

'''simple docstring'''

from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
    import torch

if is_tf_available():
    import tensorflow as tf





class 							_snake_case (       _snake_case						):
   _A       :     Optional[Any]       =					['''image_processor''']
   _A       :     Optional[int]       =					'''SamImageProcessor'''
   def __init__( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      int							):
     super().__init__(lowerCAmelCase__							)
     SCREAMING_SNAKE_CASE:str						      =					self.image_processor
     SCREAMING_SNAKE_CASE:Dict						      =					-10
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.image_processor.size["longest_edge"]
   def __call__( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None     ,SCREAMING_SNAKE_CASE__			:      str=None     ,SCREAMING_SNAKE_CASE__			:      str=None     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, TensorType]] = None     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.image_processor(
         lowerCAmelCase__     ,return_tensors=lowerCAmelCase__     ,**lowerCAmelCase__     ,)

     # pop arguments that are not used in the foward but used nevertheless
     SCREAMING_SNAKE_CASE:str						      =					encoding_image_processor["original_sizes"]

     if hasattr(lowerCAmelCase__     ,"numpy"							):  # Checks if Torch or TF tensor
       SCREAMING_SNAKE_CASE:Tuple						      =					original_sizes.numpy()

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					self._check_and_preprocess_points(
         input_points=lowerCAmelCase__     ,input_labels=lowerCAmelCase__     ,input_boxes=lowerCAmelCase__     ,)

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self._normalize_and_convert(
         lowerCAmelCase__     ,lowerCAmelCase__     ,input_points=lowerCAmelCase__     ,input_labels=lowerCAmelCase__     ,input_boxes=lowerCAmelCase__     ,return_tensors=lowerCAmelCase__     ,)

     return encoding_image_processor
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None     ,SCREAMING_SNAKE_CASE__			:      Tuple=None     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]="pt"     ,):
     if input_points is not None:
       if len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
         SCREAMING_SNAKE_CASE:List[Any]						      =					[
             self._normalize_coordinates(self.target_size     ,lowerCAmelCase__     ,original_sizes[0]							) for point in input_points
         ]
       else:
         SCREAMING_SNAKE_CASE:Dict						      =					[
             self._normalize_coordinates(self.target_size     ,lowerCAmelCase__     ,lowerCAmelCase__							)
             for point, original_size in zip(lowerCAmelCase__     ,lowerCAmelCase__							)
         ]
       # check that all arrays have the same shape
       if not all(point.shape == input_points[0].shape for point in input_points							):
         if input_labels is not None:
           SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					self._pad_points_and_labels(lowerCAmelCase__     ,lowerCAmelCase__							)

       SCREAMING_SNAKE_CASE:str						      =					np.array(lowerCAmelCase__							)

     if input_labels is not None:
       SCREAMING_SNAKE_CASE:List[str]						      =					np.array(lowerCAmelCase__							)

     if input_boxes is not None:
       if len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
         SCREAMING_SNAKE_CASE:Dict						      =					[
             self._normalize_coordinates(self.target_size     ,lowerCAmelCase__     ,original_sizes[0]     ,is_bounding_box=lowerCAmelCase__							)
             for box in input_boxes
         ]
       else:
         SCREAMING_SNAKE_CASE:Optional[Any]						      =					[
             self._normalize_coordinates(self.target_size     ,lowerCAmelCase__     ,lowerCAmelCase__     ,is_bounding_box=lowerCAmelCase__							)
             for box, original_size in zip(lowerCAmelCase__     ,lowerCAmelCase__							)
         ]
       SCREAMING_SNAKE_CASE:Any						      =					np.array(lowerCAmelCase__							)

     if input_boxes is not None:
       if return_tensors == "pt":
         SCREAMING_SNAKE_CASE:int						      =					torch.from_numpy(lowerCAmelCase__							)
         # boxes batch size of 1 by default
         SCREAMING_SNAKE_CASE:int						      =					input_boxes.unsqueeze(1							) if len(input_boxes.shape							) != 3 else input_boxes
       elif return_tensors == "tf":
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tf.convert_to_tensor(lowerCAmelCase__							)
         # boxes batch size of 1 by default
         SCREAMING_SNAKE_CASE:str						      =					tf.expand_dims(lowerCAmelCase__     ,1							) if len(input_boxes.shape							) != 3 else input_boxes
       encoding_image_processor.update({"input_boxes": input_boxes}							)
     if input_points is not None:
       if return_tensors == "pt":
         SCREAMING_SNAKE_CASE:Dict						      =					torch.from_numpy(lowerCAmelCase__							)
         # point batch size of 1 by default
         SCREAMING_SNAKE_CASE:List[Any]						      =					input_points.unsqueeze(1							) if len(input_points.shape							) != 4 else input_points
       elif return_tensors == "tf":
         SCREAMING_SNAKE_CASE:int						      =					tf.convert_to_tensor(lowerCAmelCase__							)
         # point batch size of 1 by default
         SCREAMING_SNAKE_CASE:Any						      =					tf.expand_dims(lowerCAmelCase__     ,1							) if len(input_points.shape							) != 4 else input_points
       encoding_image_processor.update({"input_points": input_points}							)
     if input_labels is not None:
       if return_tensors == "pt":
         SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.from_numpy(lowerCAmelCase__							)
         # point batch size of 1 by default
         SCREAMING_SNAKE_CASE:Optional[Any]						      =					input_labels.unsqueeze(1							) if len(input_labels.shape							) != 3 else input_labels
       elif return_tensors == "tf":
         SCREAMING_SNAKE_CASE:List[str]						      =					tf.convert_to_tensor(lowerCAmelCase__							)
         # point batch size of 1 by default
         SCREAMING_SNAKE_CASE:int						      =					tf.expand_dims(lowerCAmelCase__     ,1							) if len(input_labels.shape							) != 3 else input_labels
       encoding_image_processor.update({"input_labels": input_labels}							)

     return encoding_image_processor
   def     __UpperCamelCase						( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Any							):
     SCREAMING_SNAKE_CASE:List[str]						      =					max([point.shape[0] for point in input_points]							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					[]
     for i, point in enumerate(lowerCAmelCase__							):
       if point.shape[0] != expected_nb_points:
         SCREAMING_SNAKE_CASE:Any						      =					np.concatenate(
             [point, np.zeros((expected_nb_points - point.shape[0], 2)							) + self.point_pad_value]     ,axis=0							)
         SCREAMING_SNAKE_CASE:List[Any]						      =					np.append(input_labels[i]     ,[self.point_pad_value]							)
       processed_input_points.append(lowerCAmelCase__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					processed_input_points
     return input_points, input_labels
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      Any=False							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[int]						      =					original_size
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					self.image_processor._get_preprocess_shape(lowerCAmelCase__     ,longest_edge=lowerCAmelCase__							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					deepcopy(lowerCAmelCase__							).astype(lowerCAmelCase__							)

     if is_bounding_box:
       SCREAMING_SNAKE_CASE:int						      =					coords.reshape(-1     ,2     ,2							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					coords[..., 0] * (new_w / old_w)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					coords[..., 1] * (new_h / old_h)

     if is_bounding_box:
       SCREAMING_SNAKE_CASE:List[str]						      =					coords.reshape(-1     ,4							)

     return coords
   def     __UpperCamelCase						( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      int=None     ,SCREAMING_SNAKE_CASE__			:      Any=None     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=None     ,):
     if input_points is not None:
       if hasattr(lowerCAmelCase__     ,"numpy"							):  # Checks for TF or Torch tensor
         SCREAMING_SNAKE_CASE:Dict						      =					input_points.numpy().tolist()

       if not isinstance(lowerCAmelCase__     ,lowerCAmelCase__							) or not isinstance(input_points[0]     ,lowerCAmelCase__							):
         raise ValueError("Input points must be a list of list of floating points."							)
       SCREAMING_SNAKE_CASE:List[Any]						      =					[np.array(lowerCAmelCase__							) for input_point in input_points]
     else:
       SCREAMING_SNAKE_CASE:List[Any]						      =					None

     if input_labels is not None:
       if hasattr(lowerCAmelCase__     ,"numpy"							):
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					input_labels.numpy().tolist()

       if not isinstance(lowerCAmelCase__     ,lowerCAmelCase__							) or not isinstance(input_labels[0]     ,lowerCAmelCase__							):
         raise ValueError("Input labels must be a list of list integers."							)
       SCREAMING_SNAKE_CASE:Tuple						      =					[np.array(lowerCAmelCase__							) for label in input_labels]
     else:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					None

     if input_boxes is not None:
       if hasattr(lowerCAmelCase__     ,"numpy"							):
         SCREAMING_SNAKE_CASE:Any						      =					input_boxes.numpy().tolist()

       if (
           not isinstance(lowerCAmelCase__     ,lowerCAmelCase__							)
           or not isinstance(input_boxes[0]     ,lowerCAmelCase__							)
           or not isinstance(input_boxes[0][0]     ,lowerCAmelCase__							)
       ):
         raise ValueError("Input boxes must be a list of list of list of floating points."							)
       SCREAMING_SNAKE_CASE:str						      =					[np.array(lowerCAmelCase__							).astype(np.floataa							) for box in input_boxes]
     else:
       SCREAMING_SNAKE_CASE:Any						      =					None

     return input_points, input_labels, input_boxes
   @property
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.image_processor.model_input_names
     return list(dict.fromkeys(lowerCAmelCase__							)							)







   def     __UpperCamelCase						( self			:      List[str]     ,*SCREAMING_SNAKE_CASE__			:      Optional[int]     ,**SCREAMING_SNAKE_CASE__			:      Tuple							):
     return self.image_processor.post_process_masks(*lowerCAmelCase__     ,**lowerCAmelCase__							)









'''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_  ( snake_case=32      ,       snake_case=10      ,       snake_case=100      ,       snake_case=1026      ,       snake_case=True      ,       snake_case="data/tokenized_stories_train_wikitext103.jbl"      ,       snake_case="igf_context_pairs.jbl"      ,       ):
  set_seed(3 )
  # generate train_data and objective_set
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[int]						      =					generate_datasets(
      snake_case      ,       snake_case      ,       number=snake_case      ,       min_len=1026      ,       trim=snake_case )
  # 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?
  SCREAMING_SNAKE_CASE:Tuple						      =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )

  # load pretrained model
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					load_gpta("gpt2" ).to(snake_case )
  print("computing perplexity on objective set" )
  SCREAMING_SNAKE_CASE:str						      =					compute_perplexity(snake_case      ,       snake_case      ,       snake_case ).item()
  print("perplexity on objective set:"      ,       snake_case )

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

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





def    A_  ( snake_case      ,       snake_case=15      ,       snake_case=128      ,       snake_case=100      ,       snake_case="igf_model.pt"      ,       ):
  set_seed(42 )

  # Load pre-trained model
  SCREAMING_SNAKE_CASE:int						      =					GPTaLMHeadModel.from_pretrained("gpt2" )

  # Initialize secondary learner to use embedding weights of model
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					SecondaryLearner(snake_case )

  # Train secondary learner
  SCREAMING_SNAKE_CASE:int						      =					train_secondary_learner(
      snake_case      ,       snake_case      ,       max_epochs=snake_case      ,       batch_size=snake_case      ,       eval_freq=100      ,       igf_model_path=snake_case      ,       )

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case=32      ,       snake_case=1000      ,       snake_case=16      ,       snake_case=1.0      ,       snake_case=recopy_gpta      ,       snake_case=None      ,       snake_case=10      ,       snake_case="gpt2_finetuned.pt"      ,       ):
  SCREAMING_SNAKE_CASE:str						      =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
  SCREAMING_SNAKE_CASE:Dict						      =					RandomSampler(snake_case )
  SCREAMING_SNAKE_CASE:str						      =					DataLoader(snake_case      ,       sampler=snake_case )

  SCREAMING_SNAKE_CASE:str						      =					max_steps // (len(snake_case )) + 1
  SCREAMING_SNAKE_CASE:Tuple						      =					0
  SCREAMING_SNAKE_CASE:List[Any]						      =					torch.zeros((1, context_len)      ,       dtype=torch.long      ,       device=snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					recopy_model(snake_case      ,       snake_case      ,       snake_case )

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(snake_case )
    secondary_learner.eval()
  SCREAMING_SNAKE_CASE:List[Any]						      =					[]
  SCREAMING_SNAKE_CASE:Tuple						      =					0

  SCREAMING_SNAKE_CASE:Tuple						      =					[]
  SCREAMING_SNAKE_CASE:Tuple						      =					[]

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

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

        # 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 == 10:
          SCREAMING_SNAKE_CASE:int						      =					-1
        if predicted_q < threshold:
          SCREAMING_SNAKE_CASE:Optional[int]						      =					False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu() ) )
        SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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()
        SCREAMING_SNAKE_CASE:int						      =					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:
          SCREAMING_SNAKE_CASE:Optional[Any]						      =					compute_perplexity(snake_case      ,       snake_case      ,       snake_case )
          test_perps.append(snake_case )

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

    # save finetuned transformer model
  torch.save(model.state_dict()      ,       snake_case )
  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_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" )

  # Required parameters
  parser.add_argument(
      "--data_dir"      ,       default=snake_case      ,       type=snake_case      ,       required=snake_case      ,       help="The input data dir. Should contain data files for WikiText."      ,       )
  parser.add_argument(
      "--model_name_or_path"      ,       default=snake_case      ,       type=snake_case      ,       required=snake_case      ,       help="Path to pretrained model or model identifier from huggingface.co/models"      ,       )
  parser.add_argument(
      "--data_file"      ,       type=snake_case      ,       default=snake_case      ,       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=snake_case      ,       default=snake_case      ,       help="A jbl file containing the context and information gain pairs to train secondary learner."      ,       )

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

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

  parser.add_argument(
      "--context_len"      ,       default=32      ,       type=snake_case      ,       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=100      ,       type=snake_case      ,       help="number of articles that are long enough to be used as our objective set"      ,       )
  parser.add_argument(
      "--eval_freq"      ,       default=100      ,       type=snake_case      ,       help="secondary model evaluation is triggered at eval_freq" )

  parser.add_argument("--max_steps"      ,       default=1000      ,       type=snake_case      ,       help="To calculate training epochs" )

  parser.add_argument(
      "--secondary_learner_batch_size"      ,       default=128      ,       type=snake_case      ,       help="batch size of training data for secondary learner"      ,       )

  parser.add_argument(
      "--batch_size"      ,       default=16      ,       type=snake_case      ,       help="batch size of training data of language model(gpt2) " )

  parser.add_argument(
      "--eval_interval"      ,       default=10      ,       type=snake_case      ,       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=100      ,       type=snake_case      ,       help="The number of examples split to be used as objective_set/test_data" )

  parser.add_argument(
      "--min_len"      ,       default=1026      ,       type=snake_case      ,       help="The minimum length of the article to be used as objective set" )

  parser.add_argument(
      "--secondary_learner_max_epochs"      ,       default=15      ,       type=snake_case      ,       help="number of epochs to train secondary learner" )

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

  parser.add_argument(
      "--threshold"      ,       default=1.0      ,       type=snake_case      ,       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=snake_case      ,       help="finetuned_model_name" )

  parser.add_argument(
      "--recopy_model"      ,       default=snake_case      ,       type=snake_case      ,       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=32      ,       max_steps=10      ,       size_objective_set=100      ,       min_len=1026      ,       trim=snake_case      ,       data_file="data/tokenized_stories_train_wikitext103.jbl"      ,       igf_data_file="igf_context_pairs.jbl"      ,       )

  # Load train data for secondary learner
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					joblib.load("data/IGF_values.jbl" )

  # Train secondary learner
  SCREAMING_SNAKE_CASE:Optional[int]						      =					training_secondary_learner(
      snake_case      ,       secondary_learner_max_epochs=15      ,       secondary_learner_batch_size=128      ,       eval_freq=100      ,       igf_model_path="igf_model.pt"      ,       )

  # load pretrained gpt2 model
  SCREAMING_SNAKE_CASE:Dict						      =					GPTaLMHeadModel.from_pretrained("gpt2" )
  set_seed(42 )

  # Generate train and test data to train and evaluate gpt2 model
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					generate_datasets(
      context_len=32      ,       file="data/tokenized_stories_train_wikitext103.jbl"      ,       number=100      ,       min_len=1026      ,       trim=snake_case )

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      snake_case      ,       snake_case      ,       snake_case      ,       context_len=32      ,       max_steps=1000      ,       batch_size=16      ,       threshold=1.0      ,       recopy_model=snake_case      ,       secondary_learner=snake_case      ,       eval_interval=10      ,       finetuned_model_name="gpt2_finetuned.pt"      ,       )


if __name__ == "__main__":
    main()






