import torch
import time
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from multiprocessing import cpu_count

from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    squad_convert_examples_to_features
)

from transformers.data.processors.squad import SquadResult, SquadV2Processor, SquadExample
from transformers.data.metrics.squad_metrics import compute_predictions_logits

def run_prediction(question_texts, context_text, model_path, n_best_size=1):
    ### Setting hyperparameters
    max_seq_length = 512
    doc_stride = 256
    n_best_size = n_best_size
    max_query_length = 64
    max_answer_length = 512
    do_lower_case = False
    null_score_diff_threshold = 0.0

    def to_list(tensor):
        return tensor.detach().cpu().tolist()

    config_class, model_class, tokenizer_class = (AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer)
    config = config_class.from_pretrained(model_path)
    tokenizer = tokenizer_class.from_pretrained(model_path, do_lower_case=True, use_fast=False)
    model = model_class.from_pretrained(model_path, config=config)

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)

    processor = SquadV2Processor()
    examples = []

    timer = time.time()
    for i, question_text in enumerate(question_texts):
        
        example = SquadExample(
            qas_id=str(i),
            question_text=question_text,
            context_text=context_text,
            answer_text=None,
            start_position_character=None,
            title="Predict",
            answers=None,
        )

        examples.append(example)
    print(f'Created Squad Examples in {time.time()-timer} seconds')

    print(f'Number of CPUs: {cpu_count()}')
    timer = time.time()
    features, dataset = squad_convert_examples_to_features(
        examples=examples,
        tokenizer=tokenizer,
        max_seq_length=max_seq_length,
        doc_stride=doc_stride,
        max_query_length=max_query_length,
        is_training=False,
        return_dataset="pt",
        threads=cpu_count(),
    )
    print(f'Converted Examples to Features in {time.time()-timer} seconds')

    eval_sampler = SequentialSampler(dataset)
    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10)

    all_results = []

    timer = time.time()
    for batch in eval_dataloader:
        model.eval()
        batch = tuple(t.to(device) for t in batch)

        with torch.no_grad():
            inputs = {
                "input_ids": batch[0],
                "attention_mask": batch[1],
                "token_type_ids": batch[2],
            }

            example_indices = batch[3]

            outputs = model(**inputs)

            for i, example_index in enumerate(example_indices):
                eval_feature = features[example_index.item()]
                unique_id = int(eval_feature.unique_id)

                output = [to_list(output[i]) for output in outputs.to_tuple()]

                start_logits, end_logits = output
                result = SquadResult(unique_id, start_logits, end_logits)
                all_results.append(result)
    print(f'Model predictions completed in {time.time()-timer} seconds') 

    print(all_results)

    output_nbest_file = None
    if n_best_size > 1:
        output_nbest_file = "nbest.json"

    timer = time.time()
    final_predictions = compute_predictions_logits(
        all_examples=examples,
        all_features=features,
        all_results=all_results,
        n_best_size=n_best_size,
        max_answer_length=max_answer_length,
        do_lower_case=do_lower_case,
        output_prediction_file=None,
        output_nbest_file=output_nbest_file,
        output_null_log_odds_file=None,
        verbose_logging=False,
        version_2_with_negative=True,
        null_score_diff_threshold=null_score_diff_threshold,
        tokenizer=tokenizer
    )
    print(f'Logits converted to predictions in {time.time()-timer} seconds')

    return final_predictions