'''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


__UpperCAmelCase     							=logging.get_logger(__name__)




class     a__      (   UpperCAmelCase__ ):






     def __init__( self		:		Optional[Any]  ,				a		:		Union[List[ControlNetModel], Tuple[ControlNetModel]]    ):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase		=  nn.ModuleList(a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		torch.FloatTensor  ,				a		:		Union[torch.Tensor, float, int]  ,				a		:		torch.Tensor  ,				a		:		List[torch.tensor]  ,				a		:		List[float]  ,				a		:		Optional[torch.Tensor] = None  ,				a		:		Optional[torch.Tensor] = None  ,				a		:		Optional[torch.Tensor] = None  ,				a		:		Optional[Dict[str, Any]] = None  ,				a		:		bool = False  ,				a		:		bool = True  ,				):





         """simple docstring"""



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

             # merge samples
             if i == 0:
                 __lowerCamelCase	,  __lowerCamelCase		=  down_samples, mid_sample
             else:
                 __lowerCamelCase		=  [
                     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    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		Union[str, os.PathLike]  ,				a		:		bool = True  ,				a		:		Callable = None  ,				a		:		bool = False  ,				a		:		Optional[str] = None  ,				):





         """simple docstring"""



         __lowerCamelCase		=  0
         __lowerCamelCase		=  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
             __lowerCamelCase		=  model_path_to_save + f"""_{idx}"""






     @classmethod
     def    SCREAMING_SNAKE_CASE__   ( cls		:		List[str]  ,				a		:		Optional[Union[str, os.PathLike]]  ,				**a		:		Optional[Any]    ):





         """simple docstring"""



         __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(a    ):
             __lowerCamelCase		=  ControlNetModel.from_pretrained(a  ,				**a    )
             controlnets.append(a    )

             idx += 1
             __lowerCamelCase		=  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    )







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def A_	(		A__				)  ->		Tuple:

       # A local function to see if a dot lands in the circle.
       def is_in_circle(A__		,      A__				) -> bool:
              a__						:	List[str] =				sqrt((x**2) + (y**2)				)
              # Our circle has a radius of 1, so a distance
              # greater than 1 would land outside the circle.
              return distance_from_centre <= 1

       # The proportion of guesses that landed in the circle
       a__						:	List[str] =				mean(
           int(is_in_circle(uniform(-1.0		,      1.0				)		,      uniform(-1.0		,      1.0				)				)				)
           for _ in range(A__				)				)
       # The ratio of the area for circle to square is pi/4.
       a__						:	Optional[Any] =				proportion * 4
       print(F'The estimated value of pi is {pi_estimate}'				)
       print(F'The numpy value of pi is {pi}'				)
       print(F'The total error is {abs(pi - pi_estimate				)}'				)



def A_	(		A__		,      A__		,      A__ = 0.0		,      A__ = 1.0		,      )  ->		float:
       return mean(
           function_to_integrate(uniform(A__		,      A__				)				) for _ in range(A__				)				) * (max_value - min_value)



def A_	(		A__		,      A__ = 0.0		,      A__ = 1.0				)  ->		None:

       def identity_function(A__				) -> float:
              return x

       a__						:	List[Any] =				area_under_curve_estimator(
           A__		,      A__		,      A__		,      A__				)
       a__						:	Union[str, Any] =				(max_value * max_value - min_value * min_value) / 2

       print('******************'				)
       print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {expected_value}'				)
       print(F'Total error is {abs(estimated_value - expected_value				)}'				)
       print('******************'				)



def A_	(		A__				)  ->		None:

       def function_to_integrate(A__				) -> float:
              return sqrt(4.0 - x * x				)

       a__						:	Dict =				area_under_curve_estimator(
           A__		,      A__		,      0.0		,      2.0				)

       print('******************'				)
       print('Estimating pi using area_under_curve_estimator'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {pi}'				)
       print(F'Total error is {abs(estimated_value - pi				)}'				)
       print('******************'				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


