





"""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


lowerCamelCase_					:						Union[str, Any]      = torch.cuda.is_available()
lowerCamelCase_					:						Dict      = [' Sam ate lunch today.', 'Sams lunch ingredients.']
lowerCamelCase_					:						str      = ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowerCamelCase_					:						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__		(					_UpperCAmelCase			, _UpperCAmelCase ) ->    List[str]:






  """simple docstring"""





  A_  :	Optional[int]    						=  '\n'.join(_A )
  Path(_A ).open('w' ).writelines(_A )





def 						UpperCAmelCase__		(					_UpperCAmelCase ) ->    Dict:






  """simple docstring"""





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





class 			_UpperCAmelCase  (       lowerCamelCase__		):



      '''simple docstring'''

      @classmethod
      def  lowerCamelCase_      (					cls  ):

        """simple docstring"""

        return cls

      @require_torch_multi_gpu
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  {
            'no_teacher': True,
            'freeze_encoder': True,
            'gpus': 2,
            'overwrite_output_dir': True,
            'sortish_sampler': True,
        }
        self._test_distiller_cli_fork(lowercase_ ,				check_contents=lowercase_  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_=True  ):

        """simple docstring"""

        A_  :	Dict    						=  {
            '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(lowercase_  )
        A_  :	Optional[Any]    						=  CHEAP_ARGS.copy()
        A_  :	List[Any]    						=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
        A_  :	str    						=  self.get_auto_remove_tmp_dir()
        args_d.update(data_dir=lowercase_ ,				output_dir=lowercase_ ,				**lowercase_  )

        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}"""

        A_  :	Dict    						=  [x for x in (convert(lowercase_ ,				lowercase_  ) for k, v in args_d.items()) if len(lowercase_  )]
        A_  :	Tuple    						=  [sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
        execute_subprocess_async(lowercase_ ,				env=self.get_env()  )

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

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

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

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






"""simple docstring"""




import math as m





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase=0.001 ):






  """simple docstring"""





  return (f(a + h ) - f(a - h )) / (2 * h)





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=0			, _UpperCAmelCase=100			, _UpperCAmelCase=0.0_001			, _UpperCAmelCase=1E-6			, _UpperCAmelCase=False ):






  """simple docstring"""





  A_  :	Optional[int]    						=  xa  # set the initial guess
  A_  :	Any    						=  [a]
  A_  :	str    						=  abs(f(_UpperCAmelCase ) )
  A_  :	str    						=  lambda _UpperCAmelCase : calc_derivative(_UpperCAmelCase			, _UpperCAmelCase			, h=_UpperCAmelCase )  # noqa: E731  Derivative of f(x)
  for _ in range(_UpperCAmelCase ):
    if fa(_UpperCAmelCase ) == 0:
      raise ValueError('No converging solution found' )
    A_  :	Union[str, Any]    						=  a - f(_UpperCAmelCase ) / fa(_UpperCAmelCase )  # Calculate the next estimate
    if logsteps:
      steps.append(_UpperCAmelCase )
    if error < maxerror:
      break
  else:
    raise ValueError('Iteration limit reached, no converging solution found' )
  if logsteps:
    # If logstep is true, then log intermediate steps
    return a, error, steps
  return a, error


if __name__ == "__main__":
   from matplotlib import pyplot as plt

   lowerCamelCase_					:						str      = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_					:						int      = newton_raphson(
       f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
   )
   plt.plot([abs(f(x)) for x in steps])
   plt.xlabel('step')
   plt.ylabel('error')
   plt.show()
   print(F"solution = {{{solution:f}}}, error = {{{error:f}}}")