



"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType



class       SCREAMING_SNAKE_CASE__  :

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=64      ,     _SCREAMING_SNAKE_CASE=None						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	np.random.default_rng(lowerCAmelCase_						)
    UpperCAmelCase	:					Any	=	length
    UpperCAmelCase	:					List[Any]	=	rng.normal(size=(length,)						).astype(np.floataa						)
    UpperCAmelCase	:					Optional[Any]	=	a * self.x + b + rng.normal(scale=0.1      ,     size=(length,)						).astype(np.floataa						)

   def __len__(   self						)  ->      Optional[int]:





    '''simple docstring'''




    return self.length




   def __getitem__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    return {"x": self.x[i], "y": self.y[i]}



class       SCREAMING_SNAKE_CASE__  (					torch.nn.Module ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=False						)  ->      Dict:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase	:					Optional[Any]	=	torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
    UpperCAmelCase	:					Union[str, Any]	=	torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
    UpperCAmelCase	:					int	=	True




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=None						)  ->      Optional[Any]:





    '''simple docstring'''




    if self.first_batch:
     print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"						)
     UpperCAmelCase	:					List[str]	=	False
    return x * self.a[0] + self.b[0]



class       SCREAMING_SNAKE_CASE__  (					torch.nn.Module ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=False						)  ->      Optional[Any]:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase	:					Optional[int]	=	torch.nn.Parameter(torch.tensor(lowerCAmelCase_						).float()						)
    UpperCAmelCase	:					Any	=	torch.nn.Parameter(torch.tensor(lowerCAmelCase_						).float()						)
    UpperCAmelCase	:					Union[str, Any]	=	True




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=None						)  ->      int:





    '''simple docstring'''




    if self.first_batch:
     print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"						)
     UpperCAmelCase	:					Dict	=	False
    return x * self.a + self.b

def       _snake_case						(						UpperCamelCase						:		Any			,		UpperCamelCase						:		int = 16						):
 from datasets import load_dataset
 from transformers import AutoTokenizer

 UpperCAmelCase	:					Optional[Any]	=	AutoTokenizer.from_pretrained("""bert-base-cased"""						)
 UpperCAmelCase	:					Union[str, Any]	=	{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
 UpperCAmelCase	:					Optional[int]	=	load_dataset("""csv"""			,		data_files=lowerCAmelCase_						)
 UpperCAmelCase	:					Union[str, Any]	=	datasets["""train"""].unique("""label"""						)

 UpperCAmelCase	:					Any	=	{v: i for i, v in enumerate(lowerCAmelCase_						)}

 def tokenize_function(UpperCamelCase						:		str						):
  # max_length=None => use the model max length (it's actually the default)
  UpperCAmelCase	:					Union[str, Any]	=	tokenizer(
      examples["""sentence1"""]			,		examples["""sentence2"""]			,		truncation=lowerCAmelCase_			,		max_length=lowerCAmelCase_			,		padding="""max_length"""						)
  if "label" in examples:
   UpperCAmelCase	:					Optional[Any]	=	[label_to_id[l] for l in examples["""label"""]]
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 UpperCAmelCase	:					Dict	=	datasets.map(
     lowerCAmelCase_			,		batched=lowerCAmelCase_			,		remove_columns=["""sentence1""", """sentence2""", """label"""]			,		)

 def collate_fn(UpperCamelCase						:		Tuple						):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(lowerCAmelCase_			,		padding="""max_length"""			,		max_length=128			,		return_tensors="""pt"""						)
  return tokenizer.pad(lowerCAmelCase_			,		padding="""longest"""			,		return_tensors="""pt"""						)

 # Instantiate dataloaders.
 UpperCAmelCase	:					Any	=	DataLoader(tokenized_datasets["""train"""]			,		shuffle=lowerCAmelCase_			,		collate_fn=lowerCAmelCase_			,		batch_size=2						)
 UpperCAmelCase	:					Optional[int]	=	DataLoader(tokenized_datasets["""validation"""]			,		shuffle=lowerCAmelCase_			,		collate_fn=lowerCAmelCase_			,		batch_size=1						)

 return train_dataloader, eval_dataloader







from __future__ import annotations

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

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

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

def   a_    ( lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			tuple[int, ...]	):
   __lowerCAmelCase								=	""
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42

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

   return decoded

def   a_    ( lowerCAmelCase_      :			list[int]	):
   __lowerCAmelCase								=	[]
   for key in product(lowerCAmelCase_,	repeat=3	):
      __lowerCAmelCase								=	try_key(lowerCAmelCase_,	lowerCAmelCase_	)
      if encoded is not None:
         possibles.append(lowerCAmelCase_	)
   return possibles

def   a_    ( lowerCAmelCase_      :			list[str],	lowerCAmelCase_      :			str	):
   return [possible for possible in possibles if common_word in possible.lower()]

def   a_    ( lowerCAmelCase_      :			str = "p059_cipher.txt"	):
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	Path(lowerCAmelCase_	).parent.joinpath(lowerCAmelCase_	).read_text(encoding='utf-8'	)

   __lowerCAmelCase								=	[int(lowerCAmelCase_	) for number in data.strip().split(','	)]

   __lowerCAmelCase								=	filter_valid_chars(lowerCAmelCase_	)
   for common_word in COMMON_WORDS:
      __lowerCAmelCase								=	filter_common_word(lowerCAmelCase_,	lowerCAmelCase_	)
      if len(lowerCAmelCase_	) == 1:
         break

   __lowerCAmelCase								=	possibles[0]
   return sum(ord(lowerCAmelCase_	) for char in decoded_text	)


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

