


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


snake_case_  :	List[Any]							  =							torch.cuda.is_available()
snake_case_  :	List[Any]							  =							[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
snake_case_  :	Union[str, Any]							  =							['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
snake_case_  :	Tuple							  =							{
    '''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(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     ='''\n'''.join(a__)
				Path(a__).open('''w''').writelines(a__)







def   lowerCamelCase(      a__):
				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   A__							(						UpperCamelCase__     ):




						@classmethod
						def 							__UpperCamelCase (				cls    :					Tuple     )							->   int:


										"""simple docstring"""





										return cls




						@require_torch_multi_gpu
						def 							__UpperCamelCase (				self    :					Dict     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ={
										    '''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 							__UpperCamelCase (				self    :					Tuple ,			_a    :					List[Any] ,			_a    :					Any=True     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ={
										    '''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     )
										_SCREAMING_SNAKE_CASE	     =CHEAP_ARGS.copy()
										_SCREAMING_SNAKE_CASE	     =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
										_SCREAMING_SNAKE_CASE	     =self.get_auto_remove_tmp_dir()
										args_d.update(data_dir=_a ,			output_dir=_a ,			**_a     )

										def convert(_a    :					int ,			_a    :					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}"

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

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

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







"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						None:






     '''simple docstring'''



     if right <= left:
          return
     UpperCAmelCase__		:  str				=				left
     UpperCAmelCase__		:  List[str]				=				right
     UpperCAmelCase__		:  List[Any]				=				sorting[left]
     while i <= b:
          if sorting[i] < pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  List[str]				=				sorting[i], sorting[a]
               a += 1
               i += 1
          elif sorting[i] > pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  Optional[int]				=				sorting[i], sorting[b]
               b -= 1
          else:
               i += 1
     quick_sort_apartition(snake_case       , snake_case       , a - 1							)
     quick_sort_apartition(snake_case       , b + 1       , snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						None:






     '''simple docstring'''



     if left < right:
          UpperCAmelCase__		:  int				=				lomuto_partition(snake_case       , snake_case       , snake_case							)
          quick_sort_lomuto_partition(snake_case       , snake_case       , pivot_index - 1							)
          quick_sort_lomuto_partition(snake_case       , pivot_index + 1       , snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						int:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				sorting[right]
     UpperCAmelCase__		:  Optional[Any]				=				left
     for i in range(snake_case       , snake_case							):
          if sorting[i] < pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				sorting[i], sorting[store_index]
               store_index += 1
     UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				sorting[store_index], sorting[right]
     return store_index


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list							)->						list:






     '''simple docstring'''



     if len(snake_case							) <= 1:
          return sorting
     return (
         three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
         + [i for i in sorting if i == sorting[0]]
         + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
     )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

 _lowerCAmelCase     :     Optional[int]            =	input("""Enter numbers separated by a comma:\n""").strip()
 _lowerCAmelCase     :     str            =	[int(item) for item in user_input.split(""",""")]
 quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
 print(unsorted)


