import hashlib
import logging
import os
import tarfile
import threading
import urllib
import urllib.request
import zipfile
from queue import Queue
from typing import Any, Iterable, List, Optional

import torch
from torch.utils.data import Dataset
from torch.utils.model_zoo import tqdm


def stream_url(url: str,
               start_byte: Optional[int] = None,
               block_size: int = 32 * 1024,
               progress_bar: bool = True) -> Iterable:
    """Stream url by chunk

    Args:
        url (str): Url.
        start_byte (int, optional): Start streaming at that point (Default: ``None``).
        block_size (int, optional): Size of chunks to stream (Default: ``32 * 1024``).
        progress_bar (bool, optional): Display a progress bar (Default: ``True``).
    """

    # If we already have the whole file, there is no need to download it again
    req = urllib.request.Request(url, method="HEAD")
    url_size = int(urllib.request.urlopen(req).info().get("Content-Length", -1))
    if url_size == start_byte:
        return

    req = urllib.request.Request(url)
    if start_byte:
        req.headers["Range"] = "bytes={}-".format(start_byte)

    with urllib.request.urlopen(req) as upointer, tqdm(
            unit="B",
            unit_scale=True,
            unit_divisor=1024,
            total=url_size,
            disable=not progress_bar,
    ) as pbar:

        num_bytes = 0
        while True:
            chunk = upointer.read(block_size)
            if not chunk:
                break
            yield chunk
            num_bytes += len(chunk)
            pbar.update(len(chunk))


def download_url(url: str,
                 download_folder: str,
                 filename: Optional[str] = None,
                 hash_value: Optional[str] = None,
                 hash_type: str = "sha256",
                 progress_bar: bool = True,
                 resume: bool = False) -> None:
    """Download file to disk.

    Args:
        url (str): Url.
        download_folder (str): Folder to download file.
        filename (str, optional): Name of downloaded file. If None, it is inferred from the url (Default: ``None``).
        hash_value (str, optional): Hash for url (Default: ``None``).
        hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``).
        progress_bar (bool, optional): Display a progress bar (Default: ``True``).
        resume (bool, optional): Enable resuming download (Default: ``False``).
    """

    req = urllib.request.Request(url, method="HEAD")
    req_info = urllib.request.urlopen(req).info()

    # Detect filename
    filename = filename or req_info.get_filename() or os.path.basename(url)
    filepath = os.path.join(download_folder, filename)
    if resume and os.path.exists(filepath):
        mode = "ab"
        local_size: Optional[int] = os.path.getsize(filepath)

    elif not resume and os.path.exists(filepath):
        raise RuntimeError(
            "{} already exists. Delete the file manually and retry.".format(filepath)
        )
    else:
        mode = "wb"
        local_size = None

    if hash_value and local_size == int(req_info.get("Content-Length", -1)):
        with open(filepath, "rb") as file_obj:
            if validate_file(file_obj, hash_value, hash_type):
                return
        raise RuntimeError(
            "The hash of {} does not match. Delete the file manually and retry.".format(
                filepath
            )
        )

    with open(filepath, mode) as fpointer:
        for chunk in stream_url(url, start_byte=local_size, progress_bar=progress_bar):
            fpointer.write(chunk)

    with open(filepath, "rb") as file_obj:
        if hash_value and not validate_file(file_obj, hash_value, hash_type):
            raise RuntimeError(
                "The hash of {} does not match. Delete the file manually and retry.".format(
                    filepath
                )
            )


def validate_file(file_obj: Any, hash_value: str, hash_type: str = "sha256") -> bool:
    """Validate a given file object with its hash.

    Args:
        file_obj: File object to read from.
        hash_value (str): Hash for url.
        hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``).

    Returns:
        bool: return True if its a valid file, else False.
    """

    if hash_type == "sha256":
        hash_func = hashlib.sha256()
    elif hash_type == "md5":
        hash_func = hashlib.md5()
    else:
        raise ValueError

    while True:
        # Read by chunk to avoid filling memory
        chunk = file_obj.read(1024 ** 2)
        if not chunk:
            break
        hash_func.update(chunk)

    return hash_func.hexdigest() == hash_value


def extract_archive(from_path: str, to_path: Optional[str] = None, overwrite: bool = False) -> List[str]:
    """Extract archive.
    Args:
        from_path (str): the path of the archive.
        to_path (str, optional): the root path of the extraced files (directory of from_path) (Default: ``None``)
        overwrite (bool, optional): overwrite existing files (Default: ``False``)

    Returns:
        list: List of paths to extracted files even if not overwritten.

    Examples:
        >>> url = 'http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz'
        >>> from_path = './validation.tar.gz'
        >>> to_path = './'
        >>> torchaudio.datasets.utils.download_from_url(url, from_path)
        >>> torchaudio.datasets.utils.extract_archive(from_path, to_path)
    """

    if to_path is None:
        to_path = os.path.dirname(from_path)

    try:
        with tarfile.open(from_path, "r") as tar:
            logging.info("Opened tar file {}.".format(from_path))
            files = []
            for file_ in tar:  # type: Any
                file_path = os.path.join(to_path, file_.name)
                if file_.isfile():
                    files.append(file_path)
                    if os.path.exists(file_path):
                        logging.info("{} already extracted.".format(file_path))
                        if not overwrite:
                            continue
                tar.extract(file_, to_path)
            return files
    except tarfile.ReadError:
        pass

    try:
        with zipfile.ZipFile(from_path, "r") as zfile:
            logging.info("Opened zip file {}.".format(from_path))
            files = zfile.namelist()
            for file_ in files:
                file_path = os.path.join(to_path, file_)
                if os.path.exists(file_path):
                    logging.info("{} already extracted.".format(file_path))
                    if not overwrite:
                        continue
                zfile.extract(file_, to_path)
        return files
    except zipfile.BadZipFile:
        pass

    raise NotImplementedError("We currently only support tar.gz, tgz, and zip achives.")


class _DiskCache(Dataset):
    """
    Wrap a dataset so that, whenever a new item is returned, it is saved to disk.
    """

    def __init__(self, dataset: Dataset, location: str = ".cached") -> None:
        self.dataset = dataset
        self.location = location

        self._id = id(self)
        self._cache: List = [None] * len(dataset)

    def __getitem__(self, n: int) -> Any:
        if self._cache[n]:
            f = self._cache[n]
            return torch.load(f)

        f = str(self._id) + "-" + str(n)
        f = os.path.join(self.location, f)
        item = self.dataset[n]

        self._cache[n] = f
        os.makedirs(self.location, exist_ok=True)
        torch.save(item, f)

        return item

    def __len__(self) -> int:
        return len(self.dataset)


def diskcache_iterator(dataset: Dataset, location: str = ".cached") -> Dataset:
    return _DiskCache(dataset, location)


class _ThreadedIterator(threading.Thread):
    """
    Prefetch the next queue_length items from iterator in a background thread.

    Example:
    >> for i in bg_iterator(range(10)):
    >>     print(i)
    """

    class _End:
        pass

    def __init__(self, generator: Iterable, maxsize: int) -> None:
        threading.Thread.__init__(self)
        self.queue: Queue = Queue(maxsize)
        self.generator = generator
        self.daemon = True
        self.start()

    def run(self) -> None:
        for item in self.generator:
            self.queue.put(item)
        self.queue.put(self._End)

    def __iter__(self) -> Any:
        return self

    def __next__(self) -> Any:
        next_item = self.queue.get()
        if next_item == self._End:
            raise StopIteration
        return next_item

    # Required for Python 2.7 compatibility
    def next(self) -> Any:
        return self.__next__()


def bg_iterator(iterable: Iterable, maxsize: int) -> Any:
    return _ThreadedIterator(iterable, maxsize=maxsize)
