from transformers import BertTokenizer
import json


def print_stats(model_substitutes_dict, cap_at_30):
    print("\ncap at 30 set to " + str(cap_at_30))
    evaluation_path = "evaluation/"
    # synonyms_path = "synonyms.json"

    found_substitutes_path = "final_Versions/models/vers2/eval/complete_substitute_pairs_50.json"
    model_name = "final_Versions/models/vers3/output/"

    with open(evaluation_path + "engl_data/substitute_pairs_foodbert_text.json", "r") as whole_json_file:
        engl_list = json.load(whole_json_file)

    engl_dict = {}
    for foo in engl_list:
        if foo[0] in engl_dict.keys():
            engl_dict[foo[0]].append(foo[1])
        else:
            engl_dict[foo[0]] = [foo[1]]

    substitute_sum = 0
    over30 = 0
    for ingred in engl_dict.keys():
        curr_nr = len(engl_dict[ingred])
        if cap_at_30:
            if curr_nr > 30:
                substitute_sum += 30
                over30 += 1
            else:
                substitute_sum += curr_nr
        else:
            if curr_nr > 30:
                over30 += 1
            substitute_sum += curr_nr
    print("english ingredients with over 30 substitutes: " + str(over30))
    print("english nones: " + str(4372-len(engl_dict.keys())))
    print("average amount of substitutes found for english ingredients: " + str(substitute_sum / 4372))

    # with open(found_substitutes_path, "r") as whole_json_file:
    #     model_substitutes_dict = json.load(whole_json_file)[model_name]

    substitute_sum = 0
    over100 = 0
    over1000 = 0
    over30 = 0
    nones = 0

    for ingred in model_substitutes_dict.keys():

        curr_nr = len(model_substitutes_dict[ingred])
        if curr_nr == 0:
            nones += 1
        if curr_nr > 100:
            # print(ingred + ": " + str(curr_nr))
            over100 += 1
        if curr_nr > 1000:
            # print(ingred + ": " + str(curr_nr))
            over1000 += 1
        if cap_at_30:
            if curr_nr > 30:
                substitute_sum += 30
                over30 += 1
            else:
                substitute_sum += curr_nr
        else:
            if curr_nr > 30:
                over30 += 1
            substitute_sum += curr_nr
    # print(str(substitute_sum))
    print("number of ingredients in dataset: " + str(len(model_substitutes_dict.keys())))
    print("number of nones: " + str(nones))
    print("ingredients with over 30 substitutes: " + str(over30))
    print("ingredients with over 100 substitutes: " + str(over100))
    print("ingredients with over 1000 substitutes: " + str(over1000))
    print("average number of substitutes: " + str(substitute_sum / len(model_substitutes_dict.keys())))
    # print(str(len(model_substitutes_dict.keys())))


def main():
    # with open("train_model/vocab/used_ingredients.json", "r") as used_ingredients_file:
    #     used_ingredients = json.load(used_ingredients_file)
    # tokenizer = BertTokenizer(vocab_file='train_model/vocab/vocab.txt', do_lower_case=False, model_max_length=512,
    #                           never_split=used_ingredients)
    #
    # sent = ["Die Paprika schneiden. Dann die Stücke kochen."]
    #
    # batch_encoding = tokenizer.batch_encode_plus(sent, add_special_tokens=True, max_length=512, truncation=True)
    #
    # # Get the input IDs and attention mask in tensor format
    # input_ids = batch_encoding['input_ids']
    # attn_mask = batch_encoding['attention_mask']
    #
    # print(input_ids)
    # print(attn_mask)

    evaluation_path = "evaluation/"
    synonyms_path = "synonyms.json"
    data_path = "data/"

    engl_data_path = evaluation_path + "engl_data/"

    found_substitutes_path = "final_Versions/models/vers2/eval/complete_substitute_pairs_50.json"
    # model_name = "final_Versions/models/vers3/output/"

    with open(found_substitutes_path, "r") as whole_json_file:
        model_substitutes_dict = json.load(whole_json_file)

    with open(data_path + synonyms_path, "r") as whole_json_file:
        synonyms_dict = json.load(whole_json_file)

    category_subs = ["Paprika", "Apfel", "Gouda", "Huhn", "Gans", "Kaninchen", "Kalbfleisch", "Schweinefleisch", "Ente", "Lamm",
                "Pute", "Wildfleisch", "Rindfleisch", "Speck", "Fisch", "Kohl", "Blattsalat", "Schokolade", "Kuvertüre", "Kuchenglasur",
                "Honig", "Sirup", "Joghurt", "Essig", "Traubensaft", "Geflügelfleisch", "Wein", "Suppenfleisch"]

    # synonyms_dict = {"Zartbitterschokolade": ["Schokolade_Zartbitter"],
    #                  "Hähnchenfilet": ["Filet_Hähnchen", "Hühnerfilet"],
    #                  "Huhn": ["Hähnchenfilet", "Filet_Hähnchen", "Hühnchenschenkel", "Hühnerbeine"],
    #                  "Kuvertüre_Zartbitter": ["Zartbitterkuvertüre"]}
    #
    # model_substitutes_dict = {"Zartbitterschokolade": ["Schokolade_Zartbitter", "Kuvertüre_Zartbitter", "Zartbitterkuvertüre", "Nutella"],
    #                           "Schokolade_Zartbitter": ["Kuvertüre_Zartbitter", "Weiße_Schokolade", "Zartbitterschokolade"],
    #                           "Huhn": ["Hähnchenfilet", "Schweinelende"],
    #                           "Dill": ["Petersilie"]}


    final_dict = {}

    new_syn_dict = {}
    # get base word for all synonyms
    for ingred in synonyms_dict.keys():
        if ingred not in category_subs:
            for syn in synonyms_dict[ingred]:
                new_syn_dict[syn] = ingred

    #
    for ingred in model_substitutes_dict.keys():
        if ingred not in new_syn_dict.keys():
            final_dict[ingred] = set()

    for ingred in model_substitutes_dict.keys():
        curr_set = set()
        for sub in model_substitutes_dict[ingred]:
            if sub in new_syn_dict:
                curr_set.add(new_syn_dict[sub])
            else:
                curr_set.add(sub)
        if ingred not in new_syn_dict:
            final_dict[ingred] |= curr_set
        else:
            test = new_syn_dict[ingred]
            final_dict[test] |= curr_set
    # print(final_dict)
    for ingred in final_dict.keys():
        if ingred in final_dict[ingred]:
            final_dict[ingred].remove(ingred)

    new_final_dict = {}
    for ingred in final_dict.keys():
        new_final_dict[ingred] = list(final_dict[ingred])

    with open(found_substitutes_path, "r") as whole_json_file:
        new_final_dict = json.load(whole_json_file)

    print_stats(new_final_dict, cap_at_30=True)
    print_stats(new_final_dict, cap_at_30=False)

    print("--------------------------------------------\nground truth only: ")

    with open("data/ground_truth.json", "r") as whole_json_file:
        ground_truth = json.load(whole_json_file)

    ground_truth_only = {}
    for ingred in new_final_dict.keys():
        if ingred in ground_truth.keys():
            ground_truth_only[ingred] = new_final_dict[ingred]

    print_stats(ground_truth_only, cap_at_30=True)
    print_stats(ground_truth_only, cap_at_30=False)

    print("================================\nenglisch:")
    with open(engl_data_path + "substitute_pairs_foodbert_text.json", "r") as whole_json_file:
        engl_list = json.load(whole_json_file)
    with open(engl_data_path + "engl_ground_truth.json", "r") as whole_json_file:
        engl_ground_truth = json.load(whole_json_file)

    engl_dict = {}
    for foo in engl_list:
        if foo[0] in engl_dict.keys():
            engl_dict[foo[0]].append(foo[1])
        else:
            engl_dict[foo[0]] = [foo[1]]
    print_stats(engl_dict, cap_at_30=True)
    print_stats(engl_dict, cap_at_30=False)

    print("--------------------------------------------\nground truth only: ")

    ground_truth_only = {}
    for ingred in engl_dict.keys():
        if ingred in engl_ground_truth.keys():
            ground_truth_only[ingred] = engl_dict[ingred]

    print_stats(ground_truth_only, cap_at_30=True)
    print_stats(ground_truth_only, cap_at_30=False)

main()