


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class      lowerCamelCase	(  snake_case__     ):
       _UpperCamelCase    		=      42







class      lowerCamelCase	(  snake_case__,			snake_case__     ):

       @register_to_config
       def __init__(			self	,						a__ = None	,						a__ = 3	,						a__ = 3	,						a__ = False	,						a__ = "positional"	,						a__ = 0	,						a__ = True	,						a__ = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")	,						a__ = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")	,						a__ = (2_24, 4_48, 6_72, 8_96)	,						a__ = 2	,						a__ = 1	,						a__ = 1	,						a__ = "conv"	,						a__ = "conv"	,						a__ = "silu"	,						a__ = 8	,						a__ = 32	,						a__ = 1e-5	,						a__ = "default"	,						a__ = True	,						a__ = None	,						a__ = None	,						):






        '''simple docstring'''






        super().__init__()

        snake_case   : str	  =      sample_size
        snake_case   : List[Any]	  =      block_out_channels[0] * 4

        # Check inputs
        if len(__snake_case					) != len(__snake_case					):
         raise ValueError(
             f'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'''					)

        if len(__snake_case					) != len(__snake_case					):
         raise ValueError(
             f'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'''					)

        # input
        snake_case   : Optional[Any]	  =      nn.Convad(__snake_case	,						block_out_channels[0]	,						kernel_size=3	,						padding=(1, 1)					)

        # time
        if time_embedding_type == "fourier":
         snake_case   : Optional[int]	  =      GaussianFourierProjection(embedding_size=block_out_channels[0]	,						scale=16					)
         snake_case   : List[str]	  =      2 * block_out_channels[0]
        elif time_embedding_type == "positional":
         snake_case   : Optional[int]	  =      Timesteps(block_out_channels[0]	,						__snake_case	,						__snake_case					)
         snake_case   : Union[str, Any]	  =      block_out_channels[0]

        snake_case   : int	  =      TimestepEmbedding(__snake_case	,						__snake_case					)

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
         snake_case   : List[str]	  =      nn.Embedding(__snake_case	,						__snake_case					)
        elif class_embed_type == "timestep":
         snake_case   : List[str]	  =      TimestepEmbedding(__snake_case	,						__snake_case					)
        elif class_embed_type == "identity":
         snake_case   : Dict	  =      nn.Identity(__snake_case	,						__snake_case					)
        else:
         snake_case   : Any	  =      None

        snake_case   : Optional[int]	  =      nn.ModuleList([]					)
        snake_case   : Dict	  =      None
        snake_case   : List[str]	  =      nn.ModuleList([]					)

        # down
        snake_case   : Union[str, Any]	  =      block_out_channels[0]
        for i, down_block_type in enumerate(__snake_case					):
         snake_case   : Dict	  =      output_channel
         snake_case   : str	  =      block_out_channels[i]
         snake_case   : Dict	  =      i == len(__snake_case					) - 1

         snake_case   : Tuple	  =      get_down_block(
             __snake_case	,						num_layers=__snake_case	,						in_channels=__snake_case	,						out_channels=__snake_case	,						temb_channels=__snake_case	,						add_downsample=not is_final_block	,						resnet_eps=__snake_case	,						resnet_act_fn=__snake_case	,						resnet_groups=__snake_case	,						attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel	,						downsample_padding=__snake_case	,						resnet_time_scale_shift=__snake_case	,						downsample_type=__snake_case	,						)
         self.down_blocks.append(__snake_case					)

        # mid
        snake_case   : Union[str, Any]	  =      UNetMidBlockaD(
            in_channels=block_out_channels[-1]	,						temb_channels=__snake_case	,						resnet_eps=__snake_case	,						resnet_act_fn=__snake_case	,						output_scale_factor=__snake_case	,						resnet_time_scale_shift=__snake_case	,						attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]	,						resnet_groups=__snake_case	,						add_attention=__snake_case	,						)

        # up
        snake_case   : int	  =      list(reversed(__snake_case					)					)
        snake_case   : List[str]	  =      reversed_block_out_channels[0]
        for i, up_block_type in enumerate(__snake_case					):
         snake_case   : Tuple	  =      output_channel
         snake_case   : Optional[Any]	  =      reversed_block_out_channels[i]
         snake_case   : List[str]	  =      reversed_block_out_channels[min(i + 1	,						len(__snake_case					) - 1					)]

         snake_case   : Optional[int]	  =      i == len(__snake_case					) - 1

         snake_case   : Optional[int]	  =      get_up_block(
             __snake_case	,						num_layers=layers_per_block + 1	,						in_channels=__snake_case	,						out_channels=__snake_case	,						prev_output_channel=__snake_case	,						temb_channels=__snake_case	,						add_upsample=not is_final_block	,						resnet_eps=__snake_case	,						resnet_act_fn=__snake_case	,						resnet_groups=__snake_case	,						attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel	,						resnet_time_scale_shift=__snake_case	,						upsample_type=__snake_case	,						)
         self.up_blocks.append(__snake_case					)
         snake_case   : Dict	  =      output_channel

        # out
        snake_case   : int	  =      norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,						32					)
        snake_case   : Tuple	  =      nn.GroupNorm(num_channels=block_out_channels[0]	,						num_groups=__snake_case	,						eps=__snake_case					)
        snake_case   : List[Any]	  =      nn.SiLU()
        snake_case   : int	  =      nn.Convad(block_out_channels[0]	,						__snake_case	,						kernel_size=3	,						padding=1					)



       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__ = None	,						a__ = True	,						):






        '''simple docstring'''






        if self.config.center_input_sample:
         snake_case   : List[Any]	  =      2 * sample - 1.0

        # 1. time
        snake_case   : List[str]	  =      timestep
        if not torch.is_tensor(__snake_case					):
         snake_case   : int	  =      torch.tensor([timesteps]	,						dtype=torch.long	,						device=sample.device					)
        elif torch.is_tensor(__snake_case					) and len(timesteps.shape					) == 0:
         snake_case   : Optional[int]	  =      timesteps[None].to(sample.device					)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        snake_case   : Optional[int]	  =      timesteps * torch.ones(sample.shape[0]	,						dtype=timesteps.dtype	,						device=timesteps.device					)

        snake_case   : Dict	  =      self.time_proj(__snake_case					)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        snake_case   : Union[str, Any]	  =      t_emb.to(dtype=self.dtype					)
        snake_case   : Optional[Any]	  =      self.time_embedding(__snake_case					)

        if self.class_embedding is not None:
         if class_labels is None:
          raise ValueError('''class_labels should be provided when doing class conditioning'''					)

         if self.config.class_embed_type == "timestep":
          snake_case   : Any	  =      self.time_proj(__snake_case					)

         snake_case   : Tuple	  =      self.class_embedding(__snake_case					).to(dtype=self.dtype					)
         snake_case   : List[str]	  =      emb + class_emb

        # 2. pre-process
        snake_case   : Tuple	  =      sample
        snake_case   : str	  =      self.conv_in(__snake_case					)

        # 3. down
        snake_case   : List[str]	  =      (sample,)
        for downsample_block in self.down_blocks:
         if hasattr(__snake_case	,						'''skip_conv'''					):
          snake_case  ,					snake_case  ,					snake_case   : Optional[int]	  =      downsample_block(
              hidden_states=__snake_case	,						temb=__snake_case	,						skip_sample=__snake_case					)
         else:
          snake_case  ,					snake_case   : Optional[Any]	  =      downsample_block(hidden_states=__snake_case	,						temb=__snake_case					)

         down_block_res_samples += res_samples

        # 4. mid
        snake_case   : Optional[int]	  =      self.mid_block(__snake_case	,						__snake_case					)

        # 5. up
        snake_case   : List[Any]	  =      None
        for upsample_block in self.up_blocks:
         snake_case   : str	  =      down_block_res_samples[-len(upsample_block.resnets					) :]
         snake_case   : str	  =      down_block_res_samples[: -len(upsample_block.resnets					)]

         if hasattr(__snake_case	,						'''skip_conv'''					):
          snake_case  ,					snake_case   : Any	  =      upsample_block(__snake_case	,						__snake_case	,						__snake_case	,						__snake_case					)
         else:
          snake_case   : List[str]	  =      upsample_block(__snake_case	,						__snake_case	,						__snake_case					)

        # 6. post-process
        snake_case   : Dict	  =      self.conv_norm_out(__snake_case					)
        snake_case   : Optional[int]	  =      self.conv_act(__snake_case					)
        snake_case   : Dict	  =      self.conv_out(__snake_case					)

        if skip_sample is not None:
         sample += skip_sample

        if self.config.time_embedding_type == "fourier":
         snake_case   : Any	  =      timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]					)))					)
         snake_case   : List[str]	  =      sample / timesteps

        if not return_dict:
         return (sample,)

        return UNetaDOutput(sample=__snake_case					)










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()






