import requests
from collections import Counter
from requests.adapters import HTTPAdapter, Retry
import multiprocessing
import os
import time
import logging

import gradio as gr
import pandas as pd
import polars as pl
import matplotlib.pyplot as plt
import spaces
from gradio_huggingfacehub_search import HuggingfaceHubSearch
from huggingface_hub import PyTorchModelHubMixin
import torch
from torch import nn
from transformers import AutoModel, AutoTokenizer, AutoConfig
from tqdm import tqdm


logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")


session = requests.Session()
retries = Retry(total=5, backoff_factor=1, status_forcelist=[502, 503, 504])
session.mount('http://', HTTPAdapter(max_retries=retries))


class QualityModel(nn.Module, PyTorchModelHubMixin):
    def __init__(self, config):
        super(QualityModel, self).__init__()
        self.model = AutoModel.from_pretrained(config["base_model"])
        self.dropout = nn.Dropout(config["fc_dropout"])
        self.fc = nn.Linear(self.model.config.hidden_size, len(config["id2label"]))

    def forward(self, input_ids, attention_mask):
        features = self.model(
            input_ids=input_ids, attention_mask=attention_mask
        ).last_hidden_state
        dropped = self.dropout(features)
        outputs = self.fc(dropped)
        return torch.softmax(outputs[:, 0, :], dim=1)

device = "cuda" if torch.cuda.is_available() else "cpu"
config = AutoConfig.from_pretrained("nvidia/quality-classifier-deberta")
tokenizer = AutoTokenizer.from_pretrained("nvidia/quality-classifier-deberta")
model = QualityModel.from_pretrained("nvidia/quality-classifier-deberta").to(device)
# model = torch.compile(model)
model.eval()


@spaces.GPU
def predict(texts: list[str]):
    inputs = tokenizer(
        texts, return_tensors="pt", padding="longest", truncation=True
    ).to(device)
    outputs = model(inputs["input_ids"], inputs["attention_mask"])
    predicted_classes = torch.argmax(outputs, dim=1)
    predicted_domains = [
        config.id2label[class_idx.item()] for class_idx in predicted_classes.cpu().numpy()
    ]
    return predicted_domains


def plot_and_df(texts, preds):
    texts_df = pd.DataFrame({"quality": preds, "text": texts})
    counts = Counter(preds)
    counts_df = pd.DataFrame(
        {
            "quality": ["Low", "Medium", "High"],
            "count": [counts.get("Low", 0), counts.get("Medium", 0), counts.get("High", 0)]
        }
    )
    # counts.reset_index(inplace=True)
    return (
            gr.BarPlot(counts_df, x="quality", y="count", sort=None),
            texts_df[texts_df["quality"] == "Low"][["text"]][:min(texts_df.shape[0], 20)],
            texts_df[texts_df["quality"] == "Medium"][["text"]][:min(texts_df.shape[0], 20)],
            texts_df[texts_df["quality"] == "High"][["text"]][:min(texts_df.shape[0], 20)],
        )


def get_first_parquet_filename(dataset, config, split):
    parquet_resp = session.get(f"https://datasets-server.huggingface.co/parquet?dataset={dataset}&config={config}", timeout=10).json()
    if "error" in parquet_resp:
        raise ValueError(parquet_resp["error"])
    first_parquet_file_url = [file for file in parquet_resp["parquet_files"] if file["split"] == split][0]["url"]
    return "/".join(first_parquet_file_url.split("/")[-3:])


@spaces.GPU
def run_quality_check(dataset, config, split, column, nested_column, batch_size, num_examples):
    logging.info(f"Fetching data for {dataset=} {config=} {split=} {column=}")
    try:
        filename = get_first_parquet_filename(dataset, config, split)
    except Exception as error:
        yield f"❌ {error}", gr.BarPlot(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
        return

    try:
        logging.info(f"Loading hf://datasets/{dataset}@~parquet/{filename}")
        data = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{filename}", columns=[column])
    except Exception as error:
        yield f"❌ {error}", gr.BarPlot(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
        return
    logging.info("Data fetched.")

    data_sample = data.sample(num_examples, seed=16) if data.shape[0] > num_examples else data
    texts = data_sample[column].to_list()
    if nested_column:
        texts = [text[nested_column] for text in texts]
    predictions, texts_processed = [], []
    num_examples = min(len(texts), num_examples)
    for i in range(0, num_examples, batch_size):
        batch_texts = texts[i:i+batch_size]
        try:
            batch_predictions = predict(batch_texts)
        except Exception as error:
            yield f"❌ {error}", gr.BarPlot(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
            return
        predictions.extend(batch_predictions)
        texts_processed.extend(batch_texts)
        yield {"quality check in progress...": i / num_examples}, *plot_and_df(texts_processed, predictions), pd.DataFrame()

    yield {"quality check finished": 1.}, *plot_and_df(texts_processed, predictions), data_sample


PERSPECTIVE_API_KEY = os.environ.get("PERSPECTIVE_API_KEY")
PERSPECTIVE_URL = f"https://commentanalyzer.googleapis.com/v1alpha1/comments:analyze?key={PERSPECTIVE_API_KEY}"
REQUESTED_ATTRIBUTES = {"TOXICITY": {}, "SEVERE_TOXICITY": {},
                        "IDENTITY_ATTACK": {}, "INSULT": {}, "PROFANITY": {},
                        "THREAT": {}}
ATT_SCORE = "attributeScores"
SUM_SCORE = "summaryScore"


def plot_toxicity(scores):
    fig, axs = plt.subplots(2, 3)#, figsize=(10, 6))
    for x, y, score_name in zip([0,0,0,1,1,1], [0,1,2,0,1,2], scores):
        axs[x,y].hist(scores[score_name], bins=20, range=(0., 1.))
        axs[x,y].set_xlabel(score_name)
    fig.supylabel("Number of texts")
    fig.suptitle("Histogram of toxicity scores")
    fig.tight_layout()

    return fig

def call_perspective_api(texts_df, column_name, nested_column_name, dataset, config, split):#, full_check=False):
    headers = {
        "content-type": "application/json",
    }
    req_att_scores = {attr: [] for attr in REQUESTED_ATTRIBUTES}

    # fetch data if it doesn't exist yet
    if texts_df.values.tolist() == [['', '', '']]:
        logging.info(f"Fetching data for {dataset=} {config=} {split=} {column_name=}")
        try:
            filename = get_first_parquet_filename(dataset, config, split)
        except Exception as error:
            yield f"❌ {error}", gr.BarPlot(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
            return

        try:
            logging.info(f"Loading hf://datasets/{dataset}@~parquet/{filename}")
            texts_df = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{filename}", columns=[column_name])
        except Exception as error:
            yield f"❌ {error}", gr.BarPlot(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
            return
        logging.info("Data fetched.")
        texts_df = texts_df.to_pandas()

    texts = texts_df.sample(100, random_state=16)[column_name].values if texts_df.shape[0] > 100 else texts_df[column_name].values
    if nested_column_name:
        texts = [text[nested_column_name] for text in texts]

    n_samples = len(texts)
    for i, text in tqdm(enumerate(texts), desc="scanning with perspective"):
        data = {
            "comment": {"text": text},
            "languages": ["en"],
            "requestedAttributes": REQUESTED_ATTRIBUTES
        }
        time.sleep(1)
        try:
            req_response = requests.post(PERSPECTIVE_URL, json=data, headers=headers)
        except Exception as e:
            print(e)
            return req_att_scores

        if req_response.ok:
            response = req_response.json()
            if ATT_SCORE in response:
                for req_att in REQUESTED_ATTRIBUTES:
                    if req_att in response[ATT_SCORE]:
                        att_score = response[ATT_SCORE][req_att][SUM_SCORE]["value"]
                        req_att_scores[req_att].append(att_score)
                    else:
                        req_att_scores[req_att].append(0)
            else:
                raise ValueError(req_response)
        else:
            try:
                req_response.raise_for_status()
            except Exception as e:
                logging.info(e)
                return req_att_scores
        if i % 10 == 0:
            plot_toxicity(req_att_scores)
            print(len(texts[:i]), len(req_att_scores["TOXICITY"]))
            yield {"toxicity check in progress...": i / n_samples}, plt.gcf(), pd.DataFrame.from_dict({column_name: texts[:i+1], **req_att_scores})

    plot_toxicity(req_att_scores)
    yield {"toxicity check finished.": 1.}, plt.gcf(), pd.DataFrame.from_dict({column_name: texts, **req_att_scores})


with gr.Blocks() as demo:
    gr.Markdown(
        """
        # 💫 Dataset Quality Checker 💫
        This space:
         * uses [NVIDIA's quality classifier model](https://huggingface.co/nvidia/quality-classifier-deberta) 
        on a subset of any text dataset on the Hub to give a quick glance on the quality of texts. 
         * uses [Perspective](https://perspectiveapi.com/how-it-works/) to check toxicity of some random samples
        ## Select dataset and text column
        """
    )
    with gr.Row():
        with gr.Column(scale=3):
            dataset_name = HuggingfaceHubSearch(
                    label="Hub Dataset ID",
                    placeholder="Search for dataset id on Huggingface",
                    search_type="dataset",
                )
        subset_dropdown = gr.Dropdown(label="Subset", visible=False)
        split_dropdown = gr.Dropdown(label="Split", visible=False)

    # config_name = "default"  # TODO: user input
    with gr.Accordion("Dataset preview", open=False):
        @gr.render(inputs=[dataset_name, subset_dropdown, split_dropdown])
        def embed(name, subset, split):
            html_code = f"""
            <iframe
              src="https://huggingface.co/datasets/{name}/embed/viewer/{subset}/{split}"
              frameborder="0"
              width="100%"
              height="600px"
            ></iframe>
                """
            return gr.HTML(value=html_code)

    with gr.Row():
        text_column_dropdown = gr.Dropdown(label="Text column name")
        nested_text_column_dropdown = gr.Dropdown(visible=False)

    def _resolve_dataset_selection(dataset: str, default_subset: str, default_split: str, text_feature):
        if "/" not in dataset.strip().strip("/"):
            return {
                subset_dropdown: gr.Dropdown(visible=False),
                split_dropdown: gr.Dropdown(visible=False),
                text_column_dropdown: gr.Dropdown(label="Text column name"),
                nested_text_column_dropdown: gr.Dropdown(visible=False)
            }
        info_resp = session.get(f"https://datasets-server.huggingface.co/info?dataset={dataset}", timeout=3).json()
        if "error" in info_resp:
            return {
                subset_dropdown: gr.Dropdown(visible=False),
                split_dropdown: gr.Dropdown(visible=False),
                text_column_dropdown: gr.Dropdown(label="Text column name"),
                nested_text_column_dropdown: gr.Dropdown(visible=False)
            }
        subsets: list[str] = list(info_resp["dataset_info"])
        subset = default_subset if default_subset in subsets else subsets[0]
        splits: list[str] = info_resp["dataset_info"][subset]["splits"]
        split = default_split if default_split in splits else splits[0]
        features = info_resp["dataset_info"][subset]["features"]

        def _is_string_feature(feature):
            return isinstance(feature, dict) and feature.get("dtype") == "string"

        text_features = [feature_name for feature_name, feature in features.items() if _is_string_feature(feature)]
        nested_features = [feature_name for feature_name, feature in features.items() if isinstance(feature, dict) and isinstance(next(iter(feature.values())), dict)]
        nested_text_features = [feature_name for feature_name in nested_features if any(_is_string_feature(nested_feature) for nested_feature in features[feature_name].values())]
        if not text_feature:
            return {
                subset_dropdown: gr.Dropdown(value=subset, choices=subsets, visible=len(subsets) > 1),
                split_dropdown: gr.Dropdown(value=split, choices=splits, visible=len(splits) > 1),
                text_column_dropdown: gr.Dropdown(choices=text_features + nested_text_features, label="Text column name",),
                nested_text_column_dropdown: gr.Dropdown(visible=False),
            }
        logging.info(nested_text_features)
        if text_feature in nested_text_features:
            nested_keys = [feature_name for feature_name, feature in features[text_feature].items() if _is_string_feature(feature)]
            return {
                subset_dropdown: gr.Dropdown(value=subset, choices=subsets, visible=len(subsets) > 1),
                split_dropdown: gr.Dropdown(value=split, choices=splits, visible=len(splits) > 1),
                text_column_dropdown: gr.Dropdown(choices=text_features + nested_text_features,
                                                  label="Text column name"),
                nested_text_column_dropdown: gr.Dropdown(value=nested_keys[0], choices=nested_keys,
                                                         label="Nested text column name", visible=True)
            }
        return {
            subset_dropdown: gr.Dropdown(value=subset, choices=subsets, visible=len(subsets) > 1),
            split_dropdown: gr.Dropdown(value=split, choices=splits, visible=len(splits) > 1),
            text_column_dropdown: gr.Dropdown(choices=text_features + nested_text_features, label="Text column name", info="Text colum name to check (only non-nested texts are supported)"),
            nested_text_column_dropdown: gr.Dropdown(visible=False),
        }

    @dataset_name.change(inputs=[dataset_name], outputs=[subset_dropdown, split_dropdown, text_column_dropdown, nested_text_column_dropdown])
    def show_input_from_subset_dropdown(dataset: str) -> dict:
        return _resolve_dataset_selection(dataset, default_subset="default", default_split="train", text_feature=None)

    @subset_dropdown.change(inputs=[dataset_name, subset_dropdown], outputs=[subset_dropdown, split_dropdown, text_column_dropdown, nested_text_column_dropdown])
    def show_input_from_subset_dropdown(dataset: str, subset: str) -> dict:
        return _resolve_dataset_selection(dataset, default_subset=subset, default_split="train", text_feature=None)

    @split_dropdown.change(inputs=[dataset_name, subset_dropdown, split_dropdown], outputs=[subset_dropdown, split_dropdown, text_column_dropdown, nested_text_column_dropdown])
    def show_input_from_split_dropdown(dataset: str, subset: str, split: str) -> dict:
        return _resolve_dataset_selection(dataset, default_subset=subset, default_split=split, text_feature=None)

    @text_column_dropdown.change(inputs=[dataset_name, subset_dropdown, split_dropdown, text_column_dropdown], outputs=[subset_dropdown, split_dropdown, text_column_dropdown, nested_text_column_dropdown])
    def show_input_from_text_column_dropdown(dataset: str, subset: str, split: str, text_column) -> dict:
        return _resolve_dataset_selection(dataset, default_subset=subset, default_split=split, text_feature=text_column)

    gr.Markdown("## Run nvidia quality classifier")
    batch_size = gr.Slider(0, 64, 32, step=4, label="Inference batch size", info="(set this to smaller value if this space crashes.)")
    num_examples = gr.Slider(0, 1000, 500, step=10, label="Number of examples", info="Number of random examples to run quality classifier on")
    gr_check_btn = gr.Button("Check Dataset")
    progress_bar = gr.Label(show_label=False)
    plot = gr.BarPlot()


    with gr.Accordion("Explore some individual examples for each class", open=False):
        gr.Markdown("### Low")
        df_low = gr.DataFrame()
        gr.Markdown("### Medium")
        df_medium = gr.DataFrame()
        gr.Markdown("### High")
        df_high = gr.DataFrame()

    texts_df = gr.DataFrame(visible=False)

    gr.Examples(
        [
            ["HuggingFaceFW/fineweb-edu", "default", "train", "text", None, 16, 500],
            # ["fka/awesome-chatgpt-prompts", "default", "train", "prompt", 64, 200],
            # ["proj-persona/PersonaHub", "instruction", "train", "synthesized text",  32, 1000],
            ["argilla/FinePersonas-v0.1", "default", "train", "persona", None, 64, 1000],
            ["allenai/real-toxicity-prompts", "default", "train", "continuation", "text", 64, 1000],
        ],
        [dataset_name, subset_dropdown, split_dropdown, text_column_dropdown, nested_text_column_dropdown, batch_size, num_examples],
        [progress_bar, plot, df_low, df_medium, df_high, texts_df],
        fn=run_quality_check,
        run_on_click=False,
        cache_examples=False,
    )

    gr_check_btn.click(
        run_quality_check,
        inputs=[dataset_name, subset_dropdown, split_dropdown, text_column_dropdown, nested_text_column_dropdown, batch_size, num_examples],
        outputs=[progress_bar, plot, df_low, df_medium, df_high, texts_df]
    )

    gr.Markdown("""## Explore toxicity
    Run [Perspective](https://perspectiveapi.com/how-it-works/) on 100 random samples to check toxicity
    """)
    # checkbox = gr.Checkbox(value=False, label="Run on full first parquet data (better not)")
    gr_toxicity_btn = gr.Button("Run Perpspective API")
    toxicity_progress_bar = gr.Label(show_label=False)
    toxicity_hist = gr.Plot()
    with gr.Accordion("Explore examples with toxicity scores:", open=False):
        toxicity_df = gr.DataFrame()
    gr_toxicity_btn.click(
        call_perspective_api,
        inputs=[texts_df, text_column_dropdown, nested_text_column_dropdown, dataset_name, subset_dropdown, split_dropdown],#, checkbox],
        outputs=[toxicity_progress_bar, toxicity_hist, toxicity_df]
    )


demo.launch()