




"""simple docstring"""

import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


a         =      logging.get_logger(__name__)





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 def __init__(			self	:	List[Any]   ,							_UpperCAmelCase	:	Union[List[ControlNetModel], Tuple[ControlNetModel]]		):
       super().__init__()
       _A        =       nn.ModuleList(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	Union[torch.Tensor, float, int]   ,							_UpperCAmelCase	:	torch.Tensor   ,							_UpperCAmelCase	:	List[torch.tensor]   ,							_UpperCAmelCase	:	List[float]   ,							_UpperCAmelCase	:	Optional[torch.Tensor] = None   ,							_UpperCAmelCase	:	Optional[torch.Tensor] = None   ,							_UpperCAmelCase	:	Optional[torch.Tensor] = None   ,							_UpperCAmelCase	:	Optional[Dict[str, Any]] = None   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	bool = True   ,							):
       for i, (image, scale, controlnet) in enumerate(zip(_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.nets		)		):
             _A       ,   _A        =       controlnet(
                 _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							)

             # merge samples
             if i == 0:
                   _A       ,   _A        =       down_samples, mid_sample
             else:
                   _A        =       [
                       samples_prev + samples_curr
                       for samples_prev, samples_curr in zip(_UpperCAmelCase   ,							_UpperCAmelCase		)
                   ]
                   mid_block_res_sample += mid_sample

       return down_block_res_samples, mid_block_res_sample

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	Union[str, os.PathLike]   ,							_UpperCAmelCase	:	bool = True   ,							_UpperCAmelCase	:	Callable = None   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	Optional[str] = None   ,							):
       _A        =       0
       _A        =       save_directory
       for controlnet in self.nets:
             controlnet.save_pretrained(
                 _UpperCAmelCase   ,							is_main_process=_UpperCAmelCase   ,							save_function=_UpperCAmelCase   ,							safe_serialization=_UpperCAmelCase   ,							variant=_UpperCAmelCase   ,							)

             idx += 1
             _A        =       model_path_to_save + F'''_{idx}'''



 @classmethod
 def       lowerCAmelCase_							(			cls	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[Union[str, os.PathLike]]   ,							**_UpperCAmelCase	:	Tuple		):
       _A        =       0
       _A        =       []

       # load controlnet and append to list until no controlnet directory exists anymore
       # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
       # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
       _A        =       pretrained_model_path
       while os.path.isdir(_UpperCAmelCase		):
             _A        =       ControlNetModel.from_pretrained(_UpperCAmelCase   ,							**_UpperCAmelCase		)
             controlnets.append(_UpperCAmelCase		)

             idx += 1
             _A        =       pretrained_model_path + F'''_{idx}'''

       logger.info(F'''{len(_UpperCAmelCase		)} controlnets loaded from {pretrained_model_path}.'''		)

       if len(_UpperCAmelCase		) == 0:
             raise ValueError(
                 F'''No ControlNets found under {os.path.dirname(_UpperCAmelCase		)}. Expected at least {pretrained_model_path + "_0"}.'''		)

       return cls(_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()

