



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_		:  Union[str, Any]										=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
A_		:  str										=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
A_		:  Optional[int]										=  {
    '''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 						UpperCAmelCase__   (   UpperCAmelCase__			:Path			,       UpperCAmelCase__			:list       ):






 '''simple docstring'''

 a							=		"\n".join(_a       )
 Path(_a       ).open("w"       ).writelines(_a       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[int]       ):






 '''simple docstring'''

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




class 		_lowercase		(							__a						):

   @classmethod
   def 		A				(   cls      :							Optional[Any]				)							->					Union[str, Any]:


    """simple docstring"""






    return cls

   @require_torch_multi_gpu
   def 		A				(   self      :							List[Any]				)							->					List[str]:


    """simple docstring"""






    a							=		{
        "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 		A				(   self      :							Tuple				, __lowerCAmelCase      :							Tuple				, __lowerCAmelCase      :							Optional[int]=True				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		{
        "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				)
    a							=		CHEAP_ARGS.copy()
    a							=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
    a							=		self.get_auto_remove_tmp_dir()
    args_d.update(data_dir=__lowerCAmelCase				, output_dir=__lowerCAmelCase				, **__lowerCAmelCase				)

    def convert(__lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							List[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}"""

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

    a							=		os.listdir(__lowerCAmelCase				)
    a							=		{os.path.basename(__lowerCAmelCase				) for p in contents}
    a							=		[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)
    a							=		os.path.join(__lowerCAmelCase				, "metrics.json"				)
    a							=		"rouge2"

    a							=		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				)
    a							=		metrics["val"][-1]
    self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]				, 0.0_1				)
    self.assertIsInstance(last_step_stats[f"""val_avg_{val_metric}"""]				, __lowerCAmelCase				)
    self.assertEqual(len(metrics["test"]				)				, 1				)
    a							=		int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1				)
    self.assertEqual(len(metrics["val"]				)				, __lowerCAmelCase				)







'''simple docstring'''







def 		snake_case_				(_a      :				Any       ,  _a      :				Union[str, Any]	):
    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    UpperCAmelCase     			=						[0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    UpperCAmelCase     			=						1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(_a       ,  n + 1	):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()




