




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 get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class 		lowercase							(     UpperCamelCase__	):
     _a											=		4_2







class 		lowercase							(     UpperCamelCase__,UpperCamelCase__	):

     @register_to_config
     def __init__(						self	,  _a = 6_5536	,  _a = None	,  _a = 2	,  _a = 2	,  _a = 0	,  _a = "fourier"	,  _a = True	,  _a = False	,  _a = 0.0	,  _a = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")	,  _a = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")	,  _a = "UNetMidBlock1D"	,  _a = None	,  _a = (32, 32, 64)	,  _a = None	,  _a = 8	,  _a = 1	,  _a = False	,  )		->							Optional[Any]:
           super().__init__()
           _A							:  Optional[int]    =		sample_size

           # time
           if time_embedding_type == "fourier":
                 _A							:  Union[str, Any]    =		GaussianFourierProjection(
                     embedding_size=8	,  set_W_to_weight=_a	,  log=_a	,  flip_sin_to_cos=_a       )
                 _A							:  Any    =		2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                 _A							:  str    =		Timesteps(
                     block_out_channels[0]	,  flip_sin_to_cos=_a	,  downscale_freq_shift=_a       )
                 _A							:  int    =		block_out_channels[0]

           if use_timestep_embedding:
                 _A							:  List[str]    =		block_out_channels[0] * 4
                 _A							:  str    =		TimestepEmbedding(
                     in_channels=_a	,  time_embed_dim=_a	,  act_fn=_a	,  out_dim=block_out_channels[0]	,  )

           _A							:  Dict    =		nn.ModuleList([]       )
           _A							:  Optional[int]    =		None
           _A							:  List[Any]    =		nn.ModuleList([]       )
           _A							:  Optional[int]    =		None

           # down
           _A							:  Union[str, Any]    =		in_channels
           for i, down_block_type in enumerate(_a       ):
                 _A							:  List[str]    =		output_channel
                 _A							:  str    =		block_out_channels[i]

                 if i == 0:
                       input_channel += extra_in_channels

                 _A							:  Any    =		i == len(_a       ) - 1

                 _A							:  List[str]    =		get_down_block(
                     _a	,  num_layers=_a	,  in_channels=_a	,  out_channels=_a	,  temb_channels=block_out_channels[0]	,  add_downsample=not is_final_block or downsample_each_block	,  )
                 self.down_blocks.append(_a       )

           # mid
           _A							:  Union[str, Any]    =		get_mid_block(
               _a	,  in_channels=block_out_channels[-1]	,  mid_channels=block_out_channels[-1]	,  out_channels=block_out_channels[-1]	,  embed_dim=block_out_channels[0]	,  num_layers=_a	,  add_downsample=_a	,  )

           # up
           _A							:  List[Any]    =		list(reversed(_a       )       )
           _A							:  str    =		reversed_block_out_channels[0]
           if out_block_type is None:
                 _A							:  List[str]    =		out_channels
           else:
                 _A							:  Tuple    =		block_out_channels[0]

           for i, up_block_type in enumerate(_a       ):
                 _A							:  str    =		output_channel
                 _A							:  Union[str, Any]    =		(
                     reversed_block_out_channels[i + 1] if i < len(_a       ) - 1 else final_upsample_channels
                 )

                 _A							:  Any    =		i == len(_a       ) - 1

                 _A							:  Any    =		get_up_block(
                     _a	,  num_layers=_a	,  in_channels=_a	,  out_channels=_a	,  temb_channels=block_out_channels[0]	,  add_upsample=not is_final_block	,  )
                 self.up_blocks.append(_a       )
                 _A							:  Tuple    =		output_channel

           # out
           _A							:  Optional[int]    =		norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,  32       )
           _A							:  Optional[Any]    =		get_out_block(
               out_block_type=_a	,  num_groups_out=_a	,  embed_dim=block_out_channels[0]	,  out_channels=_a	,  act_fn=_a	,  fc_dim=block_out_channels[-1] // 4	,  )



     def       a__			(						self	,  _a	,  _a	,  _a = True	,  )		->							Union[UNetaDOutput, Tuple]:
           _A							:  str    =		timestep
           if not torch.is_tensor(_a       ):
                 _A							:  str    =		torch.tensor([timesteps]	,  dtype=torch.long	,  device=sample.device       )
           elif torch.is_tensor(_a       ) and len(timesteps.shape       ) == 0:
                 _A							:  int    =		timesteps[None].to(sample.device       )

           _A							:  Union[str, Any]    =		self.time_proj(_a       )
           if self.config.use_timestep_embedding:
                 _A							:  List[str]    =		self.time_mlp(_a       )
           else:
                 _A							:  Optional[Any]    =		timestep_embed[..., None]
                 _A							:  Any    =		timestep_embed.repeat([1, 1, sample.shape[2]]       ).to(sample.dtype       )
                 _A							:  str    =		timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])       )

           # 2. down
           _A							:  int    =		()
           for downsample_block in self.down_blocks:
                 _A							:  Optional[Any]    =		downsample_block(hidden_states=_a	,  temb=_a       )
                 down_block_res_samples += res_samples

           # 3. mid
           if self.mid_block:
                 _A							:  Dict    =		self.mid_block(_a	,  _a       )

           # 4. up
           for i, upsample_block in enumerate(self.up_blocks       ):
                 _A							:  str    =		down_block_res_samples[-1:]
                 _A							:  Union[str, Any]    =		down_block_res_samples[:-1]
                 _A							:  Optional[Any]    =		upsample_block(_a	,  res_hidden_states_tuple=_a	,  temb=_a       )

           # 5. post-process
           if self.out_block:
                 _A							:  Any    =		self.out_block(_a	,  _a       )

           if not return_dict:
                 return (sample,)

           return UNetaDOutput(sample=_a       )







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Union[str, Any]    =		""""""
      for i in table:
            res += inp[i - 1]
      return res







def        lowerCAmelCase_			(  snake_case_  ):
      return data[1:] + data[0]







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Dict    =		""""""
      for i in range(len(snake_case_  )  ):
            if a[i] == b[i]:
                  res += "0"
            else:
                  res += "1"
      return res







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  int    =		int("""0b""" + data[0] + data[-1],2  )
      _A							:  Any    =		int("""0b""" + data[1:3],2  )
      return bin(s[row][col]  )[2:]







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_,snake_case_  ):
      _A							:  List[str]    =		message[:4]
      _A							:  List[Any]    =		message[4:]
      _A							:  Union[str, Any]    =		apply_table(snake_case_,snake_case_  )
      _A							:  List[Any]    =		xor(snake_case_,snake_case_  )
      _A							:  Optional[Any]    =		apply_sbox(snake_case_,temp[:4]  )  # noqa: E741
      _A							:  List[Any]    =		apply_sbox(snake_case_,temp[4:]  )
      _A							:  int    =		"""0""" * (2 - len(snake_case_  )) + l  # noqa: E741
      _A							:  Union[str, Any]    =		"""0""" * (2 - len(snake_case_  )) + r
      _A							:  List[Any]    =		apply_table(l + r,snake_case_  )
      _A							:  Any    =		xor(snake_case_,snake_case_  )
      return temp + right


if __name__ == "__main__":
  _snake_case            = input("Enter 10 bit key: ")
  _snake_case            = input("Enter 8 bit message: ")

  _snake_case            = [6, 3, 7, 4, 8, 5, 10, 9]
  _snake_case            = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
  _snake_case            = [2, 4, 3, 1]
  _snake_case            = [2, 6, 3, 1, 4, 8, 5, 7]
  _snake_case            = [4, 1, 3, 5, 7, 2, 8, 6]
  _snake_case            = [4, 1, 2, 3, 2, 3, 4, 1]
  _snake_case            = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
  _snake_case            = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

  # key generation
  _snake_case            = apply_table(key, paa_table)
  _snake_case            = temp[:5]
  _snake_case            = temp[5:]
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = apply_table(left + right, pa_table)
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = apply_table(left + right, pa_table)

  # encryption
  _snake_case            = apply_table(message, IP)
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = temp[4:] + temp[:4]
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = apply_table(temp, IP_inv)
  print("Cipher text is:", CT)

  # decryption
  _snake_case            = apply_table(CT, IP)
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = temp[4:] + temp[:4]
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = apply_table(temp, IP_inv)
  print("Plain text after decypting is:", PT)

