

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()
'''simple docstring'''






from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_UpperCAmelCase    :     Any         =       '''\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
'''

_UpperCAmelCase    :     str         =       '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''

_UpperCAmelCase    :     Union[str, Any]         =       '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,    _KWARGS_DESCRIPTION			)
class lowercase_       (		datasets.Metric			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			MetricInfo:
        return datasets.MetricInfo(
            description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
                {
                    'predictions': datasets.Sequence(datasets.Value('string', id='token'     ), id='sequence'     ),
                    'references': datasets.Sequence(
                        datasets.Sequence(datasets.Value('string', id='token'     ), id='sequence'     ), id='references'     ),
                }     ), )

   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			List[List[List[str]]], UpperCamelCase__      :			List[List[str]], UpperCamelCase__      :			int = 1, UpperCamelCase__      :			int = 4, )  ->			Dict[str, float]:
        return {
            "google_bleu": gleu_score.corpus_gleu(
                list_of_references=UpperCamelCase__, hypotheses=UpperCamelCase__, min_len=UpperCamelCase__, max_len=UpperCamelCase__     )
        }



