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LLM_Inception
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import os
import pickle
import numpy as np

import shutil

mapping_dataset_directory = {'ActvityNet_hico_style_batch1':'ActivityNet_hico_batch1','charadesEgo_hico_style':'charadesego_frame', 'HAG_hico_style_new':'hag_frame','HACS_hico_style':'hacs_frame','kinetics_hico_style':'kinetics_dataset/k700-2020/train'}

train_pkl = "/home/lihong/chenyuanjie/Sandwich/Data/B123_train_KIN-FULL_with_node.pkl"
split_test_path = "/home/lihong/chenyuanjie/Sandwich/Data/B123_test_KIN-FULL_with_node.pkl"

with open(split_test_path, 'rb') as f:
    data = pickle.load(f)


# split_test_pkl = []

# action_num_dict = {}

# for data_idx, data_item in enumerate(data):
#     if data_item[0] in mapping_dataset_directory.keys():
#         dataset = mapping_dataset_directory[data_item[0]]
#     else:
#         dataset = data_item[0]
    
#     orig_label = data_item[2]
#     node_labels = data_item[3]
    
#     for nod_lab in node_labels:
#         if nod_lab in action_num_dict.keys():
#             action_num_dict[nod_lab] += 1
#         else:
#             action_num_dict[nod_lab] = 1
    
#     if data_idx %1000 == 0:
#         print(len(data),data_idx)
        

# current_action_num_dict = {}
# dataset_list = []
# for data_idx, data_item in enumerate(data):
#     if data_item[0] in mapping_dataset_directory.keys():
#         dataset = mapping_dataset_directory[data_item[0]]
#     else:
#         dataset = data_item[0]
#     image_path = '/data/xiaoqian/Images/' + dataset + '/' + data_item[1]
#     if not os.path.isfile(image_path):
#         if not dataset in dataset_list:
#             dataset_list.append(dataset)
#         continue
    
#     orig_label = data_item[2]
#     node_labels = data_item[3]
#     flag = False
#     for nod_lab in node_labels:
#         if nod_lab in current_action_num_dict.keys():
#             if current_action_num_dict[nod_lab] < action_num_dict[nod_lab] * 0.1 and not flag:
#                 split_test_pkl.append(data_item)
#                 flag = True
#             current_action_num_dict[nod_lab] += 1
#         else:
#             split_test_pkl.append(data_item)
#             flag = True
#             current_action_num_dict[nod_lab] = 1
    
#     if data_idx % 1000 == 0:
#         print(len(data),data_idx)
        
# print(action_num_dict, current_action_num_dict)
# with open(split_test_path, 'wb') as f:
#     pickle.dump(split_test_pkl, f)
# exit()

## mapping node to idx
mapping_node_index = pickle.load(open("/home/lihong/chenyuanjie/Sandwich/Data/mapping_node_index.pkl", "rb"))
verbnet_topology = pickle.load(open("/home/lihong/chenyuanjie/Sandwich/Data/verbnet_topology_898.pkl", "rb"))

Father2Son, objects = verbnet_topology["Father2Son"], verbnet_topology["objects"]

objects = np.array(objects)
objects_290 = objects[mapping_node_index]

object_to_idx = {obj: idx for idx, obj in enumerate(objects_290)}

# filtered_objects = [obj.split("-")[0] for obj in objects_290]

selected_list = ["hit", "push","run","dress","drive","cook","throw","build","shake","cut"]
true_selected_list = ["hit-18.1","push-12","run-51.3.2","dress-41.1.1-1-1","drive-11.5","cooking-45.3","throw-17.1-1","build-26.1","shake-22.3-2","cut-21.1-1"]
true_selected_list_id = [object_to_idx[node] for node in true_selected_list]
true_selected_paired_list = ['run-51.3.2_hit-18.1', 'drive-11.5_dress-41.1.1-1-1', 'cooking-45.3_build-26.1','shake-22.3-2_cut-21.1-1'] #,'throw-17.1-1_push-12'
true_label = {}

# {'hit-18.1cut-21.1-1': 86808, 'hit-18.1drive-11.5': 14935, 'hit-18.1run-51.3.2': 34237}
# {'run-51.3.2run-51.3.2': 341324, 'run-51.3.2hit-18.1': 34237, 'run-51.3.2cut-21.1-1': 20389}
# {'dress-41.1.1-1-1dress-41.1.1-1-1': 470063, 'dress-41.1.1-1-1run-51.3.2': 63862, 'dress-41.1.1-1-1cut-21.1-1': 47727, 'dress-41.1.1-1-1drive-11.5': 24965, 'dress-41.1.1-1-1hit-18.1': 23118, 'dress-41.1.1-1-1push-12': 11982, 'dress-41.1.1-1-1cooking-45.3': 469, 'dress-41.1.1-1-1build-26.1': 306}
# {'drive-11.5drive-11.5': 238175, 'drive-11.5build-26.1': 15223, 'drive-11.5hit-18.1': 14935, 'drive-11.5cut-21.1-1': 30031, 'drive-11.5dress-41.1.1-1-1': 24965}
# {'cooking-45.3cooking-45.3': 68577, 'cooking-45.3build-26.1': 37668, 'cooking-45.3cut-21.1-1': 15072, 'cooking-45.3dress-41.1.1-1-1': 469}
# {'throw-17.1-1throw-17.1-1': 394887, 'throw-17.1-1hit-18.1': 92553, 'throw-17.1-1drive-11.5': 30348, 'throw-17.1-1dress-41.1.1-1-1': 97911, 'throw-17.1-1run-51.3.2': 30097, 'throw-17.1-1push-12': 20854, 'throw-17.1-1cut-21.1-1': 20714}
# {'build-26.1cooking-45.3': 37668, 'build-26.1build-26.1': 95743, 'build-26.1drive-11.5': 15223, 'build-26.1cut-21.1-1': 23454, 'build-26.1shake-22.3-2': 23015, 'build-26.1dress-41.1.1-1-1': 306}
# {'shake-22.3-2build-26.1': 23015, 'shake-22.3-2shake-22.3-2': 23015, 'shake-22.3-2cut-21.1-1': 13005}
# {'cut-21.1-1cut-21.1-1': 553752, 'cut-21.1-1hit-18.1': 86808, 'cut-21.1-1drive-11.5': 30031, 'cut-21.1-1dress-41.1.1-1-1': 47727, 'cut-21.1-1build-26.1': 23454, 'cut-21.1-1cooking-45.3': 15072, 'cut-21.1-1run-51.3.2': 20389, 'cut-21.1-1throw-17.1-1': 20714, 'cut-21.1-1shake-22.3-2': 13005}

selected_pkl = {}
selected_paired_pkl = {}
pangea_pkl = {}

negative_pkl = []

dataset_list = []
num_images = 0

for data_idx, data_item in enumerate(data):
    save_flag = False
    if data_item[0] in mapping_dataset_directory.keys():
        dataset = mapping_dataset_directory[data_item[0]]
    else:
        dataset = data_item[0]
    
    image_path = '/data/xiaoqian/Images/' + dataset + '/' + data_item[1]
    
    if not os.path.isfile(image_path):
        if not dataset in dataset_list:
            dataset_list.append(dataset)
        continue
    
    print(data_item)
    exit()
    orig_label = data_item[2]
    node_labels = data_item[3]
    node_labels_id = [object_to_idx[node] for node in node_labels]
    
    co_objects = list(set(node_labels_id).intersection(set(true_selected_list_id)))
    
    if len(co_objects) > 0:
        
        for sel_paired_objects in true_selected_list:
            if object_to_idx[sel_paired_objects] in co_objects:
                
                if sel_paired_objects not in selected_pkl.keys():
                    selected_pkl[sel_paired_objects] = [data_idx]
                    save_flag = True
                else:
                    if len(selected_pkl[sel_paired_objects]) < 2000:
                        save_flag = True
                        selected_pkl[sel_paired_objects].append(data_idx)
        
        
        for sel_paired_objects in true_selected_paired_list:
            sel_obj1, sel_obj2 = sel_paired_objects.split("_")
            
            if object_to_idx[sel_obj1] in co_objects and object_to_idx[sel_obj2] in co_objects:
                
                if sel_paired_objects not in selected_paired_pkl.keys():
                    selected_paired_pkl[sel_paired_objects] = {}
                if sel_paired_objects not in selected_paired_pkl[sel_paired_objects].keys():
                    save_flag = True
                    selected_paired_pkl[sel_paired_objects][sel_paired_objects] = [data_idx]
                else:
                    if len(selected_paired_pkl[sel_paired_objects][sel_paired_objects]) < 2000:
                        save_flag = True
                        selected_paired_pkl[sel_paired_objects][sel_paired_objects].append(data_idx)
            elif object_to_idx[sel_obj1] in co_objects:
                
                if sel_paired_objects not in selected_paired_pkl.keys():
                    selected_paired_pkl[sel_paired_objects] = {}
                if sel_obj1 not in selected_paired_pkl[sel_paired_objects].keys():
                    save_flag = True
                    selected_paired_pkl[sel_paired_objects][sel_obj1] = [data_idx]
                else:
                    if len(selected_paired_pkl[sel_paired_objects][sel_obj1]) < 2000:
                        save_flag = True
                        selected_paired_pkl[sel_paired_objects][sel_obj1].append(data_idx)
            elif object_to_idx[sel_obj2] in co_objects:
                
                if sel_paired_objects not in selected_paired_pkl.keys():
                    selected_paired_pkl[sel_paired_objects] = {}
                if sel_obj2 not in selected_paired_pkl[sel_paired_objects].keys():
                    save_flag = True
                    selected_paired_pkl[sel_paired_objects][sel_obj2] = [data_idx]
                else:
                    if len(selected_paired_pkl[sel_paired_objects][sel_obj2]) < 2000:
                        save_flag = True
                        selected_paired_pkl[sel_paired_objects][sel_obj2].append(data_idx)
    else:
        if len(negative_pkl) < 3000:
            
            neg_flag = False
            for sel_list in selected_list:
                for nod_lab in node_labels:
                    if sel_list in nod_lab:
                        neg_flag = True
                        break
                    if neg_flag:
                        break
            
            if not neg_flag:
                save_flag = True
                negative_pkl.append(data_idx)
    if save_flag:
        num_images += 1
        if not os.path.exists(os.path.dirname(os.path.join("/home/lihong/workspace/pangea/pangea", dataset, data_item[1]))):
            os.makedirs(os.path.dirname(os.path.join("/home/lihong/workspace/pangea/pangea", dataset, data_item[1])))
        shutil.copy(image_path, os.path.join("/home/lihong/workspace/pangea/pangea", dataset, data_item[1]))
    
    if data_idx % 1000 == 0:
        print(len(data),data_idx)

for name in selected_pkl.keys():
    print(f"selected {name} affordance has {len(selected_pkl[name])} objects")
    
for name in selected_paired_pkl.keys():
    for sub_name in selected_paired_pkl[name].keys():
        print(f"selected {name} paired actions {sub_name} has {len(selected_paired_pkl[name][sub_name])} objects")

print("negative_pkl has {} objects".format(len(negative_pkl)))
print("num_images has {} objects".format(num_images))

pangea_pkl["selected_pkl"] = selected_pkl
pangea_pkl["selected_paired_pkl"] = selected_paired_pkl
pangea_pkl["negative_pkl"] = negative_pkl

with open(os.path.join("/home/lihong/workspace/pangea","pangea_test.pkl"),"wb") as fp:
    pickle.dump(pangea_pkl,fp)

print(dataset_list)

exit()         
selected_images = {}

save_data = []

for data_idx, data_item in enumerate(data):
    if data_item[0] in mapping_dataset_directory.keys():
        dataset = mapping_dataset_directory[data_item[0]]
    else:
        dataset = data_item[0]
    image_path = '/data/xiaoqian/Images/' + dataset + '/' + data_item[1]
    
    if not os.path.isfile(image_path):
        if not dataset in dataset_list:
            dataset_list.append(dataset)
        continue
    
    orig_label = data_item[2]
    node_labels = data_item[3]
    if true_selected_list[9] in node_labels:
        for i in range(len(true_selected_list)):
            if true_selected_list[i] in node_labels:
                if true_selected_list[9]+ true_selected_list[i] in selected_images.keys():
                    selected_images[true_selected_list[9]+ true_selected_list[i]] += 1
                else:
                    selected_images[true_selected_list[9]+ true_selected_list[i]] = 1
    
    if data_idx %1000 == 0:
        print(data_idx)
    
    
print(selected_images)