


'''simple docstring'''


def  __lowerCAmelCase (__lowerCAmelCase   ):
  if not all(char in "01" for char in bin_string   ):
    raise ValueError("Non-binary value was passed to the function"   )
  if not bin_string:
    raise ValueError("Empty string was passed to the function"   )
  _UpperCAmelCase		:       List[Any]								=      ""
  while len(__lowerCAmelCase   ) % 3 != 0:
    _UpperCAmelCase		:       Tuple								=      "0" + bin_string
  _UpperCAmelCase		:       Dict								=      [
      bin_string[index : index + 3]
      for index in range(len(__lowerCAmelCase   )   )
      if index % 3 == 0
  ]
  for bin_group in bin_string_in_3_list:
    _UpperCAmelCase		:       int								=      0
    for index, val in enumerate(__lowerCAmelCase   ):
      oct_val += int(2 ** (2 - index) * int(__lowerCAmelCase   )   )
    oct_string += str(__lowerCAmelCase   )
  return oct_string


if __name__ == "__main__":
      from doctest import testmod

      testmod()







'''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  __lowerCAmelCase (__lowerCAmelCase   ):
		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]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter]	,  lowerCamelCase__  :     float = 0.9_9_9_9	,  lowerCamelCase__  :     float = 0.0	,  lowerCamelCase__  :     int = 0	,  lowerCamelCase__  :     bool = False	,  lowerCamelCase__  :     Union[float, int] = 1.0	,  lowerCamelCase__  :     Union[float, int] = 2 / 3	,  lowerCamelCase__  :     Optional[Any] = None	,  lowerCamelCase__  :     Dict[str, Any] = None	,  **lowerCamelCase__  :     Optional[int]	,  )   ->Optional[Any]:



					'''simple docstring'''







					if isinstance(lowerCamelCase__	,  torch.nn.Module ):
							_UpperCAmelCase		:       List[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"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__	,  )
							_UpperCAmelCase		:       List[str]								=      parameters.parameters()

							# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
							_UpperCAmelCase		:       Optional[int]								=      True

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

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

					_UpperCAmelCase		:       Optional[Any]								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       Dict								=      [p.clone().detach() for p in parameters]

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

					_UpperCAmelCase		:       Union[str, Any]								=      None

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

					_UpperCAmelCase		:       Optional[int]								=      model_cls
					_UpperCAmelCase		:       Union[str, Any]								=      model_config




			@classmethod
			def 							lowerCAmelCase__    (		cls  :     int	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     int )   ->"EMAModel":



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[int]								=      model_cls.load_config(lowerCamelCase__	,  return_unused_kwargs=lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      model_cls.from_pretrained(lowerCamelCase__ )

					_UpperCAmelCase		:       List[str]								=      cls(model.parameters()	,  model_cls=lowerCamelCase__	,  model_config=model.config )

					ema_model.load_state_dict(lowerCamelCase__ )
					return ema_model




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     int )   ->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__." )

					_UpperCAmelCase		:       int								=      self.model_cls.from_config(self.model_config )
					_UpperCAmelCase		:       Union[str, Any]								=      self.state_dict()
					state_dict.pop("shadow_params"	,  lowerCamelCase__ )

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




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     int )   ->float:



					'''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		:       int								=      1 - (1 + step / self.inv_gamma) ** -self.power
					else:
							_UpperCAmelCase		:       Any								=      (1 + step) / (10 + step)

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




			@torch.no_grad()
			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->Dict:



					'''simple docstring'''







					if isinstance(lowerCamelCase__	,  torch.nn.Module ):
							_UpperCAmelCase		:       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"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__	,  )
							_UpperCAmelCase		:       Any								=      parameters.parameters()

					_UpperCAmelCase		:       Dict								=      list(lowerCamelCase__ )

					self.optimization_step += 1

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

					_UpperCAmelCase		:       Union[str, Any]								=      contextlib.nullcontext
					if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
							import deepspeed

					for s_param, param in zip(self.shadow_params	,  lowerCamelCase__ ):
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
									_UpperCAmelCase		:       str								=      deepspeed.zero.GatheredParameters(lowerCamelCase__	,  modifier_rank=lowerCamelCase__ )

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




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->None:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     Dict=None	,  lowerCamelCase__  :     Optional[int]=None )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      [
					    p.to(device=lowerCamelCase__	,  dtype=lowerCamelCase__ ) if p.is_floating_point() else p.to(device=lowerCamelCase__ )
					    for p in self.shadow_params
					]




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->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  :     Optional[int]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      [param.detach().cpu().clone() for param in parameters]




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->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	,  lowerCamelCase__ ):
							param.data.copy_(c_param.data )

					# Better memory-wise.
					_UpperCAmelCase		:       int								=      None






			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     dict )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      copy.deepcopy(lowerCamelCase__ )

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

					_UpperCAmelCase		:       List[str]								=      state_dict.get("optimization_step"	,  self.optimization_step )
					if not isinstance(self.optimization_step	,  lowerCamelCase__ ):
							raise ValueError("Invalid optimization_step" )

					_UpperCAmelCase		:       List[Any]								=      state_dict.get("update_after_step"	,  self.update_after_step )
					if not isinstance(self.update_after_step	,  lowerCamelCase__ ):
							raise ValueError("Invalid update_after_step" )

					_UpperCAmelCase		:       str								=      state_dict.get("use_ema_warmup"	,  self.use_ema_warmup )
					if not isinstance(self.use_ema_warmup	,  lowerCamelCase__ ):
							raise ValueError("Invalid use_ema_warmup" )

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

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

					_UpperCAmelCase		:       List[str]								=      state_dict.get("shadow_params"	,  lowerCamelCase__ )
					if shadow_params is not None:
							_UpperCAmelCase		:       Optional[Any]								=      shadow_params
							if not isinstance(self.shadow_params	,  lowerCamelCase__ ):
									raise ValueError("shadow_params must be a list" )
							if not all(isinstance(lowerCamelCase__	,  torch.Tensor ) for p in self.shadow_params ):
									raise ValueError("shadow_params must all be Tensors" )



