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


__snake_case			      =    torch.cuda.is_available()
__snake_case			      =    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__snake_case			      =    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__snake_case			      =    {
    '''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						)->  Tuple:






      '''simple docstring'''





      UpperCAmelCase    : Any      ='''\n'''.join(__lowerCAmelCase						)
      Path(__lowerCAmelCase						).open('''w'''						).writelines(__lowerCAmelCase						)





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  str:






      '''simple docstring'''





      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 			__snake_case   (     lowerCamelCase__   ):
 @classmethod
 def 	UpperCAmelCase__       (  cls						)	->							int:
       '''simple docstring'''





       return cls
 @require_torch_multi_gpu
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      ={
           '''no_teacher''': True,
           '''freeze_encoder''': True,
           '''gpus''': 2,
           '''overwrite_output_dir''': True,
           '''sortish_sampler''': True,
       }
       self._test_distiller_cli_fork(snake_case__		,   check_contents=snake_case__						)






 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__=True						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : Optional[int]      ={
           '''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(snake_case__						)
       UpperCAmelCase    : dict      =CHEAP_ARGS.copy()
       UpperCAmelCase    : Any      =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
       UpperCAmelCase    : Union[str, Any]      =self.get_auto_remove_tmp_dir()
       args_d.update(data_dir=snake_case__		,   output_dir=snake_case__		,   **snake_case__						)

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

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

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

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

       UpperCAmelCase    : List[Any]      =load_json(snake_case__						)
       # {'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(snake_case__						)
       UpperCAmelCase    : Optional[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}''']		,   snake_case__						)
       self.assertEqual(len(metrics['''test''']						)		,   1						)
       UpperCAmelCase    : Optional[Any]      =int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1						)
       self.assertEqual(len(metrics['''val''']						)		,   snake_case__						)

from collections import defaultdict





def   lowerCAmelCase_ (				__lowerCAmelCase = 5						)->  int:






      '''simple docstring'''





      UpperCAmelCase    : List[str]      =defaultdict(__lowerCAmelCase						)
      UpperCAmelCase    : Tuple      =0

      while True:
            UpperCAmelCase    : Any      =get_digits(__lowerCAmelCase						)
            freqs[digits].append(__lowerCAmelCase						)

            if len(freqs[digits]						) == max_base:
                  UpperCAmelCase    : Optional[int]      =freqs[digits][0] ** 3
                  return base

            num += 1





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  str:






      '''simple docstring'''





      return "".join(sorted(str(num**3						)						)						)


if __name__ == "__main__":
     print(f'{solution() = }')
