

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     (      A__			:  List[str]=32			,  A__			:  Dict=10			,  A__			:  int=100			,  A__			:  Optional[int]=1026			,  A__			:  Union[str, Any]=True			,  A__			:  str="data/tokenized_stories_train_wikitext103.jbl"			,  A__			:  Any="igf_context_pairs.jbl"			,  ):
    set_seed(3       )
    # generate train_data and objective_set
    SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   generate_datasets(
        A__			,  A__			,  number=A__			,  min_len=1026			,  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?
    SCREAMING_SNAKE_CASE										=   torch.device("cuda:0" if torch.cuda.is_available() else "cpu"       )

    # load pretrained model
    SCREAMING_SNAKE_CASE										=   load_gpta("gpt2"       ).to(A__       )
    print("computing perplexity on objective set"       )
    SCREAMING_SNAKE_CASE										=   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 	__a     (      A__			:  str			,  A__			:  int=15			,  A__			:  Dict=128			,  A__			:  Dict=100			,  A__			:  List[str]="igf_model.pt"			,  ):
    set_seed(42       )

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

    # Initialize secondary learner to use embedding weights of model
    SCREAMING_SNAKE_CASE										=   SecondaryLearner(A__       )

    # Train secondary learner
    SCREAMING_SNAKE_CASE										=   train_secondary_learner(
        A__			,  A__			,  max_epochs=A__			,  batch_size=A__			,  eval_freq=100			,  igf_model_path=A__			,  )

    del model, secondary_learner_train_data
    torch.cuda.empty_cache()

    return secondary_learner





def 	__a     (      A__			:  Tuple			,  A__			:  Any			,  A__			:  Optional[int]			,  A__			:  Union[str, Any]=32			,  A__			:  Any=1000			,  A__			:  List[Any]=16			,  A__			:  Tuple=1.0			,  A__			:  Union[str, Any]=recopy_gpta			,  A__			:  Optional[int]=None			,  A__			:  Optional[Any]=10			,  A__			:  Tuple="gpt2_finetuned.pt"			,  ):
    SCREAMING_SNAKE_CASE										=   torch.device("cuda:0" if torch.cuda.is_available() else "cpu"       )
    SCREAMING_SNAKE_CASE										=   RandomSampler(A__       )
    SCREAMING_SNAKE_CASE										=   DataLoader(A__			,  sampler=A__       )

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

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

    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE										=   []

    # Compute the performance of the transformer model at the beginning
    SCREAMING_SNAKE_CASE										=   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()
            SCREAMING_SNAKE_CASE										=   random.randint(0			,  example.size(2       ) - context_len - 1       )
            SCREAMING_SNAKE_CASE										=   example[0, 0, start : start + context_len]
            lm_optimizer.zero_grad()
            SCREAMING_SNAKE_CASE										=   model(A__			,  labels=A__       )
            SCREAMING_SNAKE_CASE										=   True

            if secondary_learner is not None:
                SCREAMING_SNAKE_CASE										=   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:
                    SCREAMING_SNAKE_CASE										=   -1
                if predicted_q < threshold:
                    SCREAMING_SNAKE_CASE										=   False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()       )       )
                SCREAMING_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()
                SCREAMING_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:
                    SCREAMING_SNAKE_CASE										=   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 	__a     (      ):
    SCREAMING_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=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=100			,  type=A__			,  help="number of articles that are long enough to be used as our objective set"			,  )
    parser.add_argument(
        "--eval_freq"			,  default=100			,  type=A__			,  help="secondary model evaluation is triggered at eval_freq"       )

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

    parser.add_argument(
        "--secondary_learner_batch_size"			,  default=128			,  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=100			,  type=A__			,  help="The number of examples split to be used as objective_set/test_data"       )

    parser.add_argument(
        "--min_len"			,  default=1026			,  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=100			,  min_len=1026			,  trim=A__			,  data_file="data/tokenized_stories_train_wikitext103.jbl"			,  igf_data_file="igf_context_pairs.jbl"			,  )

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

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

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

    # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
    finetune(
        A__			,  A__			,  A__			,  context_len=32			,  max_steps=1000			,  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.abc import Callable

import numpy as np





def 	__a     (      A__			:  Callable			,  A__			:  float			,  A__			:  float			,  A__			:  float			,  A__			:  float       ):
    SCREAMING_SNAKE_CASE										=   int(np.ceil((x_end - xa) / step_size       )       )
    SCREAMING_SNAKE_CASE										=   np.zeros((n + 1,)       )
    SCREAMING_SNAKE_CASE										=   ya
    SCREAMING_SNAKE_CASE										=   xa

    for k in range(A__       ):
        SCREAMING_SNAKE_CASE										=   y[k] + step_size * ode_func(A__			,  y[k]       )
        SCREAMING_SNAKE_CASE										=   y[k] + (
            (step_size / 2) * (ode_func(A__			,  y[k]       ) + ode_func(x + step_size			,  A__       ))
        )
        x += step_size

    return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()