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# Copyright 2020 The GPflow Contributors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

""" Tasks that write to TensorBoard """

from io import BytesIO
from typing import Any, Callable, Dict, List, Optional, Union

import numpy as np
import tensorflow as tf

from ..base import Parameter
from ..models import BayesianModel
from ..utilities import parameter_dict
from .base import MonitorTask

__all__ = ["ToTensorBoard", "ModelToTensorBoard", "ScalarToTensorBoard", "ImageToTensorBoard"]

class ToTensorBoard(MonitorTask):
    writers = {}

    def __init__(self, log_dir: str):
        :param log_dir: directory in which to store the tensorboard files.
            Can be nested, e.g. ./logs/my_run/
        if log_dir not in self.writers:
            self.writers[log_dir] = tf.summary.create_file_writer(log_dir)
        self.file_writer = self.writers[log_dir]

    def __call__(self, step, **kwargs):
        with self.file_writer.as_default():
            super().__call__(step, **kwargs)

class ModelToTensorBoard(ToTensorBoard):
    Monitoring task that creates a sensible TensorBoard for a model.

    Monitors all the model's parameters for which their name matches with `keywords_to_monitor`.
    By default, "kernel" and "likelihood" are elements of `keywords_to_monitor`.
        keyword = "kernel", parameter = "kernel.lengthscale" => match
        keyword = "variational", parameter = "kernel.lengthscale" => no match

    def __init__(
        log_dir: str,
        model: BayesianModel,
        max_size: int = 3,
        keywords_to_monitor: List[str] = ["kernel", "likelihood"],
        left_strip_character: str = ".",
        :param log_dir: directory in which to store the tensorboard files.
            Can be a nested: for example, './logs/my_run/'.
        :param model: model to be monitord.
        :param max_size: maximum size of arrays (incl.) to store each
            element of the array independently as a scalar in the TensorBoard.
            Setting max_size to -1 will write all values. Use with care.
        :param keywords_to_monitor: specifies keywords to be monitored.
            If the parameter's name includes any of the keywords specified it
            will be monitored. By default, parameters that match the `kernel` or
            `likelihood` keyword are monitored.
            Adding a "*" to the list will match with all parameters,
            i.e. no parameters or variables will be filtered out.
        :param left_strip_character: certain frameworks prepend their variables with
            a character. GPflow adds a '.' and Keras add a '_', for example.
            When a `left_strip_character` is specified it will be stripped from the
            parameter's name. By default the '.' is left stripped, for example:
            ".likelihood.variance" becomes "likelihood.variance".
        self.model = model
        self.max_size = max_size
        self.keywords_to_monitor = keywords_to_monitor
        self.summarize_all = "*" in self.keywords_to_monitor
        self.left_strip_character = left_strip_character

    def run(self, **unused_kwargs):
        for name, parameter in parameter_dict(self.model).items():
            # check if the parameter name matches any of the specified keywords
            if self.summarize_all or any(keyword in name for keyword in self.keywords_to_monitor):
                # keys are sometimes prepended with a character, which we strip
                name = name.lstrip(self.left_strip_character)
                self._summarize_parameter(name, parameter)

    def _summarize_parameter(self, name: str, param: Union[Parameter, tf.Variable]):
        :param name: identifier used in tensorboard
        :param param: parameter to be stored in tensorboard
        param = tf.reshape(param, (-1,))
        size = param.shape[0]

        if not isinstance(size, int):
            raise ValueError(
                f"The monitoring can not be autographed as the size of a parameter {param} "
                "is unknown at compile time. If compiling the monitor task is important, "
                "make sure the shape of all parameters is known beforehand. Otherwise, "
                "run the monitor outside the `tf.function`."
        if size == 1:
            # if there's only one element do not add a numbered suffix
            tf.summary.scalar(name, param[0], step=self.current_step)
            it = range(size) if self.max_size == -1 else range(min(size, self.max_size))
            for i in it:
                tf.summary.scalar(f"{name}[{i}]", param[i], step=self.current_step)

class ScalarToTensorBoard(ToTensorBoard):
    """Stores the return value of a callback in a TensorBoard."""

    def __init__(self, log_dir: str, callback: Callable[[], float], name: str):
        :param log_dir: directory in which to store the tensorboard files.
            For example, './logs/my_run/'.
        :param callback: callback to be executed and result written to TensorBoard.
            A callback can have arguments (e.g. data) passed to the function using
            keyword arguments.
            For example:
            lambda cb(x=None): 2 * x
            task = ScalarToTensorBoard(logdir, cb, "callback")

            # specify the argument of the function using kwargs, the names need to match.
            task(step, x=1)
        :param name: name used in TensorBoard.
        self.name = name
        self.callback = callback

    def run(self, **kwargs):
        tf.summary.scalar(self.name, self.callback(**kwargs), step=self.current_step)

class ImageToTensorBoard(ToTensorBoard):
    def __init__(
        log_dir: str,
        plotting_function: Callable[
            ["matplotlib.figure.Figure", "matplotlib.figure.Axes"], "matplotlib.figure.Figure"
        name: Optional[str] = None,
        fig_kw: Optional[Dict[str, Any]] = None,
        subplots_kw: Optional[Dict[str, Any]] = None,
        :param log_dir: directory in which to store the tensorboard files.
            Can be nested: for example, './logs/my_run/'.
        :param plotting_function: function performing the plotting.
        :param name: name used in TensorBoard.
        :params fig_kw: keyword arguments to be passed to Figure constructor, e.g. `figsize`.
        :params subplots_kw: keyword arguments to be passed to figure.subplots constructor, e.g.
            `nrows`, `ncols`, `sharex`, `sharey`. By default the default values
            from matplotlib.pyplot are used.
        self.plotting_function = plotting_function
        self.name = name
        self.fig_kw = fig_kw or {}
        self.subplots_kw = subplots_kw or {}

            from matplotlib.figure import Figure
        except ImportError:
            raise RuntimeError("ImageToTensorBoard requires the matplotlib package to be installed")

        self.fig = Figure(**self.fig_kw)
        if self.subplots_kw != {}:
            self.axes = self.fig.subplots(**self.subplots_kw)
            self.axes = self.fig.add_subplot(111)

    def _clear_axes(self):
        if isinstance(self.axes, np.ndarray):
            for ax in self.axes.flatten():

    def run(self, **unused_kwargs):
        from matplotlib.backends.backend_agg import FigureCanvasAgg

        self.plotting_function(self.fig, self.axes)
        canvas = FigureCanvasAgg(self.fig)

        # get PNG data from the figure
        png_buffer = BytesIO()
        png_encoded = png_buffer.getvalue()

        image_tensor = tf.io.decode_png(png_encoded)[None]

        # Write to TensorBoard
        tf.summary.image(self.name, image_tensor, step=self.current_step)
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