# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""

import evaluate
import datasets
import numpy as np

#from seametrics.horizon.utils import *

# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""

# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is designed to solve this great ML task and is crafted with a lot of care.
"""

# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of predictions to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Returns:
    accuracy: description of the first score,
    another_score: description of the second score,
Examples:
    Examples should be written in doctest format, and should illustrate how
    to use the function.

    >>> my_new_module = evaluate.load("my_new_module")
    >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
    >>> print(results)
    {'accuracy': 1.0}
"""

# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION,
                                                _KWARGS_DESCRIPTION)
# begin utils
def xy_points_to_slope_midpoint(xy_points):
    """
    Given two points, return the slope and midpoint of the line

    Args:
    xy_points: list of two points, each point is a list of two elements
    Points are in the form of [x, y], where x and y are normalized to [0, 1]

    Returns:
    slope: Slope of the line
    midpoint : Midpoint is in the form of [x,y], and is also normalized to [0, 1]
    """

    #x1, y1, x2, y2 = xy_points[0][0], xy_points[0][1], xy_points[1][
    #    0], xy_points[1][1]

    x1, y1, x2, y2 = xy_points[0], xy_points[1], xy_points[2], xy_points[3]
    slope = (y2 - y1) / (x2 - x1)

    midpoint_x = 0.5
    midpoint_y = slope * (0.5 - x1) + y1
    midpoint = [midpoint_x, midpoint_y]
    return slope, midpoint


def calculate_horizon_error(annotated_horizon, proposed_horizon):
    """
    Calculate the error between the annotated horizon and the proposed horizon

    Args:
    annotated_horizon: list of two points, each point is a list of two elements
    Points are in the form of [x, y], where x and y are normalized to [0, 1]
    proposed_horizon: list of two points, each point is a list of two elements
    Points are in the form of [x, y], where x and y are normalized to [0, 1]

    Returns:
    slope_error: Error in the slope of the lines
    midpoint_error: Error in the midpoint_y of the lines
    """

    slope_annotated, midpoint_annotated = xy_points_to_slope_midpoint(
        annotated_horizon)
    slope_proposed, midpoint_proposed = xy_points_to_slope_midpoint(
        proposed_horizon)

    slope_error = abs(slope_annotated - slope_proposed)
    midpoint_error = abs(midpoint_annotated[1] - midpoint_proposed[1])

    return slope_error, midpoint_error


def calculate_horizon_error_across_sequence(slope_error_list,
                                            midpoint_error_list,
                                            slope_error_jump_threshold,
                                            midpoint_error_jump_threshold):
    """
    Calculate the error statistics across a sequence of frames

    Args:
    slope_error_list: List of errors in the slope of the lines
    midpoint_error_list: List of errors in the midpoint_y of the lines

    Returns:
    average_slope_error: Average error in the slope of the lines
    average_midpoint_error: Average error in the midpoint_y of the lines
    """

    # Calculate the average and standard deviation of the errors
    average_slope_error = np.mean(slope_error_list)
    average_midpoint_error = np.mean(midpoint_error_list)

    stddev_slope_error = np.std(slope_error_list)
    stddev_midpoint_error = np.std(midpoint_error_list)

    # Calculate the maximum errors
    max_slope_error = np.max(slope_error_list)
    max_midpoint_error = np.max(midpoint_error_list)

    # Calculate the differences between errors in successive frames
    diff_slope_error = np.abs(np.diff(slope_error_list))
    diff_midpoint_error = np.abs(np.diff(midpoint_error_list))

    # Calculate the number of jumps in the errors
    num_slope_error_jumps = np.sum(
        diff_slope_error > slope_error_jump_threshold)
    num_midpoint_error_jumps = np.sum(
        diff_midpoint_error > midpoint_error_jump_threshold)

    # Create a dictionary to store the results
    sequence_results = {
        'average_slope_error': average_slope_error,
        'average_midpoint_error': average_midpoint_error,
        'stddev_slope_error': stddev_slope_error,
        'stddev_midpoint_error': stddev_midpoint_error,
        'max_slope_error': max_slope_error,
        'max_midpoint_error': max_midpoint_error,
        'num_slope_error_jumps': num_slope_error_jumps,
        'num_midpoint_error_jumps': num_midpoint_error_jumps
    }

    return sequence_results


def calculate_horizon_error(annotated_horizon, proposed_horizon):
    """
    Calculate the error between the annotated horizon and the proposed horizon

    Args:
    annotated_horizon: list of two points, each point is a list of two elements
    Points are in the form of [x, y], where x and y are normalized to [0, 1]
    proposed_horizon: list of two points, each point is a list of two elements
    Points are in the form of [x, y], where x and y are normalized to [0, 1]

    Returns:
    slope_error: Error in the slope of the lines
    midpoint_error: Error in the midpoint_y of the lines
    """

    slope_annotated, midpoint_annotated = xy_points_to_slope_midpoint(
        annotated_horizon)
    slope_proposed, midpoint_proposed = xy_points_to_slope_midpoint(
        proposed_horizon)

    slope_error = abs(slope_annotated - slope_proposed)
    midpoint_error = abs(midpoint_annotated[1] - midpoint_proposed[1])

    return slope_error, midpoint_error


def calculate_horizon_error_across_sequence(slope_error_list,
                                            midpoint_error_list,
                                            slope_error_jump_threshold,
                                            midpoint_error_jump_threshold):
    """
    Calculate the error statistics across a sequence of frames

    Args:
    slope_error_list: List of errors in the slope of the lines
    midpoint_error_list: List of errors in the midpoint_y of the lines

    Returns:
    average_slope_error: Average error in the slope of the lines
    average_midpoint_error: Average error in the midpoint_y of the lines
    """

    # Calculate the average and standard deviation of the errors
    average_slope_error = np.mean(slope_error_list)
    average_midpoint_error = np.mean(midpoint_error_list)

    stddev_slope_error = np.std(slope_error_list)
    stddev_midpoint_error = np.std(midpoint_error_list)

    # Calculate the maximum errors
    max_slope_error = np.max(slope_error_list)
    max_midpoint_error = np.max(midpoint_error_list)

    # Calculate the differences between errors in successive frames
    diff_slope_error = np.abs(np.diff(slope_error_list))
    diff_midpoint_error = np.abs(np.diff(midpoint_error_list))

    # Calculate the number of jumps in the errors
    num_slope_error_jumps = np.sum(
        diff_slope_error > slope_error_jump_threshold)
    num_midpoint_error_jumps = np.sum(
        diff_midpoint_error > midpoint_error_jump_threshold)

    # Create a dictionary to store the results
    sequence_results = {
        'average_slope_error': average_slope_error,
        'average_midpoint_error': average_midpoint_error,
        'stddev_slope_error': stddev_slope_error,
        'stddev_midpoint_error': stddev_midpoint_error,
        'max_slope_error': max_slope_error,
        'max_midpoint_error': max_midpoint_error,
        'num_slope_error_jumps': num_slope_error_jumps,
        'num_midpoint_error_jumps': num_midpoint_error_jumps
    }

    return sequence_results


def slope_to_roll(slope):
    """
    Convert the slope of the horizon to roll

    Args:
    slope: Slope of the horizon

    Returns:
    roll: Roll in degrees
    """
    roll = np.arctan(slope) * 180 / np.pi
    return roll


def roll_to_slope(roll):
    """
    Convert the roll of the horizon to slope

    Args:
    roll: Roll of the horizon in degrees

    Returns:
    slope: Slope of the horizon
    """
    slope = np.tan(roll * np.pi / 180)
    return slope


def midpoint_to_pitch(midpoint, vertical_fov_degrees):
    """
    Convert the midpoint of the horizon to pitch

    Args:
    midpoint: Midpoint of the horizon
    vertical_fov_degrees: Vertical field of view of the camera in degrees

    Returns:
    pitch: Pitch in degrees
    """
    pitch = midpoint * vertical_fov_degrees
    return pitch


def pitch_to_midpoint(pitch, vertical_fov_degrees):
    """
    Convert the pitch of the horizon to midpoint

    Args:
    pitch: Pitch of the horizon in degrees
    vertical_fov_degrees: Vertical field of view of the camera in degrees

    Returns:
    midpoint: Midpoint of the horizon
    """
    midpoint = pitch / vertical_fov_degrees
    return midpoint


# end utils
class horizonmetrics(evaluate.Metric):
    """TODO: Short description of my evaluation module."""

    def __init__(self,
                 slope_threshold=0.1,
                 midpoint_threshold=0.1,
                 vertical_fov_degrees=25.6,
                 **kwargs):
        super().__init__(**kwargs)
        self.slope_threshold = slope_threshold
        self.midpoint_threshold = midpoint_threshold
        self.vertical_fov_degrees = vertical_fov_degrees
        self.predictions = None
        self.ground_truth_det = None
        self.slope_error_list = None
        self.midpoint_error_list = None

    def _info(self):
        # TODO: Specifies the evaluate.EvaluationModuleInfo object
        return evaluate.MetricInfo(
            # This is the description that will appear on the modules page.
            module_type="metric",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            features=datasets.Features({
                'predictions': datasets.Value('int64'),
                'references': datasets.Value('int64'),
            }),
            # Homepage of the module for documentation
            homepage="http://module.homepage",
            # Additional links to the codebase or references
            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
            reference_urls=["http://path.to.reference.url/new_module"])

    def add(self, *, predictions, references, **kwargs):
        """
        Update the predictions and ground truth detections.

        Parameters
        ----------
        predictions : list
            List of predicted horizons.
        ground_truth_det : list
            List of ground truth horizons.

        """
        self.predictions = predictions
        self.ground_truth_det = references
        self.slope_error_list = []
        self.midpoint_error_list = []

        for annotated_horizon, proposed_horizon in zip(self.ground_truth_det,
                                                       self.predictions):
            slope_error, midpoint_error = calculate_horizon_error(
                annotated_horizon, proposed_horizon)
            self.slope_error_list.append(slope_error)
            self.midpoint_error_list.append(midpoint_error)

        # does not impact the metric, but is required for the interface x_x
        super(evaluate.Metric, self).add(prediction=predictions,
                                         references=references,
                                         **kwargs)

    def _compute(self):
        """
        Compute the horizon error across the sequence.

        Returns
        -------
        float
            The computed horizon error.

        """
        return calculate_horizon_error_across_sequence(
            self.slope_error_list, self.midpoint_error_list,
            self.slope_threshold, self.midpoint_threshold)

    def _download_and_prepare(self, dl_manager):
        """Optional: download external resources useful to compute the scores"""
        # TODO: Download external resources if needed
        pass