





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_snake_case           =			"""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

_snake_case           =			"""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

_snake_case           =			"""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""


def _A       (   __magic_name__  , __magic_name__	):
   return float((preds == labels).mean()	)


def _A       (   __magic_name__  , __magic_name__  , __magic_name__="binary"	):
   lowercase__			       =  simple_accuracy(__magic_name__  , __magic_name__	)
   lowercase__			       =  float(fa_score(y_true=__magic_name__  , y_pred=__magic_name__  , average=__magic_name__	)	)
   return {
       "accuracy": acc,
       "f1": fa,
   }


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  {}
   for id_pred, label in zip(__magic_name__  , __magic_name__	):
      lowercase__			       =  f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
      lowercase__			       =  id_pred["prediction"]
      if question_id in question_map:
         question_map[question_id].append((pred, label)	)
      else:
         lowercase__			       =  [(pred, label)]
   lowercase__       ,							lowercase__			       =  [], []
   for question, preds_labels in question_map.items():
      lowercase__       ,							lowercase__			       =  zip(*__magic_name__	)
      lowercase__			       =  fa_score(y_true=__magic_name__  , y_pred=__magic_name__  , average="macro"	)
      fas.append(__magic_name__	)
      lowercase__			       =  int(sum(pred == label for pred, label in preds_labels	) == len(__magic_name__	)	)
      ems.append(__magic_name__	)
   lowercase__			       =  float(sum(__magic_name__	) / len(__magic_name__	)	)
   lowercase__			       =  sum(__magic_name__	) / len(__magic_name__	)
   lowercase__			       =  float(fa_score(y_true=__magic_name__  , y_pred=[id_pred["prediction"] for id_pred in ids_preds]	)	)
   return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}







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







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     if self.config_name not in [
         "boolq",
         "cb",
         "copa",
         "multirc",
         "record",
         "rte",
         "wic",
         "wsc",
         "wsc.fixed",
         "axb",
         "axg",
     ]:
        raise KeyError(
            "You should supply a configuration name selected in "
            "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
     return datasets.MetricInfo(
         description=_DESCRIPTION   ,							citation=_CITATION   ,							inputs_description=_KWARGS_DESCRIPTION   ,							features=datasets.Features(self._get_feature_types() )   ,							codebase_urls=[]   ,							reference_urls=[]   ,							format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None   ,							)







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     if self.config_name == "record":
        return {
            "predictions": {
                "idx": {
                    "passage": datasets.Value("int64" ),
                    "query": datasets.Value("int64" ),
                },
                "prediction_text": datasets.Value("string" ),
            },
            "references": {
                "idx": {
                    "passage": datasets.Value("int64" ),
                    "query": datasets.Value("int64" ),
                },
                "answers": datasets.Sequence(datasets.Value("string" ) ),
            },
        }
     elif self.config_name == "multirc":
        return {
            "predictions": {
                "idx": {
                    "answer": datasets.Value("int64" ),
                    "paragraph": datasets.Value("int64" ),
                    "question": datasets.Value("int64" ),
                },
                "prediction": datasets.Value("int64" ),
            },
            "references": datasets.Value("int64" ),
        }
     else:
        return {
            "predictions": datasets.Value("int64" ),
            "references": datasets.Value("int64" ),
        }







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :List[Any]   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





     if self.config_name == "axb":
        return {"matthews_correlation": matthews_corrcoef(_lowercase   ,							_lowercase )}
     elif self.config_name == "cb":
        return acc_and_fa(_lowercase   ,							_lowercase   ,							fa_avg="macro" )
     elif self.config_name == "record":
        lowercase__			       =  [
            {
                "qas": [
                    {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                    for ref in references
                ]
            }
        ]
        lowercase__			       =  {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
        return evaluate_record(_lowercase   ,							_lowercase )[0]
     elif self.config_name == "multirc":
        return evaluate_multirc(_lowercase   ,							_lowercase )
     elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
        return {"accuracy": simple_accuracy(_lowercase   ,							_lowercase )}
     else:
        raise KeyError(
            "You should supply a configuration name selected in "
            "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )













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       (   __magic_name__=32  , __magic_name__=10  , __magic_name__=100  , __magic_name__=1026  , __magic_name__=True  , __magic_name__="data/tokenized_stories_train_wikitext103.jbl"  , __magic_name__="igf_context_pairs.jbl"  , ):
   set_seed(3	)
   # generate train_data and objective_set
   lowercase__       ,							lowercase__			       =  generate_datasets(
       __magic_name__  , __magic_name__  , number=__magic_name__  , min_len=1026  , trim=__magic_name__	)
   # 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?
   lowercase__			       =  torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)

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

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

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


def _A       (   __magic_name__  , __magic_name__=15  , __magic_name__=128  , __magic_name__=100  , __magic_name__="igf_model.pt"  , ):
   set_seed(42	)

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

   # Initialize secondary learner to use embedding weights of model
   lowercase__			       =  SecondaryLearner(__magic_name__	)

   # Train secondary learner
   lowercase__			       =  train_secondary_learner(
       __magic_name__  , __magic_name__  , max_epochs=__magic_name__  , batch_size=__magic_name__  , eval_freq=100  , igf_model_path=__magic_name__  , )

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner


def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__=32  , __magic_name__=1000  , __magic_name__=16  , __magic_name__=1.0  , __magic_name__=recopy_gpta  , __magic_name__=None  , __magic_name__=10  , __magic_name__="gpt2_finetuned.pt"  , ):
   lowercase__			       =  torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)
   lowercase__			       =  RandomSampler(__magic_name__	)
   lowercase__			       =  DataLoader(__magic_name__  , sampler=__magic_name__	)

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

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

   lowercase__			       =  []
   lowercase__			       =  []

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

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

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

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

               print("Test perplexity, step"  , __magic_name__  , ":"  , __magic_name__	)
            # 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()  , __magic_name__	)
   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       (   ):
   lowercase__			       =  argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"	)

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

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

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

   parser.add_argument(
       "--context_len"  , default=32  , type=__magic_name__  , 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=__magic_name__  , help="number of articles that are long enough to be used as our objective set"  , )
   parser.add_argument(
       "--eval_freq"  , default=100  , type=__magic_name__  , help="secondary model evaluation is triggered at eval_freq"	)

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

   parser.add_argument(
       "--secondary_learner_batch_size"  , default=128  , type=__magic_name__  , help="batch size of training data for secondary learner"  , )

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

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

   parser.add_argument(
       "--min_len"  , default=1026  , type=__magic_name__  , help="The minimum length of the article to be used as objective set"	)

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

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

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

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

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

   # Train secondary learner
   lowercase__			       =  training_secondary_learner(
       __magic_name__  , secondary_learner_max_epochs=15  , secondary_learner_batch_size=128  , eval_freq=100  , igf_model_path="igf_model.pt"  , )

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

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

   # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
   finetune(
       __magic_name__  , __magic_name__  , __magic_name__  , context_len=32  , max_steps=1000  , batch_size=16  , threshold=1.0  , recopy_model=__magic_name__  , secondary_learner=__magic_name__  , eval_interval=10  , finetuned_model_name="gpt2_finetuned.pt"  , )


if __name__ == "__main__":
  main()






