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      =				logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE				( _UpperCAmelCase					):





							"""simple docstring"""

							def __init__(  self:   List[str]		,     __A:   Union[List[ControlNetModel], Tuple[ControlNetModel]]   )    ->				Union[str, Any]:
												super().__init__()
												_A								= nn.ModuleList(lowerCamelCase_   )

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




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

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

												return down_block_res_samples, mid_block_res_sample

							def       __A (  self:   Any		,     __A:   Union[str, os.PathLike]		,     __A:   bool = True		,     __A:   Callable = None		,     __A:   bool = False		,     __A:   Optional[str] = None		,     )    ->				Optional[int]:
												_A								= 0
												_A								= save_directory
												for controlnet in self.nets:
																	controlnet.save_pretrained(
																	    lowerCamelCase_		,     is_main_process=lowerCamelCase_		,     save_function=lowerCamelCase_		,     safe_serialization=lowerCamelCase_		,     variant=lowerCamelCase_		,     )

																	idx += 1
																	_A								= model_path_to_save + f"""_{idx}"""



							@classmethod
							def       __A (  cls:   Dict		,     __A:   Optional[Union[str, os.PathLike]]		,     **__A:   Tuple   )    ->				List[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(lowerCamelCase_   ):
																	_A								= ControlNetModel.from_pretrained(lowerCamelCase_		,     **lowerCamelCase_   )
																	controlnets.append(lowerCamelCase_   )

																	idx += 1
																	_A								= pretrained_model_path + f"""_{idx}"""

												logger.info(f"""{len(lowerCamelCase_   )} controlnets loaded from {pretrained_model_path}."""   )

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

												return cls(lowerCamelCase_   )











import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	snake_case					):





							"""simple docstring"""

							A_						  =					1

							@register_to_config
							def __init__(  self:   Any		,     __A:   int = 10_00		,     __A:   Optional[Union[np.ndarray, List[float]]] = None   )    ->				List[str]:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__A   )

												# standard deviation of the initial noise distribution
												_A								= 1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												_A								= 4

												# running values
												_A								= []

							def       __A (  self:   str		,     __A:   int		,     __A:   Union[str, torch.device] = None   )    ->				int:
												_A								= num_inference_steps
												_A								= torch.linspace(1		,     0		,     num_inference_steps + 1   )[:-1]
												_A								= torch.cat([steps, torch.tensor([0.0]   )]   )

												if self.config.trained_betas is not None:
																	_A								= torch.tensor(self.config.trained_betas		,     dtype=torch.floataa   )
												else:
																	_A								= torch.sin(steps * math.pi / 2   ) ** 2

												_A								= (1.0 - self.betas**2) ** 0.5

												_A								= (torch.atana(self.betas		,     self.alphas   ) / math.pi * 2)[:-1]
												_A								= timesteps.to(__A   )

												_A								= []

							def       __A (  self:   Tuple		,     __A:   torch.FloatTensor		,     __A:   int		,     __A:   torch.FloatTensor		,     __A:   bool = True		,     )    ->				Union[SchedulerOutput, Tuple]:
												if self.num_inference_steps is None:
																	raise ValueError(
																	    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''   )

												_A								= (self.timesteps == timestep).nonzero().item()
												_A								= timestep_index + 1

												_A								= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__A   )

												if len(self.ets   ) == 1:
																	_A								= self.ets[-1]
												elif len(self.ets   ) == 2:
																	_A								= (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																	_A								= (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																	_A								= (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												_A								= self._get_prev_sample(__A		,     __A		,     __A		,     __A   )

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=__A   )

							def       __A (  self:   Optional[int]		,     __A:   torch.FloatTensor		,     *__A:   Tuple		,     **__A:   List[Any]   )    ->				torch.FloatTensor:
												return sample

							def       __A (  self:   List[str]		,     __A:   Optional[Any]		,     __A:   Optional[Any]		,     __A:   Any		,     __A:   List[Any]   )    ->				List[Any]:
												_A								= self.alphas[timestep_index]
												_A								= self.betas[timestep_index]

												_A								= self.alphas[prev_timestep_index]
												_A								= self.betas[prev_timestep_index]

												_A								= (sample - sigma * ets) / max(__A		,     1e-8   )
												_A								= next_alpha * pred + ets * next_sigma

												return prev_sample



							def __len__(  self:   List[str]   )    ->				Dict:
												return self.config.num_train_timesteps



