



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


__magic_name__       =     torch.cuda.is_available()
__magic_name__       =     [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__magic_name__       =     ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__magic_name__       =     {
    '''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 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       '\n'.join(_UpperCAmelCase				)
					Path(_UpperCAmelCase				).open('w'				).writelines(_UpperCAmelCase				)





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






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




class 	a__       (       _snake_case       ):


					"""simple docstring"""



					@classmethod
					def 				__UpperCAmelCase		(		cls     :Dict    ):
										return cls

					@require_torch_multi_gpu
					def 				__UpperCAmelCase		(		self     :Tuple    ):
										lowercase						   =       {
										    '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 				__UpperCAmelCase		(		self     :List[str]      ,  lowercase__     :Union[str, Any]      ,  lowercase__     :List[Any]=True    ):
										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(lowercase__    )
										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=lowercase__      ,  output_dir=lowercase__      ,  **lowercase__    )

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

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

										lowercase						   =       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__    )
										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}"""]      ,  lowercase__    )
										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']    )      ,  lowercase__    )











from __future__ import annotations





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					assert (
					    c % greatest_common_divisor(_UpperCAmelCase       ,			_UpperCAmelCase				) == 0
					)  # greatest_common_divisor(a,b) function implemented below
					((lowercase)   ,      (lowercase)   ,      (lowercase))						   =       extended_gcd(_UpperCAmelCase       ,			_UpperCAmelCase				)  # extended_gcd(a,b) function implemented below
					lowercase						   =       c / d
					return (r * x, r * y)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase = 2				):
					"""simple docstring"""






					((lowercase)   ,      (lowercase))						   =       diophantine(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)  # Initial value
					lowercase						   =       greatest_common_divisor(_UpperCAmelCase       ,			_UpperCAmelCase				)
					lowercase						   =       a // d
					lowercase						   =       b // d

					for i in range(_UpperCAmelCase				):
										lowercase						   =       xa + i * q
										lowercase						   =       ya - i * p
										print(_UpperCAmelCase       ,			_UpperCAmelCase				)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if a < b:
										lowercase   ,      lowercase						   =       b, a

					while a % b != 0:
										lowercase   ,      lowercase						   =       b, a % b

					return b





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					assert a >= 0 and b >= 0

					if b == 0:
										lowercase   ,      lowercase   ,      lowercase						   =       a, 1, 0
					else:
										((lowercase)   ,      (lowercase)   ,      (lowercase))						   =       extended_gcd(_UpperCAmelCase       ,			a % b				)
										lowercase						   =       q
										lowercase						   =       p - q * (a // b)

					assert a % d == 0 and b % d == 0
					assert d == a * x + b * y

					return (d, x, y)


if __name__ == "__main__":
		from doctest import testmod

		testmod(name='''diophantine''', verbose=True)
		testmod(name='''diophantine_all_soln''', verbose=True)
		testmod(name='''extended_gcd''', verbose=True)
		testmod(name='''greatest_common_divisor''', verbose=True)






