


'''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


_UpperCAmelCase					:			int				     =					torch.cuda.is_available()
_UpperCAmelCase					:			Any				     =					[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_UpperCAmelCase					:			str				     =					["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_UpperCAmelCase					:			str				     =					{
    """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""": 4_2,
    """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""": 1_2,
    """max_target_length""": 1_2,
    """val_max_target_length""": 1_2,
    """test_max_target_length""": 1_2,
    """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 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					'''\n'''.join(lowerCamelCase)
						Path(lowerCamelCase).open('''w''').writelines(lowerCamelCase)

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




class    a__					(			__A   ):







				"""simple docstring"""



				@classmethod
				def        _snake_case	(cls		):
										return cls




				@require_torch_multi_gpu
				def        _snake_case	(self		):
										__lowerCAmelCase											=					{
										    '''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        _snake_case	(self							,      __lowercase							,      __lowercase=True		):
										__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(__lowercase		)
										__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=__lowercase							,      output_dir=__lowercase							,      **__lowercase		)

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

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

										__lowerCAmelCase											=					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		)
										__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}"""]							,      __lowercase		)
										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''']		)							,      __lowercase		)





'''simple docstring'''

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCAmelCase					:			Any				     =					TypeVar("""T""", bound=bool)


class    a__					(			Generic[T]   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										__lowerCAmelCase											=					value
										__lowerCAmelCase											=					None
										__lowerCAmelCase											=					None




				@property
				def        _snake_case	(self		):
										return self._value




				@staticmethod
				def        _snake_case	(__lowercase							,      __lowercase		):
										if not roota:
																return roota

										if not roota:
																return roota

										if roota.value > roota.value:
																__lowerCAmelCase							,       __lowerCAmelCase											=					roota, roota

										if random.choice([True, False]		):
																__lowerCAmelCase							,       __lowerCAmelCase											=					roota.right, roota.left

										__lowerCAmelCase											=					RandomizedHeapNode.merge(roota.left							,      __lowercase		)

										return roota


class    a__					(			Generic[T]   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase = ()		):
										__lowerCAmelCase											=					None

										if data:
																for item in data:
																						self.insert(__lowercase		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					RandomizedHeapNode.merge(self._root							,      RandomizedHeapNode(__lowercase		)		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.top()

										if self._root is None:
																return None

										__lowerCAmelCase											=					RandomizedHeapNode.merge(self._root.left							,      self._root.right		)

										return result




				def        _snake_case	(self		):
										if not self._root:
																raise IndexError('''Can\'t get top element for the empty heap.'''		)
										return self._root.value




				def        _snake_case	(self		):
										__lowerCAmelCase											=					None




				def        _snake_case	(self		):
										__lowerCAmelCase											=					[]
										while self:
																result.append(self.pop()		)

										return result




				def __bool__(self		):
										return self._root is not None


if __name__ == "__main__":
	import doctest

	doctest.testmod()

