
"""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  _UpperCamelCase	(						_A      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   filter(lambda _A      : p.requires_grad					,      model.parameters()      )
    _UpperCAmelCase          =   sum([np.prod(p.size()      ) for p in model_parameters]      )
    return params


a					:	Union[str, Any]							=		logging.getLogger(__name__)


def  _UpperCamelCase	(						_A					,      _A      )    ->  Dict:





    """simple docstring"""

    if metric == "rouge2":
        _UpperCAmelCase          =   """{val_avg_rouge2:.4f}-{step_count}"""
    elif metric == "bleu":
        _UpperCAmelCase          =   """{val_avg_bleu:.4f}-{step_count}"""
    elif metric == "em":
        _UpperCAmelCase          =   """{val_avg_em:.4f}-{step_count}"""
    elif metric == "loss":
        _UpperCAmelCase          =   """{val_avg_loss:.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."""      )

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

    return checkpoint_callback


def  _UpperCamelCase	(						_A					,      _A      )    ->  Tuple:





    """simple docstring"""

    return EarlyStopping(
        monitor=F"""val_{metric}"""					,      mode="""min""" if """loss""" in metric else """max"""					,      patience=_A					,      verbose=_A					,      )







class 		a_						( pl.Callback	):







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Tuple     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   {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			:       Optional[Any]     ,					__UpperCamelCase			:       pl.Trainer     ,					__UpperCamelCase			:       pl.LightningModule     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       str=True     )		->None:






        '''simple docstring'''






        logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
        _UpperCAmelCase          =   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
        _UpperCAmelCase          =   Path(pl_module.hparams.output_dir     )
        if type_path == "test":
            _UpperCAmelCase          =   od / """test_results.txt"""
            _UpperCAmelCase          =   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.
            _UpperCAmelCase          =   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
            _UpperCAmelCase          =   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
                _UpperCAmelCase          =   metrics[key]
                if isinstance(__UpperCamelCase     ,					torch.Tensor     ):
                    _UpperCAmelCase          =   val.item()
                _UpperCAmelCase          =   f"""{key}: {val:.6f}\n"""
                writer.write(__UpperCamelCase     )

        if not save_generations:
            return

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







    @rank_zero_only
    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[Any]     )		->List[str]:






        '''simple docstring'''






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

        _UpperCAmelCase          =   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			:       Optional[int]     ,					__UpperCamelCase			:       pl.Trainer     ,					__UpperCamelCase			:       pl.LightningModule     )		->Optional[Any]:






        '''simple docstring'''






        save_json(pl_module.metrics     ,					pl_module.metrics_save_path     )
        return self._write_logs(__UpperCamelCase     ,					__UpperCamelCase     ,					"""test"""     )







    @rank_zero_only
    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       pl.Trainer     ,					__UpperCamelCase			:       Any     )		->str:






        '''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")

"""simple docstring"""



import colorsys

from PIL import Image  # type: ignore


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  float:





    """simple docstring"""

    _UpperCAmelCase          =   x
    _UpperCAmelCase          =   y
    for step in range(_A      ):  # noqa: B007
        _UpperCAmelCase          =   a * a - b * b + x
        _UpperCAmelCase          =   2 * a * b + y
        _UpperCAmelCase          =   a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return (2_5_5, 2_5_5, 2_5_5)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 2_5_5      ) for i in colorsys.hsv_to_rgb(_A					,      1					,      1      )      )


def  _UpperCamelCase	(						_A = 8_0_0					,      _A = 6_0_0					,      _A = -0.6					,      _A = 0					,      _A = 3.2					,      _A = 5_0					,      _A = True					,      )    ->  Image.Image:





    """simple docstring"""

    _UpperCAmelCase          =   Image.new("""RGB"""					,      (image_width, image_height)      )
    _UpperCAmelCase          =   img.load()

    # loop through the image-coordinates
    for image_x in range(_A      ):
        for image_y in range(_A      ):
            # determine the figure-coordinates based on the image-coordinates
            _UpperCAmelCase          =   figure_width / image_width * image_height
            _UpperCAmelCase          =   figure_center_x + (image_x / image_width - 0.5) * figure_width
            _UpperCAmelCase          =   figure_center_y + (image_y / image_height - 0.5) * figure_height

            _UpperCAmelCase          =   get_distance(_A					,      _A					,      _A      )

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                _UpperCAmelCase          =   get_color_coded_rgb(_A      )
            else:
                _UpperCAmelCase          =   get_black_and_white_rgb(_A      )

    return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 a					:	List[str]							=		get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()