'''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      :				List[str] 				=    torch.cuda.is_available()
_A      :				Dict 				=    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_A      :				Union[str, 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 					UpperCamelCase_						( snake_case_	:	Path						,		snake_case_	:	list				) ->					Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase    							=						"""\n""".join(snake_case_				)
 Path(snake_case_				).open("""w"""				).writelines(snake_case_				)







def 					UpperCamelCase_						( snake_case_	:	Optional[Any]				) ->					List[Any]:






 '''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     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 @classmethod
 def a (				cls       :     List[Any]							)  ->   Any:
  return cls
 @require_torch_multi_gpu
 def a (				self       :     Optional[int]							)  ->   str:
  __lowerCAmelCase    							=						{
      """no_teacher""": True,
      """freeze_encoder""": True,
      """gpus""": 2,
      """overwrite_output_dir""": True,
      """sortish_sampler""": True,
  }
  self._test_distiller_cli_fork(SCREAMING_SNAKE_CASE__				,	check_contents=SCREAMING_SNAKE_CASE__							)





 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     Tuple=True							)  ->   Dict:
  __lowerCAmelCase    							=						{
      """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(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						CHEAP_ARGS.copy()
  __lowerCAmelCase    							=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
  __lowerCAmelCase    							=						self.get_auto_remove_tmp_dir()
  args_d.update(data_dir=SCREAMING_SNAKE_CASE__				,	output_dir=SCREAMING_SNAKE_CASE__				,	**SCREAMING_SNAKE_CASE__							)

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

  __lowerCAmelCase    							=						os.listdir(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						{os.path.basename(SCREAMING_SNAKE_CASE__							) for p in contents}
  __lowerCAmelCase    							=						[p for p in contents if p.endswith("""ckpt"""							)]
  assert len(SCREAMING_SNAKE_CASE__							) > 0

  self.assertIn("""test_generations.txt"""				,	SCREAMING_SNAKE_CASE__							)
  self.assertIn("""test_results.txt"""				,	SCREAMING_SNAKE_CASE__							)

  # get the following from the module, (we don't have access to `model` here)
  __lowerCAmelCase    							=						os.path.join(SCREAMING_SNAKE_CASE__				,	"""metrics.json"""							)
  __lowerCAmelCase    							=						"""rouge2"""

  __lowerCAmelCase    							=						load_json(SCREAMING_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(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						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}"""]				,	SCREAMING_SNAKE_CASE__							)
  self.assertEqual(len(metrics["""test"""]							)				,	1							)
  __lowerCAmelCase    							=						int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1							)
  self.assertEqual(len(metrics["""val"""]							)				,	SCREAMING_SNAKE_CASE__							)







'''simple docstring'''

from unittest.mock import patch

import datasets
from datasets import Dataset







def 					UpperCamelCase_						( ) ->					Dict:






 '''simple docstring'''

 __lowerCAmelCase    							=						Dataset.from_dict({"""col_1""": [3, 2, 0, 1]}				)

 with patch("""tqdm.auto.tqdm"""				) as mock_tqdm:
  datasets.disable_progress_bar()
  dset.map(lambda snake_case_				: {"col_2": x["col_1"] + 1}				)
  mock_tqdm.assert_not_called()

  mock_tqdm.reset_mock()

  datasets.enable_progress_bar()
  dset.map(lambda snake_case_				: {"col_2": x["col_1"] + 1}				)
  mock_tqdm.assert_called()






