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       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







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







class 		a__						:

					def __init__(    self      :       Tuple,_A      :       Iterable[torch.nn.Parameter],_A      :       float = 0.9999,_A      :       float = 0.0,_A      :       int = 0,_A      :       bool = False,_A      :       Union[float, int] = 1.0,_A      :       Union[float, int] = 2 / 3,_A      :       Optional[Any] = None,_A      :       Dict[str, Any] = None,**_A      :       str,):







							"""simple docstring"""






							if isinstance(_A,torch.nn.Module    ):
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    (
									    "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,)
									SCREAMING_SNAKE_CASE_  :		str				      =    parameters.parameters()

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

							if kwargs.get("max_value",_A    ) is not None:
									SCREAMING_SNAKE_CASE_  :		str				      =    "The `max_value` argument is deprecated. Please use `decay` instead."
									deprecate("max_value","1.0.0",_A,standard_warn=_A    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    kwargs["max_value"]

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

							SCREAMING_SNAKE_CASE_  :		Dict				      =    list(_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [p.clone().detach() for p in parameters]

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

							SCREAMING_SNAKE_CASE_  :		Dict				      =    None

							SCREAMING_SNAKE_CASE_  :		Any				      =    decay
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    min_decay
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    update_after_step
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    use_ema_warmup
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    inv_gamma
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    power
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    None  # set in `step()`

							SCREAMING_SNAKE_CASE_  :		Dict				      =    model_cls
							SCREAMING_SNAKE_CASE_  :		Any				      =    model_config

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Dict,_A      :       Tuple,_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model_cls.load_config(_A,return_unused_kwargs=_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    model_cls.from_pretrained(_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    cls(model.parameters(),model_cls=_A,model_config=model.config    )

							ema_model.load_state_dict(_A    )
							return ema_model

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       int    ):







							"""simple docstring"""






							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__."    )

							SCREAMING_SNAKE_CASE_  :		str				      =    self.model_cls.from_config(self.model_config    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.state_dict()
							state_dict.pop("shadow_params",_A    )

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

					def 				__UpperCamelCase   (    self      :       str,_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    max(0,optimization_step - self.update_after_step - 1    )

							if step <= 0:
									return 0.0

							if self.use_ema_warmup:
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    1 - (1 + step / self.inv_gamma) ** -self.power
							else:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    (1 + step) / (10 + step)

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    min(_A,self.decay    )
							# make sure decay is not smaller than min_decay
							SCREAMING_SNAKE_CASE_  :		Any				      =    max(_A,self.min_decay    )
							return cur_decay_value

					@torch.no_grad()
					def 				__UpperCamelCase   (    self      :       str,_A      :       Iterable[torch.nn.Parameter]    ):







							"""simple docstring"""






							if isinstance(_A,torch.nn.Module    ):
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    (
									    "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,)
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    parameters.parameters()

							SCREAMING_SNAKE_CASE_  :		int				      =    list(_A    )

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.get_decay(self.optimization_step    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    decay
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    1 - decay

							SCREAMING_SNAKE_CASE_  :		str				      =    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():
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    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 				__UpperCamelCase   (    self      :       int,_A      :       Iterable[torch.nn.Parameter]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    list(_A    )
							for s_param, param in zip(self.shadow_params,_A    ):
									param.data.copy_(s_param.to(param.device    ).data    )

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Any=None,_A      :       Union[str, Any]=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [
							    p.to(device=_A,dtype=_A    ) if p.is_floating_point() else p.to(device=_A    )
							    for p in self.shadow_params
							]

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							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 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       Iterable[torch.nn.Parameter]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [param.detach().cpu().clone() for param in parameters]

					def 				__UpperCamelCase   (    self      :       int,_A      :       Iterable[torch.nn.Parameter]    ):







							"""simple docstring"""






							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.
							SCREAMING_SNAKE_CASE_  :		Dict				      =    None






					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    copy.deepcopy(_A    )

							SCREAMING_SNAKE_CASE_  :		str				      =    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"    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    state_dict.get("min_decay",self.min_decay    )
							if not isinstance(self.min_decay,_A    ):
									raise ValueError("Invalid min_decay"    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    state_dict.get("optimization_step",self.optimization_step    )
							if not isinstance(self.optimization_step,_A    ):
									raise ValueError("Invalid optimization_step"    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    state_dict.get("update_after_step",self.update_after_step    )
							if not isinstance(self.update_after_step,_A    ):
									raise ValueError("Invalid update_after_step"    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    state_dict.get("use_ema_warmup",self.use_ema_warmup    )
							if not isinstance(self.use_ema_warmup,_A    ):
									raise ValueError("Invalid use_ema_warmup"    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    state_dict.get("inv_gamma",self.inv_gamma    )
							if not isinstance(self.inv_gamma,(float, int)    ):
									raise ValueError("Invalid inv_gamma"    )

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

							SCREAMING_SNAKE_CASE_  :		str				      =    state_dict.get("shadow_params",_A    )
							if shadow_params is not None:
									SCREAMING_SNAKE_CASE_  :		int				      =    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"""




def        lowercase							(		lowerCAmelCase__ :     list			) ->      bool:
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
		if len(lowerCAmelCase__			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		if len(lowerCAmelCase__			) == 1:
				return True
		__a           =			series[1] - series[0]
		for index in range(len(lowerCAmelCase__			) - 1			):
				if series[index + 1] - series[index] != common_diff:
						return False
		return True

def        lowercase							(		lowerCAmelCase__ :     list			) ->      float:
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
		if len(lowerCAmelCase__			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		__a           =			0
		for val in series:
				answer += val
		return answer / len(lowerCAmelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






