# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import random
import numpy as np
from typing import Dict, List, Optional, Tuple

def load_video_aux(path):
    import cv2
    cap = cv2.VideoCapture(path)
    frames = []
    while True:
        ret, frame = cap.read()
        if ret:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            frames.append(frame)
        else:
            break
    #print(path, frames == [])
    frames = np.stack(frames)
    return frames

def load_video(path):
    for i in range(3):
        try:
            ## load video
            return load_video_aux(path)
        except Exception:
            print(f"failed loading {path} ({i} / 3)")
            if i == 2:
                raise ValueError(f"Unable to load {path}")

class Compose(object):
    """Compose several preprocess together.
    Args:
        preprocess (list of ``Preprocess`` objects): list of preprocess to compose.
    """

    def __init__(self, preprocess):
        self.preprocess = preprocess

    def __call__(self, sample):
        for t in self.preprocess:
            sample = t(sample)
        return sample

    def __repr__(self):
        format_string = self.__class__.__name__ + '('
        for t in self.preprocess:
            format_string += '\n'
            format_string += '    {0}'.format(t)
        format_string += '\n)'
        return format_string


class Normalize(object):
    """Normalize a ndarray image with mean and standard deviation.
    """

    def __init__(self, mean, std):
        self.mean = mean
        self.std = std

    def __call__(self, frames):
        """
        Args:
            tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
        Returns:
            Tensor: Normalized Tensor image.
        """
        frames = (frames - self.mean) / self.std
        return frames

    def __repr__(self):
        return self.__class__.__name__+'(mean={0}, std={1})'.format(self.mean, self.std)

class CenterCrop(object):
    """Crop the given image at the center
    """
    def __init__(self, size):
        self.size = size

    def __call__(self, frames):
        """
        Args:
            img (numpy.ndarray): Images to be cropped.
        Returns:
            numpy.ndarray: Cropped image.
        """
        t, h, w = frames.shape
        th, tw = self.size
        delta_w = int(round((w - tw))/2.)
        delta_h = int(round((h - th))/2.)
        frames = frames[:, delta_h:delta_h+th, delta_w:delta_w+tw]
        return frames


class RandomCrop(object):
    """Crop the given image at the center
    """

    def __init__(self, size):
        self.size = size

    def __call__(self, frames):
        """
        Args:
            img (numpy.ndarray): Images to be cropped.
        Returns:
            numpy.ndarray: Cropped image.
        """
        t, h, w = frames.shape
        th, tw = self.size
        delta_w = random.randint(0, w-tw)
        delta_h = random.randint(0, h-th)
        frames = frames[:, delta_h:delta_h+th, delta_w:delta_w+tw]
        return frames

    def __repr__(self):
        return self.__class__.__name__ + '(size={0})'.format(self.size)

class HorizontalFlip(object):
    """Flip image horizontally.
    """

    def __init__(self, flip_ratio):
        self.flip_ratio = flip_ratio

    def __call__(self, frames):
        """
        Args:
            img (numpy.ndarray): Images to be flipped with a probability flip_ratio
        Returns:
            numpy.ndarray: Cropped image.
        """
        import cv2
        t, h, w = frames.shape
        if random.random() < self.flip_ratio:
            for index in range(t):
                frames[index] = cv2.flip(frames[index], 1)
        return frames

def compute_mask_indices(
    shape: Tuple[int, int],
    padding_mask: Optional[torch.Tensor],
    mask_prob: float,
    mask_length: int,
    mask_type: str = "static",
    mask_other: float = 0.0,
    min_masks: int = 0,
    no_overlap: bool = False,
    min_space: int = 0,
) -> np.ndarray:
    """
    Computes random mask spans for a given shape
    Args:
        shape: the the shape for which to compute masks.
            should be of size 2 where first element is batch size and 2nd is timesteps
        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
        mask_type: how to compute mask lengths
            static = fixed size
            uniform = sample from uniform distribution [mask_other, mask_length*2]
            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
            poisson = sample from possion distribution with lambda = mask length
        min_masks: minimum number of masked spans
        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
    """

    bsz, all_sz = shape
    mask = np.full((bsz, all_sz), False)

    all_num_mask = int(
        # add a random number for probabilistic rounding
        mask_prob * all_sz / float(mask_length)
        + np.random.rand()
    )

    all_num_mask = max(min_masks, all_num_mask)

    mask_idcs = []
    for i in range(bsz):
        if padding_mask is not None:
            sz = all_sz - padding_mask[i].long().sum().item()
            num_mask = int(
                # add a random number for probabilistic rounding
                mask_prob * sz / float(mask_length)
                + np.random.rand()
            )
            num_mask = max(min_masks, num_mask)
        else:
            sz = all_sz
            num_mask = all_num_mask

        if mask_type == "static":
            lengths = np.full(num_mask, mask_length)
        elif mask_type == "uniform":
            lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
        elif mask_type == "normal":
            lengths = np.random.normal(mask_length, mask_other, size=num_mask)
            lengths = [max(1, int(round(x))) for x in lengths]
        elif mask_type == "poisson":
            lengths = np.random.poisson(mask_length, size=num_mask)
            lengths = [int(round(x)) for x in lengths]
        else:
            raise Exception("unknown mask selection " + mask_type)

        if sum(lengths) == 0:
            lengths[0] = min(mask_length, sz - 1)

        if no_overlap:
            mask_idc = []

            def arrange(s, e, length, keep_length):
                span_start = np.random.randint(s, e - length)
                mask_idc.extend(span_start + i for i in range(length))

                new_parts = []
                if span_start - s - min_space >= keep_length:
                    new_parts.append((s, span_start - min_space + 1))
                if e - span_start - keep_length - min_space > keep_length:
                    new_parts.append((span_start + length + min_space, e))
                return new_parts

            parts = [(0, sz)]
            min_length = min(lengths)
            for length in sorted(lengths, reverse=True):
                lens = np.fromiter(
                    (e - s if e - s >= length + min_space else 0 for s, e in parts),
                    np.int,
                )
                l_sum = np.sum(lens)
                if l_sum == 0:
                    break
                probs = lens / np.sum(lens)
                c = np.random.choice(len(parts), p=probs)
                s, e = parts.pop(c)
                parts.extend(arrange(s, e, length, min_length))
            mask_idc = np.asarray(mask_idc)
        else:
            min_len = min(lengths)
            if sz - min_len <= num_mask:
                min_len = sz - num_mask - 1

            mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)

            mask_idc = np.asarray(
                [
                    mask_idc[j] + offset
                    for j in range(len(mask_idc))
                    for offset in range(lengths[j])
                ]
            )

        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))

    min_len = min([len(m) for m in mask_idcs])
    batch_indexes, starts, ends = [], [], []
    for i, mask_idc in enumerate(mask_idcs):
        if len(mask_idc) > min_len:
            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
        mask[i, mask_idc] = True
        vals, run_starts, run_lengths = find_runs(mask[i])
        start_indices, lengths = run_starts[vals == True], run_lengths[vals == True]
        starts.append(start_indices)
        ends.append(start_indices+lengths)
        batch_indexes.append(np.zeros([len(start_indices)])+i)
    return mask, np.concatenate(starts).astype(np.int64), np.concatenate(ends).astype(np.int64), np.concatenate(batch_indexes).astype(np.int64)

def find_runs(x):
    """Find runs of consecutive items in an array."""

    # ensure array
    x = np.asanyarray(x)
    if x.ndim != 1:
        raise ValueError('only 1D array supported')
    n = x.shape[0]

    # handle empty array
    if n == 0:
        return np.array([]), np.array([]), np.array([])

    else:
        # find run starts
        loc_run_start = np.empty(n, dtype=bool)
        loc_run_start[0] = True
        np.not_equal(x[:-1], x[1:], out=loc_run_start[1:])
        run_starts = np.nonzero(loc_run_start)[0]

        # find run values
        run_values = x[loc_run_start]

        # find run lengths
        run_lengths = np.diff(np.append(run_starts, n))

        return run_values, run_starts, run_lengths
