




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_  						=       1000
class 				__a :


     """simple docstring"""


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


         '''simple docstring'''





         if isinstance(_UpperCamelCase    ,torch.Tensor						):
             SCREAMING_SNAKE_CASE__       =img.numpy().astype("""np.uint8"""						)
         if isinstance(_UpperCamelCase    ,_UpperCamelCase						):
             SCREAMING_SNAKE_CASE__       =img_tensorize(_UpperCamelCase						)
         assert isinstance(_UpperCamelCase    ,np.ndarray						)

         SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =img.shape[1], img.shape[0]
         SCREAMING_SNAKE_CASE__       =mplfigure.Figure(frameon=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =fig.get_dpi()
         SCREAMING_SNAKE_CASE__       =(width * scale + 1e-2) / dpi
         SCREAMING_SNAKE_CASE__       =(height * scale + 1e-2) / dpi
         fig.set_size_inches(_UpperCamelCase    ,_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =fig.add_axes([0.0, 0.0, 1.0, 1.0]						)
         ax.axis("""off"""						)
         ax.set_xlim(0.0    ,_UpperCamelCase						)
         ax.set_ylim(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =saveas
         SCREAMING_SNAKE_CASE__       =rgb
         SCREAMING_SNAKE_CASE__       =pynb
         SCREAMING_SNAKE_CASE__       =img
         SCREAMING_SNAKE_CASE__       =edgecolor
         SCREAMING_SNAKE_CASE__       =0.5
         SCREAMING_SNAKE_CASE__       =linestyle
         SCREAMING_SNAKE_CASE__       =int(np.sqrt(min(_UpperCamelCase    ,_UpperCamelCase						)						) * scale // 3						)
         SCREAMING_SNAKE_CASE__       =width
         SCREAMING_SNAKE_CASE__       =height
         SCREAMING_SNAKE_CASE__       =scale
         SCREAMING_SNAKE_CASE__       =fig
         SCREAMING_SNAKE_CASE__       =ax
         SCREAMING_SNAKE_CASE__       =pad
         SCREAMING_SNAKE_CASE__       =idaobj
         SCREAMING_SNAKE_CASE__       =idaattr
         SCREAMING_SNAKE_CASE__       =FigureCanvasAgg(_UpperCamelCase						)




     def   __A    (     self      :			Dict    ,_UpperCamelCase      :			Tuple    ,_UpperCamelCase      :			Dict=None						)		->       Union[str, Any]:


         '''simple docstring'''





         if color is None:
             SCREAMING_SNAKE_CASE__       =self.edgecolor
         ((SCREAMING_SNAKE_CASE__)      ,		(SCREAMING_SNAKE_CASE__)      ,		(SCREAMING_SNAKE_CASE__)      ,		(SCREAMING_SNAKE_CASE__))       =box
         SCREAMING_SNAKE_CASE__       =xa - xa
         SCREAMING_SNAKE_CASE__       =ya - ya
         self.ax.add_patch(
             mpl.patches.Rectangle(
                 (xa, ya)    ,_UpperCamelCase    ,_UpperCamelCase    ,fill=_UpperCamelCase    ,edgecolor=_UpperCamelCase    ,linewidth=self.font_size // 3    ,alpha=self.alpha    ,linestyle=self.linestyle    ,)						)




     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			Union[str, Any]    ,_UpperCamelCase      :			List[str]=None    ,_UpperCamelCase      :			List[Any]=None    ,_UpperCamelCase      :			List[Any]=None    ,_UpperCamelCase      :			List[Any]=None						)		->       Dict:


         '''simple docstring'''





         if len(boxes.shape						) > 2:
             SCREAMING_SNAKE_CASE__       =boxes[0]
         if len(obj_ids.shape						) > 1:
             SCREAMING_SNAKE_CASE__       =obj_ids[0]
         if len(obj_scores.shape						) > 1:
             SCREAMING_SNAKE_CASE__       =obj_scores[0]
         if len(attr_ids.shape						) > 1:
             SCREAMING_SNAKE_CASE__       =attr_ids[0]
         if len(attr_scores.shape						) > 1:
             SCREAMING_SNAKE_CASE__       =attr_scores[0]
         if isinstance(_UpperCamelCase    ,torch.Tensor						):
             SCREAMING_SNAKE_CASE__       =boxes.numpy()
         if isinstance(_UpperCamelCase    ,_UpperCamelCase						):
             SCREAMING_SNAKE_CASE__       =np.array(_UpperCamelCase						)
         assert isinstance(_UpperCamelCase    ,np.ndarray						)
         SCREAMING_SNAKE_CASE__       =np.prod(boxes[:, 2:] - boxes[:, :2]    ,axis=1						)
         SCREAMING_SNAKE_CASE__       =np.argsort(-areas						).tolist()
         SCREAMING_SNAKE_CASE__       =boxes[sorted_idxs] if boxes is not None else None
         SCREAMING_SNAKE_CASE__       =obj_ids[sorted_idxs] if obj_ids is not None else None
         SCREAMING_SNAKE_CASE__       =obj_scores[sorted_idxs] if obj_scores is not None else None
         SCREAMING_SNAKE_CASE__       =attr_ids[sorted_idxs] if attr_ids is not None else None
         SCREAMING_SNAKE_CASE__       =attr_scores[sorted_idxs] if attr_scores is not None else None

         SCREAMING_SNAKE_CASE__       =[self._random_color(maximum=1						) for _ in range(len(_UpperCamelCase						)						)]
         SCREAMING_SNAKE_CASE__       =[assigned_colors[idx] for idx in sorted_idxs]
         if obj_ids is not None:
             SCREAMING_SNAKE_CASE__       =self._create_text_labels_attr(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)
             for i in range(len(_UpperCamelCase						)						):
                 SCREAMING_SNAKE_CASE__       =assigned_colors[i]
                 self.add_box(boxes[i]    ,_UpperCamelCase						)
                 self.draw_labels(labels[i]    ,boxes[i]    ,_UpperCamelCase						)




     def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			Dict    ,_UpperCamelCase      :			Optional[int]						)		->       Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =box
         SCREAMING_SNAKE_CASE__       =(xa, ya)
         SCREAMING_SNAKE_CASE__       =(ya - ya) * (xa - xa)
         SCREAMING_SNAKE_CASE__       =_SMALL_OBJ * self.scale
         if instance_area < small or ya - ya < 4_0 * self.scale:
             if ya >= self.height - 5:
                 SCREAMING_SNAKE_CASE__       =(xa, ya)
             else:
                 SCREAMING_SNAKE_CASE__       =(xa, ya)

         SCREAMING_SNAKE_CASE__       =(ya - ya) / np.sqrt(self.height * self.width						)
         SCREAMING_SNAKE_CASE__       =self._change_color_brightness(_UpperCamelCase    ,brightness_factor=0.7						)
         SCREAMING_SNAKE_CASE__       =np.clip((height_ratio - 0.02) / 0.08 + 1    ,1.2    ,2						)
         font_size *= 0.75 * self.font_size

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




     def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			List[Any]    ,_UpperCamelCase      :			str="g"    ,_UpperCamelCase      :			List[Any]="left"    ,)		->       List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =0
         SCREAMING_SNAKE_CASE__       =self.font_size
         SCREAMING_SNAKE_CASE__       =np.maximum(list(mplc.to_rgb(_UpperCamelCase						)						)    ,0.2						)
         SCREAMING_SNAKE_CASE__       =max(0.8    ,np.max(_UpperCamelCase						)						)
         SCREAMING_SNAKE_CASE__       ={
             """facecolor""": """black""",
             """alpha""": self.alpha,
             """pad""": self.pad,
             """edgecolor""": """none""",
         }
         SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =position
         self.ax.text(
             _UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase    ,size=font_size * self.scale    ,family="""sans-serif"""    ,bbox=_UpperCamelCase    ,verticalalignment="""top"""    ,horizontalalignment=_UpperCamelCase    ,color=_UpperCamelCase    ,zorder=1_0    ,rotation=_UpperCamelCase    ,)




     def   __A    (     self      :			List[Any]    ,_UpperCamelCase      :			Dict=None						)		->       str:


         '''simple docstring'''





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




     def   __A    (     self      :			Any    ,_UpperCamelCase      :			List[Any]    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			Tuple    ,_UpperCamelCase      :			List[Any]						)		->       Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =[self.idaobj[i] for i in classes]
         SCREAMING_SNAKE_CASE__       =[self.idaattr[i] for i in attr_classes]
         SCREAMING_SNAKE_CASE__       =[
             f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
             for label, score, attr, attr_score in zip(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)
         ]
         return labels




     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			Dict    ,_UpperCamelCase      :			Dict						)		->       Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =[self.idaobj[i] for i in classes]
         if scores is not None:
             if labels is None:
                 SCREAMING_SNAKE_CASE__       =["""{:.0f}%""".format(s * 1_0_0						) for s in scores]
             else:
                 SCREAMING_SNAKE_CASE__       =["""{} {:.0f}%""".format(_UpperCamelCase    ,s * 1_0_0						) for li, s in zip(_UpperCamelCase    ,_UpperCamelCase						)]
         return labels




     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			str=2_5_5						)		->       Any:


         '''simple docstring'''





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




     def   __A    (     self      :			str						)		->       Optional[int]:


         '''simple docstring'''





         if not self.pynb:
             SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =self.canvas.print_to_buffer()
             if (width, height) != (self.width, self.height):
                 SCREAMING_SNAKE_CASE__       =cva.resize(self.img    ,(width, height)						)
             else:
                 SCREAMING_SNAKE_CASE__       =self.img
         else:
             SCREAMING_SNAKE_CASE__       =io.BytesIO()  # works for cairo backend
             self.canvas.print_rgba(_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =self.width, self.height
             SCREAMING_SNAKE_CASE__       =buf.getvalue()
             SCREAMING_SNAKE_CASE__       =self.img

         SCREAMING_SNAKE_CASE__       =np.frombuffer(_UpperCamelCase    ,dtype="""uint8"""						)
         SCREAMING_SNAKE_CASE__       =buffer.reshape(_UpperCamelCase    ,_UpperCamelCase    ,4						)
         SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =np.split(_UpperCamelCase    ,[3]    ,axis=2						)

         try:
             import numexpr as ne  # fuse them with numexpr

             SCREAMING_SNAKE_CASE__       =ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""						)
         except ImportError:
             SCREAMING_SNAKE_CASE__       =alpha.astype("""float32"""						) / 255.0
             SCREAMING_SNAKE_CASE__       =img * (1 - alpha) + rgb * alpha

         return visualized_image.astype("""uint8"""						)




     def   __A    (     self      :			List[Any]    ,_UpperCamelCase      :			Dict    ,_UpperCamelCase      :			Dict						)		->       Optional[Any]:


         '''simple docstring'''





         assert brightness_factor >= -1.0 and brightness_factor <= 1.0
         SCREAMING_SNAKE_CASE__       =mplc.to_rgb(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =colorsys.rgb_to_hls(*mplc.to_rgb(_UpperCamelCase						)						)
         SCREAMING_SNAKE_CASE__       =polygon_color[1] + (brightness_factor * polygon_color[1])
         SCREAMING_SNAKE_CASE__       =0.0 if modified_lightness < 0.0 else modified_lightness
         SCREAMING_SNAKE_CASE__       =1.0 if modified_lightness > 1.0 else modified_lightness
         SCREAMING_SNAKE_CASE__       =colorsys.hls_to_rgb(polygon_color[0]    ,_UpperCamelCase    ,polygon_color[2]						)
         return modified_color






lowerCamelCase_  						=       (
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)












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


lowerCamelCase_  						=       logging.getLogger(__name__)
class 				__a (			pl.Callback					):


     """simple docstring"""


     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			Optional[Any]    ,_UpperCamelCase      :			Any						)		->       int:


         '''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(_UpperCamelCase						)




     @rank_zero_only
     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			pl.Trainer    ,_UpperCamelCase      :			pl.LightningModule    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Tuple=True						)		->       None:


         '''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=_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
                 SCREAMING_SNAKE_CASE__       =metrics[key]
                 if isinstance(_UpperCamelCase    ,torch.Tensor						):
                     SCREAMING_SNAKE_CASE__       =val.item()
                 SCREAMING_SNAKE_CASE__       =f"""{key}: {val:.6f}\n"""
                 writer.write(_UpperCamelCase						)

         if not save_generations:
             return

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




     @rank_zero_only
     def   __A    (     self      :			Tuple    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			Dict						)		->       Dict:


         '''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(_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   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			pl.Trainer    ,_UpperCamelCase      :			pl.LightningModule						)		->       Any:


         '''simple docstring'''





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




     @rank_zero_only
     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			pl.Trainer    ,_UpperCamelCase      :			List[Any]						)		->       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      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase=1,    __UpperCamelCase=False     ):
    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=__UpperCamelCase,    filename=__UpperCamelCase,    monitor=f"""val_{metric}""",    mode="""min""" if """loss""" in metric else """max""",    save_top_k=__UpperCamelCase,    )
    return checkpoint_callback




def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    return EarlyStopping(
        monitor=f"""val_{metric}""",    mode="""min""" if """loss""" in metric else """max""",    patience=__UpperCamelCase,    verbose=__UpperCamelCase,    )






