





"""simple docstring"""


import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


a__       :  Tuple       		=					torch.cuda.is_available()
a__       :  Optional[int]       		=					[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a__       :  Tuple       		=					["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a__       :  List[str]       		=					{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 4_2,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1e-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 1_2,
    """max_target_length""": 1_2,
    """val_max_target_length""": 1_2,
    """test_max_target_length""": 1_2,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	Any:

 '''simple docstring'''






 snake_case__     				=					"\n".join(_UpperCAmelCase						)
 Path(_UpperCAmelCase						).open("w"						).writelines(_UpperCAmelCase						)





def 				a__      (	_UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 for split in ["train", "val", "test"]:
  _dump_articles(os.path.join(_UpperCAmelCase						,  f"{split}.source"						)						,  _UpperCAmelCase						)
  _dump_articles(os.path.join(_UpperCAmelCase						,  f"{split}.target"						)						,  _UpperCAmelCase						)
 return tmp_dir



class   lowercase_					(  __lowerCAmelCase				):

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(      cls  ):






        '''simple docstring'''

        return cls

       @require_torch_multi_gpu
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					{
            "no_teacher": True,
            "freeze_encoder": True,
            "gpus": 2,
            "overwrite_output_dir": True,
            "sortish_sampler": True,
        }
        self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE ,       check_contents=__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=True  ):






        '''simple docstring'''

        snake_case__     				=					{
            "label_smoothing": 0.0,
            "early_stopping_patience": -1,
            "train_batch_size": 1,
            "eval_batch_size": 2,
            "max_epochs": 2,
            "alpha_mlm": 0.2,
            "alpha_ce": 0.8,
            "do_predict": True,
            "model_name_or_path": "sshleifer/tinier_bart",
            "teacher": CHEAP_ARGS["model_name_or_path"],
            "val_check_interval": 0.5,
        }
        default_updates.update(__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					CHEAP_ARGS.copy()
        snake_case__     				=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
        snake_case__     				=					self.get_auto_remove_tmp_dir()
        args_d.update(data_dir=__SCREAMING_SNAKE_CASE ,       output_dir=__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )

        def convert(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):
         if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
          return ""
         if v is False or v is None:
          return ""
         if v is True:  # or len(str(v))==0:
          return F"--{k}"
         return F"--{k}={v}"

        snake_case__     				=					[x for x in (convert(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) for k, v in args_d.items()) if len(__SCREAMING_SNAKE_CASE  )]
        snake_case__     				=					[sys.executable, F"{self.test_file_dir}/distillation.py"] + cli_args
        execute_subprocess_async(__SCREAMING_SNAKE_CASE ,       env=self.get_env()  )

        snake_case__     				=					os.listdir(__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					{os.path.basename(__SCREAMING_SNAKE_CASE  ) for p in contents}
        snake_case__     				=					[p for p in contents if p.endswith("ckpt"  )]
        assert len(__SCREAMING_SNAKE_CASE  ) > 0

        self.assertIn("test_generations.txt" ,       __SCREAMING_SNAKE_CASE  )
        self.assertIn("test_results.txt" ,       __SCREAMING_SNAKE_CASE  )

        # get the following from the module, (we don't have access to `model` here)
        snake_case__     				=					os.path.join(__SCREAMING_SNAKE_CASE ,       "metrics.json"  )
        snake_case__     				=					"rouge2"

        snake_case__     				=					load_json(__SCREAMING_SNAKE_CASE  )
        # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
        print(__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					metrics["val"][-1]
        self.assertGreaterEqual(last_step_stats["val_avg_gen_time"] ,       0.01  )
        self.assertIsInstance(last_step_stats[F"val_avg_{val_metric}"] ,       __SCREAMING_SNAKE_CASE  )
        self.assertEqual(len(metrics["test"]  ) ,       1  )
        snake_case__     				=					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1  )
        self.assertEqual(len(metrics["val"]  ) ,       __SCREAMING_SNAKE_CASE  )








"""simple docstring"""


a__       :  Optional[Any]       		=					{
    """A""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """B""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """C""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """D""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """E""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """F""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """G""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """H""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """I""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """J""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """K""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """L""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """M""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """N""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """O""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """P""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """Q""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """R""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """S""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """T""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """U""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """V""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """W""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """X""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """Y""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
    """Z""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
}





def 				a__      (	_UpperCAmelCase						)     ->	list[tuple[str, str]]:

 '''simple docstring'''






 return [alphabet[char] for char in key.upper()]





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 snake_case__     				=					""
 snake_case__     				=					0
 snake_case__     				=					generate_table(_UpperCAmelCase						)
 for char in words.upper():
  cipher += get_opponent(table[count]						,  _UpperCAmelCase						)
  snake_case__     				=					(count + 1) % len(_UpperCAmelCase						)
 return cipher





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 return encrypt(_UpperCAmelCase						,  _UpperCAmelCase						)





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	tuple[int, int]:

 '''simple docstring'''






 snake_case__     				=					0 if char in table[0] else 1
 snake_case__     				=					table[row].index(_UpperCAmelCase						)
 return row, col





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 snake_case__				, snake_case__     				=					get_position(_UpperCAmelCase						,  char.upper()						)
 if row == 1:
  return table[0][col]
 else:
  return table[1][col] if row == 0 else char


if __name__ == "__main__":
   import doctest

   doctest.testmod()  # Fist ensure that all our tests are passing...
   a__       :  int       		=					input("""Enter key: """).strip()
   a__       :  Optional[int]       		=					input("""Enter text to encrypt: """).strip()
   a__       :  Dict       		=					encrypt(key, text)

   print(f'''Encrypted: {cipher_text}''')
   print(f'''Decrypted with key: {decrypt(key, cipher_text)}''')

