



"""simple docstring"""


def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				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					=    ''''''
				while len(_SCREAMING_SNAKE_CASE				) % 3 != 0:
								_UpperCAmelCase					=    '''0''' + bin_string
				_UpperCAmelCase					=    [
				    bin_string[index : index + 3]
				    for index in range(len(_SCREAMING_SNAKE_CASE				)				)
				    if index % 3 == 0
				]
				for bin_group in bin_string_in_3_list:
								_UpperCAmelCase					=    0
								for index, val in enumerate(_SCREAMING_SNAKE_CASE				):
												oct_val += int(2 ** (2 - index) * int(_SCREAMING_SNAKE_CASE				)				)
								oct_string += str(_SCREAMING_SNAKE_CASE				)
				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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				random.seed(_SCREAMING_SNAKE_CASE				)
				np.random.seed(_SCREAMING_SNAKE_CASE				)
				torch.manual_seed(_SCREAMING_SNAKE_CASE				)
				torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE				)
				# ^^ safe to call this function even if cuda is not available
class 			_a :
			"""simple docstring"""



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

							if isinstance(__UpperCamelCase				,   torch.nn.Module   ):
											_UpperCAmelCase					=    (
											    '''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				,   )
											_UpperCAmelCase					=    parameters.parameters()

											# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
											_UpperCAmelCase					=    True

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

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

							_UpperCAmelCase					=    list(__UpperCamelCase   )
							_UpperCAmelCase					=    [p.clone().detach() for p in parameters]

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

							_UpperCAmelCase					=    None

							_UpperCAmelCase					=    decay
							_UpperCAmelCase					=    min_decay
							_UpperCAmelCase					=    update_after_step
							_UpperCAmelCase					=    use_ema_warmup
							_UpperCAmelCase					=    inv_gamma
							_UpperCAmelCase					=    power
							_UpperCAmelCase					=    0
							_UpperCAmelCase					=    None  # set in `step()`

							_UpperCAmelCase					=    model_cls
							_UpperCAmelCase					=    model_config



			@classmethod
			def  lowercase__						(		cls     :    List[str]				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Any   )->"EMAModel":
							_UpperCAmelCase		,						_UpperCAmelCase					=    model_cls.load_config(__UpperCamelCase				,   return_unused_kwargs=__UpperCamelCase   )
							_UpperCAmelCase					=    model_cls.from_pretrained(__UpperCamelCase   )

							_UpperCAmelCase					=    cls(model.parameters()				,   model_cls=__UpperCamelCase				,   model_config=model.config   )

							ema_model.load_state_dict(__UpperCamelCase   )
							return ema_model



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    Any   )->Optional[Any]:
							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					=    self.model_cls.from_config(self.model_config   )
							_UpperCAmelCase					=    self.state_dict()
							state_dict.pop('''shadow_params'''				,   __UpperCamelCase   )

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



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    int   )->float:
							_UpperCAmelCase					=    max(0				,   optimization_step - self.update_after_step - 1   )

							if step <= 0:
											return 0.0

							if self.use_ema_warmup:
											_UpperCAmelCase					=    1 - (1 + step / self.inv_gamma) ** -self.power
							else:
											_UpperCAmelCase					=    (1 + step) / (1_0 + step)

							_UpperCAmelCase					=    min(__UpperCamelCase				,   self.decay   )
							# make sure decay is not smaller than min_decay
							_UpperCAmelCase					=    max(__UpperCamelCase				,   self.min_decay   )
							return cur_decay_value



			@torch.no_grad()
			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->Optional[Any]:
							if isinstance(__UpperCamelCase				,   torch.nn.Module   ):
											_UpperCAmelCase					=    (
											    '''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				,   )
											_UpperCAmelCase					=    parameters.parameters()

							_UpperCAmelCase					=    list(__UpperCamelCase   )

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							_UpperCAmelCase					=    self.get_decay(self.optimization_step   )
							_UpperCAmelCase					=    decay
							_UpperCAmelCase					=    1 - decay

							_UpperCAmelCase					=    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():
															_UpperCAmelCase					=    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  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->None:
							_UpperCAmelCase					=    list(__UpperCamelCase   )
							for s_param, param in zip(self.shadow_params				,   __UpperCamelCase   ):
											param.data.copy_(s_param.to(param.device   ).data   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Union[str, Any]=None   )->None:
							_UpperCAmelCase					=    [
							    p.to(device=__UpperCamelCase				,   dtype=__UpperCamelCase   ) if p.is_floating_point() else p.to(device=__UpperCamelCase   )
							    for p in self.shadow_params
							]



			def  lowercase__						(		self     :    Optional[Any]   )->dict:
							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     :    Any				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->None:
							_UpperCAmelCase					=    [param.detach().cpu().clone() for param in parameters]



			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->None:
							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.
							_UpperCAmelCase					=    None



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    dict   )->None:
							_UpperCAmelCase					=    copy.deepcopy(__UpperCamelCase   )

							_UpperCAmelCase					=    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					=    state_dict.get('''min_decay'''				,   self.min_decay   )
							if not isinstance(self.min_decay				,   __UpperCamelCase   ):
											raise ValueError('''Invalid min_decay'''   )

							_UpperCAmelCase					=    state_dict.get('''optimization_step'''				,   self.optimization_step   )
							if not isinstance(self.optimization_step				,   __UpperCamelCase   ):
											raise ValueError('''Invalid optimization_step'''   )

							_UpperCAmelCase					=    state_dict.get('''update_after_step'''				,   self.update_after_step   )
							if not isinstance(self.update_after_step				,   __UpperCamelCase   ):
											raise ValueError('''Invalid update_after_step'''   )

							_UpperCAmelCase					=    state_dict.get('''use_ema_warmup'''				,   self.use_ema_warmup   )
							if not isinstance(self.use_ema_warmup				,   __UpperCamelCase   ):
											raise ValueError('''Invalid use_ema_warmup'''   )

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

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

							_UpperCAmelCase					=    state_dict.get('''shadow_params'''				,   __UpperCamelCase   )
							if shadow_params is not None:
											_UpperCAmelCase					=    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'''   )



