





'''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


_A				:							Tuple							= torch.cuda.is_available()
_A				:							Optional[int]							= [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_A				:							Optional[int]							= ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_A				:							List[str]							= {
    """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        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	List[str]:
    SCREAMING_SNAKE_CASE__       							= '''\n'''.join(lowercase__  )
    Path(lowercase__  ).open('''w'''  ).writelines(lowercase__  )


def        __snake_case       (						lowerCAmelCase_  )							->	int:







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



class 				__snake_case  (     A_						):


    '''simple docstring'''







    @classmethod
    def    lowercase_				(					cls    ):





        '''simple docstring'''

        return cls



    @require_torch_multi_gpu
    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= {
            '''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    lowercase_				(					self      ,							A_      ,							A_=True    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= {
            '''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_    )
        SCREAMING_SNAKE_CASE__       							= CHEAP_ARGS.copy()
        SCREAMING_SNAKE_CASE__       							= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
        SCREAMING_SNAKE_CASE__       							= self.get_auto_remove_tmp_dir()
        args_d.update(data_dir=A_      ,							output_dir=A_      ,							**A_    )

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

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

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

        SCREAMING_SNAKE_CASE__       							= 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_    )
        SCREAMING_SNAKE_CASE__       							= 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    )
        SCREAMING_SNAKE_CASE__       							= int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1    )
        self.assertEqual(len(metrics['''val''']    )      ,							A_    )







import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


_A				:							Any							= get_logger(__name__)


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    if text.startswith(lowerCAmelCase_  ):
        return text[len(lowerCAmelCase_  ) :]
    return text  # or whatever


def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, Any]:
    return {remove_prefix(lowerCAmelCase_   ,       '''model.'''  ): v for k, v in sd.items()}


def        __snake_case       (						lowerCAmelCase_  )							->	Optional[int]:
    SCREAMING_SNAKE_CASE__       							= {}
    for k in state_dicts[0].keys():
        SCREAMING_SNAKE_CASE__       							= [sd[k] for sd in state_dicts]
        SCREAMING_SNAKE_CASE__       							= sum(lowerCAmelCase_  ) / len(lowerCAmelCase_  )
        assert isinstance(lowerCAmelCase_   ,       torch.Tensor  )
        SCREAMING_SNAKE_CASE__       							= new_t
    return new_sd


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	None:
    SCREAMING_SNAKE_CASE__       							= AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_  )
    if os.path.isfile(lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__       							= [pl_ckpt_path]
    else:
        assert os.path.isdir(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= list(Path(lowerCAmelCase_  ).glob('''*.ckpt'''  )  )
        assert ckpt_files, f'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

    if len(lowerCAmelCase_  ) > 1:
        logger.info(f'''averaging the weights of {ckpt_files}'''  )

    SCREAMING_SNAKE_CASE__       							= [sanitize(torch.load(lowerCAmelCase_   ,       map_location='''cpu'''  )['''state_dict''']  ) for x in ckpt_files]
    SCREAMING_SNAKE_CASE__       							= average_state_dicts(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= hf_model.load_state_dict(lowerCAmelCase_   ,       strict=lowerCAmelCase_  )
    assert not missing, f'''missing keys: {missing}'''
    hf_model.save_pretrained(lowerCAmelCase_  )
    try:
        SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained(lowerCAmelCase_  )
        tok.save_pretrained(lowerCAmelCase_  )
    except Exception:
        pass
        # dont copy tokenizer if cant


if __name__ == "__main__":
  fire.Fire(convert_pl_to_hf)
