




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


_lowerCamelCase					:     Optional[Any]				       =    torch.cuda.is_available()
_lowerCamelCase					:     Tuple				       =    [' Sam ate lunch today.', 'Sams lunch ingredients.']
_lowerCamelCase					:     int				       =    ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_lowerCamelCase					:     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': 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 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			List[Any]						=						'''\n'''.join(_UpperCAmelCase		)
	Path(_UpperCAmelCase		).open('''w'''		).writelines(_UpperCAmelCase		)

def 						lowercase_ (						_UpperCAmelCase		):




	"""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 (      __UpperCAmelCase):







				@classmethod
				def    a_					(							cls				:   Optional[Any]    ):



					"""simple docstring"""

					return cls







				@require_torch_multi_gpu
				def    a_					(							self				:   int    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						{
					    '''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    a_					(							self				:   str ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   Dict=True    ):



					"""simple docstring"""

					A_					:			Optional[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(_lowerCamelCase    )
					A_					:			dict						=						CHEAP_ARGS.copy()
					A_					:			int						=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
					A_					:			str						=						self.get_auto_remove_tmp_dir()
					args_d.update(data_dir=_lowerCamelCase ,       output_dir=_lowerCamelCase ,       **_lowerCamelCase    )

					def convert(_lowerCamelCase				:   int ,       _lowerCamelCase				:   Optional[int]    ):
						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_					:			Dict						=						[x for x in (convert(_lowerCamelCase ,       _lowerCamelCase    ) for k, v in args_d.items()) if len(_lowerCamelCase    )]
					A_					:			Dict						=						[sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
					execute_subprocess_async(_lowerCamelCase ,       env=self.get_env()    )

					A_					:			Tuple						=						os.listdir(_lowerCamelCase    )
					A_					:			List[Any]						=						{os.path.basename(_lowerCamelCase    ) for p in contents}
					A_					:			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)
					A_					:			Dict						=						os.path.join(_lowerCamelCase ,       '''metrics.json'''    )
					A_					:			Optional[Any]						=						'''rouge2'''

					A_					:			Any						=						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    )
					A_					:			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    )
					A_					:			Dict						=						int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1    )
					self.assertEqual(len(metrics['''val''']    ) ,       _lowerCamelCase    )











"""simple docstring"""

import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_lowerCamelCase					:     Optional[int]				       =    set()
_lowerCamelCase					:     Union[str, Any]				       =    get_logger(__name__)

def 						lowercase_ (						_UpperCAmelCase = None		):




	"""simple docstring"""






	def decorator(_UpperCAmelCase		):
		global _emitted_deprecation_warnings

		if inspect.isclass(_UpperCAmelCase		):
			A_					:			Union[str, Any]						=						deprecated_class_or_function.__init__
			A_					:			int						=						deprecated_class_or_function.__name__
		else:
			A_					:			Union[str, Any]						=						deprecated_class_or_function
			A_					:			List[Any]						=						deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			A_					:			List[Any]						=						name if name != '''__init__''' else deprecated_function.__qualname__.split('''.'''		)[-2]

		A_					:			str						=						(
		    f"""{name} is deprecated and will be removed in the next major version of datasets.""" + f""" {help_message}"""
		    if help_message
		    else ''''''
		)

		@wraps(_UpperCAmelCase		)
		def wrapper(*_UpperCAmelCase					, **_UpperCAmelCase		):
			A_					:			Any						=						hash(_UpperCAmelCase		)
			if func_hash not in _emitted_deprecation_warnings:
				warnings.warn(_UpperCAmelCase					, category=_UpperCAmelCase					, stacklevel=2		)
				_emitted_deprecation_warnings.add(_UpperCAmelCase		)
			return deprecated_function(*_UpperCAmelCase					, **_UpperCAmelCase		)

		A_					:			Any						=						'''deprecated'''

		if inspect.isclass(_UpperCAmelCase		):
			A_					:			List[Any]						=						wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator

class     lowercase (      enum.EnumMeta):







				def __getattribute__(							cls				:   Any ,       _lowerCamelCase				:   List[Any]    ):



					"""simple docstring"""

					A_					:			str						=						super().__getattribute__(_lowerCamelCase    )
					if isinstance(_lowerCamelCase ,       enum.Enum    ) and obj._on_access:
						obj._on_access()
					return obj







				def __getitem__(							cls				:   Any ,       _lowerCamelCase				:   Union[str, Any]    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						super().__getitem__(_lowerCamelCase    )
					if member._on_access:
						member._on_access()
					return member







				def __call__(							cls				:   str ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   List[Any]=None ,       *, _lowerCamelCase				:   int=None ,       _lowerCamelCase				:   str=None ,       _lowerCamelCase				:   Dict=None ,       _lowerCamelCase				:   Tuple=1    ):



					"""simple docstring"""

					A_					:			Dict						=						super().__call__(_lowerCamelCase ,       _lowerCamelCase ,       module=_lowerCamelCase ,       qualname=_lowerCamelCase ,       type=_lowerCamelCase ,       start=_lowerCamelCase    )
					if isinstance(_lowerCamelCase ,       enum.Enum    ) and obj._on_access:
						obj._on_access()
					return obj

class     lowercase (      enum.Enum       ,		metaclass=__UpperCAmelCase):







				def __new__(							cls				:   Optional[Any] ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					A_					:			Tuple						=						object.__new__(cls    )
					A_					:			Union[str, Any]						=						value
					A_					:			Union[str, Any]						=						member.deprecate
					return member







				@property
				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					return ""







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						F""" {self.help_message}""" if self.help_message else ''''''
					warnings.warn(
					    F"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
					    + help_message ,       _lowerCamelCase ,       stacklevel=3 ,       )





