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   :						Optional[int]							=  torch.cuda.is_available()
__UpperCamelCase   :						Dict							=  [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
__UpperCamelCase   :						Union[str, Any]							=  ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
__UpperCamelCase   :						Optional[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        a_	( _A ,				_A					)     ->     List[str]:







				"""simple docstring"""
				snake_case__													=       '\n'.join(_A					)
				Path(_A					).open('w'					).writelines(_A					)
def        a_	( _A					)     ->     List[str]:







				"""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 					__SCREAMING_SNAKE_CASE(		a_		):
							@classmethod
							def lowerCAmelCase_	(       cls: Tuple  )						->						Optional[int]:
											return cls
							@require_torch_multi_gpu
							def lowerCAmelCase_	(       self: int  )						->						int:
											snake_case__													=       {
											    'no_teacher': True,
											    'freeze_encoder': True,
											    'gpus': 2,
											    'overwrite_output_dir': True,
											    'sortish_sampler': True,
											}
											self._test_distiller_cli_fork(UpperCamelCase    ,		check_contents=UpperCamelCase  )


							def lowerCAmelCase_	(       self: int    ,		UpperCamelCase: Optional[int]    ,		UpperCamelCase: Tuple=True  )						->						Any:
											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(UpperCamelCase  )
											snake_case__													=       CHEAP_ARGS.copy()
											snake_case__													=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
											snake_case__													=       self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=UpperCamelCase    ,		output_dir=UpperCamelCase    ,		**UpperCamelCase  )

											def convert(UpperCamelCase: Optional[int]    ,		UpperCamelCase: List[Any]  ):
															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}'''

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

											snake_case__													=       os.listdir(UpperCamelCase  )
											snake_case__													=       {os.path.basename(UpperCamelCase  ) for p in contents}
											snake_case__													=       [p for p in contents if p.endswith('ckpt'  )]
											assert len(UpperCamelCase  ) > 0

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

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

											snake_case__													=       load_json(UpperCamelCase  )
											# {'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(UpperCamelCase  )
											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}''']    ,		UpperCamelCase  )
											self.assertEqual(len(metrics['test']  )    ,		1  )
											snake_case__													=       int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1  )
											self.assertEqual(len(metrics['val']  )    ,		UpperCamelCase  )












def        a_	( _A ,				_A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res
def        a_	( _A ,				_A ,				_A					)     ->     Dict:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												snake_case__													=       ((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res




