



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_          =				torch.cuda.is_available()
UpperCAmelCase_          =				[" Sam ate lunch today.", "Sams lunch ingredients."]
UpperCAmelCase_          =				["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
UpperCAmelCase_          =				{
    "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 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Optional[Any]  ,					_SCREAMING_SNAKE_CASE :						Optional[int]					)->Dict:
      _lowerCAmelCase			      =	'''\n'''.join(__UpperCamelCase					)
      Path(__UpperCamelCase					).open('''w'''					).writelines(__UpperCamelCase					)
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Any					)->Dict:







      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 		UpperCAmelCase    (    _lowerCAmelCase					):





   @classmethod
   def 						__lowerCAmelCase				(						cls      ):
         return cls





   @require_torch_multi_gpu
   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	{
             '''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 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase=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(_lowerCamelCase      )
         _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=_lowerCamelCase							,  output_dir=_lowerCamelCase							,  **_lowerCamelCase      )

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

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

         _lowerCAmelCase			      =	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      )
         _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}''']							,  _lowerCamelCase      )
         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''']      )							,  _lowerCamelCase      )







"""simple docstring"""


import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   @slow
   @unittest.skip('''Model is not available.'''    )
   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					'''nvidia/megatron-gpt2-345m/'''
     if "MYDIR" in os.environ:
       __A		     =					os.path.join(os.environ['''MYDIR'''],			_lowerCamelCase    )
     __A		     =					GPTaLMHeadModel.from_pretrained(_lowerCamelCase    )
     model.to(_lowerCamelCase    )
     model.half()

     __A		     =					torch.tensor(
         [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]],			device=_lowerCamelCase,			dtype=torch.long,			)

     with torch.no_grad():
       __A		     =					model(_lowerCamelCase    ).logits

     __A		     =					torch.Size((1, 9, 5_02_57)    )
     self.assertEqual(output.shape,			_lowerCamelCase    )

     __A		     =					torch.tensor(
         [
             4.94_14,
             -0.29_20,
             -1.21_48,
             -4.02_73,
             -0.51_61,
             -5.21_09,
             -1.24_12,
             -1.83_01,
             -1.77_34,
             -4.71_48,
             -0.23_17,
             -1.08_11,
             -2.17_77,
             0.41_41,
             -3.79_69,
             -4.05_86,
             -2.53_32,
             -3.38_09,
             4.38_67,
         ],			device=_lowerCamelCase,			dtype=torch.half,			)

     for i in range(19    ):
       __A     ,       __A		     =					8 * i // 17, 27_92 * i  # along the diagonal
       __A     ,       __A		     =					output[0, r, c], expected_diag[i]
       __A		     =					f'row={r} col={c} computed={computed} expected={expected}'
       self.assertAlmostEqual(_lowerCamelCase,			_lowerCamelCase,			delta=1e-4,			msg=_lowerCamelCase    )




