import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import pipeline
import numpy as np
import pandas as pd
import matplotlib.cm as cm
import html
from torch.nn.functional import softmax
import torch
from matplotlib.colors import LinearSegmentedColormap

cdict = {'red':   [[0.0,  0.8, 0.8],
                   [1.0,  1.0, 1.0]],
         'green': [[0.0,  0.0, 0.0],
                   [1.0,  1.0, 1.0]],
         'blue':  [[0.0,  0.0, 0.0],
                   [1.0,  1.0, 1.0]],
          'alpha':[[0.0,  1.0, 1.0],
                   [1.0,  0.0, 0.0]]}

cmap = LinearSegmentedColormap('codemap', segmentdata=cdict, N=256)

def value2rgba(x, cmap=cmap, alpha_mult=1.0):
    c = cmap(x)
    rgb = (np.array(c[:-1]) * 255).astype(int)
    a = c[-1] * alpha_mult
    return tuple(rgb.tolist() + [a])

def highlight_token_scores(tokens, scores, sep=' ', **kwargs):
    html_code,spans = [''], []#['<span style="font-family: monospace;">'], []
    for t, s in zip(tokens, scores):
        t = html.escape(t)
        t = t.replace("\n", " \n")
        c = str(value2rgba(s, alpha_mult=0.8, **kwargs))
        spans.append(f'<span title="{s:.3f}" style="background-color: rgba{c};">{t}</span>')
    html_code.append(sep.join(spans))
    return '<pre><code>' + ''.join(html_code)

def color_dataframe(row):
    styles = []
    c = str(value2rgba(row["scores"], alpha_mult=0.8))
    for key in row.index:
        if key in {"tokens", "scores"}:
            styles.append(f"background-color: rgba{c}") 
        else:
            styles.append(f"background-color: None") 
    return styles

@st.cache(allow_output_mutation=True)
def load_tokenizer(model_ckpt):
    return AutoTokenizer.from_pretrained(model_ckpt)

@st.cache(allow_output_mutation=True)
def load_model(model_ckpt):
    model = AutoModelForCausalLM.from_pretrained(model_ckpt)
    return model

def calculate_scores(probs, token_ids):
    probs = probs[:-1]
    token_ids = token_ids[1:]
    sorted_ids = np.argsort(probs, axis=-1)[:, ::-1]
    sorted_probs = np.sort(probs, axis=-1)[:, ::-1]
    selected_token_mask = sorted_ids == token_ids[:, None]
    masked_probs = np.ma.array(sorted_probs, mask=~selected_token_mask)
    token_probs = masked_probs.sum(axis=1).data

    masked_indices = np.cumsum(selected_token_mask[:, ::-1], axis=-1)[:, ::-1].astype(bool)
    masked_probs = np.ma.array(sorted_probs, mask=~masked_indices)
    token_rank = masked_indices.sum(axis=-1)
    cumulative_probs = masked_probs.sum(axis=1).data/token_rank
    scores = token_probs/cumulative_probs
    return [1.] + list(scores), sorted_ids

def calculate_loss(logits, labels):
    shift_logits = logits[..., :-1, :].contiguous()
    shift_labels = labels[..., 1:].contiguous()
    loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
    loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
    norm_loss = 1 - (loss/torch.max(loss))
    return [1.] + list(norm_loss.numpy())

default_code = """\
from torch import nn
from transformers import Model

class Transformer:
    def __init__(config):
        self.model = Model(config)

    def forward(inputs):
        return self.model(inputs)"""

solution_code = """\
from torch import nn
from transformers import Model

class Transformer(nn.Module):
    def __init__(self, config):
        super(Transformer, self).__init__()
        self.config = config
        self.model = Model(config)

    def forward(self, inputs):
        return self.model(inputs)
"""

st.set_page_config(page_icon=':parrot:', layout="wide")

np.random.seed(42)
model_ckpt = "lvwerra/codeparrot"
tokenizer = load_tokenizer(model_ckpt)
model = load_model(model_ckpt)
st.markdown("<h1 style='text-align: center;'>CodeParrot 🦜</h1>", unsafe_allow_html=True)
st.markdown('##')

col1, col2 = st.columns(2)

col1.subheader("Edit code")
code = col1.text_area(label="", value=default_code, height=220,).strip()
inputs = tokenizer(code, return_tensors='pt')
token_list = [tokenizer.decode(t) for t in inputs["input_ids"][0]]

with torch.no_grad():
    logits = model(input_ids=inputs["input_ids"]).logits[0]
    probs = softmax(logits, dim=-1)

loss = calculate_loss(logits, inputs["input_ids"][0])
norm_probs, sorted_token_ids = calculate_scores(probs.numpy(), inputs["input_ids"][0].numpy())

if len(inputs['input_ids'])>1024:
    st.warning("Your input is longer than the maximum 1024 tokens and will be truncated.")

st.sidebar.title("Settings:")
if st.sidebar.radio("Highlight mode:", ["Probability heuristics", "Scaled loss per token"]) == "Probability heuristics":
    scores = norm_probs
else:
    scores = loss

suggestion_threshold = st.sidebar.slider("Suggestion threshold", 0.0, 1.0, 0.2)

col2.subheader("Highlighted code")
col2.markdown('##')
html_string = highlight_token_scores(token_list, scores, sep="")
col2.markdown(html_string, unsafe_allow_html=True)
col2.markdown('##')

st.subheader("Model suggestions")
top_k = {}
for i in range(5):
    top_k[f"top-{i+1}"] = ["No prediction for first token"] + [repr(tokenizer.decode(idx)) for idx in sorted_token_ids[:, i]]
df = pd.DataFrame({"tokens": [repr(t) for t in token_list], "scores": scores, **top_k})
df.index.name = "position"
df_filter = df.loc[df["scores"]<=suggestion_threshold]
df_filter.reset_index(inplace=True)
df_filter = df_filter[["tokens", "scores", "position", "top-1", "top-2", "top-3", "top-4", "top-5",]]
df_filter = df_filter.style.apply(color_dataframe, axis=1)
st.dataframe(df_filter)

st.markdown('##')

st.subheader("Possible solution")
st.code(solution_code)