



"""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   snake_case__     (	__lowerCamelCase       :					Optional[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  __SCREAMING_SNAKE_CASE    :


					'''simple docstring'''





					def __init__( self							:						Any,						lowerCamelCase							:						Iterable[torch.nn.Parameter],						lowerCamelCase							:						float = 0.9_999,						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							:						int,						)->      List[Any]:

								if isinstance(snake_case__,						torch.nn.Module  ):
											lowerCamelCase__	:	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''',						snake_case__,						standard_warn=snake_case__,						)
											lowerCamelCase__	:	Union[str, Any]				=parameters.parameters()

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

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

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

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

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

								lowerCamelCase__	:	List[str]				=None

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

								lowerCamelCase__	:	Any				=model_cls
								lowerCamelCase__	:	Any				=model_config



					@classmethod
					def 	snake_case ( cls							:						Dict,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Any  )->      Union[str, Any]:
								lowerCamelCase__			,     lowerCamelCase__	:	Dict				=model_cls.load_config(snake_case__,						return_unused_kwargs=snake_case__  )
								lowerCamelCase__	:	Optional[int]				=model_cls.from_pretrained(snake_case__  )

								lowerCamelCase__	:	Dict				=cls(model.parameters(),						model_cls=snake_case__,						model_config=model.config  )

								ema_model.load_state_dict(snake_case__  )
								return ema_model



					def 	snake_case ( self							:						Optional[Any],						lowerCamelCase							:						List[Any]  )->      int:
								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[Any]				=self.model_cls.from_config(self.model_config  )
								lowerCamelCase__	:	List[Any]				=self.state_dict()
								state_dict.pop('''shadow_params''',						snake_case__  )

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



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						int  )->      Optional[int]:
								lowerCamelCase__	:	int				=max(0,						optimization_step - self.update_after_step - 1  )

								if step <= 0:
											return 0.0

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

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



					@torch.no_grad()
					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						Iterable[torch.nn.Parameter]  )->      Dict:
								if isinstance(snake_case__,						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''',						snake_case__,						standard_warn=snake_case__,						)
											lowerCamelCase__	:	Dict				=parameters.parameters()

								lowerCamelCase__	:	List[Any]				=list(snake_case__  )

								self.optimization_step += 1

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

								lowerCamelCase__	:	Tuple				=contextlib.nullcontext
								if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
											import deepspeed

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

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



					def 	snake_case ( self							:						List[Any],						lowerCamelCase							:						Iterable[torch.nn.Parameter]  )->      Dict:
								lowerCamelCase__	:	Any				=list(snake_case__  )
								for s_param, param in zip(self.shadow_params,						snake_case__  ):
											param.data.copy_(s_param.to(param.device  ).data  )



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						int=None,						lowerCamelCase							:						int=None  )->      str:
								lowerCamelCase__	:	Dict				=[
								    p.to(device=snake_case__,						dtype=snake_case__  ) if p.is_floating_point() else p.to(device=snake_case__  )
								    for p in self.shadow_params
								]



					def 	snake_case ( self							:						Optional[int]  )->      Optional[int]:
								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							:						Any,						lowerCamelCase							:						Iterable[torch.nn.Parameter]  )->      str:
								lowerCamelCase__	:	Optional[Any]				=[param.detach().cpu().clone() for param in parameters]



					def 	snake_case ( self							:						str,						lowerCamelCase							:						Iterable[torch.nn.Parameter]  )->      List[Any]:
								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,						snake_case__  ):
											param.data.copy_(c_param.data  )

								# Better memory-wise.
								lowerCamelCase__	:	int				=None




					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						dict  )->      Optional[int]:
								lowerCamelCase__	:	Dict				=copy.deepcopy(snake_case__  )

								lowerCamelCase__	:	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'''  )

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

								lowerCamelCase__	:	Optional[Any]				=state_dict.get('''optimization_step''',						self.optimization_step  )
								if not isinstance(self.optimization_step,						snake_case__  ):
											raise ValueError('''Invalid optimization_step'''  )

								lowerCamelCase__	:	str				=state_dict.get('''update_after_step''',						self.update_after_step  )
								if not isinstance(self.update_after_step,						snake_case__  ):
											raise ValueError('''Invalid update_after_step'''  )

								lowerCamelCase__	:	Optional[int]				=state_dict.get('''use_ema_warmup''',						self.use_ema_warmup  )
								if not isinstance(self.use_ema_warmup,						snake_case__  ):
											raise ValueError('''Invalid use_ema_warmup'''  )

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

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

								lowerCamelCase__	:	str				=state_dict.get('''shadow_params''',						snake_case__  )
								if shadow_params is not None:
											lowerCamelCase__	:	Dict				=shadow_params
											if not isinstance(self.shadow_params,						snake_case__  ):
														raise ValueError('''shadow_params must be a list'''  )
											if not all(isinstance(snake_case__,						torch.Tensor  ) for p in self.shadow_params  ):
														raise ValueError('''shadow_params must all be Tensors'''  )









"""simple docstring"""






import os






def   snake_case__     (	):
			"""simple docstring"""
			with open(os.path.dirname(__lowerCamelCase			) + '''/p022_names.txt'''			) as file:
						lowerCamelCase__	:	Tuple				=str(file.readlines()[0]			)
						lowerCamelCase__	:	int				=names.replace('''"'''							,	''''''			).split(''','''			)

			names.sort()

			lowerCamelCase__	:	Union[str, Any]				=0
			lowerCamelCase__	:	str				=0

			for i, name in enumerate(__lowerCamelCase			):
						for letter in name:
									name_score += ord(__lowerCamelCase			) - 64

						total_score += (i + 1) * name_score
						lowerCamelCase__	:	Dict				=0
			return total_score


if __name__ == "__main__":
		print(solution())




