





'''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_rag import save_json





def lowerCamelCase						(     __lowerCamelCase :    Optional[int]    )						->Union[str, 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


lowercase_					= logging.getLogger(__name__)





def lowerCamelCase						(     __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    str    )						->Optional[Any]:
    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 == "em":
        _SCREAMING_SNAKE_CASE									=			"""{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."""    )

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





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    List[Any]    )						->str:
    return EarlyStopping(
        monitor=F'val_{metric}'					,       mode="""min""" if """loss""" in metric else """max"""					,       patience=__lowerCamelCase					,       verbose=__lowerCamelCase					,       )






class 					a_     (				pl.Callback       ):

    '''simple docstring'''


    def 			snake_case_(		self ,		A ,		A       )		->   int:
        _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(A       )


    @rank_zero_only
    def 			snake_case_(		self ,		A ,		A ,		A ,		A=True       )		->   None:
        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=A       )
            generations_file.parent.mkdir(exist_ok=A       )
        with open(A ,		"""a+"""       ) as writer:
            for key in sorted(A       ):
                if key in ["log", "progress_bar", "preds"]:
                    continue
                _SCREAMING_SNAKE_CASE									=			metrics[key]
                if isinstance(A ,		torch.Tensor       ):
                    _SCREAMING_SNAKE_CASE									=			val.item()
                _SCREAMING_SNAKE_CASE									=			f'{key}: {val:.6f}\n'
                writer.write(A       )

        if not save_generations:
            return

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


    @rank_zero_only
    def 			snake_case_(		self ,		A ,		A       )		->   int:
        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(A       )
        # 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 ,		A ,		A       )		->   Optional[Any]:
        save_json(pl_module.metrics ,		pl_module.metrics_save_path       )
        return self._write_logs(A ,		A ,		"""test"""       )





    @rank_zero_only
    def 			snake_case_(		self ,		A ,		A       )		->   int:
        save_json(pl_module.metrics ,		pl_module.metrics_save_path       )
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")










'''simple docstring'''

from collections.abc import Sequence





def lowerCamelCase						(     __lowerCamelCase :    Sequence[float]					,       __lowerCamelCase :    bool = False    )						->float:
    if not arr:
        return 0

    _SCREAMING_SNAKE_CASE									=			0 if allow_empty_subarrays else float("""-inf"""    )
    _SCREAMING_SNAKE_CASE									=			0.0
    for num in arr:
        _SCREAMING_SNAKE_CASE									=			max(0 if allow_empty_subarrays else num					,       curr_sum + num    )
        _SCREAMING_SNAKE_CASE									=			max(__lowerCamelCase					,       __lowerCamelCase    )

    return max_sum


if __name__ == "__main__":
      from doctest import testmod

      testmod()

      lowercase_					= [-2, 1, -3, 4, -1, 2, 1, -5, 4]
      print(f"""{max_subarray_sum(nums) = }""")



