import io
import os
from datetime import datetime
from typing import Optional

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from ganime.visualization.images import display_true_pred, unnormalize_if_necessary


def get_logdir(parent_folder: str, experiment_name: Optional[str] = None) -> str:
    """Get the logdir used for logging in tensorboard. The logdir will be the parent folder with the experiment name and the current date and time.

    Args:
        parent_folder (str): The parent folder of the logdir
        experiment_name (str, optional): Optinal name of the experiment. Defaults to "".

    Returns:
        str: The path of the logdir that can be used by Tensorboard
    """
    current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    sub_folder = (
        f"{experiment_name}_{current_time}" if experiment_name else current_time
    )
    logdir = os.path.join(parent_folder, sub_folder)
    return logdir


def plot_to_image(figure):
    """Converts the matplotlib plot specified by 'figure' to a PNG image and
    returns it. The supplied figure is closed and inaccessible after this call."""
    # Save the plot to a PNG in memory.
    buf = io.BytesIO()
    plt.savefig(buf, format="png")
    # Closing the figure prevents it from being displayed directly inside
    # the notebook.
    plt.close(figure)
    buf.seek(0)
    # Convert PNG buffer to TF image
    image = tf.image.decode_png(buf.getvalue(), channels=4)
    # Add the batch dimension
    image = tf.expand_dims(image, 0)
    return image


class TensorboardImage(tf.keras.callbacks.Callback):
    def __init__(
        self,
        logdir: str,
        train: np.array,
        validation: np.array = None,
        n_images: int = 8,
    ):
        super().__init__()
        self.logdir = logdir
        self.train = train
        self.validation = validation
        self.file_writer = tf.summary.create_file_writer(logdir)
        self.n_images = n_images

    def on_epoch_end(self, epoch, logs):
        train_X, train_y = self.train
        train_X, train_y = self.truncate_X_y(train_X, train_y, self.n_images)
        train_pred = self.model.predict(train_X)
        self.write_to_tensorboard(train_y, train_pred, "Training data", epoch)

        if self.validation is not None:
            validation_X, validation_y = self.validation
            validation_X, validation_y = self.truncate_X_y(
                validation_X, validation_y, self.n_images
            )
            validation_pred = self.model.predict(validation_X)
            self.write_to_tensorboard(
                validation_y, validation_pred, "Validation data", epoch
            )

    def truncate_X_y(self, X, y, n_images):
        """Truncate the X and y arrays to the first n_images."""
        X = X[:n_images]
        y = y[:n_images]
        return X, y

    def write_to_tensorboard(self, y_true, y_pred, tag, step):
        with self.file_writer.as_default():
            tf.summary.image(
                tag,
                plot_to_image(display_true_pred(y_true, y_pred, n_cols=len(y_true))),
                step=step,
            )


class TensorboardVideo(tf.keras.callbacks.Callback):
    def __init__(
        self,
        logdir: str,
        train: np.array,
        validation: np.array = None,
        n_videos: int = 3,
    ):
        super().__init__()
        self.logdir = logdir
        self.train = train
        self.validation = validation
        self.file_writer = tf.summary.create_file_writer(logdir)
        self.n_videos = n_videos

    def on_epoch_end(self, epoch, logs):

        # train_X, train_y = self.train
        # train_X, train_y = self.truncate_X_y(train_X, train_y, self.n_videos)
        train_pred = self.model.predict(self.train)
        self.write_to_tensorboard(
            unnormalize_if_necessary(self.train["y"]),
            train_pred,
            "Training data",
            epoch,
        )

        if self.validation is not None:
            # validation_X, validation_y = self.validation
            # validation_X, validation_y = self.truncate_X_y(
            #     validation_X, validation_y, self.n_videos
            # )
            validation_pred = self.model.predict(self.validation)
            self.write_to_tensorboard(
                unnormalize_if_necessary(self.validation["y"]),
                validation_pred,
                "Validation data",
                epoch,
            )

    def truncate_X_y(self, X, y, n_videos):
        """Truncate the X and y arrays to the first n_videos."""
        X = X[:n_videos]
        y = y[:n_videos]
        return X, y

    def write_to_tensorboard(self, y_true, y_pred, tag, step):
        stacked = tf.concat([y_pred, y_true], axis=2)
        self.video_summary(tag, stacked, step)
        self.image_summary(tag + "/images", y_true, y_pred, step)

    def image_summary(self, tag, y_true, y_pred, step):
        batch, n_frames, height, width, channels = y_true.shape
        images = np.empty(
            (batch * 2, n_frames, height, width, channels), dtype=np.float32
        )

        images[0::2] = y_pred
        images[1::2] = y_true
        images = tf.transpose(images, (0, 2, 1, 3, 4))
        images = tf.reshape(images, (height * batch * 2, width * n_frames, channels))

        with self.file_writer.as_default():
            tf.summary.image(tag, [images], step=step)

    def add_red_border(self, image_batch):
        image_batch = image_batch.copy()
        dtype = image_batch.dtype
        min_value = 0
        max_value = 1 if dtype in [np.float16, np.float32, np.float64] else 255
        # top
        image_batch[:, 0:2, :, 0] = max_value
        image_batch[:, 0:2, :, 1] = min_value
        image_batch[:, 0:2, :, 2] = min_value
        # bottom
        image_batch[:, -2:, :, 0] = max_value
        image_batch[:, -2:, :, 1] = min_value
        image_batch[:, -2:, :, 2] = min_value
        # left
        image_batch[:, :, 0:2, 0] = max_value
        image_batch[:, :, 0:2, 1] = min_value
        image_batch[:, :, 0:2, 2] = min_value
        # right
        image_batch[:, :, -2:, 0] = max_value
        image_batch[:, :, -2:, 1] = min_value
        image_batch[:, :, -2:, 2] = min_value
        return image_batch

    def video_summary(self, name, video, step=None, fps=10):
        name = tf.constant(name).numpy().decode("utf-8")
        video = np.array(video)
        if video.dtype in (np.float32, np.float64):
            video = np.clip(255 * video, 0, 255).astype(np.uint8)
        B, T, H, W, C = video.shape
        # video[:, 0] = self.add_red_border(video[:, 0])

        with self.file_writer.as_default():
            try:
                frames = video.transpose((1, 2, 0, 3, 4)).reshape((T, H, B * W, C))
                summary = tf.compat.v1.Summary()
                image = tf.compat.v1.Summary.Image(
                    height=B * H, width=T * W, colorspace=C
                )
                image.encoded_image_string = self.encode_gif(frames, fps)
                summary.value.add(tag=name + "/gif", image=image)
                tf.summary.experimental.write_raw_pb(summary.SerializeToString(), step)
            except (IOError, OSError) as e:
                print("GIF summaries require ffmpeg in $PATH.", e)
                frames = video.transpose((0, 2, 1, 3, 4)).reshape((1, B * H, T * W, C))
                tf.summary.image(name + "/grid", frames, step)

    def encode_gif(self, frames, fps):
        from subprocess import PIPE, Popen

        h, w, c = frames[0].shape
        pxfmt = {1: "gray", 3: "rgb24"}[c]
        cmd = " ".join(
            [
                f"ffmpeg -y -f rawvideo -vcodec rawvideo",
                f"-r {fps:.02f} -s {w}x{h} -pix_fmt {pxfmt} -i - -filter_complex",
                f"[0:v]split[x][z];[z]palettegen[y];[x]fifo[x];[x][y]paletteuse",
                f"-r {fps:.02f} -f gif -",
            ]
        )
        proc = Popen(cmd.split(" "), stdin=PIPE, stdout=PIPE, stderr=PIPE)
        for image in frames:
            proc.stdin.write(image.tostring())
        out, err = proc.communicate()
        if proc.returncode:
            raise IOError("\n".join([" ".join(cmd), err.decode("utf8")]))
        del proc
        return out