






"""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		:  Optional[int]						=		torch.cuda.is_available()
_UpperCamelCase		:  str						=		[' 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		:  Optional[int]						=		{
    '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': 4_2,
    '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-0_8,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 1_2,
    'max_target_length': 1_2,
    'val_max_target_length': 1_2,
    'test_max_target_length': 1_2,
    '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 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  Path		, __snake_case      :  list   ):





  '''simple docstring'''





  lowercase							   =	'\n'.join(__snake_case   )
  Path(__snake_case   ).open('w'   ).writelines(__snake_case   )
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  List[str]   ):





  '''simple docstring'''





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



class 	a					(       a_     ):







    @classmethod
    def       UpperCamelCase_				(      cls	):
      return cls







    @require_torch_multi_gpu
    def       UpperCamelCase_				(      self	):
      lowercase							   =	{
          '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	):
      lowercase							   =	{
          '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	)
      lowercase							   =	CHEAP_ARGS.copy()
      lowercase							   =	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
      lowercase							   =	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}'

      lowercase							   =	[x for x in (convert(_lowerCamelCase							,		_lowerCamelCase	) for k, v in args_d.items()) if len(_lowerCamelCase	)]
      lowercase							   =	[sys.executable, F'{self.test_file_dir}/distillation.py'] + cli_args
      execute_subprocess_async(_lowerCamelCase							,		env=self.get_env()	)

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

      lowercase							   =	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	)
      lowercase							   =	metrics['val'][-1]
      self.assertGreaterEqual(last_step_stats['val_avg_gen_time']							,		0.0_1	)
      self.assertIsInstance(last_step_stats[F'val_avg_{val_metric}']							,		_lowerCamelCase	)
      self.assertEqual(len(metrics['test']	)							,		1	)
      lowercase							   =	int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1	)
      self.assertEqual(len(metrics['val']	)							,		_lowerCamelCase	)










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  str   ):





  '''simple docstring'''





  lowercase							   =	len(__snake_case   )
  lowercase							   =	[0] * length
  lowercase							   =	[[False for i in range(__snake_case   )] for j in range(__snake_case   )]
  for i, c in enumerate(__snake_case   ):
    lowercase							   =	i
    for j in range(i + 1   ):
      if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
        lowercase							   =	True
        lowercase							   =	min(__snake_case		, 0 if j == 0 else (cut[j - 1] + 1)   )
    lowercase							   =	mincut
  return cut[length - 1]


if __name__ == "__main__":
    _UpperCamelCase		:  List[Any]						=		input('Enter the string: ').strip()
    _UpperCamelCase		:  List[Any]						=		find_minimum_partitions(s)
    print(F'''Minimum number of partitions required for the \'{s}\' is {ans}''')


