




"""simple docstring"""

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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder





@dataclass
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					"DiagonalGaussianDistribution"





class     lowercase_	(					__lowerCAmelCase      ,     __lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					List[Any]				  =     True

 @register_to_config
 def __init__(			self	:	List[str]   ,							_UpperCAmelCase	:	int = 3   ,							_UpperCAmelCase	:	int = 3   ,							_UpperCAmelCase	:	Tuple[str] = ("DownEncoderBlock2D",)   ,							_UpperCAmelCase	:	Tuple[str] = ("UpDecoderBlock2D",)   ,							_UpperCAmelCase	:	Tuple[int] = (64,)   ,							_UpperCAmelCase	:	int = 1   ,							_UpperCAmelCase	:	str = "silu"   ,							_UpperCAmelCase	:	int = 4   ,							_UpperCAmelCase	:	int = 32   ,							_UpperCAmelCase	:	int = 32   ,							_UpperCAmelCase	:	float = 0.1_8215   ,							):
       super().__init__()

       # pass init params to Encoder
       _A        =       Encoder(
           in_channels=_UpperCAmelCase   ,							out_channels=_UpperCAmelCase   ,							down_block_types=_UpperCAmelCase   ,							block_out_channels=_UpperCAmelCase   ,							layers_per_block=_UpperCAmelCase   ,							act_fn=_UpperCAmelCase   ,							norm_num_groups=_UpperCAmelCase   ,							double_z=_UpperCAmelCase   ,							)

       # pass init params to Decoder
       _A        =       Decoder(
           in_channels=_UpperCAmelCase   ,							out_channels=_UpperCAmelCase   ,							up_block_types=_UpperCAmelCase   ,							block_out_channels=_UpperCAmelCase   ,							layers_per_block=_UpperCAmelCase   ,							norm_num_groups=_UpperCAmelCase   ,							act_fn=_UpperCAmelCase   ,							)

       _A        =       nn.Convad(2 * latent_channels   ,							2 * latent_channels   ,							1		)
       _A        =       nn.Convad(_UpperCAmelCase   ,							_UpperCAmelCase   ,							1		)

       _A        =       False
       _A        =       False

       # only relevant if vae tiling is enabled
       _A        =       self.config.sample_size
       _A        =       (
           self.config.sample_size[0]
           if isinstance(self.config.sample_size   ,							(list, tuple)		)
           else self.config.sample_size
       )
       _A        =       int(sample_size / (2 ** (len(self.config.block_out_channels		) - 1))		)
       _A        =       0.25

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Tuple=False		):
       if isinstance(_UpperCAmelCase   ,							(Encoder, Decoder)		):
             _A        =       value

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	bool = True		):
       _A        =       use_tiling

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       self.enable_tiling(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       True

 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       False

 @property
 # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       {}

       def fn_recursive_add_processors(_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	torch.nn.Module   ,							_UpperCAmelCase	:	Dict[str, AttentionProcessor]		):
             if hasattr(_UpperCAmelCase   ,							'set_processor'		):
                   _A        =       module.processor

             for sub_name, child in module.named_children():
                   fn_recursive_add_processors(F'''{name}.{sub_name}'''   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

             return processors

       for name, module in self.named_children():
             fn_recursive_add_processors(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

       return processors

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Union[AttentionProcessor, Dict[str, AttentionProcessor]]		):
       _A        =       len(self.attn_processors.keys()		)

       if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		) and len(_UpperCAmelCase		) != count:
             raise ValueError(
                 F'''A dict of processors was passed, but the number of processors {len(_UpperCAmelCase		)} does not match the'''
                 F''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''		)

       def fn_recursive_attn_processor(_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	torch.nn.Module   ,							_UpperCAmelCase	:	int		):
             if hasattr(_UpperCAmelCase   ,							'set_processor'		):
                   if not isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
                         module.set_processor(_UpperCAmelCase		)
                   else:
                         module.set_processor(processor.pop(F'''{name}.processor'''		)		)

             for sub_name, child in module.named_children():
                   fn_recursive_attn_processor(F'''{name}.{sub_name}'''   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

       for name, module in self.named_children():
             fn_recursive_attn_processor(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       self.set_attn_processor(AttnProcessor()		)

 @apply_forward_hook
 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = True		):
       if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
             return self.tiled_encode(_UpperCAmelCase   ,							return_dict=_UpperCAmelCase		)

       if self.use_slicing and x.shape[0] > 1:
             _A        =       [self.encoder(_UpperCAmelCase		) for x_slice in x.split(1		)]
             _A        =       torch.cat(_UpperCAmelCase		)
       else:
             _A        =       self.encoder(_UpperCAmelCase		)

       _A        =       self.quant_conv(_UpperCAmelCase		)
       _A        =       DiagonalGaussianDistribution(_UpperCAmelCase		)

       if not return_dict:
             return (posterior,)

       return AutoencoderKLOutput(latent_dist=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = True		):
       if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
             return self.tiled_decode(_UpperCAmelCase   ,							return_dict=_UpperCAmelCase		)

       _A        =       self.post_quant_conv(_UpperCAmelCase		)
       _A        =       self.decoder(_UpperCAmelCase		)

       if not return_dict:
             return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase		)

 @apply_forward_hook
 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = True		):
       if self.use_slicing and z.shape[0] > 1:
             _A        =       [self._decode(_UpperCAmelCase		).sample for z_slice in z.split(1		)]
             _A        =       torch.cat(_UpperCAmelCase		)
       else:
             _A        =       self._decode(_UpperCAmelCase		).sample

       if not return_dict:
             return (decoded,)

       return DecoderOutput(sample=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	Any		):
       _A        =       min(a.shape[2]   ,							b.shape[2]   ,							_UpperCAmelCase		)
       for y in range(_UpperCAmelCase		):
             _A        =       a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
       return b

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	List[Any]		):
       _A        =       min(a.shape[3]   ,							b.shape[3]   ,							_UpperCAmelCase		)
       for x in range(_UpperCAmelCase		):
             _A        =       a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
       return b

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = True		):
       _A        =       int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)		)
       _A        =       int(self.tile_latent_min_size * self.tile_overlap_factor		)
       _A        =       self.tile_latent_min_size - blend_extent

       # Split the image into 512x512 tiles and encode them separately.
       _A        =       []
       for i in range(0   ,							x.shape[2]   ,							_UpperCAmelCase		):
             _A        =       []
             for j in range(0   ,							x.shape[3]   ,							_UpperCAmelCase		):
                   _A        =       x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                   _A        =       self.encoder(_UpperCAmelCase		)
                   _A        =       self.quant_conv(_UpperCAmelCase		)
                   row.append(_UpperCAmelCase		)
             rows.append(_UpperCAmelCase		)
       _A        =       []
       for i, row in enumerate(_UpperCAmelCase		):
             _A        =       []
             for j, tile in enumerate(_UpperCAmelCase		):
                   # blend the above tile and the left tile
                   # to the current tile and add the current tile to the result row
                   if i > 0:
                         _A        =       self.blend_v(rows[i - 1][j]   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
                   if j > 0:
                         _A        =       self.blend_h(row[j - 1]   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
                   result_row.append(tile[:, :, :row_limit, :row_limit]		)
             result_rows.append(torch.cat(_UpperCAmelCase   ,							dim=3		)		)

       _A        =       torch.cat(_UpperCAmelCase   ,							dim=2		)
       _A        =       DiagonalGaussianDistribution(_UpperCAmelCase		)

       if not return_dict:
             return (posterior,)

       return AutoencoderKLOutput(latent_dist=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = True		):
       _A        =       int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)		)
       _A        =       int(self.tile_sample_min_size * self.tile_overlap_factor		)
       _A        =       self.tile_sample_min_size - blend_extent

       # Split z into overlapping 64x64 tiles and decode them separately.
       # The tiles have an overlap to avoid seams between tiles.
       _A        =       []
       for i in range(0   ,							z.shape[2]   ,							_UpperCAmelCase		):
             _A        =       []
             for j in range(0   ,							z.shape[3]   ,							_UpperCAmelCase		):
                   _A        =       z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                   _A        =       self.post_quant_conv(_UpperCAmelCase		)
                   _A        =       self.decoder(_UpperCAmelCase		)
                   row.append(_UpperCAmelCase		)
             rows.append(_UpperCAmelCase		)
       _A        =       []
       for i, row in enumerate(_UpperCAmelCase		):
             _A        =       []
             for j, tile in enumerate(_UpperCAmelCase		):
                   # blend the above tile and the left tile
                   # to the current tile and add the current tile to the result row
                   if i > 0:
                         _A        =       self.blend_v(rows[i - 1][j]   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
                   if j > 0:
                         _A        =       self.blend_h(row[j - 1]   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
                   result_row.append(tile[:, :, :row_limit, :row_limit]		)
             result_rows.append(torch.cat(_UpperCAmelCase   ,							dim=3		)		)

       _A        =       torch.cat(_UpperCAmelCase   ,							dim=2		)
       if not return_dict:
             return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	bool = True   ,							_UpperCAmelCase	:	Optional[torch.Generator] = None   ,							):
       _A        =       sample
       _A        =       self.encode(_UpperCAmelCase		).latent_dist
       if sample_posterior:
             _A        =       posterior.sample(generator=_UpperCAmelCase		)
       else:
             _A        =       posterior.mode()
       _A        =       self.decode(_UpperCAmelCase		).sample

       if not return_dict:
             return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase		)







"""simple docstring"""

def 						_snake_case					( _snake_case							: str							)   -> str:






      '''simple docstring'''


      _A        =       ''
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups






def 						_snake_case					( _snake_case							: str							)   -> dict[str, str]:






      '''simple docstring'''


      _A        =       [chr(i + 65							) for i in range(26							)]
      # Remove duplicate characters from key
      _A        =       remove_duplicates(key.upper()							)
      _A        =       len(_snake_case							)
      # First fill cipher with key characters
      _A        =       {alphabet[i]: char for i, char in enumerate(_snake_case							)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(_snake_case							)    ,  26							):
            _A        =       alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  _A        =       alphabet[i - offset]
            _A        =       char
      return cipher_alphabet






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


      return "".join(cipher_map.get(_snake_case    ,  _snake_case							) for ch in message.upper()							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


      _A        =       {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_snake_case    ,  _snake_case							) for ch in message.upper()							)






def 						_snake_case					( )   -> None:






      '''simple docstring'''


      _A        =       input('Enter message to encode or decode: '							).strip()
      _A        =       input('Enter keyword: '							).strip()
      _A        =       input('Encipher or decipher? E/D:'							).strip()[0].lower()
      try:
            _A        =       {'e': encipher, 'd': decipher}[option]
      except KeyError:
            raise KeyError('invalid input option'							)
      _A        =       create_cipher_map(_snake_case							)
      print(func(_snake_case    ,  _snake_case							)							)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()

