code: 
  
  
  '''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   				=      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": 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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Optional[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Any					   =		'''\n'''.join(_UpperCamelCase     )
        Path(_UpperCamelCase     ).open('''w'''     ).writelines(_UpperCamelCase     )
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(_UpperCamelCase,    f"""{split}.source"""     ),    _UpperCamelCase     )
              _dump_articles(os.path.join(_UpperCamelCase,    f"""{split}.target"""     ),    _UpperCamelCase     )
        return tmp_dir
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			SCREAMING_SNAKE_CASE__   ):
  
  
        @classmethod
        def        __lowerCamelCase			(							cls		):
              return cls
  
  
        @require_torch_multi_gpu
        def        __lowerCamelCase			(							self		):
              lowercase			:   Any					   =		{
                  '''no_teacher''': True,
                  '''freeze_encoder''': True,
                  '''gpus''': 2,
                  '''overwrite_output_dir''': True,
                  '''sortish_sampler''': True,
              }
              self._test_distiller_cli_fork(a_							,						check_contents=a_		)
  
  
  
  
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=True		):
              lowercase			:   List[str]					   =		{
                  '''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(a_		)
              lowercase			:   List[str]					   =		CHEAP_ARGS.copy()
              lowercase			:   Optional[int]					   =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
              lowercase			:   Union[str, Any]					   =		self.get_auto_remove_tmp_dir()
              args_d.update(data_dir=a_							,						output_dir=a_							,						**a_		)
  
              def convert(SCREAMING_SNAKE_CASE__							,						SCREAMING_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}"""
  
              lowercase			:   List[str]					   =		[x for x in (convert(a_							,						a_		) for k, v in args_d.items()) if len(a_		)]
              lowercase			:   int					   =		[sys.executable, f"""{self.test_file_dir}/distillation.py"""] + cli_args
              execute_subprocess_async(a_							,						env=self.get_env()		)
  
              lowercase			:   Optional[Any]					   =		os.listdir(a_		)
              lowercase			:   int					   =		{os.path.basename(a_		) for p in contents}
              lowercase			:   Any					   =		[p for p in contents if p.endswith('''ckpt'''		)]
              assert len(a_		) > 0
  
              self.assertIn('''test_generations.txt'''							,						a_		)
              self.assertIn('''test_results.txt'''							,						a_		)
  
              # get the following from the module, (we don't have access to `model` here)
              lowercase			:   Optional[int]					   =		os.path.join(a_							,						'''metrics.json'''		)
              lowercase			:   Tuple					   =		'''rouge2'''
  
              lowercase			:   List[Any]					   =		load_json(a_		)
              # {'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(a_		)
              lowercase			:   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}"""]							,						a_		)
              self.assertEqual(len(metrics['''test''']		)							,						1		)
              lowercase			:   str					   =		int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1		)
              self.assertEqual(len(metrics['''val''']		)							,						a_		)
  
  
style_context: 
  
  
  import math
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[Any]					   =		0
        if n % 2 == 0:
              count += 1
              while n % 2 == 0:
                    lowercase			:   Any					   =		int(n / 2     )
      # the n input value must be odd so that
      # we can skip one element (ie i += 2)
  
        lowercase			:   Optional[int]					   =		3
  
        while i <= int(math.sqrt(_UpperCamelCase     )     ):
              if n % i == 0:
                    count += 1
                    while n % i == 0:
                          lowercase			:   Optional[Any]					   =		int(n / i     )
              lowercase			:   str					   =		i + 2
  
        # this condition checks the prime
        # number n is greater than 2
  
        if n > 2:
              count += 1
        return count
  
  
  if __name__ == "__main__":
    __a   				=      51_24_21_83
    print(F'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
    print(F'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')
  
  