

'''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			:	Optional[int]								=  torch.cuda.is_available()
_a			:	Optional[Any]								=  [' Sam ate lunch today.', 'Sams lunch ingredients.']
_a			:	Any								=  ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_a			:	List[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': 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__						(  SCREAMING_SNAKE_CASE :				List[Any]    ,				SCREAMING_SNAKE_CASE :				str  ):
		UpperCAmelCase  							=		'\n'.join(lowercase__  )
		Path(lowercase__  ).open('w'  ).writelines(lowercase__  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[Any]  ):
		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     lowercase_		(						__UpperCAmelCase	):

		'''simple docstring'''





		@classmethod
		def   snake_case_     (       cls       )							->			Any:


				"""simple docstring"""
				return cls
		@require_torch_multi_gpu
		def   snake_case_     (       self       )							->			Any:


				"""simple docstring"""
				UpperCAmelCase  							=		{
				    '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   snake_case_     (       self				,	a_				,	a_=True       )							->			Optional[Any]:


				"""simple docstring"""
				UpperCAmelCase  							=		{
				    '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__       )
				UpperCAmelCase  							=		CHEAP_ARGS.copy()
				UpperCAmelCase  							=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
				UpperCAmelCase  							=		self.get_auto_remove_tmp_dir()
				args_d.update(data_dir=lowerCamelCase__				,	output_dir=lowerCamelCase__				,	**lowerCamelCase__       )

				def convert(a_				,	a_       ):
						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}'''

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

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

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





'''simple docstring'''




from collections import namedtuple

_a			:	int								=  namedtuple('from_to', 'from_ to')

_a			:	Dict								=  {
    'atm': from_to(1, 1),
    'pascal': from_to(0.0000098, 101_325),
    'bar': from_to(0.986923, 1.01325),
    'kilopascal': from_to(0.00986923, 101.325),
    'megapascal': from_to(9.86923, 0.101325),
    'psi': from_to(0.068046, 14.6959),
    'inHg': from_to(0.0334211, 29.9213),
    'torr': from_to(0.00131579, 760),
}


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				float    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				str  ):
  if from_type not in PRESSURE_CONVERSION:
    raise ValueError(
        f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
        + ', '.join(SCREAMING_SNAKE_CASE  )  )
  if to_type not in PRESSURE_CONVERSION:
    raise ValueError(
        f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
        + ', '.join(SCREAMING_SNAKE_CASE  )  )
  return (
      value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


