# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import logging
import os
import pickle
import numpy as np
import pandas as pd
import joblib

import azureml.automl.core
from azureml.automl.core.shared import logging_utilities, log_server
from azureml.telemetry import INSTRUMENTATION_KEY

from inference_schema.schema_decorators import input_schema, output_schema
from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
from inference_schema.parameter_types.pandas_parameter_type import PandasParameterType
from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType

input_sample = <<input_sample>>
quantiles_sample = StandardPythonParameterType([0.025, 0.975])

try:
    log_server.enable_telemetry(INSTRUMENTATION_KEY)
    log_server.set_verbosity('INFO')
    logger = logging.getLogger('azureml.automl.core.scoring_script_forecasting')
except Exception:
    pass


def init():
    global model
    # This name is model.id of model that we want to deploy deserialize the model file back
    # into a sklearn model
    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), '<<model_filename>>')
    path = os.path.normpath(model_path)
    path_split = path.split(os.sep)
    log_server.update_custom_dimensions({'model_name': path_split[-3], 'model_version': path_split[-2]})
    try:
        logger.info("Loading model from path.")
        model = joblib.load(model_path)
        logger.info("Loading successful.")
    except Exception as e:
        logging_utilities.log_traceback(e, logger)
        raise

@input_schema('quantiles', quantiles_sample, convert_to_provided_type=False)
@input_schema('data', <<ParameterType>>(input_sample, enforce_shape=False))
def run(data, quantiles=[0.025, 0.975]):
    try:
        y_query = None
        if 'y_query' in data.columns:
            y_query = data.pop('y_query').values
        quantiles = [min(quantiles), 0.5, max(quantiles)]
        PI = 'prediction_interval'
        model.quantiles = quantiles
        pred_quantiles = model.forecast_quantiles(data, y_query)
        pred_quantiles[PI] = pred_quantiles[[min(quantiles), max(quantiles)]].apply(lambda x: '[{}, {}]'.format(x[0], x[1]), axis=1)
    except Exception as e:
        result = str(e)
        return json.dumps({"error": result})

    index_as_df = pred_quantiles.iloc[:,:-4].reset_index(drop=True)# get time column name and grain column name
    forecast_as_list = pred_quantiles[0.5].to_list()
    PI_as_list = pred_quantiles[PI].to_list()
    
    return json.dumps({"forecast": forecast_as_list,   # return the minimum over the wire: 
                       "prediction_interval": PI_as_list,
                       "index": json.loads(index_as_df.to_json(orient='records'))  # no forecast and its featurized values
                      })
