import pandas as pd
import streamlit as st
import numpy as np
import torch
import io
import time

@st.cache(show_spinner=True,allow_output_mutation=True)
def load_model(model_name):
    if model_name.startswith('bert'):
        from transformers import BertTokenizer
        tokenizer = BertTokenizer.from_pretrained(model_name)
    elif model_name.startswith('gpt2'):
        from transformers import GPT2Tokenizer
        tokenizer = GPT2Tokenizer.from_pretrained(model_name)
    elif model_name.startswith('roberta'):
        from transformers import RobertaTokenizer
        tokenizer = RobertaTokenizer.from_pretrained(model_name)
    elif model_name.startswith('albert'):
        from transformers import AlbertTokenizer
        tokenizer = AlbertTokenizer.from_pretrained(model_name)
    return tokenizer

def generate_markdown(text,color='black',font='Arial',size=20):
    return f"<p style='text-align:center; color:{color}; font-family:{font}; font-size:{size}px;'>{text}</p>"

def TokenizeText(sentence,tokenizer_name):
    if len(sentence)>0:
        if tokenizer_name.startswith('gpt2'):
            input_sent = tokenizer(sentence)['input_ids']
        else:
            input_sent = tokenizer(sentence)['input_ids'][1:-1]
        encoded_sent = [str(token) for token in input_sent]
        decoded_sent = [tokenizer.decode([token]) for token in input_sent]
        num_tokens = len(decoded_sent)

        #char_nums = [len(word)+2 for word in decoded_sent]
        #word_cols = st.columns(char_nums)
        #for word_col,word in zip(word_cols,decoded_sent):
            #with word_col:
                #st.write(word)
        #st.write('   '.join(encoded_sent))
        #st.write('   '.join(decoded_sent))
        st.markdown(generate_markdown('   '.join(encoded_sent),size=16), unsafe_allow_html=True)
        st.markdown(generate_markdown('   '.join(decoded_sent),size=16), unsafe_allow_html=True)
        st.markdown(generate_markdown(f'{num_tokens} tokens'), unsafe_allow_html=True)

        return num_tokens

def DeTokenizeText(input_str):
    if len(input_str)>0:
        input_sent = [int(element) for element in input_str.strip().split(' ')]
        encoded_sent = [str(token) for token in input_sent]
        decoded_sent = [tokenizer.decode([token]) for token in input_sent]
        num_tokens = len(decoded_sent)

        #char_nums = [len(word)+2 for word in decoded_sent]
        #word_cols = st.columns(char_nums)
        #for word_col,word in zip(word_cols,decoded_sent):
            #with word_col:
                #st.write(word)
        #st.write('   '.join(encoded_sent))
        #st.write('   '.join(decoded_sent))
        st.markdown(generate_markdown('   '.join(decoded_sent)), unsafe_allow_html=True)
        return num_tokens

if __name__=='__main__':

    # Config
    max_width = 1500
    padding_top = 0
    padding_right = 2
    padding_bottom = 0
    padding_left = 2

    define_margins = f"""
    <style>
        .appview-container .main .block-container{{
            max-width: {max_width}px;
            padding-top: {padding_top}rem;
            padding-right: {padding_right}rem;
            padding-left: {padding_left}rem;
            padding-bottom: {padding_bottom}rem;
        }}
    </style>
    """
    hide_table_row_index = """
                <style>
                tbody th {display:none}
                .blank {display:none}
                </style>
                """
    st.markdown(define_margins, unsafe_allow_html=True)
    st.markdown(hide_table_row_index, unsafe_allow_html=True)

    # Title
    st.markdown(generate_markdown('Tokenizer Demo:',size=32), unsafe_allow_html=True)
    st.markdown(generate_markdown('quick and easy way to explore how tokenizers work',size=24), unsafe_allow_html=True)

    # Select and load the tokenizer
    tokenizer_name = st.sidebar.selectbox('Choose the tokenizer from below',
                                            ('bert-base-uncased','bert-large-cased',
                                            'gpt2','gpt2-large',
                                            'roberta-base','roberta-large',
                                            'albert-base-v2','albert-xxlarge-v2'),index=7)
    tokenizer = load_model(tokenizer_name)

    comparison_mode = st.sidebar.checkbox('Compare two texts')
    detokenize = st.sidebar.checkbox('de-tokenize (make sure to type in integers separated by single spaces)')
    if comparison_mode:
        sent_cols = st.columns(2)
        num_tokens = {}
        sents = {}
        for sent_id, sent_col in enumerate(sent_cols):
            with sent_col:
                if detokenize:
                    sentence = st.text_input(f'Tokenized IDs {sent_id+1}')
                    num_tokens[f'sent_{sent_id+1}'] = DeTokenizeText(sentence)
                else:
                    sentence = st.text_input(f'Text {sent_id+1}')
                    num_tokens[f'sent_{sent_id+1}'] = TokenizeText(sentence,tokenizer_name)
                sents[f'sent_{sent_id+1}'] = sentence

        if len(sents['sent_1'])>0 and len(sents['sent_2'])>0:
            st.markdown(generate_markdown('Result&colon; ',size=16), unsafe_allow_html=True)
            if num_tokens[f'sent_1']==num_tokens[f'sent_2']:
                st.markdown(generate_markdown('Matched! ',color='MediumAquamarine'), unsafe_allow_html=True)
            else:
                st.markdown(generate_markdown('Not Matched... ',color='Salmon'), unsafe_allow_html=True)

    else:
        if detokenize:
            sentence = st.text_input(f'Tokenized IDs')
            num_tokens = DeTokenizeText(sentence)
        else:
            sentence = st.text_input(f'Text')
            num_tokens = TokenizeText(sentence,tokenizer_name)