






"""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    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					'''\n'''.join(__UpperCamelCase					)
  Path(__UpperCamelCase					).open('''w'''					).writelines(__UpperCamelCase					)



def    lowerCAmelCase							(       __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    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	str    ):

     '''simple docstring'''
     return cls


   @require_torch_multi_gpu
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					{
         '''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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	str,			_lowerCamelCase					:	int=True    ):

     '''simple docstring'''
     __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(_lowerCamelCase    )
     __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=_lowerCamelCase,			output_dir=_lowerCamelCase,			**_lowerCamelCase    )

     def convert(_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	Union[str, Any]    ):
       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(_lowerCamelCase,			_lowerCamelCase    ) for k, v in args_d.items()) if len(_lowerCamelCase    )]
     __A		     =					[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
     execute_subprocess_async(_lowerCamelCase,			env=self.get_env()    )

     __A		     =					os.listdir(_lowerCamelCase    )
     __A		     =					{os.path.basename(_lowerCamelCase    ) for p in contents}
     __A		     =					[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		     =					os.path.join(_lowerCamelCase,			'''metrics.json'''    )
     __A		     =					'''rouge2'''

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









class 						UpperCAmelCase__     :




			'''simple docstring'''






			def __init__(  self				:					List[Any]			,			a_				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      data
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None

def 			a		( _UpperCAmelCase				:   BinaryTreeNode | None					,  _UpperCAmelCase				:   int		):







		'''simple docstring'''





		if node is None:
				__UpperCAmelCase       :	List[Any]						=      BinaryTreeNode(_UpperCAmelCase		)
				return node

		# binary search tree is not empty,
		# so we will insert it into the tree
		# if new_value is less than value of data in node,
		#  add it to left subtree and proceed recursively
		if new_value < node.data:
				__UpperCAmelCase       :	int						=      insert(node.left_child					,  _UpperCAmelCase		)
		else:
				# if new_value is greater than value of data in node,
				#  add it to right subtree and proceed recursively
				__UpperCAmelCase       :	str						=      insert(node.right_child					,  _UpperCAmelCase		)
		return node

def 			a		( _UpperCAmelCase				:   None | BinaryTreeNode		):  # if node is None,return







		'''simple docstring'''





		if node:
				__UpperCAmelCase       :	Optional[int]						=      inorder(node.left_child		)
				__UpperCAmelCase       :	int						=      [*inorder_array, node.data]
				__UpperCAmelCase       :	Optional[Any]						=      inorder_array + inorder(node.right_child		)
		else:
				__UpperCAmelCase       :	List[Any]						=      []
		return inorder_array

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Any						=      insert(_UpperCAmelCase					,  15		)
		insert(_UpperCAmelCase					,  10		)
		insert(_UpperCAmelCase					,  25		)
		insert(_UpperCAmelCase					,  6		)
		insert(_UpperCAmelCase					,  14		)
		insert(_UpperCAmelCase					,  20		)
		insert(_UpperCAmelCase					,  60		)
		return root

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      make_tree()
		print('''Printing values of binary search tree in Inorder Traversal.'''		)
		inorder(_UpperCAmelCase		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()





