





'''simple docstring'''





import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json


def        __snake_case       (						lowerCAmelCase_  )							->	List[Any]:
    SCREAMING_SNAKE_CASE__       							= filter(lambda lowerCAmelCase_  : p.requires_grad   ,       model.parameters()  )
    SCREAMING_SNAKE_CASE__       							= sum([np.prod(p.size()  ) for p in model_parameters]  )
    return params


_A				:							List[Any]							= logging.getLogger(__name__)



class 				__snake_case  (     pl.Callback						):


    '''simple docstring'''







    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= {f'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
        pl_module.logger.log_metrics(_lowerCAmelCase    )



    @rank_zero_only
    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_=True    ):





        '''simple docstring'''

        logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''    )
        SCREAMING_SNAKE_CASE__       							= trainer.callback_metrics
        trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}    )
        # Log results
        SCREAMING_SNAKE_CASE__       							= Path(pl_module.hparams.output_dir    )
        if type_path == "test":
            SCREAMING_SNAKE_CASE__       							= od / """test_results.txt"""
            SCREAMING_SNAKE_CASE__       							= od / """test_generations.txt"""
        else:
            # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
            # If people want this it will be easy enough to add back.
            SCREAMING_SNAKE_CASE__       							= od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
            SCREAMING_SNAKE_CASE__       							= od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
            results_file.parent.mkdir(exist_ok=_lowerCAmelCase    )
            generations_file.parent.mkdir(exist_ok=_lowerCAmelCase    )
        with open(_lowerCAmelCase      ,							'''a+'''    ) as writer:
            for key in sorted(_lowerCAmelCase    ):
                if key in ["log", "progress_bar", "preds"]:
                    continue
                SCREAMING_SNAKE_CASE__       							= metrics[key]
                if isinstance(_lowerCAmelCase      ,							torch.Tensor    ):
                    SCREAMING_SNAKE_CASE__       							= val.item()
                SCREAMING_SNAKE_CASE__       							= f'''{key}: {val:.6f}\n'''
                writer.write(_lowerCAmelCase    )

        if not save_generations:
            return

        if "preds" in metrics:
            SCREAMING_SNAKE_CASE__       							= """\n""".join(metrics['''preds''']    )
            generations_file.open('''w+'''    ).write(_lowerCAmelCase    )



    @rank_zero_only
    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        try:
            SCREAMING_SNAKE_CASE__       							= pl_module.model.model.num_parameters()
        except AttributeError:
            SCREAMING_SNAKE_CASE__       							= pl_module.model.num_parameters()

        SCREAMING_SNAKE_CASE__       							= count_trainable_parameters(_lowerCAmelCase    )
        # mp stands for million parameters
        trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1E6, '''grad_mp''': n_trainable_pars / 1E6}    )



    @rank_zero_only
    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        save_json(pl_module.metrics      ,							pl_module.metrics_save_path    )
        return self._write_logs(_lowerCAmelCase      ,							_lowerCAmelCase      ,							'''test'''    )




    @rank_zero_only
    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        save_json(pl_module.metrics      ,							pl_module.metrics_save_path    )
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")





def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=1   ,       lowerCAmelCase_=False  )							->	Tuple:
    if metric == "rouge2":
        SCREAMING_SNAKE_CASE__       							= """{val_avg_rouge2:.4f}-{step_count}"""
    elif metric == "bleu":
        SCREAMING_SNAKE_CASE__       							= """{val_avg_bleu:.4f}-{step_count}"""
    elif metric == "loss":
        SCREAMING_SNAKE_CASE__       							= """{val_avg_loss:.4f}-{step_count}"""
    else:
        raise NotImplementedError(
            f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
            ''' this function.'''  )

    SCREAMING_SNAKE_CASE__       							= ModelCheckpoint(
        dirpath=__lowerCamelCase   ,       filename=__lowerCamelCase   ,       monitor=f'''val_{metric}'''   ,       mode='''min''' if '''loss''' in metric else '''max'''   ,       save_top_k=__lowerCamelCase   ,       )
    return checkpoint_callback



def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Tuple:
    return EarlyStopping(
        monitor=f'''val_{metric}'''   ,       mode='''min''' if '''loss''' in metric else '''max'''   ,       patience=__lowerCamelCase   ,       verbose=__lowerCamelCase   ,       )







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)
