import json
import torch
import os
from transformers import AutoTokenizer, DistilBertForQuestionAnswering,DistilBertTokenizer

def input_handler(data, context):
    """ Pre-process request input before it is sent to TensorFlow Serving REST API
    Args:
        data (obj): the request data, in format of dict or string
        context (Context): an object containing request and configuration details
    Returns:
        (dict): a JSON-serializable dict that contains request body and headers
    """
    if context.request_content_type == 'application/json':
        # pass through json (assumes it's correctly formed)
        d = data.read().decode('utf-8')
        return d if len(d) else ''

    if context.request_content_type == 'text/csv':
        # very simple csv handler
        return json.dumps({
            'instances': [float(x) for x in data.read().decode('utf-8').split(',')]
        })

    raise ValueError('{{"error": "unsupported content type {}"}}'.format(
        context.request_content_type or "unknown"))


def output_handler(data, context):
    """Post-process TensorFlow Serving output before it is returned to the client.
    Args:
        data (obj): the TensorFlow serving response
        context (Context): an object containing request and configuration details
    Returns:
        (bytes, string): data to return to client, response content type
    """
    if data.status_code != 200:
        raise ValueError(data.content.decode('utf-8'))

    response_content_type = context.accept_header
    prediction = data.content
    return prediction, response_content_type

def model_fn(model_dir):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model_name = 'distilbert-base-uncased-distilled-squad'
    nlpmodel = DistilBertForQuestionAnswering.from_pretrained(model_name)
    tokenizer = DistilBertTokenizer.from_pretrained(model_name)    
    nlpmodel.to(device)
    model = {'model':nlpmodel, 'tokenizer':tokenizer}
    return model


#    model_name = 'distilbert-base-uncased-distilled-squad'
#    model=DistilBertForQuestionAnswering.from_pretrained(model_name)
#    with open(os.path.join(model_dir, 'pytorch_model.bin'), 'rb') as f:
#        model.load_state_dict(torch.load(f))
#    return model