



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     :int			       =logging.get_logger(__name__)



class        lowerCAmelCase__					(  __A       ):


				def __init__(							self      :  Tuple						, __UpperCamelCase      :  Union[List[ControlNetModel], Tuple[ControlNetModel]]       )		->     Optional[int]:
											super().__init__()
											A      						=    nn.ModuleList(__UpperCamelCase       )


				def __UpperCamelCase						(							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						, )		->     Union[ControlNetOutput, Tuple]:

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

																		# merge samples
																		if i == 0:
																									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 __UpperCamelCase						(							self      :  Dict						, __UpperCamelCase      :  Union[str, os.PathLike]						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Callable = None						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  Optional[str] = None						, )		->     List[Any]:
											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 __UpperCamelCase						(							cls      :  str						, __UpperCamelCase      :  Optional[Union[str, os.PathLike]]						, **__UpperCamelCase      :  Dict       )		->     Any:
											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'''



from __future__ import annotations


def 				lowercase_					(     __A			:							list[list[int]]      )							->   int:



 """simple docstring"""




 for i in range(1       ,   len(matrix[0]      )      ):
  matrix[0][i] += matrix[0][i - 1]

 # preprocessing the first column
 for i in range(1       ,   len(__A      )      ):
  matrix[i][0] += matrix[i - 1][0]

 # updating the path cost for current position
 for i in range(1       ,   len(__A      )      ):
  for j in range(1       ,   len(matrix[0]      )      ):
   matrix[i][j] += min(matrix[i - 1][j]       ,   matrix[i][j - 1]      )

 return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()



