


"""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


__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = False		,      UpperCamelCase__ = True		,      )      ->						Any:

       '''simple docstring'''




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

           # merge samples
           if i == 0:
               snake_case       : Tuple    =       down_samples, mid_sample
           else:
               snake_case       : int    =       [
                   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		,      UpperCamelCase__		,      UpperCamelCase__ = True		,      UpperCamelCase__ = None		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      )      ->						Dict:

       '''simple docstring'''




       snake_case       : Any    =       0
       snake_case       : Tuple    =       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
           snake_case       : Dict    =       model_path_to_save + F'_{idx}'







   @classmethod
   def 					lowerCamelCase						(      cls		,      UpperCamelCase__		,      **UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       snake_case       : Dict    =       0
       snake_case       : int    =       []

       # 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`, ...
       snake_case       : Optional[int]    =       pretrained_model_path
       while os.path.isdir(UpperCamelCase__		):
           snake_case       : Union[str, Any]    =       ControlNetModel.from_pretrained(UpperCamelCase__		,      **UpperCamelCase__		)
           controlnets.append(UpperCamelCase__		)

           idx += 1
           snake_case       : Optional[Any]    =       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"""

from __future__ import annotations

_UpperCamelCase		     =    []



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						bool:
 for i in range(len(lowercase__   )   ):
  if board[row][i] == 1:
   return False
 for i in range(len(lowercase__   )   ):
  if board[i][column] == 1:
   return False
 for i, j in zip(range(lowercase__					,					-1					,					-1   )					,					range(lowercase__					,					-1					,					-1   )   ):
  if board[i][j] == 1:
   return False
 for i, j in zip(range(lowercase__					,					-1					,					-1   )					,					range(lowercase__					,					len(lowercase__   )   )   ):
  if board[i][j] == 1:
   return False
 return True



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						bool:
 if row >= len(lowercase__   ):
  solution.append(lowercase__   )
  printboard(lowercase__   )
  print()
  return True
 for i in range(len(lowercase__   )   ):
  if is_safe(lowercase__					,					lowercase__					,					lowercase__   ):
   lowerCAmelCase__   :				List[Any] 			=				1
   solve(lowercase__					,					row + 1   )
   lowerCAmelCase__   :				Dict 			=				0
 return False



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						None:
 for i in range(len(lowercase__   )   ):
  for j in range(len(lowercase__   )   ):
   if board[i][j] == 1:
    print("Q"					,					end=" "   )
   else:
    print("."					,					end=" "   )
  print()


# n=int(input("The no. of queens"))
_UpperCamelCase		     =    8
_UpperCamelCase		     =    [[0 for i in range(n)] for j in range(n)]
solve(board, 0)






print("""The total no. of solutions are :""", len(solution))






