'''
    This code is partially borrowed from IFRNet (https://github.com/ltkong218/IFRNet). 
'''
import re
import sys
import torch
import random
import numpy as np
from PIL import ImageFile
import torch.nn.functional as F
from imageio import imread, imwrite
ImageFile.LOAD_TRUNCATED_IMAGES = True

class InputPadder:
    """ Pads images such that dimensions are divisible by divisor """
    def __init__(self, dims, divisor=16):
        self.ht, self.wd = dims[-2:]
        pad_ht = (((self.ht // divisor) + 1) * divisor - self.ht) % divisor
        pad_wd = (((self.wd // divisor) + 1) * divisor - self.wd) % divisor
        self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2]

    def pad(self, *inputs):
        if len(inputs) == 1:
            return F.pad(inputs[0], self._pad, mode='replicate')
        else:
            return [F.pad(x, self._pad, mode='replicate') for x in inputs]

    def unpad(self, *inputs):
        if len(inputs) == 1:
            return self._unpad(inputs[0])
        else:
            return [self._unpad(x) for x in inputs]
    
    def _unpad(self, x):
        ht, wd = x.shape[-2:]
        c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]]
        return x[..., c[0]:c[1], c[2]:c[3]]

def img2tensor(img):
    return torch.tensor(img).permute(2, 0, 1).unsqueeze(0) / 255.0

def tensor2img(img_t):
    return (img_t * 255.).detach(
                        ).squeeze(0).permute(1, 2, 0).cpu().numpy(
                        ).clip(0, 255).astype(np.uint8)


def read(file):
    if file.endswith('.float3'): return readFloat(file)
    elif file.endswith('.flo'): return readFlow(file)
    elif file.endswith('.ppm'): return readImage(file)
    elif file.endswith('.pgm'): return readImage(file)
    elif file.endswith('.png'): return readImage(file)
    elif file.endswith('.jpg'): return readImage(file)
    elif file.endswith('.pfm'): return readPFM(file)[0]
    else: raise Exception('don\'t know how to read %s' % file)

def write(file, data):
    if file.endswith('.float3'): return writeFloat(file, data)
    elif file.endswith('.flo'): return writeFlow(file, data)
    elif file.endswith('.ppm'): return writeImage(file, data)
    elif file.endswith('.pgm'): return writeImage(file, data)
    elif file.endswith('.png'): return writeImage(file, data)
    elif file.endswith('.jpg'): return writeImage(file, data)
    elif file.endswith('.pfm'): return writePFM(file, data)
    else: raise Exception('don\'t know how to write %s' % file)

def readPFM(file):
    file = open(file, 'rb')

    color = None
    width = None
    height = None
    scale = None
    endian = None

    header = file.readline().rstrip()
    if header.decode("ascii") == 'PF':
        color = True
    elif header.decode("ascii") == 'Pf':
        color = False
    else:
        raise Exception('Not a PFM file.')

    dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline().decode("ascii"))
    if dim_match:
        width, height = list(map(int, dim_match.groups()))
    else:
        raise Exception('Malformed PFM header.')

    scale = float(file.readline().decode("ascii").rstrip())
    if scale < 0:
        endian = '<'
        scale = -scale
    else:
        endian = '>'

    data = np.fromfile(file, endian + 'f')
    shape = (height, width, 3) if color else (height, width)

    data = np.reshape(data, shape)
    data = np.flipud(data)
    return data, scale

def writePFM(file, image, scale=1):
    file = open(file, 'wb')

    color = None

    if image.dtype.name != 'float32':
        raise Exception('Image dtype must be float32.')

    image = np.flipud(image)

    if len(image.shape) == 3 and image.shape[2] == 3:
        color = True
    elif len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1:
        color = False
    else:
        raise Exception('Image must have H x W x 3, H x W x 1 or H x W dimensions.')

    file.write('PF\n' if color else 'Pf\n'.encode())
    file.write('%d %d\n'.encode() % (image.shape[1], image.shape[0]))

    endian = image.dtype.byteorder

    if endian == '<' or endian == '=' and sys.byteorder == 'little':
        scale = -scale

    file.write('%f\n'.encode() % scale)

    image.tofile(file)

def readFlow(name):
    if name.endswith('.pfm') or name.endswith('.PFM'):
        return readPFM(name)[0][:,:,0:2]

    f = open(name, 'rb')

    header = f.read(4)
    if header.decode("utf-8") != 'PIEH':
        raise Exception('Flow file header does not contain PIEH')

    width = np.fromfile(f, np.int32, 1).squeeze()
    height = np.fromfile(f, np.int32, 1).squeeze()

    flow = np.fromfile(f, np.float32, width * height * 2).reshape((height, width, 2))

    return flow.astype(np.float32)

def readImage(name):
    if name.endswith('.pfm') or name.endswith('.PFM'):
        data = readPFM(name)[0]
        if len(data.shape)==3:
            return data[:,:,0:3]
        else:
            return data
    return imread(name)

def writeImage(name, data):
    if name.endswith('.pfm') or name.endswith('.PFM'):
        return writePFM(name, data, 1)
    return imwrite(name, data)

def writeFlow(name, flow):
    f = open(name, 'wb')
    f.write('PIEH'.encode('utf-8'))
    np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
    flow = flow.astype(np.float32)
    flow.tofile(f)

def readFloat(name):
    f = open(name, 'rb')

    if(f.readline().decode("utf-8"))  != 'float\n':
        raise Exception('float file %s did not contain <float> keyword' % name)

    dim = int(f.readline())

    dims = []
    count = 1
    for i in range(0, dim):
        d = int(f.readline())
        dims.append(d)
        count *= d

    dims = list(reversed(dims))

    data = np.fromfile(f, np.float32, count).reshape(dims)
    if dim > 2:
        data = np.transpose(data, (2, 1, 0))
        data = np.transpose(data, (1, 0, 2))

    return data

def writeFloat(name, data):
    f = open(name, 'wb')

    dim=len(data.shape)
    if dim>3:
        raise Exception('bad float file dimension: %d' % dim)

    f.write(('float\n').encode('ascii'))
    f.write(('%d\n' % dim).encode('ascii'))

    if dim == 1:
        f.write(('%d\n' % data.shape[0]).encode('ascii'))
    else:
        f.write(('%d\n' % data.shape[1]).encode('ascii'))
        f.write(('%d\n' % data.shape[0]).encode('ascii'))
        for i in range(2, dim):
            f.write(('%d\n' % data.shape[i]).encode('ascii'))

    data = data.astype(np.float32)
    if dim==2:
        data.tofile(f)

    else:
        np.transpose(data, (2, 0, 1)).tofile(f)

def warp(img, flow):
    B, _, H, W = flow.shape
    xx = torch.linspace(-1.0, 1.0, W).view(1, 1, 1, W).expand(B, -1, H, -1)
    yy = torch.linspace(-1.0, 1.0, H).view(1, 1, H, 1).expand(B, -1, -1, W)
    grid = torch.cat([xx, yy], 1).to(img)
    flow_ = torch.cat([flow[:, 0:1, :, :] / ((W - 1.0) / 2.0), flow[:, 1:2, :, :] / ((H - 1.0) / 2.0)], 1)
    grid_ = (grid + flow_).permute(0, 2, 3, 1)
    output = F.grid_sample(input=img, grid=grid_, mode='bilinear', padding_mode='border', align_corners=True)
    return output

def check_dim_and_resize(tensor_list):
    shape_list = []
    for t in tensor_list:
        shape_list.append(t.shape[2:])

    if len(set(shape_list)) > 1:
        desired_shape = shape_list[0]
        print(f'Inconsistent size of input video frames. All frames will be resized to {desired_shape}')
        
        resize_tensor_list = []
        for t in tensor_list:
            resize_tensor_list.append(torch.nn.functional.interpolate(t, size=tuple(desired_shape), mode='bilinear'))

        tensor_list = resize_tensor_list

    return tensor_list