
'''simple docstring'''



def  _A		(							lowercase__		,		lowercase__		,		lowercase__	):
						if right <= left:
												return
						lowercase__							 =       lowercase__							 =       left
						lowercase__							 =       right
						lowercase__							 =       sorting[left]
						while i <= b:
												if sorting[i] < pivot:
																		lowercase__ ,						lowercase__							 =       sorting[i], sorting[a]
																		a += 1
																		i += 1
												elif sorting[i] > pivot:
																		lowercase__ ,						lowercase__							 =       sorting[i], sorting[b]
																		b -= 1
												else:
																		i += 1
						quick_sort_apartition(lowercase__		,		lowercase__		,		a - 1	)
						quick_sort_apartition(lowercase__		,		b + 1		,		lowercase__	)





def  _A		(							lowercase__		,		lowercase__		,		lowercase__	):
						if left < right:
												lowercase__							 =       lomuto_partition(lowercase__		,		lowercase__		,		lowercase__	)
												quick_sort_lomuto_partition(lowercase__		,		lowercase__		,		pivot_index - 1	)
												quick_sort_lomuto_partition(lowercase__		,		pivot_index + 1		,		lowercase__	)





def  _A		(							lowercase__		,		lowercase__		,		lowercase__	):
						lowercase__							 =       sorting[right]
						lowercase__							 =       left
						for i in range(lowercase__		,		lowercase__	):
												if sorting[i] < pivot:
																		lowercase__ ,						lowercase__							 =       sorting[i], sorting[store_index]
																		store_index += 1
						lowercase__ ,						lowercase__							 =       sorting[store_index], sorting[right]
						return store_index





def  _A		(							lowercase__	):
						if len(lowercase__	) <= 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)

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








'''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-0_8,
    "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  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       """\n""".join(lowercase__	)
						Path(lowercase__	).open("""w"""	).writelines(lowercase__	)





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







class 			A							(       __UpperCAmelCase				):



			@classmethod
			def A__ (					cls       )			->		Any:




									'''simple docstring'''



									return cls



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




									'''simple docstring'''



									lowercase__							 =       {
									    """no_teacher""": True,
									    """freeze_encoder""": True,
									    """gpus""": 2,
									    """overwrite_output_dir""": True,
									    """sortish_sampler""": True,
									}
									self._test_distiller_cli_fork(lowerCamelCase__       ,     check_contents=lowerCamelCase__       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=True       )			->		Dict:




									'''simple docstring'''



									lowercase__							 =       {
									    """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(lowerCamelCase__       )
									lowercase__							 =       CHEAP_ARGS.copy()
									lowercase__							 =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
									lowercase__							 =       self.get_auto_remove_tmp_dir()
									args_d.update(data_dir=lowerCamelCase__       ,     output_dir=lowerCamelCase__       ,     **lowerCamelCase__       )

									def convert(lowerCamelCase__       ,     lowerCamelCase__       ):
															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__							 =       [x for x in (convert(lowerCamelCase__       ,     lowerCamelCase__       ) for k, v in args_d.items()) if len(lowerCamelCase__       )]
									lowercase__							 =       [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
									execute_subprocess_async(lowerCamelCase__       ,     env=self.get_env()       )

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

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

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

									lowercase__							 =       load_json(lowerCamelCase__       )
									# {'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(lowerCamelCase__       )
									lowercase__							 =       metrics["""val"""][-1]
									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]       ,     0.01       )
									self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']       ,     lowerCamelCase__       )
									self.assertEqual(len(metrics["""test"""]       )       ,     1       )
									lowercase__							 =       int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1       )
									self.assertEqual(len(metrics["""val"""]       )       ,     lowerCamelCase__       )






