

"""simple docstring"""





from manim import *




class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		Rectangle(height=0.5,      width=0.5			)
    __lowercase			=		Rectangle(height=0.46,      width=0.46			).set_stroke(width=0			)
    __lowercase			=		Rectangle(height=0.25,      width=0.25			)

    __lowercase			=		[mem.copy() for i in range(6			)]
    __lowercase			=		[mem.copy() for i in range(6			)]
    __lowercase			=		VGroup(*UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0			)
    __lowercase			=		VGroup(*UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0			)
    __lowercase			=		VGroup(UpperCAmelCase__,      UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0			)
    __lowercase			=		Text("CPU",      font_size=2_4			)
    __lowercase			=		Group(UpperCAmelCase__,      UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0.5,      aligned_edge=UpperCAmelCase__			)
    cpu.move_to([-2.5, -0.5, 0]			)
    self.add(UpperCAmelCase__			)

    __lowercase			=		[mem.copy() for i in range(4			)]
    __lowercase			=		VGroup(*UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0			)
    __lowercase			=		Text("GPU",      font_size=2_4			)
    __lowercase			=		Group(UpperCAmelCase__,      UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0.5,      aligned_edge=UpperCAmelCase__			)
    gpu.move_to([-1, -1, 0]			)
    self.add(UpperCAmelCase__			)

    __lowercase			=		[mem.copy() for i in range(6			)]
    __lowercase			=		VGroup(*UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0			)

    __lowercase			=		Text("Model",      font_size=2_4			)
    __lowercase			=		Group(UpperCAmelCase__,      UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0.5,      aligned_edge=UpperCAmelCase__			)
    model.move_to([3, -1.0, 0]			)
    self.add(UpperCAmelCase__			)

    __lowercase			=		[]
    __lowercase			=		[]

    for i, rect in enumerate(UpperCAmelCase__			):
       rect.set_stroke(UpperCAmelCase__			)

       __lowercase			=		Rectangle(height=0.46 / 4,      width=0.46 / 3			).set_stroke(width=0.0			).set_fill(UpperCAmelCase__,      opacity=0.7			)

       if i == 0:
          cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT			),      buff=0.02,      direction=UpperCAmelCase__			)
          cpu_target.set_x(cpu_target.get_x() + 0.1			)
       elif i == 3:
          cpu_target.next_to(model_cpu_arr[0],      direction=UpperCAmelCase__,      buff=0.0			)
       else:
          cpu_target.next_to(model_cpu_arr[i - 1],      direction=UpperCAmelCase__,      buff=0.0			)
       self.add(UpperCAmelCase__			)
       model_cpu_arr.append(UpperCAmelCase__			)

    self.add(*UpperCAmelCase__,      *UpperCAmelCase__			)

    __lowercase			=		[meta_mem.copy() for i in range(6			)]
    __lowercase			=		[meta_mem.copy() for i in range(6			)]
    __lowercase			=		VGroup(*UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0			)
    __lowercase			=		VGroup(*UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0			)
    __lowercase			=		VGroup(UpperCAmelCase__,      UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0			)
    __lowercase			=		Text("Disk",      font_size=2_4			)
    __lowercase			=		Group(UpperCAmelCase__,      UpperCAmelCase__			).arrange(UpperCAmelCase__,      buff=0.5,      aligned_edge=UpperCAmelCase__			)
    disk.move_to([-4.0, -1.25, 0]			)
    self.add(UpperCAmelCase__,      UpperCAmelCase__			)

    __lowercase			=		[]

    for i in range(6			):
       __lowercase			=		fill.copy().set_fill(UpperCAmelCase__,      opacity=0.8			)
       target.move_to(disk_left_col_base[i]			).scale(0.5			)
       cpu_disk_arr.append(UpperCAmelCase__			)

    self.add(*UpperCAmelCase__			)

    __lowercase			=		Square(side_length=2.2			)
    key.move_to([-5, 2, 0]			)

    __lowercase			=		MarkupText(
        F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""",      font_size=1_8,      )

    key_text.move_to([-5, 2.4, 0]			)

    self.add(UpperCAmelCase__,      UpperCAmelCase__			)

    __lowercase			=		MarkupText(
        F"""<span fgcolor='{BLUE}'>●</span> Checkpoint""",      font_size=1_8,      )

    blue_text.next_to(UpperCAmelCase__,      DOWN * 2.4,      aligned_edge=key_text.get_left()			)
    self.add(UpperCAmelCase__			)

    __lowercase			=		MarkupText(F"""The offloaded weights are all sent to the CPU.""",      font_size=2_4			)
    step_a.move_to([2, 2, 0]			)

    self.play(Write(UpperCAmelCase__,      run_time=3			)			)

    for i in range(6			):
       __lowercase			=		cpu_disk_arr[i]
       __lowercase			=		rect.copy().set_fill(UpperCAmelCase__,      opacity=0.8			).scale(2.0			)
       cpa.generate_target()
       cpa.target.move_to(model_base[i]			)

       if i == 0:
          rect.set_fill(UpperCAmelCase__,      opacity=0.8			)
          rect.generate_target()
          rect.target.move_to(cpu_left_col_base[0]			).scale(2.0			)

          self.remove(
              *UpperCAmelCase__,      *UpperCAmelCase__,      )

       else:
          rect.generate_target()
          rect.target.move_to(cpu_left_col_base[i]			).scale(2.0			)
       self.play(MoveToTarget(UpperCAmelCase__			),      MoveToTarget(UpperCAmelCase__			),      model_base[i].animate.set_stroke(UpperCAmelCase__			)			)
    self.play(FadeOut(UpperCAmelCase__			)			)

    __lowercase			=		MarkupText(
        F"""Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed.""",      font_size=2_4,      )
    step_a.move_to([2, 2, 0]			)

    self.play(Write(UpperCAmelCase__,      run_time=3			)			)

    __lowercase			=		[]
    __lowercase			=		[]
    for i in range(6			):
       __lowercase			=		Arrow(start=UpperCAmelCase__,      end=UpperCAmelCase__,      color=UpperCAmelCase__,      buff=0.5			)
       a.next_to(model_base[i].get_left(),      UpperCAmelCase__,      buff=0.2			)
       arrows.append(UpperCAmelCase__			)
       animations.append(Write(UpperCAmelCase__			)			)
    self.play(*UpperCAmelCase__			)
    self.wait()







"""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)




