



'''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_							: Optional[int]						 =	logging.get_logger(__name__)



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 def __init__(							self  ,a_							)		->   int:
        super().__init__()
        _UpperCAmelCase  :						List[str]			     = nn.ModuleList(a_							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = False  ,a_ = True  ,)		->   Union[ControlNetOutput, Tuple]:
        for i, (image, scale, controlnet) in enumerate(zip(a_  ,a_  ,self.nets							)							):
               _UpperCAmelCase     ,_UpperCAmelCase  :						List[Any]			     = controlnet(
                   a_  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_  ,a_  ,)

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

        return down_block_res_samples, mid_block_res_sample







 def _snake_case (							self  ,a_  ,a_ = True  ,a_ = None  ,a_ = False  ,a_ = None  ,)		->   Dict:
        _UpperCAmelCase  :						List[Any]			     = 0
        _UpperCAmelCase  :						Dict			     = save_directory
        for controlnet in self.nets:
               controlnet.save_pretrained(
                   a_  ,is_main_process=a_  ,save_function=a_  ,safe_serialization=a_  ,variant=a_  ,)

               idx += 1
               _UpperCAmelCase  :						Optional[Any]			     = model_path_to_save + f'''_{idx}'''







 @classmethod
 def _snake_case (							cls  ,a_  ,**a_							)		->   Dict:
        _UpperCAmelCase  :						str			     = 0
        _UpperCAmelCase  :						Union[str, Any]			     = []

        # 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  :						Optional[int]			     = pretrained_model_path
        while os.path.isdir(a_							):
               _UpperCAmelCase  :						List[str]			     = ControlNetModel.from_pretrained(a_  ,**a_							)
               controlnets.append(a_							)

               idx += 1
               _UpperCAmelCase  :						Tuple			     = pretrained_model_path + f'''_{idx}'''

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

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

        return cls(a_							)








'''simple docstring'''






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = False		)->		list[float]:





       '''simple docstring'''


       if radian_mode:
              return [magnitude * cos(lowerCAmelCase_		), magnitude * sin(lowerCAmelCase_		)]
       return [magnitude * cos(radians(lowerCAmelCase_		)		), magnitude * sin(radians(lowerCAmelCase_		)		)]




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = 10**-1		)->		bool:





       '''simple docstring'''


       _UpperCAmelCase  :						NDArray[floataa]			     = cross(lowerCAmelCase_       ,   lowerCAmelCase_		)
       _UpperCAmelCase  :						float			     = sum(lowerCAmelCase_		)
       return abs(lowerCAmelCase_		) < eps


if __name__ == "__main__":
   # Test to check if it works
   A_							: str						 =	array(
       [
           polar_force(718.4, 1_8_0 - 3_0),
           polar_force(879.54, 4_5),
           polar_force(1_0_0, -9_0),
       ]
   )

   A_							: NDArray[floataa]						 =	array([[0, 0], [0, 0], [0, 0]])

   assert in_static_equilibrium(forces, location)

   # Problem 1 in image_data/2D_problems.jpg
   A_							: List[str]						 =	array(
       [
           polar_force(3_0 * 9.81, 1_5),
           polar_force(2_1_5, 1_8_0 - 4_5),
           polar_force(2_6_4, 9_0 - 3_0),
       ]
   )

   A_							: Tuple						 =	array([[0, 0], [0, 0], [0, 0]])

   assert in_static_equilibrium(forces, location)

   # Problem in image_data/2D_problems_1.jpg
   A_							: Dict						 =	array([[0, -2_0_0_0], [0, -1_2_0_0], [0, 1_5_6_0_0], [0, -1_2_4_0_0]])

   A_							: Union[str, Any]						 =	array([[0, 0], [6, 0], [1_0, 0], [1_2, 0]])

   assert in_static_equilibrium(forces, location)

   import doctest

   doctest.testmod()



