"""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  (    _lowerCamelCase		: int			) ->   int:
  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       ,  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		: 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__       ,  )
         _lowerCAmelCase		: Optional[Any]						    =  parameters.parameters()

         # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
         _lowerCAmelCase		: List[Any]						    =  True

       if kwargs.get("""max_value"""       ,  a__						) is not None:
         _lowerCAmelCase		: List[Any]						    =  """The `max_value` argument is deprecated. Please use `decay` instead."""
         deprecate("""max_value"""       ,  """1.0.0"""       ,  a__       ,  standard_warn=a__						)
         _lowerCAmelCase		: str						    =  kwargs["""max_value"""]

       if kwargs.get("""min_value"""       ,  a__						) is not None:
         _lowerCAmelCase		: List[Any]						    =  """The `min_value` argument is deprecated. Please use `min_decay` instead."""
         deprecate("""min_value"""       ,  """1.0.0"""       ,  a__       ,  standard_warn=a__						)
         _lowerCAmelCase		: Tuple						    =  kwargs["""min_value"""]

       _lowerCAmelCase		: Optional[int]						    =  list(a__						)
       _lowerCAmelCase		: List[Any]						    =  [p.clone().detach() for p in parameters]

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

       _lowerCAmelCase		: Any						    =  None

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

       _lowerCAmelCase		: List[str]						    =  model_cls
       _lowerCAmelCase		: List[str]						    =  model_config






     @classmethod
     def 					__A						(	cls       ,  a__       ,  a__						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  model_cls.load_config(a__       ,  return_unused_kwargs=a__						)
       _lowerCAmelCase		: Dict						    =  model_cls.from_pretrained(a__						)

       _lowerCAmelCase		: Tuple						    =  cls(model.parameters()       ,  model_cls=a__       ,  model_config=model.config						)

       ema_model.load_state_dict(a__						)
       return ema_model






     def 					__A						(	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		: Optional[int]						    =  self.model_cls.from_config(self.model_config						)
       _lowerCAmelCase		: Any						    =  self.state_dict()
       state_dict.pop("""shadow_params"""       ,  a__						)

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






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: str						    =  max(0       ,  optimization_step - self.update_after_step - 1						)

       if step <= 0:
         return 0.0

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

       _lowerCAmelCase		: List[Any]						    =  min(a__       ,  self.decay						)
       # make sure decay is not smaller than min_decay
       _lowerCAmelCase		: Tuple						    =  max(a__       ,  self.min_decay						)
       return cur_decay_value






     @torch.no_grad()
     def 					__A						(	self       ,  a__						):
       if isinstance(a__       ,  torch.nn.Module						):
         _lowerCAmelCase		: 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__       ,  )
         _lowerCAmelCase		: Union[str, Any]						    =  parameters.parameters()

       _lowerCAmelCase		: str						    =  list(a__						)

       self.optimization_step += 1

       # Compute the decay factor for the exponential moving average.
       _lowerCAmelCase		: int						    =  self.get_decay(self.optimization_step						)
       _lowerCAmelCase		: Any						    =  decay
       _lowerCAmelCase		: str						    =  1 - decay

       _lowerCAmelCase		: Optional[int]						    =  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		: str						    =  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 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Optional[int]						    =  list(a__						)
       for s_param, param in zip(self.shadow_params       ,  a__						):
         param.data.copy_(s_param.to(param.device						).data						)






     def 					__A						(	self       ,  a__=None       ,  a__=None						):
       _lowerCAmelCase		: List[Any]						    =  [
           p.to(device=a__       ,  dtype=a__						) if p.is_floating_point() else p.to(device=a__						)
           for p in self.shadow_params
       ]






     def 					__A						(	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 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Dict						    =  [param.detach().cpu().clone() for param in parameters]






     def 					__A						(	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		: List[Any]						    =  None






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: List[str]						    =  copy.deepcopy(a__						)

       _lowerCAmelCase		: Union[str, Any]						    =  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		: int						    =  state_dict.get("""min_decay"""       ,  self.min_decay						)
       if not isinstance(self.min_decay       ,  a__						):
         raise ValueError("""Invalid min_decay"""						)

       _lowerCAmelCase		: Union[str, Any]						    =  state_dict.get("""optimization_step"""       ,  self.optimization_step						)
       if not isinstance(self.optimization_step       ,  a__						):
         raise ValueError("""Invalid optimization_step"""						)

       _lowerCAmelCase		: str						    =  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		: Dict						    =  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		: List[str]						    =  state_dict.get("""inv_gamma"""       ,  self.inv_gamma						)
       if not isinstance(self.inv_gamma       ,  (float, int)						):
         raise ValueError("""Invalid inv_gamma"""						)

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

       _lowerCAmelCase		: List[Any]						    =  state_dict.get("""shadow_params"""       ,  a__						)
       if shadow_params is not None:
         _lowerCAmelCase		: 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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int			) ->   bool:
  return sum(i for i in range(1    ,number // 2 + 1			) if number % i == 0			) == number


if __name__ == "__main__":
     print('Program to check whether a number is a Perfect number or not...')
     _a   :	int 					=     int(input('Enter number: ').strip())
     print(F"""{number} is {"" if perfect(number) else "not "}a Perfect Number.""")




