import os
import sys
import time
import numpy as np
from urllib.request import urlretrieve
from urllib.error import HTTPError
from urllib.error import URLError


def get_file(fname,
             origin,
             cache_dir=None):
    """A function to download file from url.
    Heritage from keras.utils
    """
    if cache_dir is None:
        cache_dir = os.path.join(os.path.expanduser('~'), '.local')
    else:
        cache_dir = os.path.expanduser(cache_dir)
    
    os.makedirs(cache_dir, exist_ok=True)
    fpath = os.path.join(cache_dir, fname)

    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 == -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 HTTPError as e:
            raise Exception(error_msg.format(origin, e.code, e.msg))
        except URLError as e:
            raise Exception(error_msg.format(origin, e.errno, e.reason))
    except (Exception, KeyboardInterrupt) as e:
        if os.path.exists(fpath):
            os.remove(fpath)
        raise
    ProgressTracker.progbar = None

    return fpath


class Progbar(object):
    """Displays a progress bar.
    Arguments:
        target: Total number of steps expected, None if unknown.
        width: Progress bar width on screen.
        verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
        stateful_metrics: Iterable of string names of metrics that should *not* be
          averaged over time. Metrics in this list will be displayed as-is. All
          others will be averaged by the progbar before display.
        interval: Minimum visual progress update interval (in seconds).
        unit_name: Display name for step counts (usually "step" or "sample").
    """

    def __init__(self,
                 target,
                 width=30,
                 verbose=1,
                 interval=0.05,
                 stateful_metrics=None,
                 unit_name='step'):
        self.target = target
        self.width = width
        self.verbose = verbose
        self.interval = interval
        self.unit_name = unit_name
        if stateful_metrics:
            self.stateful_metrics = set(stateful_metrics)
        else:
            self.stateful_metrics = set()

        self._dynamic_display = ((hasattr(sys.stdout, 'isatty') and
                                  sys.stdout.isatty()) or
                                 'ipykernel' in sys.modules or
                                 'posix' in sys.modules or
                                 'PYCHARM_HOSTED' in os.environ)
        self._total_width = 0
        self._seen_so_far = 0
        # We use a dict + list to avoid garbage collection
        # issues found in OrderedDict
        self._values = {}
        self._values_order = []
        self._start = time.time()
        self._last_update = 0

    def update(self, current, values=None, finalize=None):
        """Updates the progress bar.
        Arguments:
            current: Index of current step.
            values: List of tuples: `(name, value_for_last_step)`. If `name` is in
              `stateful_metrics`, `value_for_last_step` will be displayed as-is.
              Else, an average of the metric over time will be displayed.
            finalize: Whether this is the last update for the progress bar. If
              `None`, defaults to `current >= self.target`.
        """
        if finalize is None:
            if self.target is None:
                finalize = False
            else:
                finalize = current >= self.target

        values = values or []
        for k, v in values:
            if k not in self._values_order:
                self._values_order.append(k)
            if k not in self.stateful_metrics:
                # In the case that progress bar doesn't have a target value in the first
                # epoch, both on_batch_end and on_epoch_end will be called, which will
                # cause 'current' and 'self._seen_so_far' to have the same value. Force
                # the minimal value to 1 here, otherwise stateful_metric will be 0s.
                value_base = max(current - self._seen_so_far, 1)
                if k not in self._values:
                    self._values[k] = [v * value_base, value_base]
                else:
                    self._values[k][0] += v * value_base
                    self._values[k][1] += value_base
            else:
                # Stateful metrics output a numeric value. This representation
                # means "take an average from a single value" but keeps the
                # numeric formatting.
                self._values[k] = [v, 1]
        self._seen_so_far = current

        now = time.time()
        info = ' - %.0fs' % (now - self._start)
        if self.verbose == 1:
            if now - self._last_update < self.interval and not finalize:
                return

            prev_total_width = self._total_width
            if self._dynamic_display:
                sys.stdout.write('\b' * prev_total_width)
                sys.stdout.write('\r')
            else:
                sys.stdout.write('\n')

            if self.target is not None:
                numdigits = int(np.log10(self.target)) + 1
                bar = ('%' + str(numdigits) +
                       'd/%d [') % (current, self.target)
                prog = float(current) / self.target
                prog_width = int(self.width * prog)
                if prog_width > 0:
                    bar += ('=' * (prog_width - 1))
                    if current < self.target:
                        bar += '>'
                    else:
                        bar += '='
                bar += ('.' * (self.width - prog_width))
                bar += ']'
            else:
                bar = '%7d/Unknown' % current

            self._total_width = len(bar)
            sys.stdout.write(bar)

            if current:
                time_per_unit = (now - self._start) / current
            else:
                time_per_unit = 0

            if self.target is None or finalize:
                if time_per_unit >= 1 or time_per_unit == 0:
                    info += ' %.0fs/%s' % (time_per_unit, self.unit_name)
                elif time_per_unit >= 1e-3:
                    info += ' %.0fms/%s' % (time_per_unit *
                                            1e3, self.unit_name)
                else:
                    info += ' %.0fus/%s' % (time_per_unit *
                                            1e6, self.unit_name)
            else:
                eta = time_per_unit * (self.target - current)
                if eta > 3600:
                    eta_format = '%d:%02d:%02d' % (eta // 3600,
                                                   (eta % 3600) // 60, eta % 60)
                elif eta > 60:
                    eta_format = '%d:%02d' % (eta // 60, eta % 60)
                else:
                    eta_format = '%ds' % eta

                info = ' - ETA: %s' % eta_format

            for k in self._values_order:
                info += ' - %s:' % k
                if isinstance(self._values[k], list):
                    avg = np.mean(
                        self._values[k][0] / max(1, self._values[k][1]))
                    if abs(avg) > 1e-3:
                        info += ' %.4f' % avg
                    else:
                        info += ' %.4e' % avg
                else:
                    info += ' %s' % self._values[k]

            self._total_width += len(info)
            if prev_total_width > self._total_width:
                info += (' ' * (prev_total_width - self._total_width))

            if finalize:
                info += '\n'

            sys.stdout.write(info)
            sys.stdout.flush()

        elif self.verbose == 2:
            if finalize:
                numdigits = int(np.log10(self.target)) + 1
                count = ('%' + str(numdigits) +
                         'd/%d') % (current, self.target)
                info = count + info
                for k in self._values_order:
                    info += ' - %s:' % k
                    avg = np.mean(
                        self._values[k][0] / max(1, self._values[k][1]))
                    if avg > 1e-3:
                        info += ' %.4f' % avg
                    else:
                        info += ' %.4e' % avg
                info += '\n'

                sys.stdout.write(info)
                sys.stdout.flush()

        self._last_update = now

    def add(self, n, values=None):
        self.update(self._seen_so_far + n, values)
