



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_		:		int):





				'''simple docstring'''

				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__      :




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase = 0.9999		,__lowerCamelCase = 0.0		,__lowerCamelCase = 0		,__lowerCamelCase = False		,__lowerCamelCase = 1.0		,__lowerCamelCase = 2 / 3		,__lowerCamelCase = None		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      Optional[Any]:

						"""simple docstring"""






						if isinstance(__lowerCamelCase		,torch.nn.Module			):
										lowerCAmelCase__					:    Optional[int]												=					(
										    '''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		,)
										lowerCAmelCase__					:    Optional[Any]												=					parameters.parameters()

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

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

						if kwargs.get('''min_value'''		,__lowerCamelCase			) is not None:
										lowerCAmelCase__					:    Optional[int]												=					'''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
										deprecate('''min_value'''		,'''1.0.0'''		,__lowerCamelCase		,standard_warn=__lowerCamelCase			)
										lowerCAmelCase__					:    Dict												=					kwargs['''min_value''']

						lowerCAmelCase__					:    Union[str, Any]												=					list(__lowerCamelCase			)
						lowerCAmelCase__					:    List[str]												=					[p.clone().detach() for p in parameters]

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

						lowerCAmelCase__					:    Any												=					None

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

						lowerCAmelCase__					:    Optional[Any]												=					model_cls
						lowerCAmelCase__					:    int												=					model_config



		@classmethod
		def 		lowerCAmelCase__				(cls		,__lowerCamelCase		,__lowerCamelCase			)					->      "EMAModel":

						"""simple docstring"""






						lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[Any]												=					model_cls.load_config(__lowerCamelCase		,return_unused_kwargs=__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					model_cls.from_pretrained(__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					cls(model.parameters()		,model_cls=__lowerCamelCase		,model_config=model.config			)

						ema_model.load_state_dict(__lowerCamelCase			)
						return ema_model



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      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__.'''			)

						lowerCAmelCase__					:    List[str]												=					self.model_cls.from_config(self.model_config			)
						lowerCAmelCase__					:    List[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		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					max(0		,optimization_step - self.update_after_step - 1			)

						if step <= 0:
										return 0.0

						if self.use_ema_warmup:
										lowerCAmelCase__					:    Union[str, Any]												=					1 - (1 + step / self.inv_gamma) ** -self.power
						else:
										lowerCAmelCase__					:    Any												=					(1 + step) / (10 + step)

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



		@torch.no_grad()
		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Optional[int]:

						"""simple docstring"""






						if isinstance(__lowerCamelCase		,torch.nn.Module			):
										lowerCAmelCase__					:    List[str]												=					(
										    '''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		,)
										lowerCAmelCase__					:    str												=					parameters.parameters()

						lowerCAmelCase__					:    Optional[Any]												=					list(__lowerCamelCase			)

						self.optimization_step += 1

						# Compute the decay factor for the exponential moving average.
						lowerCAmelCase__					:    Dict												=					self.get_decay(self.optimization_step			)
						lowerCAmelCase__					:    Union[str, Any]												=					decay
						lowerCAmelCase__					:    Union[str, Any]												=					1 - decay

						lowerCAmelCase__					:    Optional[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():
														lowerCAmelCase__					:    Union[str, Any]												=					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		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					list(__lowerCamelCase			)
						for s_param, param in zip(self.shadow_params		,__lowerCamelCase			):
										param.data.copy_(s_param.to(param.device			).data			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase=None		,__lowerCamelCase=None			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					[
						    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			)					->      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		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					[param.detach().cpu().clone() for param in parameters]



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      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.
						lowerCAmelCase__					:    Any												=					None



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					copy.deepcopy(__lowerCamelCase			)

						lowerCAmelCase__					:    int												=					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__					:    List[Any]												=					state_dict.get('''min_decay'''		,self.min_decay			)
						if not isinstance(self.min_decay		,__lowerCamelCase			):
										raise ValueError('''Invalid min_decay'''			)

						lowerCAmelCase__					:    str												=					state_dict.get('''optimization_step'''		,self.optimization_step			)
						if not isinstance(self.optimization_step		,__lowerCamelCase			):
										raise ValueError('''Invalid optimization_step'''			)

						lowerCAmelCase__					:    Optional[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'''			)

						lowerCAmelCase__					:    int												=					state_dict.get('''use_ema_warmup'''		,self.use_ema_warmup			)
						if not isinstance(self.use_ema_warmup		,__lowerCamelCase			):
										raise ValueError('''Invalid use_ema_warmup'''			)

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

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

						lowerCAmelCase__					:    Optional[int]												=					state_dict.get('''shadow_params'''		,__lowerCamelCase			)
						if shadow_params is not None:
										lowerCAmelCase__					:    Tuple												=					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'''			)






def      lowerCAmelCase__		(   lowerCamelCase_		:		int = 1000000):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					set(range(3	,lowerCamelCase_	,2))
				primes.add(2)
				for p in range(3	,lowerCamelCase_	,2):
								if p not in primes:
												continue
								primes.difference_update(set(range(p * p	,lowerCamelCase_	,lowerCamelCase_)))

				lowerCAmelCase__					:    int												=					[float(lowerCamelCase_) for n in range(limit + 1)]

				for p in primes:
								for n in range(lowerCamelCase_	,limit + 1	,lowerCamelCase_):
												phi[n] *= 1 - 1 / p

				return int(sum(phi[2:]))


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

