





'''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 				snake_case_      (		SCREAMING_SNAKE_CASE__=32					,	SCREAMING_SNAKE_CASE__=10					,	SCREAMING_SNAKE_CASE__=1_00					,	SCREAMING_SNAKE_CASE__=10_26					,	SCREAMING_SNAKE_CASE__=True					,	SCREAMING_SNAKE_CASE__="data/tokenized_stories_train_wikitext103.jbl"					,	SCREAMING_SNAKE_CASE__="igf_context_pairs.jbl"					,	):







  '''simple docstring'''



  set_seed(3       )
  # generate train_data and objective_set
  _snake_case		,		_snake_case     		=				generate_datasets(
      SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	number=SCREAMING_SNAKE_CASE__					,	min_len=10_26					,	trim=SCREAMING_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?
  _snake_case     		=				torch.device("cuda:0" if torch.cuda.is_available() else "cpu"       )

  # load pretrained model
  _snake_case     		=				load_gpta("gpt2"       ).to(SCREAMING_SNAKE_CASE__       )
  print("computing perplexity on objective set"       )
  _snake_case     		=				compute_perplexity(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ).item()
  print("perplexity on objective set:"					,	SCREAMING_SNAKE_CASE__       )

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

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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=15					,	SCREAMING_SNAKE_CASE__=1_28					,	SCREAMING_SNAKE_CASE__=1_00					,	SCREAMING_SNAKE_CASE__="igf_model.pt"					,	):







  '''simple docstring'''



  set_seed(42       )

  # Load pre-trained model
  _snake_case     		=				GPTaLMHeadModel.from_pretrained("gpt2"       )

  # Initialize secondary learner to use embedding weights of model
  _snake_case     		=				SecondaryLearner(SCREAMING_SNAKE_CASE__       )

  # Train secondary learner
  _snake_case     		=				train_secondary_learner(
      SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	max_epochs=SCREAMING_SNAKE_CASE__					,	batch_size=SCREAMING_SNAKE_CASE__					,	eval_freq=1_00					,	igf_model_path=SCREAMING_SNAKE_CASE__					,	)

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=32					,	SCREAMING_SNAKE_CASE__=10_00					,	SCREAMING_SNAKE_CASE__=16					,	SCREAMING_SNAKE_CASE__=1.0					,	SCREAMING_SNAKE_CASE__=recopy_gpta					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=10					,	SCREAMING_SNAKE_CASE__="gpt2_finetuned.pt"					,	):







  '''simple docstring'''



  _snake_case     		=				torch.device("cuda:0" if torch.cuda.is_available() else "cpu"       )
  _snake_case     		=				RandomSampler(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				DataLoader(SCREAMING_SNAKE_CASE__					,	sampler=SCREAMING_SNAKE_CASE__       )

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

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

  _snake_case     		=				[]
  _snake_case     		=				[]

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

      if secondary_learner is not None:
        _snake_case     		=				secondary_learner.forward(
            torch.tensor(SCREAMING_SNAKE_CASE__					,	dtype=torch.long					,	device=SCREAMING_SNAKE_CASE__       ).unsqueeze(0       )       )[0].item()
        observed_qs.append(float(SCREAMING_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:
          _snake_case     		=				-1
        if predicted_q < threshold:
          _snake_case     		=				False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()       )       )
        _snake_case     		=				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()
        _snake_case     		=				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:
          _snake_case     		=				compute_perplexity(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
          test_perps.append(SCREAMING_SNAKE_CASE__       )

          print("Test perplexity, step"					,	SCREAMING_SNAKE_CASE__					,	":"					,	SCREAMING_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()					,	SCREAMING_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 				snake_case_      (		):







  '''simple docstring'''



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

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

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

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

  parser.add_argument(
      "--context_len"					,	default=32					,	type=SCREAMING_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=1_00					,	type=SCREAMING_SNAKE_CASE__					,	help="number of articles that are long enough to be used as our objective set"					,	)
  parser.add_argument(
      "--eval_freq"					,	default=1_00					,	type=SCREAMING_SNAKE_CASE__					,	help="secondary model evaluation is triggered at eval_freq"       )

  parser.add_argument("--max_steps"					,	default=10_00					,	type=SCREAMING_SNAKE_CASE__					,	help="To calculate training epochs"       )

  parser.add_argument(
      "--secondary_learner_batch_size"					,	default=1_28					,	type=SCREAMING_SNAKE_CASE__					,	help="batch size of training data for secondary learner"					,	)

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

  parser.add_argument(
      "--eval_interval"					,	default=10					,	type=SCREAMING_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=1_00					,	type=SCREAMING_SNAKE_CASE__					,	help="The number of examples split to be used as objective_set/test_data"       )

  parser.add_argument(
      "--min_len"					,	default=10_26					,	type=SCREAMING_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=SCREAMING_SNAKE_CASE__					,	help="number of epochs to train secondary learner"       )

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

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

  parser.add_argument(
      "--recopy_model"					,	default=SCREAMING_SNAKE_CASE__					,	type=SCREAMING_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=1_00					,	min_len=10_26					,	trim=SCREAMING_SNAKE_CASE__					,	data_file="data/tokenized_stories_train_wikitext103.jbl"					,	igf_data_file="igf_context_pairs.jbl"					,	)

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

  # Train secondary learner
  _snake_case     		=				training_secondary_learner(
      SCREAMING_SNAKE_CASE__					,	secondary_learner_max_epochs=15					,	secondary_learner_batch_size=1_28					,	eval_freq=1_00					,	igf_model_path="igf_model.pt"					,	)

  # load pretrained gpt2 model
  _snake_case     		=				GPTaLMHeadModel.from_pretrained("gpt2"       )
  set_seed(42       )

  # Generate train and test data to train and evaluate gpt2 model
  _snake_case		,		_snake_case     		=				generate_datasets(
      context_len=32					,	file="data/tokenized_stories_train_wikitext103.jbl"					,	number=1_00					,	min_len=10_26					,	trim=SCREAMING_SNAKE_CASE__       )

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	context_len=32					,	max_steps=10_00					,	batch_size=16					,	threshold=1.0					,	recopy_model=SCREAMING_SNAKE_CASE__					,	secondary_learner=SCREAMING_SNAKE_CASE__					,	eval_interval=10					,	finetuned_model_name="gpt2_finetuned.pt"					,	)


if __name__ == "__main__":
  main()









'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__   :						Tuple    =			logging.get_logger(__name__)


if is_vision_available():
 import PIL


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   ['''pixel_values''']

     def __init__(					self						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = True						,   lowerCamelCase = 1 / 255						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = True						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)
       _snake_case     		=				size if size is not None else {"shortest_edge": 224}
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				crop_size if crop_size is not None else {"height": 224, "width": 224}
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase						,   param_name="crop_size"	)

       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				resample
       _snake_case     		=				do_center_crop
       _snake_case     		=				crop_size
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       _snake_case     		=				image_std if image_std is not None else OPENAI_CLIP_STD
       _snake_case     		=				do_convert_rgb

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       if "shortest_edge" not in size:
         raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
       _snake_case     		=				get_resize_output_image_size(lowerCamelCase						,   size=size["shortest_edge"]						,   default_to_square=lowerCamelCase	)
       return resize(lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase	)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
       return center_crop(lowerCamelCase						,   size=(size["height"], size["width"])						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return rescale(lowerCamelCase						,   scale=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return normalize(lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = ChannelDimension.FIRST						,   **lowerCamelCase						,   ):
       _snake_case     		=				do_resize if do_resize is not None else self.do_resize
       _snake_case     		=				size if size is not None else self.size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				resample if resample is not None else self.resample
       _snake_case     		=				do_center_crop if do_center_crop is not None else self.do_center_crop
       _snake_case     		=				crop_size if crop_size is not None else self.crop_size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="crop_size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				do_rescale if do_rescale is not None else self.do_rescale
       _snake_case     		=				rescale_factor if rescale_factor is not None else self.rescale_factor
       _snake_case     		=				do_normalize if do_normalize is not None else self.do_normalize
       _snake_case     		=				image_mean if image_mean is not None else self.image_mean
       _snake_case     		=				image_std if image_std is not None else self.image_std
       _snake_case     		=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       _snake_case     		=				make_list_of_images(lowerCamelCase	)

       if not valid_images(lowerCamelCase	):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."	)

       if do_resize and size is None:
         raise ValueError("Size must be specified if do_resize is True."	)

       if do_center_crop and crop_size is None:
         raise ValueError("Crop size must be specified if do_center_crop is True."	)

       if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."	)

       if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."	)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
         _snake_case     		=				[convert_to_rgb(lowerCamelCase	) for image in images]

       # All transformations expect numpy arrays.
       _snake_case     		=				[to_numpy_array(lowerCamelCase	) for image in images]

       if do_resize:
         _snake_case     		=				[self.resize(image=lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase	) for image in images]

       if do_center_crop:
         _snake_case     		=				[self.center_crop(image=lowerCamelCase						,   size=lowerCamelCase	) for image in images]

       if do_rescale:
         _snake_case     		=				[self.rescale(image=lowerCamelCase						,   scale=lowerCamelCase	) for image in images]

       if do_normalize:
         _snake_case     		=				[self.normalize(image=lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase	) for image in images]

       _snake_case     		=				[to_channel_dimension_format(lowerCamelCase						,   lowerCamelCase	) for image in images]

       _snake_case     		=				{"pixel_values": images}
       return BatchFeature(data=lowerCamelCase						,   tensor_type=lowerCamelCase	)


