import cma
from multiprocessing import Pool
from os import cpu_count
import time
import path_constant as pc
import packing_penalty as pp
from os import makedirs
from copy import deepcopy

class cma_approach(object):
    def __init__(self,
        # data path
        path_to_datasrc = "alexnet_data.csv",
        path_to_topology = "alexnet.csv",
        target_col = "Cycles",

        # problem definition
        number_of_partition = 4, max_iteration = 100,
        sigma = 0.5, population_size = 10,

        # constraint
        max_res_unit = 960, initial_res = 0,
        res_step = 1,
        penalty_offest = 10000000000,
        seeding_type="optimised",
        hybrid = True,
        print_to_csv = True,
        max_pack_size = 129
        ):
        self.target_col = target_col
        self.start = time.time()
        self.k = number_of_partition
        self.max_iter = max_iteration
        self.sigma = sigma
        self.max_res_unit = max_res_unit
        self.res_step = res_step
        self.population_size = population_size
        self.penalty_offest = penalty_offest
        self.ending_iter = 0
        self.is_hybrid = hybrid
        self.data_src = {}

        self.topology_file = path_to_topology
        self.layers = self.parse_topology_file()
        self.parse_data_set_file(path_to_datasrc)

        self.best_layer = number_of_partition * [0]
        self.best_res = number_of_partition * [0]

        self.total_valid_solution = 0
        self.trial = 1
        self.seeding_type = seeding_type

        self.max_res_available = max_res_unit
        self.print_to_csv = print_to_csv

        self.max_pack_size = max_pack_size

    def parse_topology_file(self):
        layers = []
        with open(pc.TOPOLOGIES_PATH+self.topology_file, 'r') as f:
            next(f)
            for line in f:
                elems = line.strip().split(',')
                layers.append(elems[0])

        for layer in layers:
            self.data_src[layer] = {}
        return layers

    def parse_data_set_file(self, path_to_data_csv):
        first = True
        target_idx = 2
        with open(pc.DATA_SOURCE_PATH+path_to_data_csv, 'r') as f:
            for line in f:
                elems = line.strip().split(',')
                # print(elems)
                if first:
                    for idx, col in enumerate(elems):
                        if self.target_col in col:
                            target_idx = idx
                            break
                    first = False
                else:
                    self.data_src[elems[1]][int(elems[0])] = int(float(elems[target_idx]))

    def regroup_layers(self, sample):
        # #print("DEBUG", sample)
        detail_sample = []
        idx = 0
        for size in sample:
            part = []

            if size == 1:
                part.append(self.layers[idx])
                idx += 1
            else:
                for i in range(0, size):
                    part.append(self.layers[i + idx])
                idx += size

            detail_sample.append(part)

        return detail_sample

    def decode(self, val, max_val):
        return int(val * max_val)

    def encode(self, val, max_val):
        return float(val / max_val)

    def filter_layer(self, layer):
        for idx in range(self.k):
            if layer[idx] <= 0:
                return False

        if sum(layer) != len(self.layers):
            return False

        return True

    def filter_res(self, res):
        # #print(layer, res)
        for idx in range(self.k):
            if res[idx] <= 0:
                return False

        if sum(res) != self.max_res_unit:
            return False

        return True

    def penalty_layer(self, layer):
        penalty_score = self.penalty_offest

        if sum(layer) != len(self.layers):
            penalty_score += self.penalty_offest
        else:
            layer = [abs(val) for val in layer]

        for idx in range(self.k):
            if layer[idx] <= 0:
                penalty_score *= 1.05

        percent_diff = (abs(sum(layer) - len(self.layers)) / len(self.layers))
        penalty_score += percent_diff * self.penalty_offest

        return penalty_score

    def find_max_latency(self, layer_partition, res_partitions):
        latencies = [0] * len(layer_partition)
        max_latency_idx = 0
        # print(layer_partition)
        # print(res_partitions)
        for idx, part in enumerate(layer_partition):
            res = res_partitions[idx]
            for layer in part:
                latencies[idx] += self.data_src[layer][res]
            if latencies[idx] > latencies[max_latency_idx]:
                max_latency_idx = idx

        return latencies, max_latency_idx

    """
    Decide partition sizes and evaluate the soln.  Should always return a 
    packable solution.
    """
    def eva_hybrid_sq(self, layer):
        # res is a list where each element corresponds to a partition.  res_step
        # is the minimum amount by which partition edge length increases.
        res = [self.res_step] * self.k
        latencies = []

        # max_res_unit = 1920*9*1 from sq_approach_faster
        variable_max_res_unit = self.max_res_unit
        
        # Do a binary search to find the largest packable variable_max_res_unit.
        search_upper_bound = self.max_res_unit
        search_lower_bound = sum([r*r for r in res])

        last_packable_res = []
        last_packable_max_res_unit = 0
        while search_upper_bound > search_lower_bound:
            variable_max_res_unit = \
                int((search_upper_bound + search_lower_bound)/2)
            limit_reached = False
            while not limit_reached:
                latencies, max_idx = self.find_max_latency(layer, res)
                res[max_idx] += self.res_step
                # If this addition puts the solution over the limit, we need to
                # revert the last partition addition.  TODO write some code to
                # see if we can assign the remaining units.
                if sum([r**2 for r in res]) > variable_max_res_unit:
                    res[max_idx] -= self.res_step
                    limit_reached = True
            if pp.isPackable(res, self.max_pack_size):
                last_packable_res = deepcopy(res)
                last_packable_max_res_unit = variable_max_res_unit
                # The desired max_res_unit value is greater than its current
                # value.
                search_lower_bound = variable_max_res_unit
            else:
                # The desired max_res_unit value is less than its current 
                # value.
                search_upper_bound = variable_max_res_unit

        # Calculate latencies of final solution.
        latencies, max_idx = self.find_max_latency(layer, last_packable_res)

        # TODO we want to penalize based on how much we had to decrease
        # variable_max_res_unit.
        max_res_unit_decrease = self.max_res_unit - variable_max_res_unit
        packing_penalty = pp.calculatePackingPenalty(max_res_unit_decrease)
        return latencies[max_idx] + packing_penalty, latencies, last_packable_res, layer

    def evaluation_top_level(self, in_val):
        pid, sampling = in_val
        layer = [self.decode(val, len(self.layers)) for val in sampling]
        layer.append(len(self.layers) - sum(layer))
        penalty = 0

        if not self.filter_layer(layer):
            penalty = self.penalty_layer(layer)
            if self.is_hybrid:
                return pid, penalty
            else:
                return pid, penalty*4

        # regroup_layers assigns layers to the partitions.  Returns a list of
        # partition lists which contain layers.
        layer = self.regroup_layers(layer)

        return pid, self.eva_hybrid_sq(layer)[0]

    def run(self):
        self.trial += self.trial
        if (self.seeding_type=="allzeros"):
            self.seed = [0]*(self.k-1)
            self.seed_od = self.seed
        elif (self.seeding_type=="optimised"):
            self.seed = []
            for i in range(self.k - 1):
                self.seed.append(int(len(self.layers)/self.k))
            self.seed.append(len(self.layers) - sum(self.seed))
            self.seed_od = self.seed
            self.seed = [self.encode(val, len(self.layers)) for val in self.seed[:-1]]
        else:
            raise ValueError('Invalid Seeding Strategy')

        self.es = cma.CMAEvolutionStrategy(self.seed, self.sigma, \
            {'popsize' : self.population_size})

        best_overall = self.penalty_offest
        self.i = 0
        temp_out = []
        while not self.es.stop() and self.i < self.max_iter:
            samples = self.es.ask()
            id_list = [(idx, sample) for idx, sample in enumerate(samples)]
            scores = [0] * self.es.popsize
            invalid_sampling = 0

            res_combintaions = [0] * self.es.popsize

            # pool = Pool(processes = cpu_count() - 4)
            # for result in pool.imap_unordered(self.evaluation_top_level, id_list):
            #     scores[result[0]] = result[1]
            #     if result[1] >= self.penalty_offest:
            #         invalid_sampling += 1
            #     else:
            #         if not self.is_hybrid:
            #             res_combintaions[result[0]] = result[2]
            # pool.close()
            # pool.join()

            for tup in id_list:
                _, scores[tup[0]] = self.evaluation_top_level(tup)
                if scores[tup[0]] >= self.penalty_offest:
                    invalid_sampling += 1

            if not self.is_hybrid:
                best_in_iteration = min(scores)

                if best_in_iteration < best_overall and best_in_iteration < self.penalty_offest:
                    best_overall = best_in_iteration
                    self.best_res = res_combintaions[scores.index(min(scores))]

            ##print(str(self.i) + ":", \
            #    "Sigma:",round(self.es.sigma, 4), \
            #    "|| Valid sampling percentage:", \
            #        (self.population_size - invalid_sampling) /self.population_size *100)
            ##print("invalid sampling", invalid_sampling)
            self.valid_sampling_percentage = (self.population_size - invalid_sampling) /self.population_size *100
            self.total_valid_solution += self.population_size - invalid_sampling
            self.samples = samples
            self.scores = scores
            self.es.tell(samples, scores)

            self.end = time.time()
            self.best_layer = [self.decode(val, len(self.layers)) for val in self.es.result[0]]
            self.best_layer.append(len(self.layers) - sum(self.best_layer))
            temp_out.append(self.report(False)[1])
            self.i += 1

        self.ending_iter = self.i
        return temp_out

    def report(self, output_png):
        ##print(self.i, self.es.sigma)
        max_latency = 0
        layer = []
        res = []
        latencies = []
        if not self.filter_layer(self.best_layer):
            ##print("RESULT NOT VALID")
            ##print("Layer:", self.best_layer, "sum: ", sum(self.best_layer))
            #print(self.penalty_layer(self.best_layer))
            if self.print_to_csv:
                with open(pc.RESULT_CSV_PATH+'cma_logmore_sq.csv', 'a') as csvFile:
                    writer = csv.writer(csvFile, delimiter=',', lineterminator="\n")
                    writer.writerow([self.target_col,self.i,self.k, self.topology_file, 0, 0, 0, 0, 0, 0, 0, layer, res, self.end-self.start, self.es.sigma, self.seed_od,self.valid_sampling_percentage, self.trial, self.population_size, self.max_res_unit, self.seeding_type])
                csvFile.close

            result = [self.target_col,self.i,self.k, self.topology_file, 0, 0, 0, 0, 0, 0, 0, layer, res, self.end-self.start, self.es.sigma, self.seed_od,self.valid_sampling_percentage, self.trial, self.population_size, self.max_res_unit, self.seeding_type]

            return False, result

        layer = self.regroup_layers(self.best_layer)
        max_latency, latencies, res, _ = self.eva_hybrid_sq(layer)

        # generate data for mapping the full array (129 * 129)
        full_latency, full_max_idx = self.find_max_latency([self.layers], [129]*len(self.layers))

        # PLEASE UNCOMMENT OUT THIS PART IF YOU NOT USING THE BASH SCRIPT WE HAVE PROVIDED 
        # print("================================= RESULT =================================")
        # print("Solution: (out of", self.total_valid_solution, "solutions)")
        # print(layer)
        # print("Res mapping:")
        # print(res)
        # print("Latency for each partition: ")
        # print(latencies)
        # print("Final Latency:", max_latency*self.k, "|| Throught put:", 1/max_latency)
        # print("==========================================================================")
        # print("Map to full array (", self.max_res_unit, ")")
        # print("Final Latency:", full_latency[full_max_idx], "|| Throught put:", 1/full_latency[full_max_idx])
        # print("==========================================================================")
        # print("Throughtput Ratio:", (1/max_latency)/(1/full_latency[full_max_idx]))
        # print("Latency increase:", (max_latency*self.k)/full_latency[full_max_idx])

        if self.print_to_csv:
            with open(pc.RESULT_CSV_PATH+'cma_logmore_sq.csv', 'a') as csvFile:
                writer = csv.writer(csvFile, delimiter=',', lineterminator="\n")
                writer.writerow([self.target_col,self.i,self.k, self.topology_file, 1,(1/max_latency), max_latency*self.k, 1/full_latency[full_max_idx], full_latency[full_max_idx], (1/max_latency)/(1/full_latency[full_max_idx]), (max_latency*self.k)/full_latency[full_max_idx], layer, res, self.end-self.start, self.es.sigma, self.seed_od,self.valid_sampling_percentage, self.trial, self.population_size, self.max_res_unit, self.seeding_type])
            csvFile.close
        
        if self.valid_sampling_percentage > 0:
            directory_path = pc.RESULT_SCREENSHOT_PATH + \
                str(self.topology_file.replace(".csv", "")) + "/" + \
                "pack_size_" + str(self.max_pack_size) + "/" + \
                "penalty_constant_" + str(pp.PENALTY_CONSTANT) + "/"
            makedirs(directory_path, exist_ok = True)
            pngFileName = "k=" + str(self.k) + "_max=" + str(self.max_res_unit) \
                + "_latency=" + str(max_latency) + ".png"
            if pp.isPackable(res, self.max_pack_size) and output_png:
                bin_area = self.max_pack_size ** 2
                packed_area = 0
                for rect in res:
                    square_area = rect ** 2
                    packed_area += square_area
                percentage_wasted = 100 * (bin_area - packed_area) / bin_area
                consumed_area = 0
                pp.printPNG(res, self.max_pack_size, directory_path + pngFileName)
        
        result = [self.target_col,self.i,self.k, self.topology_file, 1,(1/max_latency), max_latency*self.k, 1/full_latency[full_max_idx], full_latency[full_max_idx], (1/max_latency)/(1/full_latency[full_max_idx]), (max_latency*self.k)/full_latency[full_max_idx], layer, res, self.end-self.start, self.es.sigma, self.seed_od,self.valid_sampling_percentage, self.trial, self.population_size, self.max_res_unit, self.seeding_type]
        return True, result

if __name__ == "__main__":
    import csv
    import sys

    topology = sys.argv[1]
    k = int(sys.argv[2])
    population_size = int(sys.argv[3])
    max_res_unit = int(sys.argv[4])
    seeding_type = sys.argv[5]
    target_col = sys.argv[6]

    es_hybrid = cma_approach(
        path_to_datasrc = str(topology)+"_square_mem_bound.csv",
        path_to_topology = str(topology)+".csv",
        target_col = str(target_col),

        number_of_partition = k, max_iteration = 10000,
        sigma = 0.5, population_size = population_size,

        max_res_unit = max_res_unit, initial_res = 0,
        res_step = 3,
        penalty_offest = 100000000000,
        seeding_type = seeding_type,
        hybrid = True,
        print_to_csv = True
    )

    trials = 1
    es_hybrid.run()
    while not es_hybrid.report(True) and trials < 20:
        es_hybrid.run()
        trials += 1

    # k += 1
    #print("convergence takes", trials, "trials")