

"""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


SCREAMING_SNAKE_CASE_     :			Any	  =			logging.get_logger(__name__)


class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         super().__init__()
         lowerCamelCase__						=			nn.ModuleList(SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = False    ,						SCREAMING_SNAKE_CASE__ = True    ,						)   ->					Union[ControlNetOutput, Tuple]:
         for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						self.nets		)		):
            lowerCamelCase__					,       lowerCamelCase__						=			controlnet(
                SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						)

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

         return down_block_res_samples, mid_block_res_sample





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = False    ,						SCREAMING_SNAKE_CASE__ = None    ,						)   ->					str:
         lowerCamelCase__						=			0
         lowerCamelCase__						=			save_directory
         for controlnet in self.nets:
            controlnet.save_pretrained(
                SCREAMING_SNAKE_CASE__    ,						is_main_process=SCREAMING_SNAKE_CASE__    ,						save_function=SCREAMING_SNAKE_CASE__    ,						safe_serialization=SCREAMING_SNAKE_CASE__    ,						variant=SCREAMING_SNAKE_CASE__    ,						)

            idx += 1
            lowerCamelCase__						=			model_path_to_save + f'_{idx}'





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					Any:
         lowerCamelCase__						=			0
         lowerCamelCase__						=			[]

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

            idx += 1
            lowerCamelCase__						=			pretrained_model_path + f'_{idx}'

         logger.info(f'{len(SCREAMING_SNAKE_CASE__		)} controlnets loaded from {pretrained_model_path}.'		)

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

         return cls(SCREAMING_SNAKE_CASE__		)







"""simple docstring"""




# Algorithm for the pigeonhole sorting





def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			min(A__   )  # min() finds the minimum value
   lowerCamelCase__						=			max(A__   )  # max() finds the maximum value

   lowerCamelCase__						=			max_val - min_val + 1  # size is difference of max and min values plus one

   # list of pigeonholes of size equal to the variable size
   lowerCamelCase__						=			[0] * size

   # Populate the pigeonholes.
   for x in a:
      assert isinstance(A__	,    A__   ), "integers only please"
      holes[x - min_val] += 1

   # Putting the elements back into the array in an order.
   lowerCamelCase__						=			0
   for count in range(A__   ):
      while holes[count] > 0:
         holes[count] -= 1
         lowerCamelCase__						=			count + min_val
         i += 1



def 	UpperCAmelCase__   (				)      ->      Optional[int]:




   """simple docstring"""
   lowerCamelCase__						=			[8, 3, 2, 7, 4, 6, 8]
   pigeonhole_sort(A__   )
   print("Sorted order is:"	,    " ".join(A__   )   )


if __name__ == "__main__":
     main()




