import warnings
import torchtext
if torchtext._WARN:
    warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)

import gzip
import hashlib
import logging
import os
import tarfile
import zipfile

import torch
from filelock import FileLock
from torchtext import _CACHE_DIR

from ._download_hooks import _DATASET_DOWNLOAD_MANAGER

logger = logging.getLogger(__name__)

LOCK_TIMEOUT = 600


def get_lock_dir():
    lock_dir = os.path.join(_CACHE_DIR, "locks")
    if not os.path.exists(lock_dir):
        os.makedirs(lock_dir, exist_ok=True)
    return lock_dir


def reporthook(t):
    """
    https://github.com/tqdm/tqdm.
    """
    last_b = [0]

    def inner(b=1, bsize=1, tsize=None):
        """
        b: int, optional
        Number of blocks just transferred [default: 1].
        bsize: int, optional
        Size of each block (in tqdm units) [default: 1].
        tsize: int, optional
        Total size (in tqdm units). If [default: None] remains unchanged.
        """
        if tsize is not None:
            t.total = tsize
        t.update((b - last_b[0]) * bsize)
        last_b[0] = b

    return inner


def validate_file(file_obj, hash_value, hash_type="sha256"):
    """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 in ("sha256", "md5"):
        hash_func = hashlib.new(hash_type, usedforsecurity=False)
    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 _check_hash(path, hash_value, hash_type):
    logger.info("Validating hash {} matches hash of {}".format(hash_value, path))
    with open(path, "rb") as file_obj:
        if not validate_file(file_obj, hash_value, hash_type):
            raise RuntimeError(
                "The hash of {} does not match. Delete the file manually and retry.".format(os.path.abspath(path))
            )


def download_from_url(url, path=None, root=".data", overwrite=False, hash_value=None, hash_type="sha256"):
    """Download file, with logic (from tensor2tensor) for Google Drive. Returns
    the path to the downloaded file.
    Args:
        url: the url of the file from URL header. (None)
        path: path where file will be saved
        root: download folder used to store the file in (.data)
        overwrite: overwrite existing files (False)
        hash_value (str, optional): hash for url (Default: ``None``).
        hash_type (str, optional): hash type, among "sha256" and "md5" (Default: ``"sha256"``).
    Examples:
        >>> url = 'http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz'
        >>> torchtext.utils.download_from_url(url)
        >>> url = 'http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz'
        >>> torchtext.utils.download_from_url(url)
        >>> '.data/validation.tar.gz'
    """
    # figure out filename and root
    if path is None:
        _, filename = os.path.split(url)
        root = os.path.abspath(root)
        path = os.path.join(root, filename)
    else:
        path = os.path.abspath(path)
        root, filename = os.path.split(os.path.abspath(path))

    # In a concurrent setting, adding a file lock ensures the first thread to acquire will actually download the model
    # and the other ones will just use the existing path (which will not contain a partially downloaded model).
    lock_dir = get_lock_dir()
    lock = FileLock(os.path.join(lock_dir, filename + ".lock"), timeout=LOCK_TIMEOUT)
    with lock:
        # skip download if path exists and overwrite is not True
        if os.path.exists(path):
            logger.info("File %s already exists." % path)
            if not overwrite:
                if hash_value:
                    _check_hash(path, hash_value, hash_type)
                return path

        # make root dir if does not exist
        if not os.path.exists(root):
            try:
                os.makedirs(root)
            except OSError:
                raise OSError("Can't create the download directory {}.".format(root))

        # download data and move to path
        _DATASET_DOWNLOAD_MANAGER.get_local_path(url, destination=path)

        logger.info("File {} downloaded.".format(path))

        # validate
        if hash_value:
            _check_hash(path, hash_value, hash_type)

        # all good
        return path


def extract_archive(from_path, to_path=None, overwrite=False):
    """Extract archive.
    Args:
        from_path: the path of the archive.
        to_path: the root path of the extracted files (directory of from_path)
        overwrite: overwrite existing files (False)
    Returns:
        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 = './'
        >>> torchtext.utils.download_from_url(url, from_path)
        >>> torchtext.utils.extract_archive(from_path, to_path)
        >>> ['.data/val.de', '.data/val.en']
        >>> torchtext.utils.download_from_url(url, from_path)
        >>> torchtext.utils.extract_archive(from_path, to_path)
        >>> ['.data/val.de', '.data/val.en']
    """

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

    if from_path.endswith((".tar.gz", ".tgz")):
        logger.info("Opening tar file {}.".format(from_path))
        with tarfile.open(from_path, "r") as tar:
            files = []
            for file_ in tar:
                file_path = os.path.join(to_path, file_.name)
                if file_.isfile():
                    files.append(file_path)
                    if os.path.exists(file_path):
                        logger.info("{} already extracted.".format(file_path))
                        if not overwrite:
                            continue
                tar.extract(file_, to_path)
            logger.info("Finished extracting tar file {}.".format(from_path))
            return files

    elif from_path.endswith(".zip"):
        assert zipfile.is_zipfile(from_path), from_path
        logger.info("Opening zip file {}.".format(from_path))
        with zipfile.ZipFile(from_path, "r") as zfile:
            files = []
            for file_ in zfile.namelist():
                file_path = os.path.join(to_path, file_)
                files.append(file_path)
                if os.path.exists(file_path):
                    logger.info("{} already extracted.".format(file_path))
                    if not overwrite:
                        continue
                zfile.extract(file_, to_path)
        files = [f for f in files if os.path.isfile(f)]
        logger.info("Finished extracting zip file {}.".format(from_path))
        return files

    elif from_path.endswith(".gz"):
        logger.info("Opening gz file {}.".format(from_path))
        default_block_size = 65536
        filename = from_path[:-3]
        files = [filename]
        with gzip.open(from_path, "rb") as gzfile, open(filename, "wb") as d_file:
            while True:
                block = gzfile.read(default_block_size)
                if not block:
                    break
                else:
                    d_file.write(block)
            d_file.write(block)
        logger.info("Finished extracting gz file {}.".format(from_path))
        return files

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


def _log_class_usage(klass):
    identifier = "torchtext"
    if klass and hasattr(klass, "__name__"):
        identifier += f".{klass.__name__}"
    torch._C._log_api_usage_once(identifier)


def get_asset_local_path(asset_path: str, overwrite=False) -> str:
    """Get local path for assets. Download if path does not exist locally
    Args:
        asset_path: Local path to asset or remote URL
        overwrite: Indicate whether to overwrite the file when downloading from URL (default: False)
    Returns:
        bool: local path of the asset after downloading or reading from cache
    Examples:
        >>> url = 'http://<HOST>/file.txt'
        >>> torchtext.utils.get_asset_local_path(url)
        >>> '.data/file.txt'
        >>> torchtext.utils.get_asset_local_path('/home/user/file.txt')
        >>> '/home/user/file.txt'
    """
    if os.path.exists(asset_path):
        local_path = asset_path
    else:
        local_path = download_from_url(url=asset_path, root=_CACHE_DIR, overwrite=overwrite)
    return local_path
