowercase_     =       argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
  )

  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help='The config json file corresponding to the architecture.',
  )

  parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

  lowercase_     =       parser.parse_args()
  lowercase_     =       torch.load(args.checkpoint_path)

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

  # unet case
  lowercase_     =       {key.split('.')[0] for key in checkpoint.keys()}
  if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    lowercase_     =       convert_vq_autoenc_checkpoint(checkpoint, config)
  else:
    lowercase_     =       convert_ddpm_checkpoint(checkpoint, config)

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

  if config["_class_name"] == "VQModel":
    lowercase_     =       VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  elif config["_class_name"] == "AutoencoderKL":
    lowercase_     =       AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  else:
    lowercase_     =       UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    lowercase_     =       DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

    lowercase_     =       DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)





import os
import sys

from . import rsa_key_generator as rkg

lowercase_     =       1_2_8
lowercase_     =       2_5_6
def 					a				(      A__     :       str				,       A__     :       int = DEFAULT_BLOCK_SIZE					)   ->				list[int]:





      """simple docstring"""



      _lowercase     	=message.encode('ascii'					)

      _lowercase     	=[]
      for block_start in range(0				,       len(A__					)				,       A__					):
            _lowercase     	=0
            for i in range(A__				,       min(block_start + block_size				,       len(A__					)					)					):
                  block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
            block_ints.append(A__					)
      return block_ints
def 					a				(      A__     :       list[int]				,       A__     :       int				,       A__     :       int = DEFAULT_BLOCK_SIZE					)   ->				str:





      """simple docstring"""



      _lowercase     	=[]
      for block_int in block_ints:
            _lowercase     	=[]
            for i in range(block_size - 1				,       -1				,       -1					):
                  if len(A__					) + i < message_length:
                        _lowercase     	=block_int // (BYTE_SIZE**i)
                        _lowercase     	=block_int % (BYTE_SIZE**i)
                        block_message.insert(0				,       chr(A__					)					)
            message.extend(A__					)
      return "".join(A__					)
def 					a				(      A__     :       str				,       A__     :       tuple[int, int]				,       A__     :       int = DEFAULT_BLOCK_SIZE					)   ->				list[int]:





      """simple docstring"""



      _lowercase     	=[]
      _lowercase	,	_lowercase     	=key

      for block in get_blocks_from_text(A__				,       A__					):
            encrypted_blocks.append(pow(A__				,       A__				,       A__					)					)
      return encrypted_blocks
def 					a				(      A__     :       list[int]				,       A__     :       int				,       A__     :       tuple[int, int]				,       A__     :       int = DEFAULT_BLOCK_SIZE				,       )   ->				str:





      """simple docstring"""



      _lowercase     	=[]
      _lowercase	,	_lowercase     	=key
      for block in encrypted_blocks:
            decrypted_blocks.append(pow(A__				,       A__				,       A__					)					)
      return get_text_from_blocks(A__				,       A__				,       A__					)
def 					a				(      A__     :       str					)   ->				tuple[int, int, int]:





      """simple docstring"""



      with open(A__					) as fo:
            _lowercase     	=fo.read()
      _lowercase	,	_lowercase	,	_lowercase     	=content.split(','					)
      return (int(A__					), int(A__					), int(A__					))
def 					a				(      A__     :       str				,       A__     :       str				,       A__     :       str				,       A__     :       int = DEFAULT_BLOCK_SIZE				,       )   ->				str:





      """simple docstring"""



      _lowercase	,	_lowercase	,	_lowercase     	=read_key_file(A__					)
      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				,       A__					)					)

      _lowercase     	=[str(A__					) for i in encrypt_message(A__				,       (n, e)				,       A__					)]

      _lowercase     	=','.join(A__					)
      _lowercase     	=F'''{len(A__					)}_{block_size}_{encrypted_content}'''
      with open(A__				,       'w'					) as fo:
            fo.write(A__					)
      return encrypted_content
def 					a				(      A__     :       str				,       A__     :       str					)   ->				str:





      """simple docstring"""



      _lowercase	,	_lowercase	,	_lowercase     	=read_key_file(A__					)
      with open(A__					) as fo:
            _lowercase     	=fo.read()
      _lowercase	,	_lowercase	,	_lowercase     	=content.split('_'					)
      _lowercase     	=int(A__					)
      _lowercase     	=int(A__					)

      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				,       A__					)					)

      _lowercase     	=[]
      for block in encrypted_message.split(','					):
            encrypted_blocks.append(int(A__					)					)

      return decrypt_message(A__				,       A__				,       (n, d)				,       A__					)
def 					a				(      )   ->				None:





      """simple docstring"""



      _lowercase     	='encrypted_file.txt'
      _lowercase     	=input(r'Encrypt\Decrypt [e\d]: '					)

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

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

            _lowercase     	=input('\nEnter message: '					)
            _lowercase     	='rsa_pubkey.txt'
            print(F'''Encrypting and writing to {filename}...'''					)
            _lowercase     	=encrypt_and_write_to_file(A__				,       A__				,       A__					)

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

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

            print('\nDecryption:'					)
            print(A__					)


if __name__ == "__main__":
  main()


