


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


__UpperCamelCase    		=       logging.get_logger(__name__)







class        lowerCamelCase__	(							UpperCAmelCase      ):






   """simple docstring"""






   def __init__(							self      ,						snake_case     ):

      '''simple docstring'''


      super().__init__()
      UpperCamelCase__					=       nn.ModuleList(snake_case     )




   def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = False      ,						snake_case = True      ,						):

      '''simple docstring'''


      for i, (image, scale, controlnet) in enumerate(zip(snake_case      ,						snake_case      ,						self.nets     )     ):
         UpperCamelCase__,      UpperCamelCase__					=       controlnet(
             snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						)

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

      return down_block_res_samples, mid_block_res_sample




   def 				snake_case__				(							self      ,						snake_case      ,						snake_case = True      ,						snake_case = None      ,						snake_case = False      ,						snake_case = None      ,						):

      '''simple docstring'''


      UpperCamelCase__					=       0
      UpperCamelCase__					=       save_directory
      for controlnet in self.nets:
         controlnet.save_pretrained(
             snake_case      ,						is_main_process=snake_case      ,						save_function=snake_case      ,						safe_serialization=snake_case      ,						variant=snake_case      ,						)

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







   @classmethod
   def 				snake_case__				(							cls      ,						snake_case      ,						**snake_case     ):

      '''simple docstring'''


      UpperCamelCase__					=       0
      UpperCamelCase__					=       []

      # 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`, ...
      UpperCamelCase__					=       pretrained_model_path
      while os.path.isdir(snake_case     ):
         UpperCamelCase__					=       ControlNetModel.from_pretrained(snake_case      ,						**snake_case     )
         controlnets.append(snake_case     )

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

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

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

      return cls(snake_case     )






def        UpperCamelCase_(				_A   :str  )->		str:
			UpperCamelCase__					=       ""
			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        UpperCamelCase_(				_A   :str  )->		dict[str, str]:
			UpperCamelCase__					=       [chr(i + 65  ) for i in range(26  )]
			# Remove duplicate characters from key
			UpperCamelCase__					=       remove_duplicates(key.upper()  )
			UpperCamelCase__					=       len(_A  )
			# First fill cipher with key characters
			UpperCamelCase__					=       {alphabet[i]: char for i, char in enumerate(_A  )}
			# Then map remaining characters in alphabet to
			# the alphabet from the beginning
			for i in range(len(_A  )				,     26  ):
						UpperCamelCase__					=       alphabet[i - offset]
						# Ensure we are not mapping letters to letters previously mapped
						while char in key:
									offset -= 1
									UpperCamelCase__					=       alphabet[i - offset]
						UpperCamelCase__					=       char
			return cipher_alphabet
def        UpperCamelCase_(				_A   :str				,     _A   :dict[str, str]  )->		str:
			return "".join(cipher_map.get(_A				,     _A  ) for ch in message.upper()  )
def        UpperCamelCase_(				_A   :str				,     _A   :dict[str, str]  )->		str:
			UpperCamelCase__					=       {v: k for k, v in cipher_map.items()}
			return "".join(rev_cipher_map.get(_A				,     _A  ) for ch in message.upper()  )
def        UpperCamelCase_(				)->		None:
			UpperCamelCase__					=       input("Enter message to encode or decode: "  ).strip()
			UpperCamelCase__					=       input("Enter keyword: "  ).strip()
			UpperCamelCase__					=       input("Encipher or decipher? E/D:"  ).strip()[0].lower()
			try:
						UpperCamelCase__					=       {"e": encipher, "d": decipher}[option]
			except KeyError:
						raise KeyError("invalid input option"  )
			UpperCamelCase__					=       create_cipher_map(_A  )
			print(func(_A				,     _A  )  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()


