# ---------------------------------------------------------
# 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

data_sample = <<ParameterType>>(<<input_sample>>, enforce_shape=False)
input_sample = StandardPythonParameterType({'data': data_sample})

result_sample = <<ParameterType>>(<<output_sample>>, enforce_shape=False)
output_sample = StandardPythonParameterType({'Results': result_sample})

# left an empty sample global params for PBI usage
sample_global_params = StandardPythonParameterType({})

try:
    log_server.enable_telemetry(INSTRUMENTATION_KEY)
    log_server.set_verbosity('INFO')
    logger = logging.getLogger('azureml.automl.core.scoring_script_forecasting_pbi_v1')
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('GlobalParameters', sample_global_params, convert_to_provided_type=False)
@input_schema('Inputs', input_sample)
@output_schema(output_sample)
def run(Inputs):
    y_query = None
    data = Inputs['data']
    if 'y_query' in data.columns:
        y_query = data.pop('y_query').values
    
    quantiles = [0.025, 0.5, 0.975]
    if '_lower_quantile' in data:
        quantiles[0] = data.pop('_lower_quantile')[0]
    if '_upper_quantile' in data:
        quantiles[2] = data.pop('_upper_quantile')[0]

    model.quantiles = quantiles
    pred_quantiles = model.forecast_quantiles(data, y_query)
    pred_quantiles.rename(columns={quantiles[0]: "_lower_quantile_forecast_value", quantiles[2]: "_upper_quantile_forecast_value", 0.5: "_forecast_value"}, inplace=True)

    return {'Results': json.loads(pred_quantiles.to_json(orient='records'))}
