"""
Gradio interface for plotting attention.
"""

import chess
import chess.pgn
import io
import gradio as gr
import os
import torch

from lczerolens import LczeroBoard, LczeroModel, Lens

from demo import constants
from demo.utils import get_info

def get_model(model_name: str):
    return LczeroModel.from_onnx_path(os.path.join(constants.ONNX_MODEL_DIRECTORY, model_name))

def get_activations(model: LczeroModel, board: LczeroBoard):
    lens = Lens.from_name("activation", "block\d/conv2/relu")
    with torch.no_grad():
        results = lens.analyse(model, board)
    return [results[f"block{i}/conv2/relu_output"][0] for i in range(len(results))]

def get_board(game_pgn:str, board_fen:str):
    if game_pgn:
        try:
            board = LczeroBoard()
            pgn = io.StringIO(game_pgn)
            game = chess.pgn.read_game(pgn)
            for move in game.mainline_moves():
                board.push(move)
        except Exception as e:
            print(e)
            gr.Warning("Error parsing PGN, using starting position.")
            board = LczeroBoard()
    else:
        try:
            board = LczeroBoard(board_fen)
        except Exception as e:
            print(e)
            gr.Warning("Invalid FEN, using starting position.")
            board = LczeroBoard()
    return board

def render_activations(board: LczeroBoard, activations, layer_index:int, channel_index:int):
    if layer_index >= len(activations):
        safe_layer_index = len(activations) - 1
        gr.Warning(f"Layer index {layer_index} out of range, using last layer ({safe_layer_index}).")
    else:
        safe_layer_index = layer_index
    if channel_index >= activations[safe_layer_index].shape[0]:
        safe_channel_index = activations[safe_layer_index].shape[0] - 1
        gr.Warning(f"Channel index {channel_index} out of range, using last channel ({safe_channel_index}).")
    else:
        safe_channel_index = channel_index
    heatmap = activations[safe_layer_index][safe_channel_index].view(64)
    board.render_heatmap(
        heatmap,
        save_to=f"{constants.FIGURE_DIRECTORY}/activations.svg",
    )
    return  f"{constants.FIGURE_DIRECTORY}/activations_board.svg", f"{constants.FIGURE_DIRECTORY}/activations_colorbar.svg"

def initial_load(model_name: str, board_fen: str, game_pgn: str, layer_index: int, channel_index: int):
    model = get_model(model_name)
    board = get_board(game_pgn, board_fen)
    activations = get_activations(model, board)
    info = get_info(model, board)
    plots = render_activations(board, activations, layer_index, channel_index)
    return model, board, activations, info, *plots

def on_board_change(model: LczeroModel, game_pgn: str, board_fen: str, layer_index: int, channel_index: int):
    board = get_board(game_pgn, board_fen)
    activations = get_activations(model, board)
    info = get_info(model, board)
    plots = render_activations(board, activations, layer_index, channel_index)
    return board, activations, info, *plots

def on_model_change(model_name: str, board: LczeroBoard, layer_index: int, channel_index: int):
    model = get_model(model_name)
    activations = get_activations(model, board)
    info = get_info(model, board)
    plots = render_activations(board, activations, layer_index, channel_index)
    return model, activations, info, *plots

with gr.Blocks() as interface:
    with gr.Row():
        with gr.Column():
            with gr.Group():
                gr.Markdown(
                    "Specify the game PGN or FEN string that you want to analyse (PGN overrides FEN)."
                )
                game_pgn = gr.Textbox(
                    label="Game PGN",
                    lines=1,
                    value="",
                )
                board_fen = gr.Textbox(
                    label="Board FEN",
                    lines=1,
                    max_lines=1,
                    value=chess.STARTING_FEN,
                )
                model_name = gr.Dropdown(
                    label="Model",
                    choices=constants.ONNX_MODEL_NAMES,
                )
            with gr.Group():
                info = gr.Textbox(label="Info", lines=1, value="")
            with gr.Group(): 
                layer_index = gr.Slider(
                    label="Layer index",
                    minimum=0,
                    maximum=19,
                    step=1,
                    value=0,
                )
                channel_index = gr.Slider(
                    label="Channel index",
                    minimum=0,
                    maximum=200,
                    step=1,
                    value=0,
                )
        with gr.Column():
            image_board = gr.Image(label="Board", interactive=False)
            colorbar = gr.Image(label="Colorbar", interactive=False)

    model = gr.State(value=None)
    board = gr.State(value=None)
    activations = gr.State(value=None)

    interface.load(
        initial_load,
        inputs=[model_name, game_pgn, board_fen, layer_index, channel_index],
        outputs=[model, board, activations, info, image_board, colorbar],
        concurrency_limit=1, 
        concurrency_id="trace_queue"
    )
    game_pgn.submit(
        on_board_change,
        inputs=[model, game_pgn, board_fen, layer_index, channel_index],
        outputs=[board, activations, info, image_board, colorbar],
        concurrency_id="trace_queue"
    )
    board_fen.submit(
        on_board_change,
        inputs=[model, game_pgn, board_fen, layer_index, channel_index],
        outputs=[board, activations, info, image_board, colorbar],
        concurrency_id="trace_queue"
    )
    model_name.change(
        on_model_change,
        inputs=[model_name, board, layer_index, channel_index],
        outputs=[model, activations, info, image_board, colorbar],
        concurrency_id="trace_queue"
    )
    layer_index.change(
        render_activations,
        inputs=[board, activations, layer_index, channel_index],
        outputs=[image_board, colorbar],
    )
    channel_index.change(
        render_activations,
        inputs=[board, activations, layer_index, channel_index],
        outputs=[image_board, colorbar],
    )