"""This module contains simple helper functions """
from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
from typing import Any, List, Tuple, Union
import sys
import random

def set_seed(seed=None):
    if seed is not None:
        torch.manual_seed(seed)
        np.random.seed(seed)
        torch.backends.cudnn.deterministic = True
        random.seed(seed)
        torch.cuda.manual_seed_all(seed)
        torch.backends.cudnn.benchmark = True

class Logger(object):
    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""

    def __init__(self, file_name: str = None, file_mode: str = "a", should_flush: bool = True, append=False):
        self.file = None

        if append:
            file_mode = 'a'
        else:
            file_mode = 'w'
        self.file = open(file_name, file_mode)

        self.should_flush = should_flush
        self.stdout = sys.stdout
        self.stderr = sys.stderr

        sys.stdout = self
        sys.stderr = self

    def __enter__(self) -> "Logger":
        return self

    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
        self.close()

    def write(self, text: str) -> None:
        """Write text to stdout (and a file) and optionally flush."""
        if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
            return

        if self.file is not None:
            self.file.write(text)

        self.stdout.write(text)

        if self.should_flush:
            self.flush()

    def flush(self) -> None:
        """Flush written text to both stdout and a file, if open."""
        if self.file is not None:
            self.file.flush()

        self.stdout.flush()

    def close(self) -> None:
        """Flush, close possible files, and remove stdout/stderr mirroring."""
        self.flush()

        # if using multiple loggers, prevent closing in wrong order
        if sys.stdout is self:
            sys.stdout = self.stdout
        if sys.stderr is self:
            sys.stderr = self.stderr

        if self.file is not None:
            self.file.close()

def write_loss(iterations, trainer, train_writer, prefix):
    members = [attr for attr in dir(trainer) \
               if not callable(getattr(trainer, attr)) and not attr.startswith("__") and (
                       'loss' in attr or 'grad' in attr or 'nwd' in attr ) and 'name' not in attr and 'pool' not in attr]
    for m in members:
        train_writer.add_scalar(prefix+'/'+m, getattr(trainer, m), iterations + 1)


def format_time(seconds: Union[int, float]) -> str:
    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
    s = int(np.rint(seconds))

    if s < 60:
        return "{0}s".format(s)
    elif s < 60 * 60:
        return "{0}m {1:02}s".format(s // 60, s % 60)
    elif s < 24 * 60 * 60:
        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
    else:
        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)


def tensor2im(input_image, imtype=np.uint8):
    """"Converts a Tensor array into a numpy image array.

    Parameters:
        input_image (tensor) --  the input image tensor array
        imtype (type)        --  the desired type of the converted numpy array
    """
    if not isinstance(input_image, np.ndarray):
        if isinstance(input_image, torch.Tensor):  # get the data from a variable
            image_tensor = input_image.data
        else:
            return input_image
        image_numpy = image_tensor[0].cpu().float().numpy()  # convert it into a numpy array
        if image_numpy.shape[0] == 1:  # grayscale to RGB
            image_numpy = np.tile(image_numpy, (3, 1, 1))
        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0  # post-processing: tranpose and scaling
    else:  # if it is a numpy array, do nothing
        image_numpy = input_image
    return image_numpy.astype(imtype)

def str2bool(x):
    return x.lower() in ('true')

def diagnose_network(net, name='network'):
    """Calculate and print the mean of average absolute(gradients)

    Parameters:
        net (torch network) -- Torch network
        name (str) -- the name of the network
    """
    mean = 0.0
    count = 0
    for param in net.parameters():
        if param.grad is not None:
            mean += torch.mean(torch.abs(param.grad.data))
            count += 1
    if count > 0:
        mean = mean / count
    print(name)
    print(mean)


def save_image(image_numpy, image_path, aspect_ratio=1.0):
    """Save a numpy image to the disk

    Parameters:
        image_numpy (numpy array) -- input numpy array
        image_path (str)          -- the path of the image
    """

    image_pil = Image.fromarray(image_numpy)
    h, w, _ = image_numpy.shape

    if aspect_ratio > 1.0:
        image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)
    if aspect_ratio < 1.0:
        image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)
    image_pil.save(image_path)


def print_numpy(x, val=True, shp=False):
    """Print the mean, min, max, median, std, and size of a numpy array

    Parameters:
        val (bool) -- if print the values of the numpy array
        shp (bool) -- if print the shape of the numpy array
    """
    x = x.astype(np.float64)
    if shp:
        print('shape,', x.shape)
    if val:
        x = x.flatten()
        print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
            np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))


def get_model_list(dirname, key, exclude='latest'):
    if os.path.exists(dirname) is False:
        return None
    gen_models = [os.path.join(dirname, f) for f in os.listdir(dirname) if
                  os.path.isfile(os.path.join(dirname, f)) and key in f and ".pt" in f and exclude not in f]
    if gen_models is None:
        return None
    gen_models.sort()
    last_model_name = gen_models[-1]
    return last_model_name


def mkdirs(paths):
    """create empty directories if they don't exist

    Parameters:
        paths (str list) -- a list of directory paths
    """
    if isinstance(paths, list) and not isinstance(paths, str):
        for path in paths:
            mkdir(path)
    else:
        mkdir(paths)


def mkdir(path):
    """create a single empty directory if it didn't exist

    Parameters:
        path (str) -- a single directory path
    """
    if not os.path.exists(path):
        os.makedirs(path)

def adjust_dynamic_range(data, drange_in, drange_out):
    if drange_in != drange_out:
        scale = (np.float32(drange_out[1]) - np.float32(drange_out[0])) / (np.float32(drange_in[1]) - np.float32(drange_in[0]))
        bias = (np.float32(drange_out[0]) - np.float32(drange_in[0]) * scale)
        data = data * scale + bias
    return data

def create_image_grid(images, grid_size=None):
    assert images.ndim == 3 or images.ndim == 4
    num, img_w, img_h = images.shape[0], images.shape[-1], images.shape[-2]

    if grid_size is not None:
        grid_w, grid_h = tuple(grid_size)
    else:
        grid_w = max(int(np.ceil(np.sqrt(num))), 1)
        grid_h = max((num - 1) // grid_w + 1, 1)

    grid = np.zeros(list(images.shape[1:-2]) + [grid_h * img_h, grid_w * img_w], dtype=images.dtype)
    for idx in range(num):
        x = (idx % grid_w) * img_w
        y = (idx // grid_w) * img_h
        grid[..., y : y + img_h, x : x + img_w] = images[idx]
    return grid

def convert_to_pil_image(image, drange=[-1,1]):
    assert image.ndim == 2 or image.ndim == 3
    if image.ndim == 3:
        if image.shape[0] == 1:
            image = image[0] # grayscale CHW => HW
        elif image.shape[1]>image.shape[0]:
            image = image.transpose(1, 2, 0) # CHW -> HWC

    image = adjust_dynamic_range(image, drange, [0,255])
    image = np.rint(image).clip(0, 255).astype(np.uint8)
    fmt = 'RGB' if image.ndim == 3 else 'L'
    return Image.fromarray(image, fmt)

def save_images(image, filename, drange=[-1,1], quality=95):
    img = convert_to_pil_image(image, drange)
    if '.jpg' in filename:
        img.save(filename, "JPEG", quality=quality, optimize=True)
    else:
        img.save(filename)
def to_var( x):
    """Converts numpy to variable."""
    if torch.cuda.is_available():
        x = x.cuda()
    return torch.autograd.Variable(x)

def to_data(x):
    """Converts variable to numpy."""
    if torch.cuda.is_available():
        x = x.cpu()
    return x.data.numpy()
def save_image_grid(images, filename, drange=[-1,1], grid_size=None):
    convert_to_pil_image(create_image_grid(images, grid_size), drange).save(filename)