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 = [''], []#[''], [] 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'{t}') html_code.append(sep.join(spans)) return '

' + ''.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 = "codeparrot/codeparrot-small"
tokenizer = load_tokenizer(model_ckpt)
model = load_model(model_ckpt)
st.markdown("
CodeParrot 🦜
", 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("Info:")
st.sidebar.markdown("This demo uses CodeParrot to highlight the parts of code with low probability. Since CodeParrot is an autoregressive model the tokens at the beginning tend to have a lower probability. E.g. the model can't know what you want to import because it has no access to information later in the code. However, as you can see in the example on the right it still can highlight bugs or unconventional naming.\n\nAt the bottom of the page is an example of how a better solution might look like. Try to copy paste it and press **CMD + Enter** to update the highlighting.")
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)