import os
import sys
import time
import heapq
import semver
import random

from multiprocessing import Pool
from collections import namedtuple

KILO = 1024
KILOBYTE = 1 * KILO
MEGABYTE = KILO * KILOBYTE
GIGABYTE = KILO * MEGABYTE
TERABYTE = KILO * GIGABYTE
SIZE_PREFIX_LOOKUP = {'k': KILOBYTE, 'm': MEGABYTE, 'g': GIGABYTE, 't': TERABYTE}

ValueRange = namedtuple('ValueRange', 'start end r')


def parse_file_size(file_size):
    file_size = file_size.lower().strip()
    if len(file_size) == 0:
        return 0
    n = int(keep_only_digits(file_size))
    if file_size[-1] == 'b':
        file_size = file_size[:-1]
    e = file_size[-1]
    return SIZE_PREFIX_LOOKUP[e] * n if e in SIZE_PREFIX_LOOKUP else n


def keep_only_digits(txt):
    return ''.join(filter(str.isdigit, txt))


def secs_to_hours(secs):
    hours, remainder = divmod(secs, 3600)
    minutes, seconds = divmod(remainder, 60)
    return '%d:%02d:%02d' % (hours, minutes, seconds)


def check_ctcdecoder_version():
    ds_version_s = open(os.path.join(os.path.dirname(__file__), '../VERSION')).read().strip()

    try:
        # pylint: disable=import-outside-toplevel
        from ds_ctcdecoder import __version__ as decoder_version
    except ImportError as e:
        if e.msg.find('__version__') > 0:
            print("DeepSpeech version ({ds_version}) requires CTC decoder to expose __version__. "
                  "Please upgrade the ds_ctcdecoder package to version {ds_version}".format(ds_version=ds_version_s))
            sys.exit(1)
        raise e

    rv = semver.compare(ds_version_s, decoder_version)
    if rv != 0:
        print("DeepSpeech version ({}) and CTC decoder version ({}) do not match. "
              "Please ensure matching versions are in use.".format(ds_version_s, decoder_version))
        sys.exit(1)

    return rv


class Interleaved:
    """Collection that lazily combines sorted collections in an interleaving fashion.
    During iteration the next smallest element from all the sorted collections is always picked.
    The collections must support iter() and len()."""
    def __init__(self, *iterables, key=lambda obj: obj, reverse=False):
        self.iterables = iterables
        self.key = key
        self.reverse = reverse
        self.len = sum(map(len, iterables))

    def __iter__(self):
        return heapq.merge(*self.iterables, key=self.key, reverse=self.reverse)

    def __len__(self):
        return self.len


class LimitingPool:
    """Limits unbound ahead-processing of multiprocessing.Pool's imap method
    before items get consumed by the iteration caller.
    This prevents OOM issues in situations where items represent larger memory allocations."""
    def __init__(self, processes=None, initializer=None, initargs=None, process_ahead=None, sleeping_for=0.1):
        self.process_ahead = os.cpu_count() if process_ahead is None else process_ahead
        self.sleeping_for = sleeping_for
        self.processed = 0
        self.pool = Pool(processes=processes, initializer=initializer, initargs=initargs)

    def __enter__(self):
        return self

    def _limit(self, it):
        for obj in it:
            while self.processed >= self.process_ahead:
                time.sleep(self.sleeping_for)
            self.processed += 1
            yield obj

    def imap(self, fun, it):
        for obj in self.pool.imap(fun, self._limit(it)):
            self.processed -= 1
            yield obj

    def terminate(self):
        self.pool.terminate()

    def __exit__(self, exc_type, exc_value, traceback):
        self.pool.close()


class ExceptionBox:
    """Helper class for passing-back and re-raising an exception from inside a TensorFlow dataset generator.
    Used in conjunction with `remember_exception`."""
    def __init__(self):
        self.exception = None

    def raise_if_set(self):
        if self.exception is not None:
            exception = self.exception
            self.exception = None
            raise exception  # pylint: disable = raising-bad-type


def remember_exception(iterable, exception_box=None):
    """Wraps a TensorFlow dataset generator for catching its actual exceptions
    that would otherwise just interrupt iteration w/o bubbling up."""
    def do_iterate():
        try:
            yield from iterable()
        except StopIteration:
            return
        except Exception as ex:  # pylint: disable = broad-except
            exception_box.exception = ex
    return iterable if exception_box is None else do_iterate


def get_value_range(value, target_type):
    if isinstance(value, str):
        r = target_type(0)
        parts = value.split('~')
        if len(parts) == 2:
            value = parts[0]
            r = target_type(parts[1])
        elif len(parts) > 2:
            raise ValueError('Cannot parse value range')
        parts = value.split(':')
        if len(parts) == 1:
            parts.append(parts[0])
        elif len(parts) > 2:
            raise ValueError('Cannot parse value range')
        return ValueRange(target_type(parts[0]), target_type(parts[1]), r)
    if isinstance(value, tuple):
        if len(value) == 2:
            return ValueRange(target_type(value[0]), target_type(value[1]), 0)
        if len(value) == 3:
            return ValueRange(target_type(value[0]), target_type(value[1]), target_type(value[2]))
        raise ValueError('Cannot convert to ValueRange: Wrong tuple size')
    return ValueRange(target_type(value), target_type(value), 0)


def int_range(value):
    return get_value_range(value, int)


def float_range(value):
    return get_value_range(value, float)


def pick_value_from_range(value_range, clock=None):
    clock = random.random() if clock is None else max(0.0, min(1.0, float(clock)))
    value = value_range.start + clock * (value_range.end - value_range.start)
    value = random.uniform(value - value_range.r, value + value_range.r)
    return round(value) if isinstance(value_range.start, int) else value


def tf_pick_value_from_range(value_range, clock=None, double_precision=False):
    import tensorflow as tf  # pylint: disable=import-outside-toplevel
    clock = (tf.random.stateless_uniform([], seed=(-1, 1), dtype=tf.float64) if clock is None
             else tf.maximum(tf.constant(0.0, dtype=tf.float64), tf.minimum(tf.constant(1.0, dtype=tf.float64), clock)))
    value = value_range.start + clock * (value_range.end - value_range.start)
    value = tf.random.stateless_uniform([],
                                        minval=value - value_range.r,
                                        maxval=value + value_range.r,
                                        seed=(clock * tf.int32.min, clock * tf.int32.max),
                                        dtype=tf.float64)
    if isinstance(value_range.start, int):
        return tf.cast(tf.math.round(value), tf.int64 if double_precision else tf.int32)
    return tf.cast(value, tf.float64 if double_precision else tf.float32)
