





"""simple docstring"""







import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel







def     A__			(       __lowerCamelCase=3_2,		__lowerCamelCase=1_0,		__lowerCamelCase=1_0_0,		__lowerCamelCase=1_0_2_6,		__lowerCamelCase=True,		__lowerCamelCase="data/tokenized_stories_train_wikitext103.jbl",		__lowerCamelCase="igf_context_pairs.jbl",		):





  """simple docstring"""
  set_seed(3					)
  # generate train_data and objective_set
  _lowerCAmelCase      ,   _lowerCAmelCase	      =					generate_datasets(
      _UpperCamelCase,		_UpperCamelCase,		number=_UpperCamelCase,		min_len=1_0_2_6,		trim=_UpperCamelCase					)
  # keeps model same across runs
  set_seed(4					)
  # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
  # can we train on GPU?
  _lowerCAmelCase	      =					torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'					)

  # load pretrained model
  _lowerCAmelCase	      =					load_gpta('gpt2'					).to(_UpperCamelCase					)
  print('computing perplexity on objective set'					)
  _lowerCAmelCase	      =					compute_perplexity(_UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase					).item()
  print('perplexity on objective set:',		_UpperCamelCase					)

  # collect igf pairs and save to file demo.jbl
  collect_objective_set(_UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase					)

  # clean up, delete model and data we don't need anymore
  del model, train_data, objective_set
  torch.cuda.empty_cache()







def     A__			(       __lowerCamelCase,		__lowerCamelCase=1_5,		__lowerCamelCase=1_2_8,		__lowerCamelCase=1_0_0,		__lowerCamelCase="igf_model.pt",		):





  """simple docstring"""
  set_seed(4_2					)

  # Load pre-trained model
  _lowerCAmelCase	      =					GPTaLMHeadModel.from_pretrained('gpt2'					)

  # Initialize secondary learner to use embedding weights of model
  _lowerCAmelCase	      =					SecondaryLearner(_UpperCamelCase					)

  # Train secondary learner
  _lowerCAmelCase	      =					train_secondary_learner(
      _UpperCamelCase,		_UpperCamelCase,		max_epochs=_UpperCamelCase,		batch_size=_UpperCamelCase,		eval_freq=1_0_0,		igf_model_path=_UpperCamelCase,		)

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner







def     A__			(       __lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase=3_2,		__lowerCamelCase=1_0_0_0,		__lowerCamelCase=1_6,		__lowerCamelCase=1.0,		__lowerCamelCase=recopy_gpta,		__lowerCamelCase=None,		__lowerCamelCase=1_0,		__lowerCamelCase="gpt2_finetuned.pt",		):





  """simple docstring"""
  _lowerCAmelCase	      =					torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'					)
  _lowerCAmelCase	      =					RandomSampler(_UpperCamelCase					)
  _lowerCAmelCase	      =					DataLoader(_UpperCamelCase,		sampler=_UpperCamelCase					)

  _lowerCAmelCase	      =					max_steps // (len(_UpperCamelCase					)) + 1
  _lowerCAmelCase	      =					0
  _lowerCAmelCase	      =					torch.zeros((1, context_len),		dtype=torch.long,		device=_UpperCamelCase					)
  _lowerCAmelCase      ,   _lowerCAmelCase      ,   _lowerCAmelCase	      =					recopy_model(_UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase					)

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(_UpperCamelCase					)
    secondary_learner.eval()
  _lowerCAmelCase	      =					[]
  _lowerCAmelCase	      =					0

  _lowerCAmelCase	      =					[]
  _lowerCAmelCase	      =					[]

  # Compute the performance of the transformer model at the beginning
  _lowerCAmelCase	      =					compute_perplexity(_UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase					)
  test_perps.append(_UpperCamelCase					)
  print('Test perplexity, step',		_UpperCamelCase,		':',		_UpperCamelCase					)
  for epoch in range(int(_UpperCamelCase					)					):
    for step, example in enumerate(_UpperCamelCase					):
      torch.cuda.empty_cache()
      _lowerCAmelCase	      =					random.randint(0,		example.size(2					) - context_len - 1					)
      _lowerCAmelCase	      =					example[0, 0, start : start + context_len]
      lm_optimizer.zero_grad()
      _lowerCAmelCase	      =					model(_UpperCamelCase,		labels=_UpperCamelCase					)
      _lowerCAmelCase	      =					True

      if secondary_learner is not None:
        _lowerCAmelCase	      =					secondary_learner.forward(
            torch.tensor(_UpperCamelCase,		dtype=torch.long,		device=_UpperCamelCase					).unsqueeze(0					)					)[0].item()
        observed_qs.append(float(_UpperCamelCase					)					)

        # Here we implement the simple non-constant threshold for the predicted IG(X) value
        # We will decay the selectivity of our secondary learner filter from
        # 1 standard deviation above average to 1 below average after 10 batches.

        if global_step == 1_0:
          _lowerCAmelCase	      =					-1
        if predicted_q < threshold:
          _lowerCAmelCase	      =					False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()					)					)
        _lowerCAmelCase	      =					outputs[0]
        lm_loss.backward()
        examples += 1

      del outputs

      # Once the batch is filled with enough contexts, backprop on the batch.
      if examples == batch_size:
        torch.cuda.empty_cache()
        _lowerCAmelCase	      =					0
        # Do LM backprop
        torch.nn.utils.clip_grad_norm_(model.parameters(),		3.0					)
        lm_optimizer.step()
        lm_scheduler.step()  # Update learning rate schedule
        global_step += 1
        # Compute the performance of the transformer model at this batch
        if global_step % eval_interval == 0:
          _lowerCAmelCase	      =					compute_perplexity(_UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase					)
          test_perps.append(_UpperCamelCase					)

          print('Test perplexity, step',		_UpperCamelCase,		':',		_UpperCamelCase					)
            # Break out of the loop after 60 batches
      if max_steps > 0 and global_step > 6_0:
        break
    if max_steps > 0 and global_step > 6_0:
      break

    # save finetuned transformer model
  torch.save(model.state_dict(),		_UpperCamelCase					)
  torch.cuda.empty_cache()
  # Do some cleaning up so we can reinitialize for the next run of this function
  del lm_optimizer
  del lm_scheduler
  return model







def     A__			(       ):





  """simple docstring"""
  _lowerCAmelCase	      =					argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'					)

  # Required parameters
  parser.add_argument(
      '--data_dir',		default=_UpperCamelCase,		type=_UpperCamelCase,		required=_UpperCamelCase,		help='The input data dir. Should contain data files for WikiText.',		)
  parser.add_argument(
      '--model_name_or_path',		default=_UpperCamelCase,		type=_UpperCamelCase,		required=_UpperCamelCase,		help='Path to pretrained model or model identifier from huggingface.co/models',		)
  parser.add_argument(
      '--data_file',		type=_UpperCamelCase,		default=_UpperCamelCase,		help=(
          'A jbl file containing tokenized data which can be split as objective dataset, '
          'train_dataset and test_dataset.'
      ),		)

  parser.add_argument(
      '--igf_data_file',		type=_UpperCamelCase,		default=_UpperCamelCase,		help='A jbl file containing the context and information gain pairs to train secondary learner.',		)

  parser.add_argument(
      '--output_dir',		default=_UpperCamelCase,		type=_UpperCamelCase,		required=_UpperCamelCase,		help='The output directory where the final fine-tuned model is stored.',		)

  parser.add_argument(
      '--tokenizer_name',		default=_UpperCamelCase,		type=_UpperCamelCase,		help='Pretrained tokenizer name or path if not the same as model_name',		)
  parser.add_argument('--seed',		type=_UpperCamelCase,		default=_UpperCamelCase,		help='A seed for reproducible training.'					)

  parser.add_argument(
      '--context_len',		default=3_2,		type=_UpperCamelCase,		help=(
          'The maximum total input sequence length after tokenization. Sequences longer '
          'than this will be truncated, sequences shorter will be padded.'
      ),		)

  parser.add_argument(
      '--size_objective_set',		default=1_0_0,		type=_UpperCamelCase,		help='number of articles that are long enough to be used as our objective set',		)
  parser.add_argument(
      '--eval_freq',		default=1_0_0,		type=_UpperCamelCase,		help='secondary model evaluation is triggered at eval_freq'					)

  parser.add_argument('--max_steps',		default=1_0_0_0,		type=_UpperCamelCase,		help='To calculate training epochs'					)

  parser.add_argument(
      '--secondary_learner_batch_size',		default=1_2_8,		type=_UpperCamelCase,		help='batch size of training data for secondary learner',		)

  parser.add_argument(
      '--batch_size',		default=1_6,		type=_UpperCamelCase,		help='batch size of training data of language model(gpt2) '					)

  parser.add_argument(
      '--eval_interval',		default=1_0,		type=_UpperCamelCase,		help=(
          'decay the selectivity of our secondary learner filter from'
          '1 standard deviation above average to 1 below average after 10 batches'
      ),		)

  parser.add_argument(
      '--number',		default=1_0_0,		type=_UpperCamelCase,		help='The number of examples split to be used as objective_set/test_data'					)

  parser.add_argument(
      '--min_len',		default=1_0_2_6,		type=_UpperCamelCase,		help='The minimum length of the article to be used as objective set'					)

  parser.add_argument(
      '--secondary_learner_max_epochs',		default=1_5,		type=_UpperCamelCase,		help='number of epochs to train secondary learner'					)

  parser.add_argument('--trim',		default=_UpperCamelCase,		type=_UpperCamelCase,		help='truncate the example if it exceeds context length'					)

  parser.add_argument(
      '--threshold',		default=1.0,		type=_UpperCamelCase,		help=(
          'The threshold value used by secondary learner to filter the train_data and allow only'
          ' informative data as input to the model'
      ),		)

  parser.add_argument('--finetuned_model_name',		default='gpt2_finetuned.pt',		type=_UpperCamelCase,		help='finetuned_model_name'					)

  parser.add_argument(
      '--recopy_model',		default=_UpperCamelCase,		type=_UpperCamelCase,		help='Reset the model to the original pretrained GPT-2 weights after each iteration',		)

  # function calls
  # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
  generate_n_pairs(
      context_len=3_2,		max_steps=1_0,		size_objective_set=1_0_0,		min_len=1_0_2_6,		trim=_UpperCamelCase,		data_file='data/tokenized_stories_train_wikitext103.jbl',		igf_data_file='igf_context_pairs.jbl',		)

  # Load train data for secondary learner
  _lowerCAmelCase	      =					joblib.load('data/IGF_values.jbl'					)

  # Train secondary learner
  _lowerCAmelCase	      =					training_secondary_learner(
      _UpperCamelCase,		secondary_learner_max_epochs=1_5,		secondary_learner_batch_size=1_2_8,		eval_freq=1_0_0,		igf_model_path='igf_model.pt',		)

  # load pretrained gpt2 model
  _lowerCAmelCase	      =					GPTaLMHeadModel.from_pretrained('gpt2'					)
  set_seed(4_2					)

  # Generate train and test data to train and evaluate gpt2 model
  _lowerCAmelCase      ,   _lowerCAmelCase	      =					generate_datasets(
      context_len=3_2,		file='data/tokenized_stories_train_wikitext103.jbl',		number=1_0_0,		min_len=1_0_2_6,		trim=_UpperCamelCase					)

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      _UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase,		context_len=3_2,		max_steps=1_0_0_0,		batch_size=1_6,		threshold=1.0,		recopy_model=_UpperCamelCase,		secondary_learner=_UpperCamelCase,		eval_interval=1_0,		finetuned_model_name='gpt2_finetuned.pt',		)


if __name__ == "__main__":
   main()











from manim import *





class 					A__     (     __snake_case ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  Rectangle(height=0.5   ,						width=0.5 )
           UpperCamelCase					       =  Rectangle(height=0.4_6   ,						width=0.4_6 ).set_stroke(width=0 )

           UpperCamelCase					       =  Rectangle(height=0.2_5   ,						width=0.2_5 )

           UpperCamelCase					       =  [mem.copy() for i in range(6 )]
           UpperCamelCase					       =  [mem.copy() for i in range(6 )]
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  VGroup(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  Text('CPU'   ,						font_size=24 )
           UpperCamelCase					       =  Group(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0.5   ,						aligned_edge=_SCREAMING_SNAKE_CASE )
           cpu.move_to([-2.5, -0.5, 0] )
           self.add(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  [mem.copy() for i in range(4 )]
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  Text('GPU'   ,						font_size=24 )
           UpperCamelCase					       =  Group(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0.5   ,						aligned_edge=_SCREAMING_SNAKE_CASE )
           gpu.move_to([-1, -1, 0] )
           self.add(_SCREAMING_SNAKE_CASE )

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

           UpperCamelCase					       =  Text('Model'   ,						font_size=24 )
           UpperCamelCase					       =  Group(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0.5   ,						aligned_edge=_SCREAMING_SNAKE_CASE )
           model.move_to([3, -1.0, 0] )
           self.add(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  []
           UpperCamelCase					       =  []

           for i, rect in enumerate(_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  fill.copy().set_fill(_SCREAMING_SNAKE_CASE   ,						opacity=0.8 )
                 target.move_to(_SCREAMING_SNAKE_CASE )
                 model_arr.append(_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  Rectangle(height=0.4_6   ,						width=0.4_6 ).set_stroke(width=0.0 ).set_fill(_SCREAMING_SNAKE_CASE   ,						opacity=0.8 )
                 cpu_target.move_to(cpu_left_col_base[i] )
                 model_cpu_arr.append(_SCREAMING_SNAKE_CASE )

           self.add(*_SCREAMING_SNAKE_CASE   ,						*_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  [meta_mem.copy() for i in range(6 )]
           UpperCamelCase					       =  [meta_mem.copy() for i in range(6 )]
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  VGroup(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  Text('Disk'   ,						font_size=24 )
           UpperCamelCase					       =  Group(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0.5   ,						aligned_edge=_SCREAMING_SNAKE_CASE )
           disk.move_to([-4, -1.2_5, 0] )
           self.add(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

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

           UpperCamelCase					       =  MarkupText(
               f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'   ,						font_size=18   ,						)

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

           self.add(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  MarkupText(
               f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'   ,						font_size=18   ,						)

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

           UpperCamelCase					       =  MarkupText(
               f'Now watch as an input is passed through the model\nand how the memory is utilized and handled.'   ,						font_size=24   ,						)
           step_a.move_to([2, 2, 0] )

           self.play(Write(_SCREAMING_SNAKE_CASE ) )

           UpperCamelCase					       =  Square(0.3 )
           input.set_fill(_SCREAMING_SNAKE_CASE   ,						opacity=1.0 )
           input.set_stroke(width=0.0 )
           input.next_to(model_base[0]   ,						_SCREAMING_SNAKE_CASE   ,						buff=0.5 )

           self.play(Write(_SCREAMING_SNAKE_CASE ) )

           input.generate_target()
           input.target.next_to(model_arr[0]   ,						direction=_SCREAMING_SNAKE_CASE   ,						buff=0.0_2 )
           self.play(MoveToTarget(_SCREAMING_SNAKE_CASE ) )

           self.play(FadeOut(_SCREAMING_SNAKE_CASE ) )

           UpperCamelCase					       =  Arrow(start=_SCREAMING_SNAKE_CASE   ,						end=_SCREAMING_SNAKE_CASE   ,						color=_SCREAMING_SNAKE_CASE   ,						buff=0.5 )
           a.next_to(model_arr[0].get_left()   ,						_SCREAMING_SNAKE_CASE   ,						buff=0.2 )

           model_cpu_arr[0].generate_target()
           model_cpu_arr[0].target.move_to(gpu_rect[0] )

           UpperCamelCase					       =  MarkupText(
               f'As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'   ,						font_size=24   ,						)
           step_a.move_to([2, 2, 0] )

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

           UpperCamelCase					       =  {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.0_2}

           self.play(
               Write(_SCREAMING_SNAKE_CASE )   ,						Circumscribe(model_arr[0]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(model_cpu_arr[0]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(gpu_rect[0]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						)
           self.play(MoveToTarget(model_cpu_arr[0] ) )

           UpperCamelCase					       =  a.copy()
           for i in range(6 ):
                 a_c.next_to(model_arr[i].get_right() + 0.0_2   ,						_SCREAMING_SNAKE_CASE   ,						buff=0.2 )

                 input.generate_target()
                 input.target.move_to(model_arr[i].get_right() + 0.0_2 )

                 UpperCamelCase					       =  AnimationGroup(
                     FadeOut(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						MoveToTarget(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						FadeIn(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						lag_ratio=0.2 )

                 self.play(_SCREAMING_SNAKE_CASE )

                 model_cpu_arr[i].generate_target()
                 model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )

                 if i < 5:
                       model_cpu_arr[i + 1].generate_target()
                       model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
                       if i >= 1:
                             UpperCamelCase					       =  0.7

                       self.play(
                           Circumscribe(model_arr[i]   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(cpu_left_col_base[i]   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(cpu_left_col_base[i + 1]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(gpu_rect[0]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(model_arr[i + 1]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						)
                       if i < 1:
                             self.play(
                                 MoveToTarget(model_cpu_arr[i] )   ,						MoveToTarget(model_cpu_arr[i + 1] )   ,						)
                       else:
                             self.play(
                                 MoveToTarget(model_cpu_arr[i]   ,						run_time=0.7 )   ,						MoveToTarget(model_cpu_arr[i + 1]   ,						run_time=0.7 )   ,						)
                 else:
                       model_cpu_arr[i].generate_target()
                       model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
                       input.generate_target()
                       input.target.next_to(model_arr[-1].get_right()   ,						RIGHT + 0.0_2   ,						buff=0.2 )

                       self.play(
                           Circumscribe(model_arr[-1]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(cpu_left_col_base[-1]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(gpu_rect[0]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						)

                       self.play(MoveToTarget(model_cpu_arr[i] ) )

                 UpperCamelCase					       =  a_c
                 UpperCamelCase					       =  a_c.copy()

           input.generate_target()
           input.target.next_to(model_base[-1]   ,						RIGHT + 0.0_2   ,						buff=0.5 )
           self.play(
               FadeOut(_SCREAMING_SNAKE_CASE )   ,						FadeOut(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						)

           UpperCamelCase					       =  MarkupText(f'Inference on a model too large for GPU memory\nis successfully completed.'   ,						font_size=24 )
           step_a.move_to([2, 2, 0] )

           self.play(Write(_SCREAMING_SNAKE_CASE   ,						run_time=3 )   ,						MoveToTarget(_SCREAMING_SNAKE_CASE ) )

           self.wait()



