


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     __a     (       unittest.TestCase ):
 @slow
 @unittest.skip('Model is not available.'					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Dict         =  'nvidia/megatron-gpt2-345m/'
        if "MYDIR" in os.environ:
               lowercase__:  Tuple         =  os.path.join(os.environ['MYDIR']							,					__lowercase					)
        lowercase__:  Tuple         =  GPTaLMHeadModel.from_pretrained(__lowercase					)
        model.to(__lowercase					)
        model.half()

        lowercase__:  Any         =  torch.tensor(
            [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]							,					device=__lowercase							,					dtype=torch.long							,					)

        with torch.no_grad():
               lowercase__:  str         =  model(__lowercase					).logits

        lowercase__:  Optional[Any]         =  torch.Size((1, 9, 50_257)					)
        self.assertEqual(output.shape							,					__lowercase					)

        lowercase__:  List[str]         =  torch.tensor(
            [
                4.9_4_1_4,
                -0.2_9_2_0,
                -1.2_1_4_8,
                -4.0_2_7_3,
                -0.5_1_6_1,
                -5.2_1_0_9,
                -1.2_4_1_2,
                -1.8_3_0_1,
                -1.7_7_3_4,
                -4.7_1_4_8,
                -0.2_3_1_7,
                -1.0_8_1_1,
                -2.1_7_7_7,
                0.4_1_4_1,
                -3.7_9_6_9,
                -4.0_5_8_6,
                -2.5_3_3_2,
                -3.3_8_0_9,
                4.3_8_6_7,
            ]							,					device=__lowercase							,					dtype=torch.half							,					)

        for i in range(19					):
               lowercase__     ,		lowercase__:  List[str]         =  8 * i // 17, 2_792 * i  # along the diagonal
               lowercase__     ,		lowercase__:  Any         =  output[0, r, c], expected_diag[i]
               lowercase__:  str         =  F'row={r} col={c} computed={computed} expected={expected}'
               self.assertAlmostEqual(__lowercase							,					__lowercase							,					delta=1E-4							,					msg=__lowercase					)










"""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() = }''')



