




"""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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     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       a__	:







     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  ,		)  ->   Optional[Any]:



          '''simple docstring'''



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

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

          a     =				list(_SCREAMING_SNAKE_CASE			)
          a     =				[p.clone().detach() for p in parameters]

          if kwargs.get("device"  ,		_SCREAMING_SNAKE_CASE			) is not None:
               a     =				"The `device` argument is deprecated. Please use `to` instead."
               deprecate("device"  ,		"1.0.0"  ,		_SCREAMING_SNAKE_CASE  ,		standard_warn=_SCREAMING_SNAKE_CASE			)
               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       lowerCAmelCase_    (     cls  ,		A  ,		A			)  ->   "EMAModel":



          '''simple docstring'''



          a  ,				a     =				model_cls.load_config(_SCREAMING_SNAKE_CASE  ,		return_unused_kwargs=_SCREAMING_SNAKE_CASE			)
          a     =				model_cls.from_pretrained(_SCREAMING_SNAKE_CASE			)

          a     =				cls(model.parameters()  ,		model_cls=_SCREAMING_SNAKE_CASE  ,		model_config=model.config			)

          ema_model.load_state_dict(_SCREAMING_SNAKE_CASE			)
          return ema_model







     def       lowerCAmelCase_    (     self  ,		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__."			)

          a     =				self.model_cls.from_config(self.model_config			)
          a     =				self.state_dict()
          state_dict.pop("shadow_params"  ,		_SCREAMING_SNAKE_CASE			)

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







     def       lowerCAmelCase_    (     self  ,		A			)  ->   float:



          '''simple docstring'''



          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(_SCREAMING_SNAKE_CASE  ,		self.decay			)
          # make sure decay is not smaller than min_decay
          a     =				max(_SCREAMING_SNAKE_CASE  ,		self.min_decay			)
          return cur_decay_value







     @torch.no_grad()
     def       lowerCAmelCase_    (     self  ,		A			)  ->   Tuple:



          '''simple docstring'''



          if isinstance(_SCREAMING_SNAKE_CASE  ,		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"  ,		_SCREAMING_SNAKE_CASE  ,		standard_warn=_SCREAMING_SNAKE_CASE  ,		)
               a     =				parameters.parameters()

          a     =				list(_SCREAMING_SNAKE_CASE			)

          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  ,		_SCREAMING_SNAKE_CASE			):
               if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                    a     =				deepspeed.zero.GatheredParameters(_SCREAMING_SNAKE_CASE  ,		modifier_rank=_SCREAMING_SNAKE_CASE			)

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







     def       lowerCAmelCase_    (     self  ,		A			)  ->   None:



          '''simple docstring'''



          a     =				list(_SCREAMING_SNAKE_CASE			)
          for s_param, param in zip(self.shadow_params  ,		_SCREAMING_SNAKE_CASE			):
               param.data.copy_(s_param.to(param.device			).data			)







     def       lowerCAmelCase_    (     self  ,		A=None  ,		A=None			)  ->   None:



          '''simple docstring'''



          a     =				[
              p.to(device=_SCREAMING_SNAKE_CASE  ,		dtype=_SCREAMING_SNAKE_CASE			) if p.is_floating_point() else p.to(device=_SCREAMING_SNAKE_CASE			)
              for p in self.shadow_params
          ]







     def       lowerCAmelCase_    (     self			)  ->   dict:



          '''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       lowerCAmelCase_    (     self  ,		A			)  ->   None:



          '''simple docstring'''



          a     =				[param.detach().cpu().clone() for param in parameters]







     def       lowerCAmelCase_    (     self  ,		A			)  ->   None:



          '''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  ,		_SCREAMING_SNAKE_CASE			):
               param.data.copy_(c_param.data			)

          # Better memory-wise.
          a     =				None







     def       lowerCAmelCase_    (     self  ,		A			)  ->   None:



          '''simple docstring'''



          a     =				copy.deepcopy(_SCREAMING_SNAKE_CASE			)

          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  ,		_SCREAMING_SNAKE_CASE			):
               raise ValueError("Invalid min_decay"			)

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

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

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







from __future__ import annotations

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					list[int]:
     a     =				2
     a     =				[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__UpperCamelCase)
     if n > 1:
          factors.append(__UpperCamelCase)
     return factors


if __name__ == "__main__":
   import doctest

   doctest.testmod()

