




'''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(							SCREAMING_SNAKE_CASE_     )		->		int:
			random.seed(SCREAMING_SNAKE_CASE_     )
			np.random.seed(SCREAMING_SNAKE_CASE_     )
			torch.manual_seed(SCREAMING_SNAKE_CASE_     )
			torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE_     )
			# ^^ safe to call this function even if cuda is not available






class   __UpperCAmelCase				:



					'''simple docstring'''





					def __init__(	self     :						Optional[Any]      ,       _lowercase     :						Iterable[torch.nn.Parameter]      ,       _lowercase     :						float = 0.99_99      ,       _lowercase     :						float = 0.0      ,       _lowercase     :						int = 0      ,       _lowercase     :						bool = False      ,       _lowercase     :						Union[float, int] = 1.0      ,       _lowercase     :						Union[float, int] = 2 / 3      ,       _lowercase     :						Optional[Any] = None      ,       _lowercase     :						Dict[str, Any] = None      ,       **_lowercase     :						Dict      ,       )    ->							int:

								if isinstance(_lowercase      ,       torch.nn.Module):
											A_									= (
											    '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'      ,       _lowercase      ,       standard_warn=_lowercase      ,       )
											A_									= parameters.parameters()

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

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

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

								A_									= list(_lowercase)
								A_									= [p.clone().detach() for p in parameters]

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

								A_									= None

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

								A_									= model_cls
								A_									= model_config



					@classmethod
					def     __snake_case						(	cls     :						Optional[Any]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						str)    ->							"EMAModel":
								A_		, A_									= model_cls.load_config(_lowercase      ,       return_unused_kwargs=_lowercase)
								A_									= model_cls.from_pretrained(_lowercase)

								A_									= cls(model.parameters()      ,       model_cls=_lowercase      ,       model_config=model.config)

								ema_model.load_state_dict(_lowercase)
								return ema_model



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						Optional[Any])    ->							Any:
								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__.')

								A_									= self.model_cls.from_config(self.model_config)
								A_									= self.state_dict()
								state_dict.pop('shadow_params'      ,       _lowercase)

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



					def     __snake_case						(	self     :						str      ,       _lowercase     :						int)    ->							float:
								A_									= max(0      ,       optimization_step - self.update_after_step - 1)

								if step <= 0:
											return 0.0

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

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



					@torch.no_grad()
					def     __snake_case						(	self     :						int      ,       _lowercase     :						Iterable[torch.nn.Parameter])    ->							Dict:
								if isinstance(_lowercase      ,       torch.nn.Module):
											A_									= (
											    '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'      ,       _lowercase      ,       standard_warn=_lowercase      ,       )
											A_									= parameters.parameters()

								A_									= list(_lowercase)

								self.optimization_step += 1

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

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

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

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



					def     __snake_case						(	self     :						int      ,       _lowercase     :						Iterable[torch.nn.Parameter])    ->							None:
								A_									= list(_lowercase)
								for s_param, param in zip(self.shadow_params      ,       _lowercase):
											param.data.copy_(s_param.to(param.device).data)



					def     __snake_case						(	self     :						Any      ,       _lowercase     :						Optional[Any]=None      ,       _lowercase     :						Optional[Any]=None)    ->							None:
								A_									= [
								    p.to(device=_lowercase      ,       dtype=_lowercase) if p.is_floating_point() else p.to(device=_lowercase)
								    for p in self.shadow_params
								]



					def     __snake_case						(	self     :						List[str])    ->							dict:
								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     :						Union[str, Any]      ,       _lowercase     :						Iterable[torch.nn.Parameter])    ->							None:
								A_									= [param.detach().cpu().clone() for param in parameters]



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						Iterable[torch.nn.Parameter])    ->							None:
								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      ,       _lowercase):
											param.data.copy_(c_param.data)

								# Better memory-wise.
								A_									= None



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						dict)    ->							None:
								A_									= copy.deepcopy(_lowercase)

								A_									= 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')

								A_									= state_dict.get('min_decay'      ,       self.min_decay)
								if not isinstance(self.min_decay      ,       _lowercase):
											raise ValueError('Invalid min_decay')

								A_									= state_dict.get('optimization_step'      ,       self.optimization_step)
								if not isinstance(self.optimization_step      ,       _lowercase):
											raise ValueError('Invalid optimization_step')

								A_									= state_dict.get('update_after_step'      ,       self.update_after_step)
								if not isinstance(self.update_after_step      ,       _lowercase):
											raise ValueError('Invalid update_after_step')

								A_									= state_dict.get('use_ema_warmup'      ,       self.use_ema_warmup)
								if not isinstance(self.use_ema_warmup      ,       _lowercase):
											raise ValueError('Invalid use_ema_warmup')

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

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

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










'''simple docstring'''
from PIL import Image





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Image:

			def brightness(SCREAMING_SNAKE_CASE_     ) -> float:
						return 128 + level + (c - 128)

			if not -255.0 <= level <= 255.0:
						raise ValueError('level must be between -255.0 (black) and 255.0 (white)'     )
			return img.point(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change brightness to 100
														_SCREAMING_SNAKE_CASE			=						change_brightness(img, 1_00)
														brigt_img.save("image_data/lena_brightness.png", format="png")




