import math
import numbers
import warnings
from typing import Any, Optional

import numpy as np
from PIL import Image

import torch
from torch import Tensor
from torch.jit.annotations import List, Tuple

try:
    import accimage
except ImportError:
    accimage = None

from . import functional_pil as F_pil
from . import functional_tensor as F_t


_is_pil_image = F_pil._is_pil_image
_parse_fill = F_pil._parse_fill


def _get_image_size(img: Tensor) -> List[int]:
    """Returns image sizea as (w, h)
    """
    if isinstance(img, torch.Tensor):
        return F_t._get_image_size(img)

    return F_pil._get_image_size(img)


def _get_image_num_channels(img: Tensor) -> int:
    if isinstance(img, torch.Tensor):
        return F_t._get_image_num_channels(img)

    return F_pil._get_image_num_channels(img)


@torch.jit.unused
def _is_numpy(img: Any) -> bool:
    return isinstance(img, np.ndarray)


@torch.jit.unused
def _is_numpy_image(img: Any) -> bool:
    return img.ndim in {2, 3}


def to_tensor(pic):
    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.

    See ``ToTensor`` for more details.

    Args:
        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.

    Returns:
        Tensor: Converted image.
    """
    if not(F_pil._is_pil_image(pic) or _is_numpy(pic)):
        raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))

    if _is_numpy(pic) and not _is_numpy_image(pic):
        raise ValueError('pic should be 2/3 dimensional. Got {} dimensions.'.format(pic.ndim))

    if isinstance(pic, np.ndarray):
        # handle numpy array
        if pic.ndim == 2:
            pic = pic[:, :, None]

        img = torch.from_numpy(pic.transpose((2, 0, 1))).contiguous()
        # backward compatibility
        if isinstance(img, torch.ByteTensor):
            return img.float().div(255)
        else:
            return img

    if accimage is not None and isinstance(pic, accimage.Image):
        nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
        pic.copyto(nppic)
        return torch.from_numpy(nppic)

    # handle PIL Image
    if pic.mode == 'I':
        img = torch.from_numpy(np.array(pic, np.int32, copy=False))
    elif pic.mode == 'I;16':
        img = torch.from_numpy(np.array(pic, np.int16, copy=False))
    elif pic.mode == 'F':
        img = torch.from_numpy(np.array(pic, np.float32, copy=False))
    elif pic.mode == '1':
        img = 255 * torch.from_numpy(np.array(pic, np.uint8, copy=False))
    else:
        img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))

    img = img.view(pic.size[1], pic.size[0], len(pic.getbands()))
    # put it from HWC to CHW format
    img = img.permute((2, 0, 1)).contiguous()
    if isinstance(img, torch.ByteTensor):
        return img.float().div(255)
    else:
        return img


def pil_to_tensor(pic):
    """Convert a ``PIL Image`` to a tensor of the same type.

    See :class:`~torchvision.transforms.PILToTensor` for more details.

    Args:
        pic (PIL Image): Image to be converted to tensor.

    Returns:
        Tensor: Converted image.
    """
    if not F_pil._is_pil_image(pic):
        raise TypeError('pic should be PIL Image. Got {}'.format(type(pic)))

    if accimage is not None and isinstance(pic, accimage.Image):
        nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
        pic.copyto(nppic)
        return torch.as_tensor(nppic)

    # handle PIL Image
    img = torch.as_tensor(np.asarray(pic))
    img = img.view(pic.size[1], pic.size[0], len(pic.getbands()))
    # put it from HWC to CHW format
    img = img.permute((2, 0, 1))
    return img


def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor:
    """Convert a tensor image to the given ``dtype`` and scale the values accordingly

    Args:
        image (torch.Tensor): Image to be converted
        dtype (torch.dtype): Desired data type of the output

    Returns:
        Tensor: Converted image

    .. note::

        When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly.
        If converted back and forth, this mismatch has no effect.

    Raises:
        RuntimeError: When trying to cast :class:`torch.float32` to :class:`torch.int32` or :class:`torch.int64` as
            well as for trying to cast :class:`torch.float64` to :class:`torch.int64`. These conversions might lead to
            overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range
            of the integer ``dtype``.
    """
    if not isinstance(image, torch.Tensor):
        raise TypeError('Input img should be Tensor Image')

    return F_t.convert_image_dtype(image, dtype)


def to_pil_image(pic, mode=None):
    """Convert a tensor or an ndarray to PIL Image.

    See :class:`~torchvision.transforms.ToPILImage` for more details.

    Args:
        pic (Tensor or numpy.ndarray): Image to be converted to PIL Image.
        mode (`PIL.Image mode`_): color space and pixel depth of input data (optional).

    .. _PIL.Image mode: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes

    Returns:
        PIL Image: Image converted to PIL Image.
    """
    if not(isinstance(pic, torch.Tensor) or isinstance(pic, np.ndarray)):
        raise TypeError('pic should be Tensor or ndarray. Got {}.'.format(type(pic)))

    elif isinstance(pic, torch.Tensor):
        if pic.ndimension() not in {2, 3}:
            raise ValueError('pic should be 2/3 dimensional. Got {} dimensions.'.format(pic.ndimension()))

        elif pic.ndimension() == 2:
            # if 2D image, add channel dimension (CHW)
            pic = pic.unsqueeze(0)

    elif isinstance(pic, np.ndarray):
        if pic.ndim not in {2, 3}:
            raise ValueError('pic should be 2/3 dimensional. Got {} dimensions.'.format(pic.ndim))

        elif pic.ndim == 2:
            # if 2D image, add channel dimension (HWC)
            pic = np.expand_dims(pic, 2)

    npimg = pic
    if isinstance(pic, torch.Tensor):
        if pic.is_floating_point() and mode != 'F':
            pic = pic.mul(255).byte()
        npimg = np.transpose(pic.cpu().numpy(), (1, 2, 0))

    if not isinstance(npimg, np.ndarray):
        raise TypeError('Input pic must be a torch.Tensor or NumPy ndarray, ' +
                        'not {}'.format(type(npimg)))

    if npimg.shape[2] == 1:
        expected_mode = None
        npimg = npimg[:, :, 0]
        if npimg.dtype == np.uint8:
            expected_mode = 'L'
        elif npimg.dtype == np.int16:
            expected_mode = 'I;16'
        elif npimg.dtype == np.int32:
            expected_mode = 'I'
        elif npimg.dtype == np.float32:
            expected_mode = 'F'
        if mode is not None and mode != expected_mode:
            raise ValueError("Incorrect mode ({}) supplied for input type {}. Should be {}"
                             .format(mode, np.dtype, expected_mode))
        mode = expected_mode

    elif npimg.shape[2] == 2:
        permitted_2_channel_modes = ['LA']
        if mode is not None and mode not in permitted_2_channel_modes:
            raise ValueError("Only modes {} are supported for 2D inputs".format(permitted_2_channel_modes))

        if mode is None and npimg.dtype == np.uint8:
            mode = 'LA'

    elif npimg.shape[2] == 4:
        permitted_4_channel_modes = ['RGBA', 'CMYK', 'RGBX']
        if mode is not None and mode not in permitted_4_channel_modes:
            raise ValueError("Only modes {} are supported for 4D inputs".format(permitted_4_channel_modes))

        if mode is None and npimg.dtype == np.uint8:
            mode = 'RGBA'
    else:
        permitted_3_channel_modes = ['RGB', 'YCbCr', 'HSV']
        if mode is not None and mode not in permitted_3_channel_modes:
            raise ValueError("Only modes {} are supported for 3D inputs".format(permitted_3_channel_modes))
        if mode is None and npimg.dtype == np.uint8:
            mode = 'RGB'

    if mode is None:
        raise TypeError('Input type {} is not supported'.format(npimg.dtype))

    return Image.fromarray(npimg, mode=mode)


def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool = False) -> Tensor:
    """Normalize a tensor image with mean and standard deviation.

    .. note::
        This transform acts out of place by default, i.e., it does not mutates the input tensor.

    See :class:`~torchvision.transforms.Normalize` for more details.

    Args:
        tensor (Tensor): Tensor image of size (C, H, W) or (B, C, H, W) to be normalized.
        mean (sequence): Sequence of means for each channel.
        std (sequence): Sequence of standard deviations for each channel.
        inplace(bool,optional): Bool to make this operation inplace.

    Returns:
        Tensor: Normalized Tensor image.
    """
    if not isinstance(tensor, torch.Tensor):
        raise TypeError('Input tensor should be a torch tensor. Got {}.'.format(type(tensor)))

    if tensor.ndim < 3:
        raise ValueError('Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = '
                         '{}.'.format(tensor.size()))

    if not inplace:
        tensor = tensor.clone()

    dtype = tensor.dtype
    mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)
    std = torch.as_tensor(std, dtype=dtype, device=tensor.device)
    if (std == 0).any():
        raise ValueError('std evaluated to zero after conversion to {}, leading to division by zero.'.format(dtype))
    if mean.ndim == 1:
        mean = mean.view(-1, 1, 1)
    if std.ndim == 1:
        std = std.view(-1, 1, 1)
    tensor.sub_(mean).div_(std)
    return tensor


def resize(img: Tensor, size: List[int], interpolation: int = Image.BILINEAR) -> Tensor:
    r"""Resize the input image to the given size.
    The image can be a PIL Image or a torch Tensor, in which case it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions

    Args:
        img (PIL Image or Tensor): Image to be resized.
        size (sequence or int): Desired output size. If size is a sequence like
            (h, w), the output size will be matched to this. If size is an int,
            the smaller edge of the image will be matched to this number maintaining
            the aspect ratio. i.e, if height > width, then image will be rescaled to
            :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`.
            In torchscript mode size as single int is not supported, use a tuple or
            list of length 1: ``[size, ]``.
        interpolation (int, optional): Desired interpolation enum defined by `filters`_.
            Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
            and ``PIL.Image.BICUBIC`` are supported.

    Returns:
        PIL Image or Tensor: Resized image.
    """
    if not isinstance(img, torch.Tensor):
        return F_pil.resize(img, size=size, interpolation=interpolation)

    return F_t.resize(img, size=size, interpolation=interpolation)


def scale(*args, **kwargs):
    warnings.warn("The use of the transforms.Scale transform is deprecated, " +
                  "please use transforms.Resize instead.")
    return resize(*args, **kwargs)


def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant") -> Tensor:
    r"""Pad the given image on all sides with the given "pad" value.
    The image can be a PIL Image or a torch Tensor, in which case it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions

    Args:
        img (PIL Image or Tensor): Image to be padded.
        padding (int or tuple or list): Padding on each border. If a single int is provided this
            is used to pad all borders. If tuple of length 2 is provided this is the padding
            on left/right and top/bottom respectively. If a tuple of length 4 is provided
            this is the padding for the left, top, right and bottom borders respectively.
            In torchscript mode padding as single int is not supported, use a tuple or
            list of length 1: ``[padding, ]``.
        fill (int or str or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of
            length 3, it is used to fill R, G, B channels respectively.
            This value is only used when the padding_mode is constant. Only int value is supported for Tensors.
        padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
            Mode symmetric is not yet supported for Tensor inputs.

            - constant: pads with a constant value, this value is specified with fill

            - edge: pads with the last value on the edge of the image

            - reflect: pads with reflection of image (without repeating the last value on the edge)