

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


lowercase_									=    torch.cuda.is_available()
lowercase_									=    [" Sam ate lunch today.", "Sams lunch ingredients."]
lowercase_									=    ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowercase_									=    {
    "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 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Path       ,     SCREAMING_SNAKE_CASE__     :      list )				-> str:





    '''simple docstring'''






    A__        =  '''\n'''.join(UpperCamelCase__ )
    Path(UpperCamelCase__ ).open('w' ).writelines(UpperCamelCase__ )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[Any] )				-> Dict:





    '''simple docstring'''






    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 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    @classmethod
    def 		snake_case__	( cls					:			str						)->       Dict:





        '''simple docstring'''







        return cls


    @require_torch_multi_gpu
    def 		snake_case__	( self					:			Tuple						)->       Any:





        '''simple docstring'''







        A__        =  {
            '''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					:			Optional[int],lowercase_					:			List[Any],lowercase_					:			Dict=True						)->       List[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(_UpperCAmelCase						)
        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=_UpperCAmelCase,output_dir=_UpperCAmelCase,**_UpperCAmelCase						)

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

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

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








'''simple docstring'''






import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def 				_lowerCamelCase(	UpperCamelCase__     :			Any		)		->	List[str]:
    return EnvironmentCommand()





class 	_lowercase    (				a					):


   @staticmethod
   def        snake_case					(			_UpperCAmelCase       ):
       A							:							List[str]				=    parser.add_parser('''env'''     , help='''Print relevant system environment info.'''       )
       download_parser.set_defaults(func=_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							Optional[int]				=    {
           '''`datasets` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''PyArrow version''': pyarrow.__version__,
           '''Pandas version''': pandas.__version__,
       }

       print('''\nCopy-and-paste the text below in your GitHub issue.\n'''       )
       print(self.format_dict(_UpperCAmelCase       )       )

       return info


   @staticmethod
   def        snake_case					(			_UpperCAmelCase       ):
       return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]       ) + "\n"
