"""simple docstring"""







import os
import sys
import time

from . import transposition_cipher as trans_cipher


def 	a__   (		)->						None:
						UpperCAmelCase__       						=		"""Prehistoric Men.txt"""
						UpperCAmelCase__       						=		"""Output.txt"""
						UpperCAmelCase__       						=		int(input("""Enter key: """      )      )
						UpperCAmelCase__       						=		input("""Encrypt/Decrypt [e/d]: """      )

						if not os.path.exists(_SCREAMING_SNAKE_CASE      ):
												print(f'File {input_file} does not exist. Quitting...'      )
												sys.exit()
						if os.path.exists(_SCREAMING_SNAKE_CASE      ):
												print(f'Overwrite {output_file}? [y/n]'      )
												UpperCAmelCase__       						=		input("""> """      )
												if not response.lower().startswith("""y"""      ):
																		sys.exit()

						UpperCAmelCase__       						=		time.time()
						if mode.lower().startswith("""e"""      ):
												with open(_SCREAMING_SNAKE_CASE      ) as f:
																		UpperCAmelCase__       						=		f.read()
												UpperCAmelCase__       						=		trans_cipher.encrypt_message(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE      )
						elif mode.lower().startswith("""d"""      ):
												with open(_SCREAMING_SNAKE_CASE      ) as f:
																		UpperCAmelCase__       						=		f.read()
												UpperCAmelCase__       						=		trans_cipher.decrypt_message(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE      )

						with open(_SCREAMING_SNAKE_CASE		,      """w"""      ) as output_obj:
												output_obj.write(_SCREAMING_SNAKE_CASE      )

						UpperCAmelCase__       						=		round(time.time() - start_time		,      2      )
						print(("""Done (""", total_time, """seconds )""")      )


if __name__ == "__main__":
						main()









"""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			)



