





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


UpperCamelCase__          =torch.cuda.is_available()
UpperCamelCase__          =[' Sam ate lunch today.', 'Sams lunch ingredients.']
UpperCamelCase__          =['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
UpperCamelCase__          ={
    '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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							"\n".join(__lowerCamelCase		)
     Path(__lowerCamelCase		).open("w"		).writelines(__lowerCamelCase		)






def 			lowerCamelCase__	(__lowerCamelCase		):
     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 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


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




       @require_torch_multi_gpu
       def 			UpperCamelCase_	(					self							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		str  			=							{
                "no_teacher": True,
                "freeze_encoder": True,
                "gpus": 2,
                "overwrite_output_dir": True,
                "sortish_sampler": True,
            }
            self._test_distiller_cli_fork(__lowerCamelCase			,  check_contents=__lowerCamelCase							)







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase=True							)	->   List[str]:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							{
                "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(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		dict  			=							CHEAP_ARGS.copy()
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_auto_remove_tmp_dir()
            args_d.update(data_dir=__lowerCamelCase			,  output_dir=__lowerCamelCase			,  **__lowerCamelCase							)

            def convert(__lowerCamelCase			,  __lowerCamelCase							):
                 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}"""

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

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							os.listdir(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							{os.path.basename(__lowerCamelCase							) for p in contents}
            _SCREAMING_SNAKE_CASE				:		str  			=							[p for p in contents if p.endswith("ckpt"							)]
            assert len(__lowerCamelCase							) > 0

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

            # get the following from the module, (we don't have access to `model` here)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							os.path.join(__lowerCamelCase			,  "metrics.json"							)
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							"rouge2"

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							load_json(__lowerCamelCase							)
            # {'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(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							metrics["val"][-1]
            self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]			,  0.01							)
            self.assertIsInstance(last_step_stats[F"""val_avg_{val_metric}"""]			,  __lowerCamelCase							)
            self.assertEqual(len(metrics["test"]							)			,  1							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1							)
            self.assertEqual(len(metrics["val"]							)			,  __lowerCamelCase							)






from typing import Literal

UpperCamelCase__          ='ABCDEFGHIJKLMNOPQRSTUVWXYZ'






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							LETTERS if mode == "decrypt" else key
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							key if mode == "decrypt" else LETTERS
     _SCREAMING_SNAKE_CASE				:		Dict  			=							""
     # loop through each symbol in the message
     for symbol in message:
          if symbol.upper() in chars_a:
               # encrypt/decrypt the symbol
               _SCREAMING_SNAKE_CASE				:		List[str]  			=							chars_a.find(symbol.upper()		)
               if symbol.isupper():
                    translated += chars_b[sym_index].upper()
               else:
                    translated += chars_b[sym_index].lower()
          else:
               # symbol is not in LETTERS, just add it
               translated += symbol
     return translated






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     return translate_message(__lowerCamelCase,				__lowerCamelCase,				"encrypt"		)






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     return translate_message(__lowerCamelCase,				__lowerCamelCase,				"decrypt"		)






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		str  			=							"Hello World"
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							"QWERTYUIOPASDFGHJKLZXCVBNM"
     _SCREAMING_SNAKE_CASE				:		Any  			=							"decrypt"  # set to 'encrypt' or 'decrypt'

     if mode == "encrypt":
          _SCREAMING_SNAKE_CASE				:		int  			=							encrypt_message(__lowerCamelCase,				__lowerCamelCase		)
     elif mode == "decrypt":
          _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							decrypt_message(__lowerCamelCase,				__lowerCamelCase		)
     print(f"""Using the key {key}, the {mode}ed message is: {translated}"""		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()