# %%
import torch
import json
import random 
import numpy as np
from transformers import AutoTokenizer
from transformers import AutoModel
from loss import batch_all_triplet_loss, batch_hard_triplet_loss
from sklearn.neighbors import KNeighborsClassifier
from tqdm import tqdm
import pandas as pd
import re
from torch.utils.data import Dataset, DataLoader
import torch.optim as optim


# %%
SHUFFLES=0
AMPLIFY_FACTOR=0
LEARNING_RATE=1e-5


# %%
def generate_train_entity_sets(entity_id_mentions, entity_id_name, group_size, anchor=True):
    # split entity mentions into groups
    # anchor = False, don't add entity name to each group, simply treat it as a normal mention
    entity_sets = []
    if anchor:
        for id, mentions in entity_id_mentions.items():
            random.shuffle(mentions)
            positives = [mentions[i:i + group_size] for i in range(0, len(mentions), group_size)]
            anchor_positive = [([entity_id_name[id]]+p, id) for p in positives]
            entity_sets.extend(anchor_positive)
    else:
        for id, mentions in entity_id_mentions.items():
            group = list(set([entity_id_name[id]] + mentions))
            random.shuffle(group)
            positives = [(mentions[i:i + group_size], id) for i in range(0, len(mentions), group_size)]
            entity_sets.extend(positives)
    return entity_sets

def batchGenerator(data, batch_size):
    for i in range(0, len(data), batch_size):
        batch = data[i:i+batch_size]
        x, y = [], []
        for t in batch:
            x.extend(t[0])
            y.extend([t[1]]*len(t[0]))
        yield x, y


with open('../esAppMod/tca_entities.json', 'r') as file:
    entities = json.load(file)
all_entity_id_name = {entity['entity_id']: entity['entity_name'] for _, entity in entities['data'].items()}

with open('../esAppMod/train.json', 'r') as file:
    train = json.load(file)
train_entity_id_mentions = {data['entity_id']: data['mentions'] for _, data in train['data'].items()}
train_entity_id_name = {data['entity_id']: all_entity_id_name[data['entity_id']] for _, data in train['data'].items()}

# %%
###############
# alternate data import strategy
###################################################
# import code
# import training file
data_path = '../esAppMod_data_import/train.csv'
df = pd.read_csv(data_path, skipinitialspace=True)
# rather than use pattern, we use the real thing and property
entity_ids = df['entity_id'].to_list()
target_id_list = sorted(list(set(entity_ids)))

id2label = {}
label2id = {}
for idx, val in enumerate(target_id_list):
    id2label[idx] = val
    label2id[val] = idx

df["training_id"] = df["entity_id"].map(label2id)

# %%
##############################################################
# augmentation code

# basic preprocessing
def preprocess_text(text):
    # 1. Make all uppercase
    text = text.lower()

    # standardize spacing
    text = re.sub(r'\s+', ' ', text).strip()

    return text


def generate_random_shuffles(text, n):
    words = text.split()  # Split the input into words
    shuffled_variations = []
    
    for _ in range(n):
        shuffled = words[:]  # Copy the word list to avoid in-place modification
        random.shuffle(shuffled)  # Randomly shuffle the words
        shuffled_variations.append(" ".join(shuffled))  # Join the words back into a string
    
    return shuffled_variations


def shuffle_text(text, n_shuffles=SHUFFLES):
    all_processed = []
    # add the original text
    all_processed.append(text)
        
    # Generate random shuffles
    shuffled_variations = generate_random_shuffles(text, n_shuffles)
    all_processed.extend(shuffled_variations)
    
    return all_processed

def corrupt_word(word):
    """Corrupt a single word using random corruption techniques."""
    if len(word) <= 1:  # Skip corruption for single-character words
        return word
    
    corruption_type = random.choice(["delete", "swap"])
    
    if corruption_type == "delete":
        # Randomly delete a character
        idx = random.randint(0, len(word) - 1)
        word = word[:idx] + word[idx + 1:]
    
    elif corruption_type == "swap":
        # Swap two adjacent characters
        if len(word) > 1:
            idx = random.randint(0, len(word) - 2)
            word = (word[:idx] + word[idx + 1] + word[idx] + word[idx + 2:])
    
    
    return word

def corrupt_string(sentence, corruption_probability=0.01):
    """Corrupt each word in the string with a given probability."""
    words = sentence.split()
    corrupted_words = [
        corrupt_word(word) if random.random() < corruption_probability else word
        for word in words
    ]
    return " ".join(corrupted_words)


def create_example(index, mention, entity_name):
    return {'entity_id': index, 'mention': mention, 'entity_name': entity_name}

# augment whole dataset
def augment_data(df):
    output_list = []

    for idx,row in df.iterrows():
        index = row['entity_id']
        entity_name = row['entity_name']
        parent_desc = row['mention']
        parent_desc = preprocess_text(parent_desc) 

        # add basic example
        output_list.append(create_example(index, parent_desc, entity_name))

        # add shuffled strings
        processed_descs = shuffle_text(parent_desc, n_shuffles=SHUFFLES)
        for desc in processed_descs:
            if (desc != parent_desc):
                output_list.append(create_example(index, desc, entity_name))
        
        # add corrupted strings
        desc = corrupt_string(parent_desc, corruption_probability=0.01)
        if (desc != parent_desc):
            output_list.append(create_example(index, desc, entity_name))

        # add example with stripped non-alphanumerics
        desc = re.sub(r'[^\w\s]', ' ', parent_desc)  # Retains only alphanumeric and spaces
        if (desc != parent_desc):
            output_list.append(create_example(index, desc, entity_name))

        # short sequence amplifier
        # short sequences are rare, and we must compensate by including more examples
        # also, short sequence don't usually get affected by shuffle
        words = parent_desc.split()
        word_count = len(words)
        if word_count <= 2:
            for _ in range(AMPLIFY_FACTOR):
                output_list.append(create_example(index, desc, entity_name))

    new_df = pd.DataFrame(output_list)
    return new_df



# %%
def make_entity_id_mentions(df):
    entity_id_mentions = {}
    entity_id_list = list(set(df['entity_id']))
    for entity_id in entity_id_list:
        entity_id_mentions[entity_id] = df[df['entity_id']==entity_id]['mention'].to_list()
    return entity_id_mentions

def make_entity_id_name(df):
    entity_id_name = {}
    entity_id_list = list(set(df['entity_id']))
    for entity_id in entity_id_list:
        # entity_id always matches entity_name, so first value would work
        entity_id_name[entity_id] = df[df['entity_id']==entity_id]['entity_name'].to_list()[0]
    return entity_id_name


# %%
num_sample_per_class = 10  # samples in each group
batch_size = 16 # number of groups, effective batch_size for computing triplet loss = batch_size * num_sample_per_class
margin = 2
epochs = 200
DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# MODEL_NAME = 'distilbert-base-cased' #'prajjwal1/bert-small' #'bert-base-cased'   
MODEL_NAME = 'prajjwal1/bert-small' # 'prajjwal1/bert-small' 'bert-base-cased'  'distilbert-base-cased' 

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModel.from_pretrained(MODEL_NAME)
optimizer = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE)
# scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)

model.to(DEVICE)
model.train()

losses = []



for epoch in tqdm(range(epochs)):
    total_loss = 0.0
    batch_number = 0

    augmented_df = augment_data(df)
    train_entity_id_mentions = make_entity_id_mentions(augmented_df)
    train_entity_id_name = make_entity_id_name(augmented_df)
    
    data = generate_train_entity_sets(train_entity_id_mentions, train_entity_id_name, num_sample_per_class-1, anchor=True)
    random.shuffle(data)
    for x,y in batchGenerator(data, batch_size):

        # print(len(x), len(y), end='-->')
        optimizer.zero_grad()

        inputs = tokenizer(x, padding=True, return_tensors='pt')
        inputs.to(DEVICE)
        outputs = model(**inputs)
        cls = outputs.last_hidden_state[:,0,:]
        # for training less than half the time, train on easy
        y = torch.tensor(y).to(DEVICE)
        if epoch < epochs / 2:
            loss, _ = batch_all_triplet_loss(y, cls, margin, squared=False)
        # for training after half the time, train on hard
        else:
            loss = batch_hard_triplet_loss(y, cls, margin, squared=False)
        loss.backward()
        optimizer.step()
        total_loss += loss.detach().item()
        batch_number += 1

        del x, y, outputs, cls, loss
        torch.cuda.empty_cache()

    # scheduler.step()  # Update the learning rate
    print(f'epoch loss: {total_loss/batch_number}')
    # print(f"Epoch {epoch+1}: lr={scheduler.get_last_lr()[0]}")
    if epoch % 5 == 0:
        torch.save(model.state_dict(), './checkpoint/siamese_simple.pt')


torch.save(model.state_dict(), './checkpoint/siamese_simple.pt')
# %%