import enum
import functools
import gzip
import io
import lzma
import mmap
import os
import os.path
import pathlib
import pickle
import platform
from typing import BinaryIO
from typing import (
    Sequence,
    Callable,
    Union,
    Any,
    Tuple,
    TypeVar,
    Iterator,
    Dict,
    Optional,
    IO,
    Sized,
)
from typing import cast

import numpy as np
import PIL.Image
import torch
import torch.distributed as dist
import torch.utils.data
from torchdata.datapipes.iter import IoPathFileLister, IoPathFileOpener, IterDataPipe, ShardingFilter, Shuffler
from torchdata.datapipes.utils import StreamWrapper


__all__ = [
    "INFINITE_BUFFER_SIZE",
    "BUILTIN_DIR",
    "read_mat",
    "image_buffer_from_array",
    "SequenceIterator",
    "MappingIterator",
    "Enumerator",
    "getitem",
    "path_accessor",
    "path_comparator",
    "Decompressor",
    "fromfile",
    "read_flo",
    "hint_sharding",
]

K = TypeVar("K")
D = TypeVar("D")

# pseudo-infinite until a true infinite buffer is supported by all datapipes
INFINITE_BUFFER_SIZE = 1_000_000_000

BUILTIN_DIR = pathlib.Path(__file__).parent.parent / "_builtin"


def read_mat(buffer: io.IOBase, **kwargs: Any) -> Any:
    try:
        import scipy.io as sio
    except ImportError as error:
        raise ModuleNotFoundError("Package `scipy` is required to be installed to read .mat files.") from error

    if isinstance(buffer, StreamWrapper):
        buffer = buffer.file_obj

    return sio.loadmat(buffer, **kwargs)


def image_buffer_from_array(array: np.ndarray, *, format: str = "png") -> io.BytesIO:
    image = PIL.Image.fromarray(array)
    buffer = io.BytesIO()
    image.save(buffer, format=format)
    buffer.seek(0)
    return buffer


class SequenceIterator(IterDataPipe[D]):
    def __init__(self, datapipe: IterDataPipe[Sequence[D]]):
        self.datapipe = datapipe

    def __iter__(self) -> Iterator[D]:
        for sequence in self.datapipe:
            yield from iter(sequence)


class MappingIterator(IterDataPipe[Union[Tuple[K, D], D]]):
    def __init__(self, datapipe: IterDataPipe[Dict[K, D]], *, drop_key: bool = False) -> None:
        self.datapipe = datapipe
        self.drop_key = drop_key

    def __iter__(self) -> Iterator[Union[Tuple[K, D], D]]:
        for mapping in self.datapipe:
            yield from iter(mapping.values() if self.drop_key else mapping.items())


class Enumerator(IterDataPipe[Tuple[int, D]]):
    def __init__(self, datapipe: IterDataPipe[D], start: int = 0) -> None:
        self.datapipe = datapipe
        self.start = start

    def __iter__(self) -> Iterator[Tuple[int, D]]:
        yield from enumerate(self.datapipe, self.start)


def _getitem_closure(obj: Any, *, items: Tuple[Any, ...]) -> Any:
    for item in items:
        obj = obj[item]
    return obj


def getitem(*items: Any) -> Callable[[Any], Any]:
    return functools.partial(_getitem_closure, items=items)


def _path_attribute_accessor(path: pathlib.Path, *, name: str) -> D:
    return cast(D, getattr(path, name))


def _path_accessor_closure(data: Tuple[str, Any], *, getter: Callable[[pathlib.Path], D]) -> D:
    return getter(pathlib.Path(data[0]))


def path_accessor(getter: Union[str, Callable[[pathlib.Path], D]]) -> Callable[[Tuple[str, Any]], D]:
    if isinstance(getter, str):
        getter = functools.partial(_path_attribute_accessor, name=getter)

    return functools.partial(_path_accessor_closure, getter=getter)


def _path_comparator_closure(data: Tuple[str, Any], *, accessor: Callable[[Tuple[str, Any]], D], value: D) -> bool:
    return accessor(data) == value


def path_comparator(getter: Union[str, Callable[[pathlib.Path], D]], value: D) -> Callable[[Tuple[str, Any]], bool]:
    return functools.partial(_path_comparator_closure, accessor=path_accessor(getter), value=value)


class CompressionType(enum.Enum):
    GZIP = "gzip"
    LZMA = "lzma"


class Decompressor(IterDataPipe[Tuple[str, io.IOBase]]):
    types = CompressionType

    _DECOMPRESSORS = {
        types.GZIP: lambda file: gzip.GzipFile(fileobj=file),
        types.LZMA: lambda file: lzma.LZMAFile(file),
    }

    def __init__(
        self,
        datapipe: IterDataPipe[Tuple[str, io.IOBase]],
        *,
        type: Optional[Union[str, CompressionType]] = None,
    ) -> None:
        self.datapipe = datapipe
        if isinstance(type, str):
            type = self.types(type.upper())
        self.type = type

    def _detect_compression_type(self, path: str) -> CompressionType:
        if self.type:
            return self.type

        # TODO: this needs to be more elaborate
        ext = os.path.splitext(path)[1]
        if ext == ".gz":
            return self.types.GZIP
        elif ext == ".xz":
            return self.types.LZMA
        else:
            raise RuntimeError("FIXME")

    def __iter__(self) -> Iterator[Tuple[str, io.IOBase]]:
        for path, file in self.datapipe:
            type = self._detect_compression_type(path)
            decompressor = self._DECOMPRESSORS[type]
            yield path, decompressor(file)


class PicklerDataPipe(IterDataPipe):
    def __init__(self, source_datapipe: IterDataPipe[Tuple[str, IO[bytes]]]) -> None:
        self.source_datapipe = source_datapipe

    def __iter__(self) -> Iterator[Any]:
        for _, fobj in self.source_datapipe:
            data = pickle.load(fobj)
            for _, d in enumerate(data):
                yield d


class SharderDataPipe(torch.utils.data.datapipes.iter.grouping.ShardingFilterIterDataPipe):
    def __init__(self, source_datapipe: IterDataPipe) -> None:
        super().__init__(source_datapipe)
        self.rank = 0
        self.world_size = 1
        if dist.is_available() and dist.is_initialized():
            self.rank = dist.get_rank()
            self.world_size = dist.get_world_size()
        self.apply_sharding(self.world_size, self.rank)

    def __iter__(self) -> Iterator[Any]:
        num_workers = self.world_size
        worker_id = self.rank
        worker_info = torch.utils.data.get_worker_info()
        if worker_info is not None:
            worker_id = worker_id + worker_info.id * num_workers
            num_workers *= worker_info.num_workers
        self.apply_sharding(num_workers, worker_id)
        yield from super().__iter__()


class TakerDataPipe(IterDataPipe):
    def __init__(self, source_datapipe: IterDataPipe, num_take: int) -> None:
        super().__init__()
        self.source_datapipe = source_datapipe
        self.num_take = num_take
        self.world_size = 1
        if dist.is_available() and dist.is_initialized():
            self.world_size = dist.get_world_size()

    def __iter__(self) -> Iterator[Any]:
        num_workers = self.world_size
        worker_info = torch.utils.data.get_worker_info()
        if worker_info is not None:
            num_workers *= worker_info.num_workers

        # TODO: this is weird as it drops more elements than it should
        num_take = self.num_take // num_workers

        for i, data in enumerate(self.source_datapipe):
            if i < num_take:
                yield data
            else:
                break

    def __len__(self) -> int:
        num_take = self.num_take // self.world_size
        if isinstance(self.source_datapipe, Sized):
            if len(self.source_datapipe) < num_take:
                num_take = len(self.source_datapipe)
        # TODO: might be weird to not take `num_workers` into account
        return num_take


def _make_sharded_datapipe(root: str, dataset_size: int) -> IterDataPipe[Dict[str, Any]]:
    dp = IoPathFileLister(root=root)
    dp = SharderDataPipe(dp)
    dp = dp.shuffle(buffer_size=INFINITE_BUFFER_SIZE)
    dp = IoPathFileOpener(dp, mode="rb")
    dp = PicklerDataPipe(dp)
    # dp = dp.cycle(2)
    dp = TakerDataPipe(dp, dataset_size)
    return dp


def _read_mutable_buffer_fallback(file: BinaryIO, count: int, item_size: int) -> bytearray:
    # A plain file.read() will give a read-only bytes, so we convert it to bytearray to make it mutable
    return bytearray(file.read(-1 if count == -1 else count * item_size))


def fromfile(
    file: BinaryIO,
    *,
    dtype: torch.dtype,
    byte_order: str,
    count: int = -1,
) -> torch.Tensor:
    """Construct a tensor from a binary file.

    .. note::

        This function is similar to :func:`numpy.fromfile` with two notable differences:

        1. This function only accepts an open binary file, but not a path to it.
        2. This function has an additional ``byte_order`` parameter, since PyTorch's ``dtype``'s do not support that
            concept.

    .. note::

        If the ``file`` was opened in update mode, i.e. "r+b" or "w+b", reading data is much faster. Be aware that as
        long as the file is still open, inplace operations on the returned tensor will reflect back to the file.

    Args:
        file (IO): Open binary file.
        dtype (torch.dtype): Data type of the underlying data as well as of the returned tensor.
        byte_order (str): Byte order of the data. Can be "little" or "big" endian.
        count (int): Number of values of the returned tensor. If ``-1`` (default), will read the complete file.
    """
    byte_order = "<" if byte_order == "little" else ">"
    char = "f" if dtype.is_floating_point else ("i" if dtype.is_signed else "u")
    item_size = (torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits // 8
    np_dtype = byte_order + char + str(item_size)

    buffer: Union[memoryview, bytearray]
    if platform.system() != "Windows":
        # PyTorch does not support tensors with underlying read-only memory. In case
        # - the file has a .fileno(),
        # - the file was opened for updating, i.e. 'r+b' or 'w+b',
        # - the file is seekable
        # we can avoid copying the data for performance. Otherwise we fall back to simply .read() the data and copy it
        # to a mutable location afterwards.
        try:
            buffer = memoryview(mmap.mmap(file.fileno(), 0))[file.tell() :]
            # Reading from the memoryview does not advance the file cursor, so we have to do it manually.
            file.seek(*(0, io.SEEK_END) if count == -1 else (count * item_size, io.SEEK_CUR))
        except (PermissionError, io.UnsupportedOperation):
            buffer = _read_mutable_buffer_fallback(file, count, item_size)
    else:
        # On Windows just trying to call mmap.mmap() on a file that does not support it, may corrupt the internal state
        # so no data can be read afterwards. Thus, we simply ignore the possible speed-up.
        buffer = _read_mutable_buffer_fallback(file, count, item_size)

    # We cannot use torch.frombuffer() directly, since it only supports the native byte order of the system. Thus, we
    # read the data with np.frombuffer() with the correct byte order and convert it to the native one with the
    # successive .astype() call.
    return torch.from_numpy(np.frombuffer(buffer, dtype=np_dtype, count=count).astype(np_dtype[1:], copy=False))


def read_flo(file: BinaryIO) -> torch.Tensor:
    if file.read(4) != b"PIEH":
        raise ValueError("Magic number incorrect. Invalid .flo file")

    width, height = fromfile(file, dtype=torch.int32, byte_order="little", count=2)
    flow = fromfile(file, dtype=torch.float32, byte_order="little", count=height * width * 2)
    return flow.reshape((height, width, 2)).permute((2, 0, 1))


def hint_sharding(datapipe: IterDataPipe[D]) -> IterDataPipe[D]:
    return ShardingFilter(datapipe)


def hint_shuffling(datapipe: IterDataPipe[D]) -> IterDataPipe[D]:
    return Shuffler(datapipe, default=False, buffer_size=INFINITE_BUFFER_SIZE)
