


"""simple docstring"""


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
	import transformers


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			random.seed(_UpperCamelCase )
			np.random.seed(_UpperCamelCase )
			torch.manual_seed(_UpperCamelCase )
			torch.cuda.manual_seed_all(_UpperCamelCase )
			# ^^ safe to call this function even if cuda is not available


class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a = 0.9_9_9_9       ,  __a = 0.0       ,  __a = 0       ,  __a = False       ,  __a = 1.0       ,  __a = 2 / 3       ,  __a = None       ,  __a = None       ,  **__a       ,  ):

										if isinstance(__a       ,  torch.nn.Module      ):
													__lowerCAmelCase							       =     (
													    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
													    "Please pass the parameters of the module instead."
													)
													deprecate(
													    "passing a `torch.nn.Module` to `ExponentialMovingAverage`"       ,  "1.0.0"       ,  __a       ,  standard_warn=__a       ,  )
													__lowerCAmelCase							       =     parameters.parameters()

													# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
													__lowerCAmelCase							       =     True

										if kwargs.get("max_value"       ,  __a      ) is not None:
													__lowerCAmelCase							       =     "The `max_value` argument is deprecated. Please use `decay` instead."
													deprecate("max_value"       ,  "1.0.0"       ,  __a       ,  standard_warn=__a      )
													__lowerCAmelCase							       =     kwargs["max_value"]

										if kwargs.get("min_value"       ,  __a      ) is not None:
													__lowerCAmelCase							       =     "The `min_value` argument is deprecated. Please use `min_decay` instead."
													deprecate("min_value"       ,  "1.0.0"       ,  __a       ,  standard_warn=__a      )
													__lowerCAmelCase							       =     kwargs["min_value"]

										__lowerCAmelCase							       =     list(__a      )
										__lowerCAmelCase							       =     [p.clone().detach() for p in parameters]

										if kwargs.get("device"       ,  __a      ) is not None:
													__lowerCAmelCase							       =     "The `device` argument is deprecated. Please use `to` instead."
													deprecate("device"       ,  "1.0.0"       ,  __a       ,  standard_warn=__a      )
													self.to(device=kwargs["device"]      )

										__lowerCAmelCase							       =     None

										__lowerCAmelCase							       =     decay
										__lowerCAmelCase							       =     min_decay
										__lowerCAmelCase							       =     update_after_step
										__lowerCAmelCase							       =     use_ema_warmup
										__lowerCAmelCase							       =     inv_gamma
										__lowerCAmelCase							       =     power
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     None  # set in `step()`

										__lowerCAmelCase							       =     model_cls
										__lowerCAmelCase							       =     model_config

							@classmethod
							def      snake_case							(	cls       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     model_cls.load_config(__a       ,  return_unused_kwargs=__a      )
										__lowerCAmelCase							       =     model_cls.from_pretrained(__a      )

										__lowerCAmelCase							       =     cls(model.parameters()       ,  model_cls=__a       ,  model_config=model.config      )

										ema_model.load_state_dict(__a      )
										return ema_model

							def      snake_case							(	self       ,  __a      ):
										if self.model_cls is None:
													raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."      )

										if self.model_config is None:
													raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."      )

										__lowerCAmelCase							       =     self.model_cls.from_config(self.model_config      )
										__lowerCAmelCase							       =     self.state_dict()
										state_dict.pop("shadow_params"       ,  __a      )

										model.register_to_config(**__a      )
										self.copy_to(model.parameters()      )
										model.save_pretrained(__a      )

							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     max(0       ,  optimization_step - self.update_after_step - 1      )

										if step <= 0:
													return 0.0

										if self.use_ema_warmup:
													__lowerCAmelCase							       =     1 - (1 + step / self.inv_gamma) ** -self.power
										else:
													__lowerCAmelCase							       =     (1 + step) / (10 + step)

										__lowerCAmelCase							       =     min(__a       ,  self.decay      )
										# make sure decay is not smaller than min_decay
										__lowerCAmelCase							       =     max(__a       ,  self.min_decay      )
										return cur_decay_value

							@torch.no_grad()
							def      snake_case							(	self       ,  __a      ):
										if isinstance(__a       ,  torch.nn.Module      ):
													__lowerCAmelCase							       =     (
													    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
													    "Please pass the parameters of the module instead."
													)
													deprecate(
													    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"       ,  "1.0.0"       ,  __a       ,  standard_warn=__a       ,  )
													__lowerCAmelCase							       =     parameters.parameters()

										__lowerCAmelCase							       =     list(__a      )

										self.optimization_step += 1

										# Compute the decay factor for the exponential moving average.
										__lowerCAmelCase							       =     self.get_decay(self.optimization_step      )
										__lowerCAmelCase							       =     decay
										__lowerCAmelCase							       =     1 - decay

										__lowerCAmelCase							       =     contextlib.nullcontext
										if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
													import deepspeed

										for s_param, param in zip(self.shadow_params       ,  __a      ):
													if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																__lowerCAmelCase							       =     deepspeed.zero.GatheredParameters(__a       ,  modifier_rank=__a      )

													with context_manager():
																if param.requires_grad:
																			s_param.sub_(one_minus_decay * (s_param - param)      )
																else:
																			s_param.copy_(__a      )

							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )
										for s_param, param in zip(self.shadow_params       ,  __a      ):
													param.data.copy_(s_param.to(param.device      ).data      )

							def      snake_case							(	self       ,  __a=None       ,  __a=None      ):
										__lowerCAmelCase							       =     [
										    p.to(device=__a       ,  dtype=__a      ) if p.is_floating_point() else p.to(device=__a      )
										    for p in self.shadow_params
										]

							def      snake_case							(	self      ):
										return {
										    "decay": self.decay,
										    "min_decay": self.min_decay,
										    "optimization_step": self.optimization_step,
										    "update_after_step": self.update_after_step,
										    "use_ema_warmup": self.use_ema_warmup,
										    "inv_gamma": self.inv_gamma,
										    "power": self.power,
										    "shadow_params": self.shadow_params,
										}

							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     [param.detach().cpu().clone() for param in parameters]

							def      snake_case							(	self       ,  __a      ):
										if self.temp_stored_params is None:
													raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"      )
										for c_param, param in zip(self.temp_stored_params       ,  __a      ):
													param.data.copy_(c_param.data      )

										# Better memory-wise.
										__lowerCAmelCase							       =     None



							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     copy.deepcopy(__a      )

										__lowerCAmelCase							       =     state_dict.get("decay"       ,  self.decay      )
										if self.decay < 0.0 or self.decay > 1.0:
													raise ValueError("Decay must be between 0 and 1"      )

										__lowerCAmelCase							       =     state_dict.get("min_decay"       ,  self.min_decay      )
										if not isinstance(self.min_decay       ,  __a      ):
													raise ValueError("Invalid min_decay"      )

										__lowerCAmelCase							       =     state_dict.get("optimization_step"       ,  self.optimization_step      )
										if not isinstance(self.optimization_step       ,  __a      ):
													raise ValueError("Invalid optimization_step"      )

										__lowerCAmelCase							       =     state_dict.get("update_after_step"       ,  self.update_after_step      )
										if not isinstance(self.update_after_step       ,  __a      ):
													raise ValueError("Invalid update_after_step"      )

										__lowerCAmelCase							       =     state_dict.get("use_ema_warmup"       ,  self.use_ema_warmup      )
										if not isinstance(self.use_ema_warmup       ,  __a      ):
													raise ValueError("Invalid use_ema_warmup"      )

										__lowerCAmelCase							       =     state_dict.get("inv_gamma"       ,  self.inv_gamma      )
										if not isinstance(self.inv_gamma       ,  (float, int)      ):
													raise ValueError("Invalid inv_gamma"      )

										__lowerCAmelCase							       =     state_dict.get("power"       ,  self.power      )
										if not isinstance(self.power       ,  (float, int)      ):
													raise ValueError("Invalid power"      )

										__lowerCAmelCase							       =     state_dict.get("shadow_params"       ,  __a      )
										if shadow_params is not None:
													__lowerCAmelCase							       =     shadow_params
													if not isinstance(self.shadow_params       ,  __a      ):
																raise ValueError("shadow_params must be a list"      )
													if not all(isinstance(__a       ,  torch.Tensor      ) for p in self.shadow_params      ):
																raise ValueError("shadow_params must all be Tensors"      )








"""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	:  List[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										super().__init__()
										__lowerCAmelCase							       =     nn.ModuleList(__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = False       ,  __a = True       ,  ):
										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      snake_case							(	self       ,  __a       ,  __a = True       ,  __a = None       ,  __a = False       ,  __a = None       ,  ):
										__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      snake_case							(	cls       ,  __a       ,  **__a      ):
										__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      )




