






'''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


_UpperCamelCase		:		int								=			torch.cuda.is_available()
_UpperCamelCase		:		Union[str, Any]								=			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_UpperCamelCase		:		Optional[int]								=			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_UpperCamelCase		:		Any								=			{
    """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 (			A			:	Path	,   A			:	list						)  -> List[str]:
   UpperCAmelCase_     :   Dict       =   "\n".join(_UpperCAmelCase						)
   Path(_UpperCAmelCase						).open('''w'''						).writelines(_UpperCAmelCase						)
def __UpperCAmelCase (			A			:	List[Any]						)  -> Optional[int]:







   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 				snake_case__     (			snake_case__):




     @classmethod
     def 						A     (       cls       :						Any     )  ->    List[str]:
        return cls




     @require_torch_multi_gpu
     def 						A     (       self       :						Optional[int]     )  ->    Any:
        UpperCAmelCase_     :   str       =   {
            "no_teacher": True,
            "freeze_encoder": True,
            "gpus": 2,
            "overwrite_output_dir": True,
            "sortish_sampler": True,
        }
        self._test_distiller_cli_fork(_A							,				check_contents=_A     )





     def 						A     (       self       :						List[str]							,				_A       :						List[str]							,				_A       :						int=True     )  ->    Optional[Any]:
        UpperCAmelCase_     :   List[Any]       =   {
            "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(_A     )
        UpperCAmelCase_     :   dict       =   CHEAP_ARGS.copy()
        UpperCAmelCase_     :   int       =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
        UpperCAmelCase_     :   Any       =   self.get_auto_remove_tmp_dir()
        args_d.update(data_dir=_A							,				output_dir=_A							,				**_A     )

        def convert(_A       :						Dict							,				_A       :						Tuple     ):
           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}"

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

        UpperCAmelCase_     :   Optional[int]       =   os.listdir(_A     )
        UpperCAmelCase_     :   List[Any]       =   {os.path.basename(_A     ) for p in contents}
        UpperCAmelCase_     :   Optional[Any]       =   [p for p in contents if p.endswith('''ckpt'''     )]
        assert len(_A     ) > 0

        self.assertIn('''test_generations.txt'''							,				_A     )
        self.assertIn('''test_results.txt'''							,				_A     )

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

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










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_UpperCamelCase		:		str								=			{}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[Any]								=			['MBart50Tokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[str]								=			['MBart50TokenizerFast']


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa import MBartaaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
						import sys

						_UpperCamelCase		:		Optional[int]								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


