

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 					A		(				unittest.TestCase					):







    """simple docstring"""





    @slow
    @unittest.skip('Model is not available.'						)
    def 		snake_case__	( self					:			Optional[int]						)->       int:





        '''simple docstring'''







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

        A__        =  torch.tensor(
            [[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]],device=_UpperCAmelCase,dtype=torch.long,)

        with torch.no_grad():
            A__        =  model(_UpperCAmelCase						).logits

        A__        =  torch.Size((1, 9, 5_0_2_5_7)						)
        self.assertEqual(output.shape,_UpperCAmelCase						)

        A__        =  torch.tensor(
            [
                4.9_414,
                -0.2_920,
                -1.2_148,
                -4.0_273,
                -0.5_161,
                -5.2_109,
                -1.2_412,
                -1.8_301,
                -1.7_734,
                -4.7_148,
                -0.2_317,
                -1.0_811,
                -2.1_777,
                0.4_141,
                -3.7_969,
                -4.0_586,
                -2.5_332,
                -3.3_809,
                4.3_867,
            ],device=_UpperCAmelCase,dtype=torch.half,)

        for i in range(1_9						):
            A__        =  8 * i // 1_7, 2_7_9_2 * i  # along the diagonal
            A__        =  output[0, r, c], expected_diag[i]
            A__        =  F'row={r} col={c} computed={computed} expected={expected}'
            self.assertAlmostEqual(_UpperCAmelCase,_UpperCAmelCase,delta=1E-4,msg=_UpperCAmelCase						)








'''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


snake_case_     			=						torch.cuda.is_available()
snake_case_     			=						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
snake_case_     			=						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
snake_case_     			=						{
    """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(	UpperCamelCase__     :			Path     ,							UpperCamelCase__     :			list		)		->	str:
    A							:							List[Any]				=    '''\n'''.join(UpperCamelCase__		)
    Path(UpperCamelCase__		).open('''w'''		).writelines(UpperCamelCase__		)
def 				_lowerCamelCase(	UpperCamelCase__     :			List[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 	_lowercase    (				a					):


   @classmethod
   def        snake_case					(			cls       ):
       return cls


   @require_torch_multi_gpu
   def        snake_case					(			self       ):
       A							:							Optional[int]				=    {
           '''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        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=True       ):
       A							:							List[Any]				=    {
           '''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       )
       A							:							dict				=    CHEAP_ARGS.copy()
       A							:							Union[str, Any]				=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
       A							:							Optional[int]				=    self.get_auto_remove_tmp_dir()
       args_d.update(data_dir=_UpperCAmelCase     , output_dir=_UpperCAmelCase     , **_UpperCAmelCase       )

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

       A							:							int				=    [x for x in (convert(_UpperCAmelCase     , _UpperCAmelCase       ) for k, v in args_d.items()) if len(_UpperCAmelCase       )]
       A							:							Union[str, Any]				=    [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
       execute_subprocess_async(_UpperCAmelCase     , env=self.get_env()       )

       A							:							Dict				=    os.listdir(_UpperCAmelCase       )
       A							:							Optional[int]				=    {os.path.basename(_UpperCAmelCase       ) for p in contents}
       A							:							Union[str, Any]				=    [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)
       A							:							Optional[Any]				=    os.path.join(_UpperCAmelCase     , '''metrics.json'''       )
       A							:							List[Any]				=    '''rouge2'''

       A							:							Union[str, Any]				=    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       )
       A							:							Optional[Any]				=    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       )
       A							:							Tuple				=    int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1       )
       self.assertEqual(len(metrics['''val''']       )     , _UpperCAmelCase       )
