





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_rag import save_json





def      _lowercase					(						_UpperCAmelCase				)						->  List[str]:
    lowerCamelCase	 =filter(lambda _UpperCAmelCase				: p.requires_grad			,      model.parameters()				)
    lowerCamelCase	 =sum([np.prod(p.size()				) for p in model_parameters]				)
    return params


UpperCAmelCase__						:			Dict									=logging.getLogger(__name__)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Optional[Any]:




    if metric == "rouge2":
        lowerCamelCase	 ="""{val_avg_rouge2:.4f}-{step_count}"""
    elif metric == "bleu":
        lowerCamelCase	 ="""{val_avg_bleu:.4f}-{step_count}"""
    elif metric == "em":
        lowerCamelCase	 ="""{val_avg_em:.4f}-{step_count}"""
    else:
        raise NotImplementedError(
            F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
            """ function."""				)

    lowerCamelCase	 =ModelCheckpoint(
        dirpath=_UpperCAmelCase			,      filename=_UpperCAmelCase			,      monitor=F"""val_{metric}"""			,      mode="""max"""			,      save_top_k=3			,      every_n_epochs=1			,      )
    return checkpoint_callback





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
    return EarlyStopping(
        monitor=F"""val_{metric}"""			,      mode="""min""" if """loss""" in metric else """max"""			,      patience=_UpperCAmelCase			,      verbose=_UpperCAmelCase			,      )




class       __A     (						pl.Callback    ):


    def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
        lowerCamelCase	 ={f"""lr_group_{i}""": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
        pl_module.logger.log_metrics(UpperCAmelCase_							)


    @rank_zero_only
    def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=True							):
        logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
        lowerCamelCase	 =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
        lowerCamelCase	 =Path(pl_module.hparams.output_dir							)
        if type_path == "test":
            lowerCamelCase	 =od / """test_results.txt"""
            lowerCamelCase	 =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.
            lowerCamelCase	 =od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
            lowerCamelCase	 =od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
            results_file.parent.mkdir(exist_ok=UpperCAmelCase_							)
            generations_file.parent.mkdir(exist_ok=UpperCAmelCase_							)
        with open(UpperCAmelCase_   , """a+"""							) as writer:
            for key in sorted(UpperCAmelCase_							):
                if key in ["log", "progress_bar", "preds"]:
                    continue
                lowerCamelCase	 =metrics[key]
                if isinstance(UpperCAmelCase_   , torch.Tensor							):
                    lowerCamelCase	 =val.item()
                lowerCamelCase	 =f"""{key}: {val:.6f}\n"""
                writer.write(UpperCAmelCase_							)

        if not save_generations:
            return

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


    @rank_zero_only
    def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
        try:
            lowerCamelCase	 =pl_module.model.model.num_parameters()
        except AttributeError:
            lowerCamelCase	 =pl_module.model.num_parameters()

        lowerCamelCase	 =count_trainable_parameters(UpperCAmelCase_							)
        # 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 _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
        save_json(pl_module.metrics   , pl_module.metrics_save_path							)
        return self._write_logs(UpperCAmelCase_   , UpperCAmelCase_   , """test"""							)


    @rank_zero_only
    def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
        save_json(pl_module.metrics   , pl_module.metrics_save_path							)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")












from abc import ABC, abstractmethod
from argparse import ArgumentParser

class       __A     (						a    ):


				@staticmethod
				@abstractmethod
				def _snake_case       (		UpperCAmelCase_							):
								raise NotImplementedError()


				@abstractmethod
				def _snake_case       (		self							):
								raise NotImplementedError()





