

'''simple docstring'''

import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Any						      =					"https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
  SCREAMING_SNAKE_CASE:List[Any]						      =					Image.open(requests.get(snake_case      ,       stream=snake_case ).raw ).convert("RGB" )

  return image





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Dict						      =					[]
  # fmt: off

  # vision encoder
  rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding") )
  rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding") )
  rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight") )
  rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias") )
  rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight") )
  rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias") )

  for i in range(config.vision_config.num_hidden_layers ):
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
    rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
    rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
    rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )

  # QFormer
  rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight") )
  rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias") )

  # fmt: on
  return rename_keys





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					dct.pop(snake_case )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					val





def    A_  ( snake_case      ,       snake_case ):
  for i in range(config.vision_config.num_hidden_layers ):
    # read in original q and v biases
    SCREAMING_SNAKE_CASE:List[Any]						      =					state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''' )
    SCREAMING_SNAKE_CASE:List[str]						      =					state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''' )

    # next, set bias in the state dict
    SCREAMING_SNAKE_CASE:int						      =					torch.cat((q_bias, torch.zeros_like(snake_case      ,       requires_grad=snake_case ), v_bias) )
    SCREAMING_SNAKE_CASE:List[Any]						      =					qkv_bias





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:int						      =					364 if "coco" in model_name else 224
  SCREAMING_SNAKE_CASE:str						      =					BlipaVisionConfig(image_size=snake_case ).to_dict()

  # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
  # seems like flan-T5 models don't have bos_token_id properly set?
  if "opt-2.7b" in model_name:
    SCREAMING_SNAKE_CASE:List[Any]						      =					OPTConfig.from_pretrained("facebook/opt-2.7b"      ,       eos_token_id=snake_case ).to_dict()
  elif "opt-6.7b" in model_name:
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					OPTConfig.from_pretrained("facebook/opt-6.7b"      ,       eos_token_id=snake_case ).to_dict()
  elif "t5-xl" in model_name:
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					TaConfig.from_pretrained("google/flan-t5-xl"      ,       dense_act_fn="gelu"      ,       bos_token_id=1 ).to_dict()
  elif "t5-xxl" in model_name:
    SCREAMING_SNAKE_CASE:Any						      =					TaConfig.from_pretrained("google/flan-t5-xxl"      ,       dense_act_fn="gelu"      ,       bos_token_id=1 ).to_dict()

  SCREAMING_SNAKE_CASE:Any						      =					BlipaConfig(vision_config=snake_case      ,       text_config=snake_case )

  return config, image_size





@torch.no_grad()
def    A_  ( snake_case      ,       snake_case=None      ,       snake_case=False ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					(
      AutoTokenizer.from_pretrained("facebook/opt-2.7b" )
      if "opt" in model_name
      else AutoTokenizer.from_pretrained("google/flan-t5-xl" )
  )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tokenizer("\n"      ,       add_special_tokens=snake_case ).input_ids[0]
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[Any]						      =					get_blipa_config(snake_case      ,       eos_token_id=snake_case )

  SCREAMING_SNAKE_CASE:List[str]						      =					BlipaForConditionalGeneration(snake_case ).eval()

  SCREAMING_SNAKE_CASE:int						      =					{
      "blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
      "blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
      "blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
      "blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
      "blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
      "blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
      "blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
  }

  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					model_name_to_original[model_name]

  # load original model
  print("Loading original model..." )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					"cuda" if torch.cuda.is_available() else "cpu"
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					load_model_and_preprocess(
      name=snake_case      ,       model_type=snake_case      ,       is_eval=snake_case      ,       device=snake_case )
  original_model.eval()
  print("Done!" )

  # update state dict keys
  SCREAMING_SNAKE_CASE:str						      =					original_model.state_dict()
  SCREAMING_SNAKE_CASE:Any						      =					create_rename_keys(snake_case )
  for src, dest in rename_keys:
    rename_key(snake_case      ,       snake_case      ,       snake_case )

  # some keys can be renamed efficiently
  for key, val in state_dict.copy().items():
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					state_dict.pop(snake_case )
    if key.startswith("Qformer.bert" ):
      SCREAMING_SNAKE_CASE:int						      =					key.replace("Qformer.bert"      ,       "qformer" )
    if "attention.self" in key:
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					key.replace("self"      ,       "attention" )
    if "opt_proj" in key:
      SCREAMING_SNAKE_CASE:Optional[int]						      =					key.replace("opt_proj"      ,       "language_projection" )
    if "t5_proj" in key:
      SCREAMING_SNAKE_CASE:List[Any]						      =					key.replace("t5_proj"      ,       "language_projection" )
    if key.startswith("opt" ):
      SCREAMING_SNAKE_CASE:Tuple						      =					key.replace("opt"      ,       "language" )
    if key.startswith("t5" ):
      SCREAMING_SNAKE_CASE:Tuple						      =					key.replace("t5"      ,       "language" )
    SCREAMING_SNAKE_CASE:Any						      =					val

  # read in qv biases
  read_in_q_v_bias(snake_case      ,       snake_case )

  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:str						      =					hf_model.load_state_dict(snake_case      ,       strict=snake_case )
  assert len(snake_case ) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  SCREAMING_SNAKE_CASE:List[Any]						      =					load_demo_image()
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					vis_processors["eval"](snake_case ).unsqueeze(0 ).to(snake_case )
  SCREAMING_SNAKE_CASE:List[Any]						      =					tokenizer(["\n"]      ,       return_tensors="pt" ).input_ids.to(snake_case )

  # create processor
  SCREAMING_SNAKE_CASE:Dict						      =					BlipImageProcessor(
      size={"height": image_size, "width": image_size}      ,       image_mean=snake_case      ,       image_std=snake_case )
  SCREAMING_SNAKE_CASE:Any						      =					BlipaProcessor(image_processor=snake_case      ,       tokenizer=snake_case )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					processor(images=snake_case      ,       return_tensors="pt" ).pixel_values.to(snake_case )

  # make sure processor creates exact same pixel values
  assert torch.allclose(snake_case      ,       snake_case )

  original_model.to(snake_case )
  hf_model.to(snake_case )
  with torch.no_grad():
    if "opt" in model_name:
      SCREAMING_SNAKE_CASE:str						      =					original_model({"image": original_pixel_values, "text_input": [""]} ).logits
      SCREAMING_SNAKE_CASE:int						      =					hf_model(snake_case      ,       snake_case ).logits
    else:
      SCREAMING_SNAKE_CASE:str						      =					original_model(
          {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]} ).logits
      SCREAMING_SNAKE_CASE:str						      =					input_ids.masked_fill(input_ids == tokenizer.pad_token_id      ,       -100 )
      SCREAMING_SNAKE_CASE:Optional[int]						      =					hf_model(snake_case      ,       snake_case      ,       labels=snake_case ).logits

  assert original_logits.shape == logits.shape
  print("First values of original logits:"      ,       original_logits[0, :3, :3] )
  print("First values of HF logits:"      ,       logits[0, :3, :3] )

  # assert values
  if model_name == "blip2-flan-t5-xl":
    SCREAMING_SNAKE_CASE:List[str]						      =					torch.tensor(
        [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]      ,       device=snake_case )
    assert torch.allclose(logits[0, :3, :3]      ,       snake_case      ,       atol=1e-4 )
  elif model_name == "blip2-flan-t5-xl-coco":
    SCREAMING_SNAKE_CASE:Tuple						      =					torch.tensor(
        [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]      ,       device=snake_case )
  else:
    # cast to same type
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					logits.dtype
    assert torch.allclose(original_logits.to(snake_case )      ,       snake_case      ,       atol=1e-2 )
  print("Looks ok!" )

  print("Generating a caption..." )
  SCREAMING_SNAKE_CASE:List[str]						      =					""
  SCREAMING_SNAKE_CASE:str						      =					tokenizer(snake_case      ,       return_tensors="pt" ).input_ids.to(snake_case )

  SCREAMING_SNAKE_CASE:Optional[int]						      =					original_model.generate({"image": original_pixel_values} )
  SCREAMING_SNAKE_CASE:str						      =					hf_model.generate(
      snake_case      ,       snake_case      ,       do_sample=snake_case      ,       num_beams=5      ,       max_length=30      ,       min_length=1      ,       top_p=0.9      ,       repetition_penalty=1.0      ,       length_penalty=1.0      ,       temperature=1      ,       )
  print("Original generation:"      ,       snake_case )
  SCREAMING_SNAKE_CASE:List[Any]						      =					input_ids.shape[1]
  SCREAMING_SNAKE_CASE:int						      =					processor.batch_decode(outputs[:, prompt_length:]      ,       skip_special_tokens=snake_case )
  SCREAMING_SNAKE_CASE:str						      =					[text.strip() for text in output_text]
  print("HF generation:"      ,       snake_case )

  if pytorch_dump_folder_path is not None:
    processor.save_pretrained(snake_case )
    hf_model.save_pretrained(snake_case )

  if push_to_hub:
    processor.push_to_hub(F'''nielsr/{model_name}''' )
    hf_model.push_to_hub(F'''nielsr/{model_name}''' )


if __name__ == "__main__":
    A_       						=							argparse.ArgumentParser()
    A_       						=							[
        "blip2-opt-2.7b",
        "blip2-opt-6.7b",
        "blip2-opt-2.7b-coco",
        "blip2-opt-6.7b-coco",
        "blip2-flan-t5-xl",
        "blip2-flan-t5-xl-coco",
        "blip2-flan-t5-xxl",
    ]
    parser.add_argument(
        "--model_name",
        default="blip2-opt-2.7b",
        choices=choices,
        type=str,
        help="Path to hf config.json of model to convert",
    )
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument(
        "--push_to_hub",
        action="store_true",
        help="Whether to push the model and processor to the hub after converting",
    )

    A_       						=							parser.parse_args()

    convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''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()






