






from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

__a:     str   					=					(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__a:     list[int]   					=					[ord(letter) for letter in string.ascii_lowercase]
__a:     set[int]   					=					{ord(char) for char in VALID_CHARS}

__a:     list[str]   					=					["the", "be", "to", "of", "and", "in", "that", "have"]




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						str | None:
       _UpperCAmelCase							=    ""
       _UpperCAmelCase							=    42
       _UpperCAmelCase							=    42
       _UpperCAmelCase							=    42

       for keychar, cipherchar in zip(cycle(__snake_case )   ,      __snake_case ):
              _UpperCAmelCase							=    cipherchar ^ keychar
              if decodedchar not in VALID_INTS:
                     return None
              decoded += chr(__snake_case )

       return decoded




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						list[str]:
       _UpperCAmelCase							=    []
       for key in product(__snake_case   ,      repeat=3 ):
              _UpperCAmelCase							=    try_key(__snake_case   ,      __snake_case )
              if encoded is not None:
                     possibles.append(__snake_case )
       return possibles




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						list[str]:
       return [possible for possible in possibles if common_word in possible.lower()]




def       _SCREAMING_SNAKE_CASE (							__snake_case = "p059_cipher.txt" )				->						int:
       _UpperCAmelCase							=    42
       _UpperCAmelCase							=    42
       _UpperCAmelCase							=    42
       _UpperCAmelCase							=    42
       _UpperCAmelCase							=    Path(__snake_case ).parent.joinpath(__snake_case ).read_text(encoding="""utf-8""" )

       _UpperCAmelCase							=    [int(__snake_case ) for number in data.strip().split(""",""" )]

       _UpperCAmelCase							=    filter_valid_chars(__snake_case )
       for common_word in COMMON_WORDS:
              _UpperCAmelCase							=    filter_common_word(__snake_case   ,      __snake_case )
              if len(__snake_case ) == 1:
                     break

       _UpperCAmelCase							=    possibles[0]
       return sum(ord(__snake_case ) for char in decoded_text )


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


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A			:					Optional[int]						=			logging.get_logger(__name__)

__A			:					Optional[Any]						=			{
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "gpt_neox"





   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			List[str]=50432     , __lowerCamelCase					:			int=6144     , __lowerCamelCase					:			Optional[Any]=44     , __lowerCamelCase					:			Tuple=64     , __lowerCamelCase					:			Optional[int]=24576     , __lowerCamelCase					:			List[str]="gelu"     , __lowerCamelCase					:			Any=0.25     , __lowerCamelCase					:			List[Any]=10000     , __lowerCamelCase					:			List[Any]=0.0     , __lowerCamelCase					:			int=0.0     , __lowerCamelCase					:			str=0.1     , __lowerCamelCase					:			List[Any]=2048     , __lowerCamelCase					:			Tuple=0.02     , __lowerCamelCase					:			Tuple=1e-5     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			int=0     , __lowerCamelCase					:			Optional[Any]=2     , __lowerCamelCase					:			List[str]=False     , __lowerCamelCase					:			Optional[Any]=True     , __lowerCamelCase					:			Optional[int]=None     , **__lowerCamelCase					:			str     , ):
       super().__init__(bos_token_id=__lowerCamelCase     , eos_token_id=__lowerCamelCase     , **__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   vocab_size
       SCREAMING_SNAKE_CASE										=   max_position_embeddings
       SCREAMING_SNAKE_CASE										=   hidden_size
       SCREAMING_SNAKE_CASE										=   num_hidden_layers
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   rotary_pct
       SCREAMING_SNAKE_CASE										=   rotary_emb_base
       SCREAMING_SNAKE_CASE										=   attention_dropout
       SCREAMING_SNAKE_CASE										=   hidden_dropout
       SCREAMING_SNAKE_CASE										=   classifier_dropout
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   layer_norm_eps
       SCREAMING_SNAKE_CASE										=   use_cache
       SCREAMING_SNAKE_CASE										=   tie_word_embeddings
       SCREAMING_SNAKE_CASE										=   use_parallel_residual
       SCREAMING_SNAKE_CASE										=   rope_scaling
       self._rope_scaling_validation()

       if self.hidden_size % self.num_attention_heads != 0:
           raise ValueError(
               "The hidden size is not divisble by the number of attention heads! Make sure to update them!"			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       if self.rope_scaling is None:
           return

       if not isinstance(self.rope_scaling     , __lowerCamelCase			) or len(self.rope_scaling			) != 2:
           raise ValueError(
               "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
               f"got {self.rope_scaling}"			)
       SCREAMING_SNAKE_CASE										=   self.rope_scaling.get("type"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.rope_scaling.get("factor"     , __lowerCamelCase			)
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
           raise ValueError(
               f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"			)
       if rope_scaling_factor is None or not isinstance(__lowerCamelCase     , __lowerCamelCase			) or rope_scaling_factor <= 1.0:
           raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"			)