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import numpy as np
from utilities.utilities import is_platform_pg
import plpy


############### Constants used in other deep learning files #########
# Name of columns in model summary table.
CLASS_VALUES_COLNAME = "class_values"
NORMALIZING_CONST_COLNAME = "normalizing_const"
COMPILE_PARAMS_COLNAME = "compile_params"
DEPENDENT_VARNAME_COLNAME = "dependent_varname"
DEPENDENT_VARTYPE_COLNAME = "dependent_vartype"
INDEPENDENT_VARNAME_COLNAME = "independent_varname"
MODEL_ARCH_TABLE_COLNAME = "model_arch_table"
MODEL_ARCH_ID_COLNAME = "model_arch_id"
MODEL_DATA_COLNAME = "model_data"
METRIC_TYPE_COLNAME = "metrics_type"

# Name of independent and dependent colnames in batched table.
# These are readonly variables, do not modify.
# MADLIB-1300 Adding these variables for DL only at this time.
MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL = "dependent_var"
MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL = "independent_var"

## sql variable types
FLOAT32_SQL_TYPE = 'REAL'
SMALLINT_SQL_TYPE = 'SMALLINT'

#####################################################################

# Prepend a dimension to np arrays using expand_dims.
def expand_input_dims(input_data):
    input_data = np_array_float32(input_data)
    input_data = np.expand_dims(input_data, axis=0)
    return input_data

def np_array_float32(var):
    return np.array(var, dtype=np.float32)

def np_array_int16(var):
    return np.array(var, dtype=np.int16)

def strip_trailing_nulls_from_class_values(class_values):
    """
        class_values is a list of unique class levels in training data. This
        could have multiple Nones in it, and this function strips out all the
        Nones that occur after the first element in the list.
        Examples:
            1) input class_values = ['cat', 'dog']
               output class_values = ['cat', 'dog']

            2) input class_values = [None, 'cat', 'dog']
               output class_values = [None, 'cat', 'dog']

            3) input class_values = [None, 'cat', 'dog', None, None]
               output class_values = [None, 'cat', 'dog']

            4) input class_values = ['cat', 'dog', None, None]
               output class_values = ['cat', 'dog']

            5) input class_values = [None, None]
               output class_values = [None]
        @args:
            @param: class_values, list
        @returns:
            updated class_values list
    """
    num_of_valid_class_values = 0
    if class_values is not None:
        for ele in class_values:
            if ele is None and num_of_valid_class_values > 0:
                break
            num_of_valid_class_values += 1
        # Pass only the valid class_values for creating columns
        class_values = class_values[:num_of_valid_class_values]
    return class_values

def get_image_count_per_seg_from_array(current_seg_id, seg_ids, images_per_seg):
    """
    Get the image count from the array containing all the images
    per segment. Based on the platform, we find the index of the current segment.
    This function is only called from inside the transition function.
    """
    if is_platform_pg():
        total_images = images_per_seg[0]
    else:
        total_images = images_per_seg[seg_ids.index(current_seg_id)]
    return total_images

def get_image_count_per_seg_for_minibatched_data_from_db(table_name):
    """
    Query the given minibatch formatted table and return the total rows per segment.
    Since we cannot pass a dictionary to the keras fit step function we create
    arrays out of the segment numbers and the rows per segment values.
    This function assumes that the table is not empty.
    :param table_name:
    :return: Returns two arrays
    1. An array containing all the segment numbers in ascending order
    1. An array containing the total images on each of the segments in the
    segment array.
    """

    mb_dep_var_col = MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL

    if is_platform_pg():
        res = plpy.execute(
            """ SELECT SUM(ARRAY_LENGTH({0}, 1)) AS images_per_seg
                FROM {1}
            """.format(mb_dep_var_col, table_name))
        images_per_seg = [int(res[0]['images_per_seg'])]
        seg_ids = [0]
    else:
        images_per_seg = plpy.execute(
            """ SELECT gp_segment_id, SUM(ARRAY_LENGTH({0}, 1)) AS images_per_seg
                FROM {1}
                GROUP BY gp_segment_id
            """.format(mb_dep_var_col, table_name))
        seg_ids = [int(each_segment["gp_segment_id"])
                   for each_segment in images_per_seg]
        images_per_seg = [int(each_segment["images_per_seg"])
                          for each_segment in images_per_seg]
    return seg_ids, images_per_seg

def get_image_count_per_seg_for_non_minibatched_data_from_db(table_name):
    """
    Query the given non minibatch formatted table and return the total rows per segment.
    Since we cannot pass a dictionary to the keras fit step function we create arrays
    out of the segment numbers and the rows per segment values.
    This function assumes that the table is not empty.
    :param table_name:
    :return: gp segment id col name and two arrays
    1. An array containing all the segment numbers in ascending order
    2. An array containing the total rows for each of the segments in the
    segment array
    """
    if is_platform_pg():
        images_per_seg = plpy.execute(
            """ SELECT count(*) AS images_per_seg
                FROM {0}
            """.format(table_name))
        seg_ids = [0]
        gp_segment_id_col = '0'
    else:
        # Compute total buffers on each segment
        images_per_seg = plpy.execute(
            """ SELECT gp_segment_id, count(*) AS images_per_seg
                FROM {0}
                GROUP BY gp_segment_id
            """.format(table_name))
        seg_ids = [int(image["gp_segment_id"]) for image in images_per_seg]
        gp_segment_id_col = '{0}.gp_segment_id'.format(table_name)

    images_per_seg = [int(image["images_per_seg"]) for image in images_per_seg]
    return gp_segment_id_col, seg_ids, images_per_seg