



from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      1
     lowerCAmelCase       =      2
     lowerCAmelCase       =      3





     def __repr__(							self			:				Tuple					)->int:
      return F"""{self.__class__.__name__}.{self.name}"""






def 						_lowerCAmelCase    (					__lowerCamelCase :  tuple[int, int]		,						__lowerCamelCase :  int		,						__lowerCamelCase :  int  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Tuple							=						point
 return degrees(atana(y - miny		,						x - minx  )  )






def 						_lowerCAmelCase    (					__lowerCamelCase :  tuple[int, int]		,						__lowerCamelCase :  tuple[int, int]		,						__lowerCamelCase :  tuple[int, int]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[str]							=						starting
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						via
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     str							=						target
 __SCREAMING_SNAKE_CASE							:     Any							=						degrees(atana(ya - ya		,						xa - xa  )  )
 via_angle %= 360
 __SCREAMING_SNAKE_CASE							:     List[str]							=						degrees(atana(ya - ya		,						xa - xa  )  )
 target_angle %= 360
 # t-
 #  \ \
 #   \ v
 #    \|
 #     s
 # via_angle is always lower than target_angle, if direction is left.
 # If they are same, it means they are on a same line of convex hull.
 if target_angle > via_angle:
  return Direction.left
 elif target_angle == via_angle:
  return Direction.straight
 else:
  return Direction.right






def 						_lowerCAmelCase    (					__lowerCamelCase :  list[tuple[int, int]]  ):



 """simple docstring"""





 if len(__lowerCamelCase  ) <= 2:
  # There is no convex hull
  raise ValueError("graham_scan: argument must contain more than 3 points."  )
 if len(__lowerCamelCase  ) == 3:
  return points
 # find the lowest and the most left point
 __SCREAMING_SNAKE_CASE							:     List[str]							=						0
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						maxsize, maxsize
 for i, point in enumerate(__lowerCamelCase  ):
  __SCREAMING_SNAKE_CASE							:     Any							=						point[0]
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						point[1]
  if y < miny:
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						y
   __SCREAMING_SNAKE_CASE							:     Dict							=						x
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						i
  if y == miny and x < minx:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						x
   __SCREAMING_SNAKE_CASE							:     Dict							=						i

    # remove the lowest and the most left point from points for preparing for sort
 points.pop(__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						sorted(__lowerCamelCase		,						key=lambda __lowerCamelCase  : angle_comparer(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )  )
 # This insert actually costs complexity,
 # and you should instead add (minx, miny) into stack later.
 # I'm using insert just for easy understanding.
 sorted_points.insert(0		,						(minx, miny)  )

 __SCREAMING_SNAKE_CASE							:     deque[tuple[int, int]]							=						deque()
 stack.append(sorted_points[0]  )
 stack.append(sorted_points[1]  )
 stack.append(sorted_points[2]  )
 # In any ways, the first 3 points line are towards left.
 # Because we sort them the angle from minx, miny.
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						Direction.left

 for i in range(3		,						len(__lowerCamelCase  )  ):
  while True:
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						stack[-2]
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						stack[-1]
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						sorted_points[i]
   __SCREAMING_SNAKE_CASE							:     str							=						check_direction(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )

   if next_direction == Direction.left:
    __SCREAMING_SNAKE_CASE							:     Optional[int]							=						Direction.left
    break
   if next_direction == Direction.straight:
    if current_direction == Direction.left:
     # We keep current_direction as left.
     # Because if the straight line keeps as straight,
     # we want to know if this straight line is towards left.
     break
    elif current_direction == Direction.right:
     # If the straight line is towards right,
     # every previous points on those straigh line is not convex hull.
     stack.pop()
   if next_direction == Direction.right:
    stack.pop()
  stack.append(sorted_points[i]  )
 return list(__lowerCamelCase  )









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        __lowercase   (  _A      )  ->    Dict:
      SCREAMING_SNAKE_CASE			:   Dict	  = filter(lambda _A      : p.requires_grad    ,     model.parameters()      )
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = sum([np.prod(p.size()      ) for p in model_parameters]      )
      return params


UpperCAmelCase__			:  List[Any]					     =     logging.getLogger(__name__)







class 							a__							( pl.Callback							):







  """simple docstring"""




  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Any		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = {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 			_lowercase   (      self     :			List[Any]					,					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			:   Union[str, Any]	  = 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			:   Union[str, Any]	  = Path(pl_module.hparams.output_dir		)
        if type_path == "test":
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = od / """test_results.txt"""
              SCREAMING_SNAKE_CASE			:   int	  = 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			:   Any	  = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
              SCREAMING_SNAKE_CASE			:   int	  = 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			:   Optional[int]	  = metrics[key]
                    if isinstance(UpperCAmelCase__					,					torch.Tensor		):
                          SCREAMING_SNAKE_CASE			:   Optional[int]	  = val.item()
                    SCREAMING_SNAKE_CASE			:   List[str]	  = f"{key}: {val:.6f}\n"
                    writer.write(UpperCAmelCase__		)

        if not save_generations:
              return

        if "preds" in metrics:
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = """\n""".join(metrics["""preds"""]		)
              generations_file.open("""w+"""		).write(UpperCAmelCase__		)


  @rank_zero_only
  def 			_lowercase   (      self     :			Optional[int]					,					UpperCAmelCase__     :			Optional[int]					,					UpperCAmelCase__     :			Union[str, Any]		)       ->int:
        """simple docstring"""







        try:
              SCREAMING_SNAKE_CASE			:   int	  = pl_module.model.model.num_parameters()
        except AttributeError:
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = pl_module.model.num_parameters()

        SCREAMING_SNAKE_CASE			:   Dict	  = 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 			_lowercase   (      self     :			str					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			pl.LightningModule		)       ->Optional[int]:
        """simple docstring"""







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




  @rank_zero_only
  def 			_lowercase   (      self     :			List[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        __lowercase   (  _A    ,     _A    ,     _A=1    ,     _A=False      )  ->    Tuple:
      if metric == "rouge2":
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            SCREAMING_SNAKE_CASE			:   Tuple	  = """{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "loss":
            SCREAMING_SNAKE_CASE			:   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."""      )

      SCREAMING_SNAKE_CASE			:   Tuple	  = ModelCheckpoint(
          dirpath=_A    ,     filename=_A    ,     monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     save_top_k=_A    ,     )
      return checkpoint_callback







def        __lowercase   (  _A    ,     _A      )  ->    Optional[int]:
      return EarlyStopping(
          monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     patience=_A    ,     verbose=_A    ,     )




