import gradio as gr

import torch
from torch import Tensor, nn
from torch.nn import TransformerEncoder, TransformerEncoderLayer
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import dataset
from torch.utils.tensorboard import SummaryWriter

import regex as re
import os
import time
from tqdm import tqdm
import copy
import math

from model import TransformerModel
from utils import preProcessText, getTokenizer
from config import getConfig


model_config, app_config = getConfig()
print(model_config)
print(app_config)

bptt=model_config["bptt"]

device = "cuda" if torch.cuda.is_available() else "cpu"
print(device)

softmax = nn.Softmax(dim=2)

tokenizer, vocab = getTokenizer()
ntokens = len(vocab)


def get_model(model_config, ntokens):
    emsize = model_config["emsize"]
    d_hid = model_config["d_hid"]
    nlayers = model_config["nlayers"]
    nhead = model_config["nhead"]
    dropout = model_config["dropout"]
    model = TransformerModel(ntokens, emsize,nhead, d_hid, nlayers, dropout)
    return model

def loadModel(best_model_path):
    global model
    if os.path.exists(best_model_path):
        print(f"Preloading model {best_model_path}")
        if torch.cuda.is_available():
            state = torch.load(best_model_path)
        else:
            state = torch.load(best_model_path, map_location=torch.device('cpu'))
        model.load_state_dict(state['model_state_dict'])
        return model
    else:
        raise Exception("Model Not Found")
    
def data_process(raw_text_iter: dataset.IterableDataset) -> Tensor:
    """Converts raw text into a flat Tensor."""
    # obtain the data in tensor format for each line
    data = [torch.tensor(vocab(tokenizer(item)), dtype=torch.long)
            for item in raw_text_iter]
    # concatenate all the lines
    return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))

def batchify(data: Tensor, batch_size: int) -> Tensor:
    """Divides the data into batch_size separate sequences, removing extra elements
    that wouldn't cleanly fit.
    Args:
        data: Tensor, shape [N]
        batch_size: int, batch size
    Returns:
        Tensor of shape [N // bsz, bsz]
    """
    seq_len = data.size(0) // batch_size
    data = data[:seq_len * batch_size]
    data = data.view(batch_size, seq_len).t().contiguous()
    return data.to(device)

def generate_square_subsequent_mask(sz: int) -> Tensor:
    """Generates an upper-triangular matrix of -inf, with zeros on diag."""
    return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)

def nonnaive_generator(model: nn.Module, gen_data: Tensor, no_words=5, k=50):
    model.eval()
    src_mask = generate_square_subsequent_mask(bptt).to(device)
    pred_text = []
    for i in range(no_words):
        batch_size = gen_data.size(0)
        if batch_size != bptt:
            src_mask_ = src_mask[:batch_size, :batch_size]
            
        # generate the probability of the next word
        output_softmax = model(gen_data, src_mask_)
        output_softmax_permuted = output_softmax.permute(1, 0, 2)
        
        # obtain the "k" top probable words index
        # both indices and values are of size (no. of words, k=50)
        indices = torch.topk(output_softmax_permuted, k, dim=2).indices.squeeze(0)
        # obtain the top "k" probability of the probable words
        values = torch.topk(softmax(output_softmax_permuted), k, dim=2).values
        values = values/torch.sum(values, dim=2, keepdims=True)
        values = values.squeeze(0)        
        
        # create categorical distribution and take sample from values
        # categorical distribution take 1 sample from k=50 samples of each dimension
        for _ in range(10): 
            ind_sampled = torch.distributions.Categorical(values).sample()
            next_index = indices[-1][ind_sampled[-1]]
            # if the obtained token is not <unk>, then no need to sample again
            if vocab.lookup_token(next_index) != '<unk>':
                break
            
        pred_text.append([vocab.lookup_token(next_index)][0])
        if(batch_size < 15):
            gen_data = torch.cat((gen_data[:, :], next_index.unsqueeze(0).unsqueeze(0)), 0)
            batch_size = gen_data.size(0)
        else:
            gen_data = torch.cat((gen_data[1:, :], next_index.unsqueeze(0).unsqueeze(0)), 0)
            batch_size = gen_data.size(0)

    return pred_text

def predText(text : str, num_words : int):
    text = [text]
    num_words = int(num_words)
    sample_data = data_process(text)
    sample_data = batchify(sample_data, 1)
    pred_text = nonnaive_generator(loaded_model,  sample_data[:,-1].unsqueeze(1), no_words=num_words, k=50)
    whole_text = text[0] + ' ' + ' '.join(pred_text)
    return whole_text


if __name__ == '__main__':
    model = get_model(model_config, ntokens).to(device)
    best_model_path = 'models/best_model.pt'
    loaded_model = loadModel(best_model_path)

    title = "Nepali Language Model For Text Generation"
    description = """
    Click **Submit** to generate text 

    **Flag** as **(Bad, Fine or Good)** as per the generated text

    You can also choose **examples** given below. Click them and then submit
    """
    examples = [["म घर", 10], ["मलाई", 40], ["आज", 70]]


    # inpt and output for the interface
    input_text_box = gr.Textbox(label="Input Text", value="म घर", lines=5, placeholder="Enter Nepali Text")
    input_num_words = gr.Number(label="Number of word to generate", value=5)
    output_text_box = gr.Textbox(label="Generated Text", lines=5, placeholder="Generated Text Appears here")

    flagging_options = ["Bad", "Fine", "Good"]


    interface = gr.Interface(fn=predText, inputs=[input_text_box, input_num_words], outputs=output_text_box, flagging_options=flagging_options, title=title, description=description, examples=examples)
    interface.launch()