# Copyright 2021 MosaicML. All Rights Reserved.

"""Utility and helper functions for datasets."""

import logging
import textwrap
from typing import Callable, List, Tuple, Union

import numpy as np
import torch
from PIL import Image
from torchvision import transforms
from torchvision.datasets import VisionDataset

from composer.core.types import Batch

__all__ = [
    "add_vision_dataset_transform",
    "NormalizationFn",
    "pil_image_collate",
]

log = logging.getLogger(__name__)


class NormalizationFn:
    """Normalizes input data and removes the background class from target data if desired.

    An instance of this class can be used as the ``device_transforms`` argument
    when constructing a :class:`~composer.core.data_spec.DataSpec`. When used here,
    the data will normalized after it has been loaded onto the device (i.e., GPU).

    Args:
        mean (Tuple[float, float, float]): The mean pixel value for each channel (RGB) for
            the dataset.
        std (Tuple[float, float, float]): The standard deviation pixel value for each
            channel (RGB) for the dataset.
        ignore_background (bool): If ``True``, ignore the background class in the training
            loss. Only used in semantic segmentation. Default: ``False``.
    """

    def __init__(self,
                 mean: Tuple[float, float, float],
                 std: Tuple[float, float, float],
                 ignore_background: bool = False):
        self.mean = mean
        self.std = std
        self.ignore_background = ignore_background

    def __call__(self, batch: Batch):
        xs, ys = batch
        assert isinstance(xs, torch.Tensor)
        assert isinstance(ys, torch.Tensor)
        device = xs.device

        if not isinstance(self.mean, torch.Tensor):
            self.mean = torch.tensor(self.mean, device=device)
            self.mean = self.mean.view(1, 3, 1, 1)
        if not isinstance(self.std, torch.Tensor):
            self.std = torch.tensor(self.std, device=device)
            self.std = self.std.view(1, 3, 1, 1)

        xs = xs.float()
        xs = xs.sub_(self.mean).div_(self.std)
        if self.ignore_background:
            ys = ys.sub_(1)
        return xs, ys


def pil_image_collate(
        batch: List[Tuple[Image.Image, Union[Image.Image, np.ndarray]]],
        memory_format: torch.memory_format = torch.contiguous_format) -> Tuple[torch.Tensor, torch.Tensor]:
    """Constructs a length 2 tuple of torch.Tensors from datasets that yield samples of type
    :class:`PIL.Image.Image`.

    This function can be used as the ``collate_fn`` argument of a :class:`torch.utils.data.DataLoader`.

    Args:
        batch (List[Tuple[Image.Image, Union[Image.Image, np.ndarray]]]): List of (image, target) tuples
            that will be aggregated and converted into a single (:class:`~torch.Tensor`, :class:`~torch.Tensor`)
            tuple.

        memory_format (torch.memory_format): The memory format for the input and target tensors.

    Returns:
        (torch.Tensor, torch.Tensor): Tuple of (image tensor, target tensor)
            The image tensor will be four-dimensional (NCHW or NHWC, depending on the ``memory_format``).
    """
    imgs = [sample[0] for sample in batch]
    w, h = imgs[0].size
    image_tensor = torch.zeros((len(imgs), 3, h, w), dtype=torch.uint8).contiguous(memory_format=memory_format)

    # Convert targets to torch tensor
    targets = [sample[1] for sample in batch]
    if isinstance(targets[0], Image.Image):
        target_dims = (len(targets), targets[0].size[1], targets[0].size[0])
    else:
        target_dims = (len(targets),)
    target_tensor = torch.zeros(target_dims, dtype=torch.int64).contiguous(memory_format=memory_format)

    for i, img in enumerate(imgs):
        nump_array = np.asarray(img, dtype=np.uint8)
        if nump_array.ndim < 3:
            nump_array = np.expand_dims(nump_array, axis=-1)

        nump_array = np.rollaxis(nump_array, 2).copy()
        if nump_array.shape[0] != 3:
            assert nump_array.shape[0] == 1, "unexpected shape"
            nump_array = np.resize(nump_array, (3, h, w))
        assert image_tensor.shape[1:] == nump_array.shape, "shape mismatch"

        image_tensor[i] += torch.from_numpy(nump_array)
        target_tensor[i] += torch.from_numpy(np.array(targets[i], dtype=np.int64))

    return image_tensor, target_tensor


def add_vision_dataset_transform(dataset: VisionDataset, transform: Callable, is_tensor_transform: bool = False):
    """Add a transform to a dataset's collection of transforms.

    Args:
        dataset (VisionDataset): A torchvision dataset.
        transform (Callable): Function to be added to the dataset's collection of
            transforms.
        is_tensor_transform (bool): Whether ``transform`` acts on data of the type
            :class:`~torch.Tensor`. default: ``False``.

            * If ``True``, and :class:`~torchvision.transforms.ToTensor` is present in
              ``dataset``'s transforms, ``transform`` will be inserted after the
              :class:`~torchvision.transforms.ToTensor` transform.
            * If ``False`` and :class:`~torchvision.transforms.ToTensor` is present, the ``transform`` will be
              inserted before :class:`~torchvision.transforms.ToTensor`.
            * If :class:`~torchvision.transforms.ToTensor` is not present, the transform will be appended to
              the end of collection of transforms.

    Returns:
        None: The ``dataset`` is modified in-place.
    """

    transform_added_logstring = textwrap.dedent(f"""\
        Transform {transform} added to dataset.
        Dataset now has the following transforms: {dataset.transform}""")

    if dataset.transform is None:
        dataset.transform = transform
        log.warning(transform_added_logstring)
    elif isinstance(dataset.transform, transforms.Compose):
        insertion_index = len(dataset.transform.transforms)
        for i, t in enumerate(dataset.transform.transforms):
            if isinstance(t, transforms.ToTensor):
                insertion_index = i
                break
        if is_tensor_transform:
            insertion_index += 1
        dataset.transform.transforms.insert(insertion_index, transform)
        log.warning(transform_added_logstring)
    else:  # transform is some other basic transform, join using Compose
        if isinstance(dataset.transform, transforms.ToTensor) and not is_tensor_transform:
            dataset.transform = transforms.Compose([transform, dataset.transform])
            log.warning(transform_added_logstring)
        else:
            dataset.transform = transforms.Compose([dataset.transform, transform])
            log.warning(transform_added_logstring)

# Copyright 2021 MosaicML. All Rights Reserved.

"""Utility and helper functions for datasets."""

import logging
import textwrap
from typing import Callable, List, Tuple, Union

import numpy as np
import torch
from PIL import Image
from torchvision import transforms
from torchvision.datasets import VisionDataset

from composer.core.types import Batch

__all__ = [
    "add_vision_dataset_transform",
    "NormalizationFn",
    "pil_image_collate",
]

log = logging.getLogger(__name__)


class NormalizationFn:
    """Normalizes input data and removes the background class from target data if desired.

    An instance of this class can be used as the ``device_transforms`` argument
    when constructing a :class:`~composer.core.data_spec.DataSpec`. When used here,
    the data will normalized after it has been loaded onto the device (i.e., GPU).

    Args:
        mean (Tuple[float, float, float]): The mean pixel value for each channel (RGB) for
            the dataset.
        std (Tuple[float, float, float]): The standard deviation pixel value for each
            channel (RGB) for the dataset.
        ignore_background (bool): If ``True``, ignore the background class in the training
            loss. Only used in semantic segmentation. Default: ``False``.
    """

    def __init__(self,
                 mean: Tuple[float, float, float],
                 std: Tuple[float, float, float],
                 ignore_background: bool = False):
        self.mean = mean
        self.std = std
        self.ignore_background = ignore_background

    def __call__(self, batch: Batch):
        xs, ys = batch
        assert isinstance(xs, torch.Tensor)
        assert isinstance(ys, torch.Tensor)
        device = xs.device

        if not isinstance(self.mean, torch.Tensor):
            self.mean = torch.tensor(self.mean, device=device)
            self.mean = self.mean.view(1, 3, 1, 1)
        if not isinstance(self.std, torch.Tensor):
            self.std = torch.tensor(self.std, device=device)
            self.std = self.std.view(1, 3, 1, 1)

        xs = xs.float()
        xs = xs.sub_(self.mean).div_(self.std)
        if self.ignore_background:
            ys = ys.sub_(1)
        return xs, ys


def pil_image_collate(
        batch: List[Tuple[Image.Image, Union[Image.Image, np.ndarray]]],
        memory_format: torch.memory_format = torch.contiguous_format) -> Tuple[torch.Tensor, torch.Tensor]:
    """Constructs a length 2 tuple of torch.Tensors from datasets that yield samples of type
    :class:`PIL.Image.Image`.

    This function can be used as the ``collate_fn`` argument of a :class:`torch.utils.data.DataLoader`.

    Args:
        batch (List[Tuple[Image.Image, Union[Image.Image, np.ndarray]]]): List of (image, target) tuples
            that will be aggregated and converted into a single (:class:`~torch.Tensor`, :class:`~torch.Tensor`)
            tuple.

        memory_format (torch.memory_format): The memory format for the input and target tensors.

    Returns:
        (torch.Tensor, torch.Tensor): Tuple of (image tensor, target tensor)
            The image tensor will be four-dimensional (NCHW or NHWC, depending on the ``memory_format``).
    """
    imgs = [sample[0] for sample in batch]
    w, h = imgs[0].size
    image_tensor = torch.zeros((len(imgs), 3, h, w), dtype=torch.uint8).contiguous(memory_format=memory_format)

    # Convert targets to torch tensor
    targets = [sample[1] for sample in batch]
    if isinstance(targets[0], Image.Image):
        target_dims = (len(targets), targets[0].size[1], targets[0].size[0])
    else:
        target_dims = (len(targets),)
    target_tensor = torch.zeros(target_dims, dtype=torch.int64).contiguous(memory_format=memory_format)

    for i, img in enumerate(imgs):
        nump_array = np.asarray(img, dtype=np.uint8)
        if nump_array.ndim < 3:
            nump_array = np.expand_dims(nump_array, axis=-1)

        nump_array = np.rollaxis(nump_array, 2).copy()
        if nump_array.shape[0] != 3:
            assert nump_array.shape[0] == 1, "unexpected shape"
            nump_array = np.resize(nump_array, (3, h, w))
        assert image_tensor.shape[1:] == nump_array.shape, "shape mismatch"

        image_tensor[i] += torch.from_numpy(nump_array)
        target_tensor[i] += torch.from_numpy(np.array(targets[i], dtype=np.int64))

    return image_tensor, target_tensor


def add_vision_dataset_transform(dataset: VisionDataset, transform: Callable, is_tensor_transform: bool = False):
    """Add a transform to a dataset's collection of transforms.

    Args:
        dataset (VisionDataset): A torchvision dataset.
        transform (Callable): Function to be added to the dataset's collection of
            transforms.
        is_tensor_transform (bool): Whether ``transform`` acts on data of the type
            :class:`~torch.Tensor`. default: ``False``.

            * If ``True``, and :class:`~torchvision.transforms.ToTensor` is present in
              ``dataset``'s transforms, ``transform`` will be inserted after the
              :class:`~torchvision.transforms.ToTensor` transform.
            * If ``False`` and :class:`~torchvision.transforms.ToTensor` is present, the ``transform`` will be
              inserted before :class:`~torchvision.transforms.ToTensor`.
            * If :class:`~torchvision.transforms.ToTensor` is not present, the transform will be appended to
              the end of collection of transforms.

    Returns:
        None: The ``dataset`` is modified in-place.
    """

    transform_added_logstring = textwrap.dedent(f"""\
        Transform {transform} added to dataset.
        Dataset now has the following transforms: {dataset.transform}""")

    if dataset.transform is None:
        dataset.transform = transform
        log.warning(transform_added_logstring)
    elif isinstance(dataset.transform, transforms.Compose):
        insertion_index = len(dataset.transform.transforms)
        for i, t in enumerate(dataset.transform.transforms):
            if isinstance(t, transforms.ToTensor):
                insertion_index = i
                break
        if is_tensor_transform:
            insertion_index += 1
        dataset.transform.transforms.insert(insertion_index, transform)
        log.warning(transform_added_logstring)
    else:  # transform is some other basic transform, join using Compose
        if isinstance(dataset.transform, transforms.ToTensor) and not is_tensor_transform:
            dataset.transform = transforms.Compose([transform, dataset.transform])
            log.warning(transform_added_logstring)
        else:
            dataset.transform = transforms.Compose([dataset.transform, transform])
            log.warning(transform_added_logstring)

# Copyright 2021 MosaicML. All Rights Reserved. Utility and helper functions for datasets. Normalizes input data and removes the background class from target data if desired.      An instance of this class can be used as the ``device_transforms`` argument     when constructing a :class:`~composer.core.data_spec.DataSpec`. When used here,     the data will normalized after it has been loaded onto the device (i.e., GPU).      Args:         mean (Tuple[float, float, float]): The mean pixel value for each channel (RGB) for             the dataset.         std (Tuple[float, float, float]): The standard deviation pixel value for each             channel (RGB) for the dataset.         ignore_background (bool): If ``True``, ignore the background class in the training             loss. Only used in semantic segmentation. Default: ``False``. Constructs a length 2 tuple of torch.Tensors from datasets that yield samples of type     :class:`PIL.Image.Image`.      This function can be used as the ``collate_fn`` argument of a :class:`torch.utils.data.DataLoader`.      Args:         batch (List[Tuple[Image.Image, Union[Image.Image, np.ndarray]]]): List of (image, target) tuples             that will be aggregated and converted into a single (:class:`~torch.Tensor`, :class:`~torch.Tensor`)             tuple.          memory_format (torch.memory_format): The memory format for the input and target tensors.      Returns:         (torch.Tensor, torch.Tensor): Tuple of (image tensor, target tensor)             The image tensor will be four-dimensional (NCHW or NHWC, depending on the ``memory_format``). # Convert targets to torch tensor Add a transform to a dataset's collection of transforms.      Args:         dataset (VisionDataset): A torchvision dataset.         transform (Callable): Function to be added to the dataset's collection of             transforms.         is_tensor_transform (bool): Whether ``transform`` acts on data of the type             :class:`~torch.Tensor`. default: ``False``.              * If ``True``, and :class:`~torchvision.transforms.ToTensor` is present in               ``dataset``'s transforms, ``transform`` will be inserted after the               :class:`~torchvision.transforms.ToTensor` transform.             * If ``False`` and :class:`~torchvision.transforms.ToTensor` is present, the ``transform`` will be               inserted before :class:`~torchvision.transforms.ToTensor`.             * If :class:`~torchvision.transforms.ToTensor` is not present, the transform will be appended to               the end of collection of transforms.      Returns:         None: The ``dataset`` is modified in-place. \         Transform {transform} added to dataset.         Dataset now has the following transforms: {dataset.transform} # transform is some other basic transform, join using Compose