






"""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


A__       :			str=						torch.cuda.is_available()
A__       :			Union[str, Any]=						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
A__       :			Optional[Any]=						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
A__       :			Union[str, Any]=						{
    """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_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						int:
    """simple docstring"""

    UpperCamelCase__								=     '\n'.join(SCREAMING_SNAKE_CASE       )
    Path(SCREAMING_SNAKE_CASE       ).open('w'       ).writelines(SCREAMING_SNAKE_CASE       )







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						int:
    """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  __lowerCamelCase     (	_a	):

   @classmethod
   def     SCREAMING_SNAKE_CASE__			(       cls   )		->							int:
       return cls

   @require_torch_multi_gpu
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       UpperCamelCase__								=     {
           '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     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_=True   )		->							Optional[Any]:
       UpperCamelCase__								=     {
           '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__								=     CHEAP_ARGS.copy()
       UpperCamelCase__								=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
       UpperCamelCase__								=     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__								=     [x for x in (convert(snake_case_			,					snake_case_   ) for k, v in args_d.items()) if len(snake_case_   )]
       UpperCamelCase__								=     [sys.executable, F'{self.test_file_dir}/distillation.py'] + cli_args
       execute_subprocess_async(snake_case_			,					env=self.get_env()   )

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

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












"""simple docstring"""





def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						int:
    """simple docstring"""

    if not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ):
        raise ValueError('double_factorial() only accepts integral values'       )
    if num < 0:
        raise ValueError('double_factorial() not defined for negative values'       )
    UpperCamelCase__								=     1
    for i in range(SCREAMING_SNAKE_CASE			,						0			,						-2       ):
        value *= i
    return value


if __name__ == "__main__":
  import doctest

  doctest.testmod()




