

'''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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       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						,			UpperCamelCase__						,			UpperCamelCase__ = 0.9999						,			UpperCamelCase__ = 0.0						,			UpperCamelCase__ = 0						,			UpperCamelCase__ = False						,			UpperCamelCase__ = 1.0						,			UpperCamelCase__ = 2 / 3						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> str:





           '''simple docstring'''

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

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

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

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





           '''simple docstring'''
           A_   ,     A_		   = model_cls.load_config(UpperCamelCase__						,			return_unused_kwargs=UpperCamelCase__    )
           A_		   = model_cls.from_pretrained(UpperCamelCase__    )

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

           ema_model.load_state_dict(UpperCamelCase__    )
           return ema_model



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Dict:





           '''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"""						,			UpperCamelCase__    )

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



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> 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(UpperCamelCase__						,			self.decay    )
           # make sure decay is not smaller than min_decay
           A_		   = max(UpperCamelCase__						,			self.min_decay    )
           return cur_decay_value



    @torch.no_grad()
    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Union[str, Any]:





           '''simple docstring'''
           if isinstance(UpperCamelCase__						,			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"""						,			UpperCamelCase__						,			standard_warn=UpperCamelCase__						,			)
                  A_		   = parameters.parameters()

           A_		   = list(UpperCamelCase__    )

           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						,			UpperCamelCase__    ):
                  if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                         A_		   = deepspeed.zero.GatheredParameters(UpperCamelCase__						,			modifier_rank=UpperCamelCase__    )

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



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> None:





           '''simple docstring'''
           A_		   = list(UpperCamelCase__    )
           for s_param, param in zip(self.shadow_params						,			UpperCamelCase__    ):
                  param.data.copy_(s_param.to(param.device    ).data    )



    def    snake_case_     (   self						,			UpperCamelCase__=None						,			UpperCamelCase__=None    )  -> None:





           '''simple docstring'''
           A_		   = [
               p.to(device=UpperCamelCase__						,			dtype=UpperCamelCase__    ) if p.is_floating_point() else p.to(device=UpperCamelCase__    )
               for p in self.shadow_params
           ]



    def    snake_case_     (   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    snake_case_     (   self						,			UpperCamelCase__    )  -> None:





           '''simple docstring'''
           A_		   = [param.detach().cpu().clone() for param in parameters]



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> 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						,			UpperCamelCase__    ):
                  param.data.copy_(c_param.data    )

           # Better memory-wise.
           A_		   = None







    def    snake_case_     (   self						,			UpperCamelCase__    )  -> None:





           '''simple docstring'''
           A_		   = copy.deepcopy(UpperCamelCase__    )

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

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

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

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



'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     bool:
       A_		   = len(UpperCAmelCase__			)
       A_		   = [[False] * (required_sum + 1) for _ in range(arr_len + 1			)]

       # for each arr value, a sum of zero(0) can be formed by not taking any element
       # hence True/1
       for i in range(arr_len + 1			):
              A_		   = True

       # sum is not zero and set is empty then false
       for i in range(1,    required_sum + 1			):
              A_		   = False

       for i in range(1,    arr_len + 1			):
              for j in range(1,    required_sum + 1			):
                     if arr[i - 1] > j:
                            A_		   = subset[i - 1][j]
                     if arr[i - 1] <= j:
                            A_		   = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

       return subset[arr_len][required_sum]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
