


'''simple docstring'''


import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


lowerCamelCase__     						=  1_000






class 						lowerCAmelCase__      :




			def __init__(		self	:  int						,	lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  List[Any]=1.2						,	lowerCamelCase__	:  Dict="g"						,	lowerCamelCase__	:  Any=0.5						,	lowerCamelCase__	:  Optional[int]="-"						,	lowerCamelCase__	:  Optional[Any]="test_out.jpg"						,	lowerCamelCase__	:  Dict=True						,	lowerCamelCase__	:  Dict=False						,	lowerCamelCase__	:  Any=None						,	lowerCamelCase__	:  List[Any]=None						,	lowerCamelCase__	:  Optional[Any]=0.7						,	) ->Union[str, Any]:



					'''simple docstring'''







					if isinstance(lowerCamelCase__						,	torch.Tensor			):
							_UpperCAmelCase		:							Dict             =   img.numpy().astype("np.uint8"			)
					if isinstance(lowerCamelCase__						,	lowerCamelCase__			):
							_UpperCAmelCase		:							Optional[int]             =   img_tensorize(lowerCamelCase__			)
					assert isinstance(lowerCamelCase__						,	np.ndarray			)

					_UpperCAmelCase       ,     _UpperCAmelCase		:							List[str]             =   img.shape[1], img.shape[0]
					_UpperCAmelCase		:							str             =   mplfigure.Figure(frameon=lowerCamelCase__			)
					_UpperCAmelCase		:							int             =   fig.get_dpi()
					_UpperCAmelCase		:							Optional[int]             =   (width * scale + 1E-2) / dpi
					_UpperCAmelCase		:							int             =   (height * scale + 1E-2) / dpi
					fig.set_size_inches(lowerCamelCase__						,	lowerCamelCase__			)
					_UpperCAmelCase		:							Dict             =   fig.add_axes([0.0, 0.0, 1.0, 1.0]			)
					ax.axis("off"			)
					ax.set_xlim(0.0						,	lowerCamelCase__			)
					ax.set_ylim(lowerCamelCase__			)

					_UpperCAmelCase		:							str             =   saveas
					_UpperCAmelCase		:							Optional[Any]             =   rgb
					_UpperCAmelCase		:							Union[str, Any]             =   pynb
					_UpperCAmelCase		:							List[Any]             =   img
					_UpperCAmelCase		:							Optional[int]             =   edgecolor
					_UpperCAmelCase		:							str             =   0.5
					_UpperCAmelCase		:							Optional[Any]             =   linestyle
					_UpperCAmelCase		:							List[Any]             =   int(np.sqrt(min(lowerCamelCase__						,	lowerCamelCase__			)			) * scale // 3			)
					_UpperCAmelCase		:							List[Any]             =   width
					_UpperCAmelCase		:							List[Any]             =   height
					_UpperCAmelCase		:							Dict             =   scale
					_UpperCAmelCase		:							Tuple             =   fig
					_UpperCAmelCase		:							Dict             =   ax
					_UpperCAmelCase		:							List[Any]             =   pad
					_UpperCAmelCase		:							List[Any]             =   idaobj
					_UpperCAmelCase		:							Union[str, Any]             =   idaattr
					_UpperCAmelCase		:							Union[str, Any]             =   FigureCanvasAgg(lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  Dict						,	lowerCamelCase__	:  Optional[Any]=None			) ->Dict:



					'''simple docstring'''







					if color is None:
							_UpperCAmelCase		:							Optional[int]             =   self.edgecolor
					((_UpperCAmelCase)       ,     (_UpperCAmelCase)       ,     (_UpperCAmelCase)       ,     (_UpperCAmelCase))		:							Any             =   box
					_UpperCAmelCase		:							List[str]             =   xa - xa
					_UpperCAmelCase		:							str             =   ya - ya
					self.ax.add_patch(
					    mpl.patches.Rectangle(
					        (xa, ya)						,	lowerCamelCase__						,	lowerCamelCase__						,	fill=lowerCamelCase__						,	edgecolor=lowerCamelCase__						,	linewidth=self.font_size // 3						,	alpha=self.alpha						,	linestyle=self.linestyle						,	)			)




			def 				lowerCAmelCase__     (		self	:  Any						,	lowerCamelCase__	:  Dict						,	lowerCamelCase__	:  Tuple=None						,	lowerCamelCase__	:  List[str]=None						,	lowerCamelCase__	:  Optional[int]=None						,	lowerCamelCase__	:  List[Any]=None			) ->Union[str, Any]:



					'''simple docstring'''







					if len(boxes.shape			) > 2:
							_UpperCAmelCase		:							Tuple             =   boxes[0]
					if len(obj_ids.shape			) > 1:
							_UpperCAmelCase		:							List[str]             =   obj_ids[0]
					if len(obj_scores.shape			) > 1:
							_UpperCAmelCase		:							Union[str, Any]             =   obj_scores[0]
					if len(attr_ids.shape			) > 1:
							_UpperCAmelCase		:							List[str]             =   attr_ids[0]
					if len(attr_scores.shape			) > 1:
							_UpperCAmelCase		:							List[str]             =   attr_scores[0]
					if isinstance(lowerCamelCase__						,	torch.Tensor			):
							_UpperCAmelCase		:							Optional[Any]             =   boxes.numpy()
					if isinstance(lowerCamelCase__						,	lowerCamelCase__			):
							_UpperCAmelCase		:							Union[str, Any]             =   np.array(lowerCamelCase__			)
					assert isinstance(lowerCamelCase__						,	np.ndarray			)
					_UpperCAmelCase		:							str             =   np.prod(boxes[:, 2:] - boxes[:, :2]						,	axis=1			)
					_UpperCAmelCase		:							Union[str, Any]             =   np.argsort(-areas			).tolist()
					_UpperCAmelCase		:							Any             =   boxes[sorted_idxs] if boxes is not None else None
					_UpperCAmelCase		:							Optional[Any]             =   obj_ids[sorted_idxs] if obj_ids is not None else None
					_UpperCAmelCase		:							Any             =   obj_scores[sorted_idxs] if obj_scores is not None else None
					_UpperCAmelCase		:							str             =   attr_ids[sorted_idxs] if attr_ids is not None else None
					_UpperCAmelCase		:							Dict             =   attr_scores[sorted_idxs] if attr_scores is not None else None

					_UpperCAmelCase		:							Optional[int]             =   [self._random_color(maximum=1			) for _ in range(len(lowerCamelCase__			)			)]
					_UpperCAmelCase		:							List[str]             =   [assigned_colors[idx] for idx in sorted_idxs]
					if obj_ids is not None:
							_UpperCAmelCase		:							List[str]             =   self._create_text_labels_attr(lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__			)
							for i in range(len(lowerCamelCase__			)			):
									_UpperCAmelCase		:							List[Any]             =   assigned_colors[i]
									self.add_box(boxes[i]						,	lowerCamelCase__			)
									self.draw_labels(labels[i]						,	boxes[i]						,	lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  Union[str, Any]						,	lowerCamelCase__	:  Optional[Any]						,	lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  str			) ->str:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							Dict             =   box
					_UpperCAmelCase		:							Optional[int]             =   (xa, ya)
					_UpperCAmelCase		:							int             =   (ya - ya) * (xa - xa)
					_UpperCAmelCase		:							Dict             =   _SMALL_OBJ * self.scale
					if instance_area < small or ya - ya < 40 * self.scale:
							if ya >= self.height - 5:
									_UpperCAmelCase		:							Dict             =   (xa, ya)
							else:
									_UpperCAmelCase		:							Union[str, Any]             =   (xa, ya)

					_UpperCAmelCase		:							int             =   (ya - ya) / np.sqrt(self.height * self.width			)
					_UpperCAmelCase		:							str             =   self._change_color_brightness(lowerCamelCase__						,	brightness_factor=0.7			)
					_UpperCAmelCase		:							List[Any]             =   np.clip((height_ratio - 0.0_2) / 0.0_8 + 1						,	1.2						,	2			)
					font_size *= 0.7_5 * self.font_size

					self.draw_text(
					    text=lowerCamelCase__						,	position=lowerCamelCase__						,	color=lowerCamelCase__						,	)




			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  Dict						,	lowerCamelCase__	:  Optional[Any]						,	lowerCamelCase__	:  str="g"						,	lowerCamelCase__	:  Union[str, Any]="left"						,	) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   0
					_UpperCAmelCase		:							Optional[Any]             =   self.font_size
					_UpperCAmelCase		:							Any             =   np.maximum(list(mplc.to_rgb(lowerCamelCase__			)			)						,	0.2			)
					_UpperCAmelCase		:							List[str]             =   max(0.8						,	np.max(lowerCamelCase__			)			)
					_UpperCAmelCase		:							int             =   {
					    "facecolor": "black",
					    "alpha": self.alpha,
					    "pad": self.pad,
					    "edgecolor": "none",
					}
					_UpperCAmelCase       ,     _UpperCAmelCase		:							List[Any]             =   position
					self.ax.text(
					    lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	size=font_size * self.scale						,	family="sans-serif"						,	bbox=lowerCamelCase__						,	verticalalignment="top"						,	horizontalalignment=lowerCamelCase__						,	color=lowerCamelCase__						,	zorder=10						,	rotation=lowerCamelCase__						,	)




			def 				lowerCAmelCase__     (		self	:  Union[str, Any]						,	lowerCamelCase__	:  Optional[int]=None			) ->List[Any]:



					'''simple docstring'''







					if saveas is None:
							_UpperCAmelCase		:							Tuple             =   self.saveas
					if saveas.lower().endswith(".jpg"			) or saveas.lower().endswith(".png"			):
							cva.imwrite(
							    lowerCamelCase__						,	self._get_buffer()[:, :, ::-1]						,	)
					else:
							self.fig.savefig(lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  List[Any]						,	lowerCamelCase__	:  List[str]						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  Union[str, Any]						,	lowerCamelCase__	:  List[str]			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   [self.idaobj[i] for i in classes]
					_UpperCAmelCase		:							Optional[Any]             =   [self.idaattr[i] for i in attr_classes]
					_UpperCAmelCase		:							List[str]             =   [
					    F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
					    for label, score, attr, attr_score in zip(lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__			)
					]
					return labels




			def 				lowerCAmelCase__     (		self	:  Optional[int]						,	lowerCamelCase__	:  Tuple						,	lowerCamelCase__	:  Union[str, Any]			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   [self.idaobj[i] for i in classes]
					if scores is not None:
							if labels is None:
									_UpperCAmelCase		:							int             =   ["{:.0f}%".format(s * 1_00			) for s in scores]
							else:
									_UpperCAmelCase		:							Tuple             =   ["{} {:.0f}%".format(lowerCamelCase__						,	s * 1_00			) for li, s in zip(lowerCamelCase__						,	lowerCamelCase__			)]
					return labels




			def 				lowerCAmelCase__     (		self	:  Tuple						,	lowerCamelCase__	:  int=2_55			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Tuple             =   np.random.randint(0						,	len(_COLORS			)			)
					_UpperCAmelCase		:							Optional[Any]             =   _COLORS[idx] * maximum
					if not self.rgb:
							_UpperCAmelCase		:							Any             =   ret[::-1]
					return ret




			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->Optional[int]:



					'''simple docstring'''







					if not self.pynb:
							_UpperCAmelCase       ,     _UpperCAmelCase		:							Union[str, Any]             =   self.canvas.print_to_buffer()
							if (width, height) != (self.width, self.height):
									_UpperCAmelCase		:							Tuple             =   cva.resize(self.img						,	(width, height)			)
							else:
									_UpperCAmelCase		:							List[str]             =   self.img
					else:
							_UpperCAmelCase		:							List[str]             =   io.BytesIO()  # works for cairo backend
							self.canvas.print_rgba(lowerCamelCase__			)
							_UpperCAmelCase       ,     _UpperCAmelCase		:							List[str]             =   self.width, self.height
							_UpperCAmelCase		:							str             =   buf.getvalue()
							_UpperCAmelCase		:							Dict             =   self.img

					_UpperCAmelCase		:							Optional[int]             =   np.frombuffer(lowerCamelCase__						,	dtype="uint8"			)
					_UpperCAmelCase		:							Union[str, Any]             =   buffer.reshape(lowerCamelCase__						,	lowerCamelCase__						,	4			)
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Any             =   np.split(lowerCamelCase__						,	[3]						,	axis=2			)

					try:
							import numexpr as ne  # fuse them with numexpr

							_UpperCAmelCase		:							List[str]             =   ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"			)
					except ImportError:
							_UpperCAmelCase		:							List[Any]             =   alpha.astype("float32"			) / 2_5_5.0
							_UpperCAmelCase		:							Union[str, Any]             =   img * (1 - alpha) + rgb * alpha

					return visualized_image.astype("uint8"			)






			def 				lowerCAmelCase__     (		self	:  Any						,	lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  List[str]			) ->List[Any]:



					'''simple docstring'''







					assert brightness_factor >= -1.0 and brightness_factor <= 1.0
					_UpperCAmelCase		:							int             =   mplc.to_rgb(lowerCamelCase__			)
					_UpperCAmelCase		:							Union[str, Any]             =   colorsys.rgb_to_hls(*mplc.to_rgb(lowerCamelCase__			)			)
					_UpperCAmelCase		:							Union[str, Any]             =   polygon_color[1] + (brightness_factor * polygon_color[1])
					_UpperCAmelCase		:							Union[str, Any]             =   0.0 if modified_lightness < 0.0 else modified_lightness
					_UpperCAmelCase		:							Any             =   1.0 if modified_lightness > 1.0 else modified_lightness
					_UpperCAmelCase		:							Optional[int]             =   colorsys.hls_to_rgb(polygon_color[0]						,	lowerCamelCase__						,	polygon_color[2]			)
					return modified_color




lowerCamelCase__     						=  (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)







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


lowerCamelCase__     						=  logging.getLogger(__name__)






class 						lowerCAmelCase__      (      pl.Callback						):




			def 				lowerCAmelCase__     (		self	:  Optional[Any]						,	lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Union[str, Any]			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   {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 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  Any=True			) ->None:



					'''simple docstring'''







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

					if not save_generations:
							return

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




			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[Any]						,	lowerCamelCase__	:  Dict						,	lowerCamelCase__	:  int			) ->Optional[int]:



					'''simple docstring'''







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

					_UpperCAmelCase		:							Dict             =   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 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule			) ->int:



					'''simple docstring'''







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






			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  Tuple			) ->Dict:



					'''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 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase=1   ,			__lowerCAmelCase=False ):
		if metric == "rouge2":
				_UpperCAmelCase		:							Tuple             =   "{val_avg_rouge2:.4f}-{step_count}"
		elif metric == "bleu":
				_UpperCAmelCase		:							List[str]             =   "{val_avg_bleu:.4f}-{step_count}"
		elif metric == "loss":
				_UpperCAmelCase		:							Union[str, Any]             =   "{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." )

		_UpperCAmelCase		:							Optional[int]             =   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 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		return EarlyStopping(
		    monitor=F"""val_{metric}"""   ,			mode="min" if "loss" in metric else "max"   ,			patience=__lowerCAmelCase   ,			verbose=__lowerCAmelCase   ,			)



