

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


_a									=  torch.cuda.is_available()
_a									=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_a									=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_a									=  {
    '''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-08,
    '''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 _a					(			SCREAMING_SNAKE_CASE						:     Path       ,   SCREAMING_SNAKE_CASE						:     list						)       ->       Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:  str        = '\n'.join(SCREAMING_SNAKE_CASE						)
 Path(SCREAMING_SNAKE_CASE						).open('w'						).writelines(SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]						)       ->       List[str]:

 """simple docstring"""






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






class 				A_					(						snake_case__   ):


     @classmethod
     def     UpperCAmelCase    (				cls     :						Optional[Any]   )				->      Union[str, Any]:
      return cls


     @require_torch_multi_gpu
     def     UpperCAmelCase    (				self     :						Dict   )				->      str:
      __lowerCAmelCase:  Optional[Any]        = {
          'no_teacher': True,
          'freeze_encoder': True,
          'gpus': 2,
          'overwrite_output_dir': True,
          'sortish_sampler': True,
      }
      self._test_distiller_cli_fork(UpperCAmelCase     ,    check_contents=UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						int     ,    UpperCAmelCase     :						Tuple     ,    UpperCAmelCase     :						Dict=True   )				->      Optional[int]:
      __lowerCAmelCase:  Tuple        = {
          '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(UpperCAmelCase   )
      __lowerCAmelCase:  dict        = CHEAP_ARGS.copy()
      __lowerCAmelCase:  Dict        = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
      __lowerCAmelCase:  Any        = self.get_auto_remove_tmp_dir()
      args_d.update(data_dir=UpperCAmelCase     ,    output_dir=UpperCAmelCase     ,    **UpperCAmelCase   )

      def convert(UpperCAmelCase     :						Tuple     ,    UpperCAmelCase     :						Dict   ):
       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}'''

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

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

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

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

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








def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 return "".join(chr(ord(SCREAMING_SNAKE_CASE						) + 32						) if 'A' <= char <= 'Z' else char for char in word						)


if __name__ == "__main__":
  from doctest import testmod

  testmod()





