


'''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


lowercase__					  =		torch.cuda.is_available()
lowercase__					  =		[" Sam ate lunch today.", "Sams lunch ingredients."]
lowercase__					  =		["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowercase__					  =		{
    "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": 42,
    "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": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "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 __UpperCamelCase (      __lowerCamelCase		:			Path   ,	__lowerCamelCase		:			list					)   ->  Union[str, Any]:







     '''simple docstring'''






     _a            =		"\n".join(__lowerCamelCase					)
     Path(__lowerCamelCase					).open("w"					).writelines(__lowerCamelCase					)






def __UpperCamelCase (      __lowerCamelCase		:			Tuple					)   ->  int:







     '''simple docstring'''






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






class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):






     @classmethod
     def 			a_       (      cls		)							->     Optional[int]:
          return cls






     @require_torch_multi_gpu
     def 			a_       (      self		)							->     Optional[int]:
          _a            =		{
              "no_teacher": True,
              "freeze_encoder": True,
              "gpus": 2,
              "overwrite_output_dir": True,
              "sortish_sampler": True,
          }
          self._test_distiller_cli_fork(__UpperCamelCase  ,				check_contents=__UpperCamelCase		)






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=True		)							->     List[str]:
          _a            =		{
              "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(__UpperCamelCase		)
          _a            =		CHEAP_ARGS.copy()
          _a            =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
          _a            =		self.get_auto_remove_tmp_dir()
          args_d.update(data_dir=__UpperCamelCase  ,				output_dir=__UpperCamelCase  ,				**__UpperCamelCase		)

          def convert(__UpperCamelCase  ,				__UpperCamelCase		):
               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}"

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

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

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

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

          _a            =		load_json(__UpperCamelCase		)
          # {'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(__UpperCamelCase		)
          _a            =		metrics["val"][-1]
          self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]  ,				0.01		)
          self.assertIsInstance(last_step_stats[f"val_avg_{val_metric}"]  ,				__UpperCamelCase		)
          self.assertEqual(len(metrics["test"]		)  ,				1		)
          _a            =		int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1		)
          self.assertEqual(len(metrics["val"]		)  ,				__UpperCamelCase		)





'''simple docstring'''

import os
import sys
import time

from . import transposition_cipher as trans_cipher






def __UpperCamelCase (      )   ->  None:







     '''simple docstring'''






     _a            =		"Prehistoric Men.txt"
     _a            =		"Output.txt"
     _a            =		int(input("Enter key: "					)					)
     _a            =		input("Encrypt/Decrypt [e/d]: "					)

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

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

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

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


if __name__ == "__main__":
   main()

