




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


__SCREAMING_SNAKE_CASE    :   List[Any]       =torch.cuda.is_available()
__SCREAMING_SNAKE_CASE    :   Tuple       =[' Sam ate lunch today.', 'Sams lunch ingredients.']
__SCREAMING_SNAKE_CASE    :   Union[str, Any]       =['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
__SCREAMING_SNAKE_CASE    :   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-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 			lowerCAmelCase_			(  lowerCamelCase:       List[str]     ,				lowerCamelCase:       int						)					->			Optional[int]:


      '''simple docstring'''




      lowerCamelCase__        =				'\n'.join(lowerCamelCase						)
      Path(lowerCamelCase						).open('w'						).writelines(lowerCamelCase						)

def 			lowerCAmelCase_			(  lowerCamelCase:       str						)					->			str:


      '''simple docstring'''




      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			(		lowercase_		):
     """simple docstring"""

     @classmethod
     def  __snake_case   (    cls  :   Union[str, Any]			)				->       Dict:
           return cls




     @require_torch_multi_gpu
     def  __snake_case   (    self  :   List[Any]			)				->       Union[str, Any]:
           lowerCamelCase__        =				{
               'no_teacher': True,
               'freeze_encoder': True,
               'gpus': 2,
               'overwrite_output_dir': True,
               'sortish_sampler': True,
           }
           self._test_distiller_cli_fork(A__	,  check_contents=A__			)




     def  __snake_case   (    self  :   str	,  A__  :   int	,  A__  :   int=True			)				->       Optional[Any]:
           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(A__			)
           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=A__	,  output_dir=A__	,  **A__			)

           def convert(A__  :   Tuple	,  A__  :   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}"""

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

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

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

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

           lowerCamelCase__        =				load_json(A__			)
           # {'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(A__			)
           lowerCamelCase__        =				metrics['val'][-1]
           self.assertGreaterEqual(last_step_stats['val_avg_gen_time']	,  0.01			)
           self.assertIsInstance(last_step_stats[F"""val_avg_{val_metric}"""]	,  A__			)
           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']			)	,  A__			)





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE    :   List[str]       =logging.getLogger()

def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      lowerCamelCase__        =				argparse.ArgumentParser()
      parser.add_argument('-f'						)
      lowerCamelCase__        =				parser.parse_args()
      return args.f






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   Optional[Any]			)				->       Optional[Any]:
           lowerCamelCase__        =				logging.StreamHandler(sys.stdout			)
           logger.addHandler(A__			)

           lowerCamelCase__        =				self.get_auto_remove_tmp_dir()
           lowerCamelCase__        =				F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

           with patch.object(A__	,  'argv'	,  A__			):
                 lowerCamelCase__        =				run_glue_with_pabee.main()
                 for value in result.values():
                       self.assertGreaterEqual(A__	,  0.75			)