"""Utilities for file download and caching."""
from __future__ import absolute_import
from __future__ import print_function

import hashlib
import multiprocessing
import os
import random
import shutil
import sys
import tarfile
import threading
import time
import zipfile
from abc import abstractmethod
from multiprocessing.pool import ThreadPool

import numpy as np
import six
from six.moves.urllib.error import HTTPError
from six.moves.urllib.error import URLError
from six.moves.urllib.request import urlopen

try:
    import queue
except ImportError:
    import Queue as queue

from ..utils.generic_utils import Progbar

if sys.version_info[0] == 2:
    def urlretrieve(url, filename, reporthook=None, data=None):
        """Replacement for `urlretrive` for Python 2.

        Under Python 2, `urlretrieve` relies on `FancyURLopener` from legacy
        `urllib` module, known to have issues with proxy management.

        # Arguments
            url: url to retrieve.
            filename: where to store the retrieved data locally.
            reporthook: a hook function that will be called once
                on establishment of the network connection and once
                after each block read thereafter.
                The hook will be passed three arguments;
                a count of blocks transferred so far,
                a block size in bytes, and the total size of the file.
            data: `data` argument passed to `urlopen`.
        """

        def chunk_read(response, chunk_size=8192, reporthook=None):
            content_type = response.info().get('Content-Length')
            total_size = -1
            if content_type is not None:
                total_size = int(content_type.strip())
            count = 0
            while 1:
                chunk = response.read(chunk_size)
                count += 1
                if not chunk:
                    reporthook(count, total_size, total_size)
                    break
                if reporthook:
                    reporthook(count, chunk_size, total_size)
                yield chunk

        response = urlopen(url, data)
        with open(filename, 'wb') as fd:
            for chunk in chunk_read(response, reporthook=reporthook):
                fd.write(chunk)
else:
    from six.moves.urllib.request import urlretrieve


def _extract_archive(file_path, path='.', archive_format='auto'):
    """Extracts an archive if it matches tar, tar.gz, tar.bz, or zip formats.

    # Arguments
        file_path: path to the archive file
        path: path to extract the archive file
        archive_format: Archive format to try for extracting the file.
            Options are 'auto', 'tar', 'zip', and None.
            'tar' includes tar, tar.gz, and tar.bz files.
            The default 'auto' is ['tar', 'zip'].
            None or an empty list will return no matches found.

    # Returns
        True if a match was found and an archive extraction was completed,
        False otherwise.
    """
    if archive_format is None:
        return False
    if archive_format is 'auto':
        archive_format = ['tar', 'zip']
    if isinstance(archive_format, six.string_types):
        archive_format = [archive_format]

    for archive_type in archive_format:
        if archive_type is 'tar':
            open_fn = tarfile.open
            is_match_fn = tarfile.is_tarfile
        if archive_type is 'zip':
            open_fn = zipfile.ZipFile
            is_match_fn = zipfile.is_zipfile

        if is_match_fn(file_path):
            with open_fn(file_path) as archive:
                try:
                    archive.extractall(path)
                except (tarfile.TarError, RuntimeError,
                        KeyboardInterrupt):
                    if os.path.exists(path):
                        if os.path.isfile(path):
                            os.remove(path)
                        else:
                            shutil.rmtree(path)
                    raise
            return True
    return False


def get_file(fname,
             origin,
             untar=False,
             md5_hash=None,
             file_hash=None,
             cache_subdir='datasets',
             hash_algorithm='auto',
             extract=False,
             archive_format='auto',
             cache_dir=None):
    """Downloads a file from a URL if it not already in the cache.

    By default the file at the url `origin` is downloaded to the
    cache_dir `~/.keras`, placed in the cache_subdir `datasets`,
    and given the filename `fname`. The final location of a file
    `example.txt` would therefore be `~/.keras/datasets/example.txt`.

    Files in tar, tar.gz, tar.bz, and zip formats can also be extracted.
    Passing a hash will verify the file after download. The command line
    programs `shasum` and `sha256sum` can compute the hash.

    # Arguments
        fname: Name of the file. If an absolute path `/path/to/file.txt` is
            specified the file will be saved at that location.
        origin: Original URL of the file.
        untar: Deprecated in favor of 'extract'.
            boolean, whether the file should be decompressed
        md5_hash: Deprecated in favor of 'file_hash'.
            md5 hash of the file for verification
        file_hash: The expected hash string of the file after download.
            The sha256 and md5 hash algorithms are both supported.
        cache_subdir: Subdirectory under the Keras cache dir where the file is
            saved. If an absolute path `/path/to/folder` is
            specified the file will be saved at that location.
        hash_algorithm: Select the hash algorithm to verify the file.
            options are 'md5', 'sha256', and 'auto'.
            The default 'auto' detects the hash algorithm in use.
        extract: True tries extracting the file as an Archive, like tar or zip.
        archive_format: Archive format to try for extracting the file.
            Options are 'auto', 'tar', 'zip', and None.
            'tar' includes tar, tar.gz, and tar.bz files.
            The default 'auto' is ['tar', 'zip'].
            None or an empty list will return no matches found.
        cache_dir: Location to store cached files, when None it
            defaults to the [Keras Directory](/faq/#where-is-the-keras-configuration-filed-stored).

    # Returns
        Path to the downloaded file
    """
    if cache_dir is None:
        cache_dir = os.path.expanduser(os.path.join('~', '.keras'))
    if md5_hash is not None and file_hash is None:
        file_hash = md5_hash
        hash_algorithm = 'md5'
    datadir_base = os.path.expanduser(cache_dir)
    if not os.access(datadir_base, os.W_OK):
        datadir_base = os.path.join('/tmp', '.keras')
    datadir = os.path.join(datadir_base, cache_subdir)
    if not os.path.exists(datadir):
        os.makedirs(datadir)

    if untar:
        untar_fpath = os.path.join(datadir, fname)
        fpath = untar_fpath + '.tar.gz'
    else:
        fpath = os.path.join(datadir, fname)

    download = False
    if os.path.exists(fpath):
        # File found; verify integrity if a hash was provided.
        if file_hash is not None:
            if not validate_file(fpath, file_hash, algorithm=hash_algorithm):
                print('A local file was found, but it seems to be '
                      'incomplete or outdated because the ' + hash_algorithm +
                      ' file hash does not match the original value of ' +
                      file_hash + ' so we will re-download the data.')
                download = True
    else:
        download = True

    if download:
        print('Downloading data from', origin)

        class ProgressTracker(object):
            # Maintain progbar for the lifetime of download.
            # This design was chosen for Python 2.7 compatibility.
            progbar = None

        def dl_progress(count, block_size, total_size):
            if ProgressTracker.progbar is None:
                if total_size is -1:
                    total_size = None
                ProgressTracker.progbar = Progbar(total_size)
            else:
                ProgressTracker.progbar.update(count * block_size)

        error_msg = 'URL fetch failure on {}: {} -- {}'
        try:
            try:
                urlretrieve(origin, fpath, dl_progress)
            except URLError as e:
                raise Exception(error_msg.format(origin, e.errno, e.reason))
            except HTTPError as e:
                raise Exception(error_msg.format(origin, e.code, e.msg))
        except (Exception, KeyboardInterrupt) as e:
            if os.path.exists(fpath):
                os.remove(fpath)
            raise
        ProgressTracker.progbar = None

    if untar:
        if not os.path.exists(untar_fpath):
            _extract_archive(fpath, datadir, archive_format='tar')
        return untar_fpath

    if extract:
        _extract_archive(fpath, datadir, archive_format)

    return fpath


def _hash_file(fpath, algorithm='sha256', chunk_size=65535):
    """Calculates a file sha256 or md5 hash.

    # Example

    ```python
       >>> from keras.data_utils import _hash_file
       >>> _hash_file('/path/to/file.zip')
       'e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855'
    ```

    # Arguments
        fpath: path to the file being validated
        algorithm: hash algorithm, one of 'auto', 'sha256', or 'md5'.
            The default 'auto' detects the hash algorithm in use.
        chunk_size: Bytes to read at a time, important for large files.

    # Returns
        The file hash
    """
    if (algorithm is 'sha256') or (algorithm is 'auto' and len(hash) is 64):
        hasher = hashlib.sha256()
    else:
        hasher = hashlib.md5()

    with open(fpath, 'rb') as fpath_file:
        for chunk in iter(lambda: fpath_file.read(chunk_size), b''):
            hasher.update(chunk)

    return hasher.hexdigest()


def validate_file(fpath, file_hash, algorithm='auto', chunk_size=65535):
    """Validates a file against a sha256 or md5 hash.

    # Arguments
        fpath: path to the file being validated
        file_hash:  The expected hash string of the file.
            The sha256 and md5 hash algorithms are both supported.
        algorithm: Hash algorithm, one of 'auto', 'sha256', or 'md5'.
            The default 'auto' detects the hash algorithm in use.
        chunk_size: Bytes to read at a time, important for large files.

    # Returns
        Whether the file is valid
    """
    if ((algorithm is 'sha256') or
            (algorithm is 'auto' and len(file_hash) is 64)):
        hasher = 'sha256'
    else:
        hasher = 'md5'

    if str(_hash_file(fpath, hasher, chunk_size)) == str(file_hash):
        return True
    else:
        return False


class Sequence(object):
    """Base object for fitting to a sequence of data, such as a dataset.

    Every `Sequence` must implements the `__getitem__` and the `__len__` methods.

    # Examples

    ```python
    from skimage.io import imread
    from skimage.transform import resize
    import numpy as np

    # Here, `x_set` is list of path to the images
    # and `y_set` are the associated classes.

    class CIFAR10Sequence(Sequence):
        def __init__(self, x_set, y_set, batch_size):
            self.X,self.y = x_set,y_set
            self.batch_size = batch_size

        def __len__(self):
            return len(self.X) // self.batch_size

        def __getitem__(self,idx):
            batch_x = self.X[idx*self.batch_size:(idx+1)*self.batch_size]
            batch_y = self.y[idx*self.batch_size:(idx+1)*self.batch_size]

            return np.array([
                resize(imread(file_name), (200,200))
                   for file_name in batch_x]), np.array(batch_y)
    ```
    """

    @abstractmethod
    def __getitem__(self, index):
        """Gets batch at position `index`.

        # Arguments
            index: position of the batch in the Sequence.

        # Returns
            A batch
        """
        raise NotImplementedError

    @abstractmethod
    def __len__(self):
        """Number of batch in the Sequence.

        # Returns
            The number of batches in the Sequence.
        """
        raise NotImplementedError


def get_index(ds, i):
    """Quick fix for Python2, otherwise, it cannot be pickled.

    # Arguments
        ds: a Sequence object
        i: index

    # Returns
        The value at index `i`.
    """
    return ds[i]


class SequenceEnqueuer(object):
    """Base class to enqueue inputs.

    The task of an Enqueuer is to use parallelism to speed up preprocessing.
    This is done with processes or threads.

    # Examples

    ```python
    enqueuer = SequenceEnqueuer(...)
    enqueuer.start()
    datas = enqueuer.get()
    for data in datas:
        # Use the inputs; training, evaluating, predicting.
        # ... stop sometime.
    enqueuer.close()
    ```

    The `enqueuer.get()` should be an infinite stream of datas.

    """

    @abstractmethod
    def is_running(self):
        raise NotImplemented

    @abstractmethod
    def start(self, workers=1, max_queue_size=10):
        """Starts the handler's workers.

        # Arguments
            workers: number of worker threads
            max_queue_size: queue size
                (when full, threads could block on `put()`).
        """
        raise NotImplemented

    @abstractmethod
    def stop(self, timeout=None):
        """Stop running threads and wait for them to exit, if necessary.

        Should be called by the same thread which called start().

        # Arguments
            timeout: maximum time to wait on thread.join()
        """
        raise NotImplemented

    @abstractmethod
    def get(self):
        """Creates a generator to extract data from the queue.

        Skip the data if it is `None`.

        # Returns
            Generator yielding tuples `(inputs, targets)`
                or `(inputs, targets, sample_weights)`.
        """
        raise NotImplemented


class OrderedEnqueuer(SequenceEnqueuer):
    """Builds a Enqueuer from a Sequence.

    Used in `fit_generator`, `evaluate_generator`, `predict_generator`.

    # Arguments
        sequence: A `keras.utils.data_utils.Sequence` object.
        use_multiprocessing: use multiprocessing if True, otherwise threading
        scheduling: Sequential querying of datas if 'sequential', random otherwise.
    """

    def __init__(self, sequence,
                 use_multiprocessing=False,
                 scheduling='sequential'):
        self.sequence = sequence
        self.use_multiprocessing = use_multiprocessing
        self.scheduling = scheduling
        self.workers = 0
        self.executor = None
        self.queue = None
        self.run_thread = None
        self.stop_signal = None

    def is_running(self):
        return self.stop_signal is not None and not self.stop_signal.is_set()

    def start(self, workers=1, max_queue_size=10):
        """Start the handler's workers.

        # Arguments
            workers: number of worker threads
            max_queue_size: queue size
                (when full, workers could block on `put()`)
        """
        if self.use_multiprocessing:
            self.executor = multiprocessing.Pool(workers)
        else:
            self.executor = ThreadPool(workers)
        self.queue = queue.Queue(max_queue_size)
        self.stop_signal = threading.Event()
        self.run_thread = threading.Thread(target=self._run)
        self.run_thread.daemon = True
        self.run_thread.start()

    def _run(self):
        """Function to submit request to the executor and queue the `Future` objects."""
        sequence = list(range(len(self.sequence)))
        while True:
            if self.scheduling is not 'sequential':
                random.shuffle(sequence)
            for i in sequence:
                if self.stop_signal.is_set():
                    return
                self.queue.put(
                    self.executor.apply_async(get_index,
                                              (self.sequence, i)), block=True)

    def get(self):
        """Creates a generator to extract data from the queue.

        Skip the data if it is `None`.

        # Returns
            Generator yielding tuples (inputs, targets)
                or (inputs, targets, sample_weights)
        """
        try:
            while self.is_running():
                inputs = self.queue.get(block=True).get()
                if inputs is not None:
                    yield inputs
        except Exception as e:
            self.stop()
            raise StopIteration(e)

    def stop(self, timeout=None):
        """Stops running threads and wait for them to exit, if necessary.

        Should be called by the same thread which called `start()`.

        # Arguments
            timeout: maximum time to wait on `thread.join()`
        """
        self.stop_signal.set()
        with self.queue.mutex:
            self.queue.queue.clear()
            self.queue.unfinished_tasks = 0
            self.queue.not_full.notify()
        self.executor.close()
        self.executor.join()
        self.run_thread.join(timeout)


class GeneratorEnqueuer(SequenceEnqueuer):
    """Builds a queue out of a data generator.

    Used in `fit_generator`, `evaluate_generator`, `predict_generator`.

    # Arguments
        generator: a generator function which endlessly yields data
        use_multiprocessing: use multiprocessing if True, otherwise threading
        wait_time: time to sleep in-between calls to `put()`
        random_seed: Initial seed for workers,
            will be incremented by one for each workers.
    """

    def __init__(self, generator,
                 use_multiprocessing=False,
                 wait_time=0.05,
                 random_seed=None):
        self.wait_time = wait_time
        self._generator = generator
        self._use_multiprocessing = use_multiprocessing
        self._threads = []
        self._stop_event = None
        self.queue = None
        self.random_seed = random_seed

    def start(self, workers=1, max_queue_size=10):
        """Kicks off threads which add data from the generator into the queue.

        # Arguments
            workers: number of worker threads
            max_queue_size: queue size
                (when full, threads could block on `put()`)
        """

        def data_generator_task():
            while not self._stop_event.is_set():
                try:
                    if self._use_multiprocessing or self.queue.qsize() < max_queue_size:
                        generator_output = next(self._generator)
                        self.queue.put(generator_output)
                    else:
                        time.sleep(self.wait_time)
                except Exception:
                    self._stop_event.set()
                    raise

        try:
            if self._use_multiprocessing:
                self.queue = multiprocessing.Queue(maxsize=max_queue_size)
                self._stop_event = multiprocessing.Event()
            else:
                self.queue = queue.Queue()
                self._stop_event = threading.Event()

            for _ in range(workers):
                if self._use_multiprocessing:
                    # Reset random seed else all children processes
                    # share the same seed
                    np.random.seed(self.random_seed)
                    thread = multiprocessing.Process(target=data_generator_task)
                    thread.daemon = True
                    if self.random_seed is not None:
                        self.random_seed += 1
                else:
                    thread = threading.Thread(target=data_generator_task)
                self._threads.append(thread)
                thread.start()
        except:
            self.stop()
            raise

    def is_running(self):
        return self._stop_event is not None and not self._stop_event.is_set()

    def stop(self, timeout=None):
        """Stops running threads and wait for them to exit, if necessary.

        Should be called by the same thread which called `start()`.

        # Arguments
            timeout: maximum time to wait on `thread.join()`.
        """
        if self.is_running():
            self._stop_event.set()

        for thread in self._threads:
            if thread.is_alive():
                if self._use_multiprocessing:
                    thread.terminate()
                else:
                    thread.join(timeout)

        if self._use_multiprocessing:
            if self.queue is not None:
                self.queue.close()

        self._threads = []
        self._stop_event = None
        self.queue = None

    def get(self):
        """Creates a generator to extract data from the queue.

        Skip the data if it is `None`.

        # Returns
            A generator
        """
        while self.is_running():
            if not self.queue.empty():
                inputs = self.queue.get()
                if inputs is not None:
                    yield inputs
            else:
                time.sleep(self.wait_time)
