import os
import math
import json

from loguru import logger
import tensorflow as tf

from lib.dataset.read_metadata import read_metadata
from ..io.readfile import readfile
from .shuffle import shuffle


# TO PARSE:
def parse_item(metadata, shape_water_desired, water_threshold=0.1):
	water_width_source, water_height_source, _water_channels_source = metadata["waterdepth"]
	water_width_target, water_height_target = shape_water_desired
	water_offset_x = math.ceil((water_width_source - water_width_target) / 2)
	water_offset_y = math.ceil((water_height_source - water_height_target) / 2)
	def parse_item_inner(item):
		parsed = tf.io.parse_single_example(item, features={
			"rainfallradar": tf.io.FixedLenFeature([], tf.string),
			"waterdepth": tf.io.FixedLenFeature([], tf.string)
		})
		rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32)
		water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32)
		
		rainfall = tf.reshape(rainfall, tf.constant(metadata["rainfallradar"], dtype=tf.int32))
		water = tf.reshape(water, tf.constant(metadata["waterdepth"], dtype=tf.int32))
		
		# SHAPES:
		# rainfall = [ feature_dim ]
		# water = [ width, height, 1 ]
		
		water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.int32)
		
		water = tf.image.crop_to_bounding_box(water, water_offset_x, water_offset_y, water_width_target, water_height_target)
		
		print("DEBUG:dataset ITEM rainfall:shape", rainfall.shape, "water:shape", water.shape)
		
		# TODO: Add any other additional parsing here, since multiple .map() calls are not optimal
		
		return rainfall, water
	
	return tf.function(parse_item_inner)

def make_dataset(filepaths, metadata, shape_water_desired=[100,100], water_threshold=0.1, compression_type="GZIP", parallel_reads_multiplier=1.5, shuffle_buffer_size=128, batch_size=64, prefetch=True, shuffle=True):
	if "NO_PREFETCH" in os.environ:
		logger.info("disabling data prefetching.")
	
	dataset = tf.data.TFRecordDataset(filepaths,
		compression_type=compression_type,
		num_parallel_reads=math.ceil(os.cpu_count() * parallel_reads_multiplier) if parallel_reads_multiplier > 0 else None
	)
	if shuffle:
		dataset = dataset.shuffle(shuffle_buffer_size)
	dataset = dataset.map(parse_item(metadata, shape_water_desired=shape_water_desired, water_threshold=water_threshold), num_parallel_calls=tf.data.AUTOTUNE)
		
	if batch_size != None:
		dataset = dataset.batch(batch_size, drop_remainder=True)
	if prefetch:
		dataset = dataset.prefetch(0 if "NO_PREFETCH" in os.environ else tf.data.AUTOTUNE)
	
	return dataset


def get_filepaths(dirpath_input, do_shuffle=True):
	result = list(filter(
		lambda filepath: str(filepath).endswith(".tfrecord.gz"),
		[ file.path for file in os.scandir(dirpath_input) ] # .path on a DirEntry object yields the absolute filepath
	))
	if do_shuffle:
		result = shuffle(result)
	else:
		result = sorted(result, key=lambda filepath: int(os.path.basename(filepath).split(".", 1)[0]))
	return result

def dataset_segmenter(dirpath_input, batch_size=64, train_percentage=0.8, parallel_reads_multiplier=1.5, water_threshold=0.1, shape_water_desired=[100,100]):
	filepaths = get_filepaths(dirpath_input)
	filepaths_count = len(filepaths)
	dataset_splitpoint = math.floor(filepaths_count * train_percentage)
	
	filepaths_train = filepaths[:dataset_splitpoint]
	filepaths_validate = filepaths[dataset_splitpoint:]
	
	metadata = read_metadata(dirpath_input)
	
	dataset_train = make_dataset(filepaths_train, metadata, batch_size=batch_size, parallel_reads_multiplier=parallel_reads_multiplier, water_threshold=water_threshold, shape_water_desired=shape_water_desired)
	dataset_validate = make_dataset(filepaths_validate, metadata, batch_size=batch_size, parallel_reads_multiplier=parallel_reads_multiplier, water_threshold=water_threshold, shape_water_desired=shape_water_desired)
	
	return dataset_train, dataset_validate #, filepaths

def dataset_predict(dirpath_input, parallel_reads_multiplier=0, prefetch=True, water_threshold=0.1, shape_water_desired=[100,100]):
	"""Creates a tf.data.Dataset() for prediction using the image segmentation head model.
	Note that this WILL MANGLE THE ORDERING if you set parallel_reads_multiplier to anything other than 0!!
	
	Args:
		dirpath_input (string): The path to the directory containing the input (.tfrecord.gz) files
		parallel_reads_multiplier (float, optional): The number of files to read in parallel. Defaults to 1.5.
		prefetch (bool, optional): Whether to prefetch data into memory or not. Defaults to True.
		water_threshold (float, optional): The water depth threshold to consider cells to contain water, in metres. Defaults to 0.1.

	Returns:
		tf.data.Dataset: A tensorflow Dataset for the given input files.
	"""
	filepaths = get_filepaths(dirpath_input, do_shuffle=False) if os.path.isdir(dirpath_input) else [ dirpath_input ]
	
	return make_dataset(
		filepaths=filepaths,
		metadata=read_metadata(dirpath_input),
		parallel_reads_multiplier=parallel_reads_multiplier,
		batch_size=None,
		prefetch=prefetch,
		shuffle=False, #even with shuffle=False we're not gonna get them all in the same order since we're reading in parallel
		water_threshold=water_threshold,
		shape_water_desired=shape_water_desired
	)

if __name__ == "__main__":
	ds_train, ds_validate = dataset_segmenter("/mnt/research-data/main/PhD-Rainfall-Radar/aimodel/output/rainfallwater_records_embed_d512e19_tfrecord/")
	for thing in ds_validate():
		as_str = str(thing)
		print(thing[:200])