


'''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 					a__       (					lowerCAmelCase__						)    ->	Optional[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 			lowerCamelCase_   :






      def __init__(					self				:    List[Any]						,						_A				:    Iterable[torch.nn.Parameter]						,						_A				:    float = 0.9_9_9_9						,						_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				:    Optional[Any]						,						):


          '''simple docstring'''


          if isinstance(_A						,						torch.nn.Module ):
              UpperCAmelCase__			:		Optional[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						,						)
              UpperCAmelCase__			:		List[str]													=				parameters.parameters()

              # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
              UpperCAmelCase__			:		Union[str, Any]													=				True

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

          if kwargs.get('''min_value'''						,						_A ) is not None:
              UpperCAmelCase__			:		List[str]													=				'''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
              deprecate('''min_value'''						,						'''1.0.0'''						,						_A						,						standard_warn=_A )
              UpperCAmelCase__			:		Any													=				kwargs['''min_value''']

          UpperCAmelCase__			:		Dict													=				list(_A )
          UpperCAmelCase__			:		int													=				[p.clone().detach() for p in parameters]

          if kwargs.get('''device'''						,						_A ) is not None:
              UpperCAmelCase__			:		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'''] )

          UpperCAmelCase__			:		Optional[int]													=				None

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

          UpperCAmelCase__			:		List[Any]													=				model_cls
          UpperCAmelCase__			:		Optional[int]													=				model_config






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


          '''simple docstring'''


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

          UpperCAmelCase__			:		Any													=				cls(model.parameters()						,						model_cls=_A						,						model_config=model.config )

          ema_model.load_state_dict(_A )
          return ema_model






      def lowercase_							(					self				:    Dict						,						_A				:    Any ):


          '''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__.''' )

          UpperCAmelCase__			:		Union[str, Any]													=				self.model_cls.from_config(self.model_config )
          UpperCAmelCase__			:		Optional[int]													=				self.state_dict()
          state_dict.pop('''shadow_params'''						,						_A )

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






      def lowercase_							(					self				:    List[str]						,						_A				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				max(0						,						optimization_step - self.update_after_step - 1 )

          if step <= 0:
              return 0.0

          if self.use_ema_warmup:
              UpperCAmelCase__			:		Any													=				1 - (1 + step / self.inv_gamma) ** -self.power
          else:
              UpperCAmelCase__			:		Optional[Any]													=				(1 + step) / (10 + step)

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






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


          '''simple docstring'''


          if isinstance(_A						,						torch.nn.Module ):
              UpperCAmelCase__			:		int													=				(
                  '''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						,						)
              UpperCAmelCase__			:		Any													=				parameters.parameters()

          UpperCAmelCase__			:		Tuple													=				list(_A )

          self.optimization_step += 1

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

          UpperCAmelCase__			:		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():
                  UpperCAmelCase__			:		Tuple													=				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 lowercase_							(					self				:    Union[str, Any]						,						_A				:    Iterable[torch.nn.Parameter] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				list(_A )
          for s_param, param in zip(self.shadow_params						,						_A ):
              param.data.copy_(s_param.to(param.device ).data )






      def lowercase_							(					self				:    List[Any]						,						_A				:    Optional[int]=None						,						_A				:    Optional[Any]=None ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				[
              p.to(device=_A						,						dtype=_A ) if p.is_floating_point() else p.to(device=_A )
              for p in self.shadow_params
          ]






      def lowercase_							(					self				:    List[str] ):


          '''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 lowercase_							(					self				:    Optional[int]						,						_A				:    Iterable[torch.nn.Parameter] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				[param.detach().cpu().clone() for param in parameters]






      def lowercase_							(					self				:    Optional[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.
          UpperCAmelCase__			:		Dict													=				None






      def lowercase_							(					self				:    Any						,						_A				:    dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				copy.deepcopy(_A )

          UpperCAmelCase__			:		List[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''' )

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

          UpperCAmelCase__			:		Tuple													=				state_dict.get('''optimization_step'''						,						self.optimization_step )
          if not isinstance(self.optimization_step						,						_A ):
              raise ValueError('''Invalid optimization_step''' )

          UpperCAmelCase__			:		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''' )

          UpperCAmelCase__			:		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''' )

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

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

          UpperCAmelCase__			:		Optional[int]													=				state_dict.get('''shadow_params'''						,						_A )
          if shadow_params is not None:
              UpperCAmelCase__			:		Tuple													=				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 					a__       (					lowerCAmelCase__						)    ->	int:
    UpperCAmelCase__			:		Tuple													=				0
    while num > 0:
        digit_sum += num % 10
        num //= 10
    return digit_sum





def 					a__       (					lowerCAmelCase__ = 1_00						)    ->	int:
    UpperCAmelCase__			:		Dict													=				1
    UpperCAmelCase__			:		str													=				2

    for i in range(2  ,					max_n + 1						):
        UpperCAmelCase__			:		Tuple													=				pre_numerator
        UpperCAmelCase__			:		Tuple													=				2 * i // 3 if i % 3 == 0 else 1
        UpperCAmelCase__			:		str													=				cur_numerator
        UpperCAmelCase__			:		List[str]													=				e_cont * pre_numerator + temp

    return sum_digits(lowerCAmelCase__						)


if __name__ == "__main__":
  print(F"""{solution() = }""")
