


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        UpperCamelCase_(				_A   :int=32				,     _A   :Dict=10				,     _A   :Optional[int]=1_00				,     _A   :Optional[Any]=10_26				,     _A   :Any=True				,     _A   :Optional[Any]="data/tokenized_stories_train_wikitext103.jbl"				,     _A   :Tuple="igf_context_pairs.jbl"				,     )->		Union[str, Any]:
   set_seed(3  )
   # generate train_data and objective_set
   UpperCamelCase__,      UpperCamelCase__					=       generate_datasets(
       _A				,     _A				,     number=_A				,     min_len=10_26				,     trim=_A  )
   # 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?
   UpperCamelCase__					=       torch.device("cuda:0" if torch.cuda.is_available() else "cpu"  )

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

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

   # clean up, delete model and data we don't need anymore
   del model, train_data, objective_set
   torch.cuda.empty_cache()
def        UpperCamelCase_(				_A   :Optional[Any]				,     _A   :List[Any]=15				,     _A   :Union[str, Any]=1_28				,     _A   :int=1_00				,     _A   :Union[str, Any]="igf_model.pt"				,     )->		Optional[Any]:
   set_seed(42  )

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

   # Initialize secondary learner to use embedding weights of model
   UpperCamelCase__					=       SecondaryLearner(_A  )

   # Train secondary learner
   UpperCamelCase__					=       train_secondary_learner(
       _A				,     _A				,     max_epochs=_A				,     batch_size=_A				,     eval_freq=1_00				,     igf_model_path=_A				,     )

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner
def        UpperCamelCase_(				_A   :Any				,     _A   :List[Any]				,     _A   :Dict				,     _A   :List[str]=32				,     _A   :int=10_00				,     _A   :Optional[int]=16				,     _A   :Dict=1.0				,     _A   :List[Any]=recopy_gpta				,     _A   :List[str]=None				,     _A   :Any=10				,     _A   :Optional[int]="gpt2_finetuned.pt"				,     )->		Dict:
   UpperCamelCase__					=       torch.device("cuda:0" if torch.cuda.is_available() else "cpu"  )
   UpperCamelCase__					=       RandomSampler(_A  )
   UpperCamelCase__					=       DataLoader(_A				,     sampler=_A  )

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

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

   UpperCamelCase__					=       []
   UpperCamelCase__					=       []

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

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

            # 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:
               UpperCamelCase__					=       -1
            if predicted_q < threshold:
               UpperCamelCase__					=       False

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

               print("Test perplexity, step"				,     _A				,     ":"				,     _A  )
            # 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()				,     _A  )
   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        UpperCamelCase_(				)->		Optional[Any]:
   UpperCamelCase__					=       argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"  )

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

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

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

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

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

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

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

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

   parser.add_argument(
       "--min_len"				,     default=10_26				,     type=_A				,     help="The minimum length of the article to be used as objective set"  )

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

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

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

   parser.add_argument(
       "--recopy_model"				,     default=_A				,     type=_A				,     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=_A				,     data_file="data/tokenized_stories_train_wikitext103.jbl"				,     igf_data_file="igf_context_pairs.jbl"				,     )

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

   # Train secondary learner
   UpperCamelCase__					=       training_secondary_learner(
       _A				,     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
   UpperCamelCase__					=       GPTaLMHeadModel.from_pretrained("gpt2"  )
   set_seed(42  )

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

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


if __name__ == "__main__":
      main()






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Any    =		'ibert'




			def __init__(							self      ,						snake_case=30522      ,						snake_case=768      ,						snake_case=12      ,						snake_case=12      ,						snake_case=3072      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=512      ,						snake_case=2      ,						snake_case=0.02      ,						snake_case=1E-1_2      ,						snake_case=1      ,						snake_case=0      ,						snake_case=2      ,						snake_case="absolute"      ,						snake_case=False      ,						snake_case="none"      ,						**snake_case      ,						):

						'''simple docstring'''


						super().__init__(pad_token_id=snake_case      ,						bos_token_id=snake_case      ,						eos_token_id=snake_case      ,						**snake_case     )

						UpperCamelCase__					=       vocab_size
						UpperCamelCase__					=       hidden_size
						UpperCamelCase__					=       num_hidden_layers
						UpperCamelCase__					=       num_attention_heads
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       intermediate_size
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       max_position_embeddings
						UpperCamelCase__					=       type_vocab_size
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       layer_norm_eps
						UpperCamelCase__					=       position_embedding_type
						UpperCamelCase__					=       quant_mode
						UpperCamelCase__					=       force_dequant







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if self.task == "multiple-choice":
									UpperCamelCase__					=       {0: "batch", 1: "choice", 2: "sequence"}
						else:
									UpperCamelCase__					=       {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]     )


