

"""simple docstring"""





from __future__ import annotations

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




class       _lowerCAmelCase						(			SCREAMING_SNAKE_CASE__     ):







 """simple docstring"""







 __UpperCAmelCase			:      Optional[Any]     	=			1
 __UpperCAmelCase			:      Tuple     	=			2
 __UpperCAmelCase			:      Optional[int]     	=			3


 def __repr__(    self							:    List[Any]			):
    return F"""{self.__class__.__name__}.{self.name}"""







def 						_A						(						UpperCamelCase_  : List[str],    UpperCamelCase_  : Dict,    UpperCamelCase_  : Dict)			->		float:
   '''simple docstring'''


   __lowercase	,__lowercase			=		point
   return degrees(atana(y - miny,    x - minx))




def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : List[Any],    UpperCamelCase_  : Any)			->		Direction:
   '''simple docstring'''


   __lowercase	,__lowercase			=		starting
   __lowercase	,__lowercase			=		via
   __lowercase	,__lowercase			=		target
   __lowercase			=		degrees(atana(ya - ya,    xa - xa))
   via_angle %= 360
   __lowercase			=		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 						_A						(						UpperCamelCase_  : int)			->		list[tuple[int, int]]:
   '''simple docstring'''


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

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

   __lowercase			=		sorted(UpperCamelCase_,    key=lambda UpperCamelCase_: angle_comparer(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_))
   # 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))

   __lowercase			=		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.
   __lowercase			=		Direction.left

   for i in range(3,    len(UpperCamelCase_)):
      while True:
         __lowercase			=		stack[-2]
         __lowercase			=		stack[-1]
         __lowercase			=		sorted_points[i]
         __lowercase			=		check_direction(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_)

         if next_direction == Direction.left:
            __lowercase			=		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(UpperCamelCase_)







"""simple docstring"""





import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
   print('#################### AVAILABLE DEVICES ####################')
   print(jax.devices())
   print('###########################################################')

   # setup for wandb sweep
   _a				   =							Args()
   _a				   =							wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   _a				   =							dict(logger.config)
   del wandb_args["batch_size"]
   _a				   =							replace(args, **wandb_args)
   _a				   =							args.base_dir + '-' + wandb.run.id
   _a				   =							replace(args, base_dir=base_dir)
   print(args)

   _a				   =							load_dataset('json', data_files=args.tr_data_path)['train']
   _a				   =							load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   _a				   =							range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   _a				   =							tr_dataset.shuffle().select(indices)
   _a				   =							range(len(val_dataset) - len(val_dataset) % args.batch_size)
   _a				   =							val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _a				   =							tr_dataset.shuffle().select(range(8_00_00))
      _a				   =							val_dataset.shuffle().select(range(80_00))

   print(tr_dataset)
   print(val_dataset)

   _a				   =							FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _a				   =							BigBirdTokenizerFast.from_pretrained(args.model_id)
   _a				   =							DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

   _a				   =							{
       'lr': args.lr,
       'init_lr': args.init_lr,
       'warmup_steps': args.warmup_steps,
       'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
       'weight_decay': args.weight_decay,
   }
   _a   ,		_a				   =							build_tx(**tx_args)

   _a				   =							Trainer(
       args=args,
       data_collator=data_collator,
       model_save_fn=model.save_pretrained,
       train_step_fn=train_step,
       val_step_fn=val_step,
       logger=logger,
       scheduler_fn=lr,
   )

   _a				   =							None
   _a				   =							trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
   try:
      trainer.train(state, tr_dataset, val_dataset)
   except KeyboardInterrupt:
      print('Oooops; TRAINING STOPPED UNFORTUNATELY')

   print('SAVING WEIGHTS IN `final-weights`')
   _a				   =							jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)




