






"""simple docstring"""



import itertools
from pathlib import Path




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->int:
 UpperCAmelCase__ 						=				[tuple(_SCREAMING_SNAKE_CASE			) for login in logins]

 UpperCAmelCase__ 						=				{char for login in split_logins for char in login}

 for permutation in itertools.permutations(_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__ 						=				True
  for login in logins:
   if not (
       permutation.index(login[0]			)
       < permutation.index(login[1]			)
       < permutation.index(login[2]			)
   ):
    UpperCAmelCase__ 						=				False
    break

  if satisfied:
   return int("""""".join(_SCREAMING_SNAKE_CASE			)			)

 raise Exception("""Unable to find the secret passcode"""			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE = "keylog.txt"			)    ->int:
 UpperCAmelCase__ 						=				Path(_SCREAMING_SNAKE_CASE			).parent.joinpath(_SCREAMING_SNAKE_CASE			).read_text().splitlines()

 return find_secret_passcode(_SCREAMING_SNAKE_CASE			)


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











"""simple docstring"""



import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class 	_UpperCamelCase ( unittest.TestCase							):



    '''simple docstring'''


    @slow
    @unittest.skip("""Model is not available."""			)
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				"""nvidia/megatron-gpt2-345m/"""
     if "MYDIR" in os.environ:
      UpperCAmelCase__ 						=				os.path.join(os.environ["""MYDIR"""]   ,			__lowercase			)
     UpperCAmelCase__ 						=				GPTaLMHeadModel.from_pretrained(__lowercase			)
     model.to(__lowercase			)
     model.half()

     UpperCAmelCase__ 						=				torch.tensor(
         [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]   ,			device=__lowercase   ,			dtype=torch.long   ,			)

     with torch.no_grad():
      UpperCAmelCase__ 						=				model(__lowercase			).logits

     UpperCAmelCase__ 						=				torch.Size((1, 9, 50257)			)
     self.assertEqual(output.shape   ,			__lowercase			)

     UpperCAmelCase__ 						=				torch.tensor(
         [
             4.9_414,
             -0.2_920,
             -1.2_148,
             -4.0_273,
             -0.5_161,
             -5.2_109,
             -1.2_412,
             -1.8_301,
             -1.7_734,
             -4.7_148,
             -0.2_317,
             -1.0_811,
             -2.1_777,
             0.4_141,
             -3.7_969,
             -4.0_586,
             -2.5_332,
             -3.3_809,
             4.3_867,
         ]   ,			device=__lowercase   ,			dtype=torch.half   ,			)

     for i in range(19			):
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				8 * i // 17, 2792 * i  # along the diagonal
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				output[0, r, c], expected_diag[i]
      UpperCAmelCase__ 						=				F'''row={r} col={c} computed={computed} expected={expected}'''
      self.assertAlmostEqual(__lowercase   ,			__lowercase   ,			delta=1e-4   ,			msg=__lowercase			)



