from typing import Any, Callable, List, Optional, Sequence, TypeVar, Iterable, Set, Tuple
import base64
import hashlib
import heapq
import itertools
import os
import re
import string
from time import sleep
from itertools import zip_longest

from django.conf import settings

T = TypeVar('T')

def statsd_key(val: Any, clean_periods: bool=False) -> str:
    if not isinstance(val, str):
        val = str(val)

    if ':' in val:
        val = val.split(':')[0]
    val = val.replace('-', "_")
    if clean_periods:
        val = val.replace('.', '_')

    return val

class StatsDWrapper:
    """Transparently either submit metrics to statsd
    or do nothing without erroring out"""

    # Backported support for gauge deltas
    # as our statsd server supports them but supporting
    # pystatsd is not released yet
    def _our_gauge(self, stat: str, value: float, rate: float=1, delta: bool=False) -> None:
        """Set a gauge value."""
        from django_statsd.clients import statsd
        if delta:
            value_str = '%+g|g' % (value,)
        else:
            value_str = '%g|g' % (value,)
        statsd._send(stat, value_str, rate)

    def __getattr__(self, name: str) -> Any:
        # Hand off to statsd if we have it enabled
        # otherwise do nothing
        if name in ['timer', 'timing', 'incr', 'decr', 'gauge']:
            if settings.STATSD_HOST != '':
                from django_statsd.clients import statsd
                if name == 'gauge':
                    return self._our_gauge
                else:
                    return getattr(statsd, name)
            else:
                return lambda *args, **kwargs: None

        raise AttributeError

statsd = StatsDWrapper()

# Runs the callback with slices of all_list of a given batch_size
def run_in_batches(all_list: Sequence[T],
                   batch_size: int,
                   callback: Callable[[Sequence[T]], None],
                   sleep_time: int=0,
                   logger: Optional[Callable[[str], None]]=None) -> None:
    if len(all_list) == 0:
        return

    limit = (len(all_list) // batch_size) + 1
    for i in range(limit):
        start = i*batch_size
        end = (i+1) * batch_size
        if end >= len(all_list):
            end = len(all_list)
        batch = all_list[start:end]

        if logger:
            logger("Executing %s in batch %s of %s" % (end-start, i+1, limit))

        callback(batch)

        if i != limit - 1:
            sleep(sleep_time)

def make_safe_digest(string: str,
                     hash_func: Callable[[bytes], Any]=hashlib.sha1) -> str:
    """
    return a hex digest of `string`.
    """
    # hashlib.sha1, md5, etc. expect bytes, so non-ASCII strings must
    # be encoded.
    return hash_func(string.encode('utf-8')).hexdigest()


def log_statsd_event(name: str) -> None:
    """
    Sends a single event to statsd with the desired name and the current timestamp

    This can be used to provide vertical lines in generated graphs,
    for example when doing a prod deploy, bankruptcy request, or
    other one-off events

    Note that to draw this event as a vertical line in graphite
    you can use the drawAsInfinite() command
    """
    event_name = "events.%s" % (name,)
    statsd.incr(event_name)

def generate_random_token(length: int) -> str:
    return str(base64.b16encode(os.urandom(length // 2)).decode('utf-8').lower())

def generate_api_key() -> str:
    choices = string.ascii_letters + string.digits
    altchars = ''.join([choices[ord(os.urandom(1)) % 62] for _ in range(2)]).encode("utf-8")
    api_key = base64.b64encode(os.urandom(24), altchars=altchars).decode("utf-8")
    return api_key

def has_api_key_format(key: str) -> bool:
    return bool(re.fullmatch(r"([A-Za-z0-9]){32}", key))

def query_chunker(queries: List[Any],
                  id_collector: Optional[Set[int]]=None,
                  chunk_size: int=1000,
                  db_chunk_size: Optional[int]=None) -> Iterable[Any]:
    '''
    This merges one or more Django ascending-id queries into
    a generator that returns chunks of chunk_size row objects
    during each yield, preserving id order across all results..

    Queries should satisfy these conditions:
        - They should be Django filters.
        - They should return Django objects with "id" attributes.
        - They should be disjoint.

    The generator also populates id_collector, which we use
    internally to enforce unique ids, but which the caller
    can pass in to us if they want the side effect of collecting
    all ids.
    '''
    if db_chunk_size is None:
        db_chunk_size = chunk_size // len(queries)

    assert db_chunk_size >= 2
    assert chunk_size >= 2

    if id_collector is not None:
        assert(len(id_collector) == 0)
    else:
        id_collector = set()

    def chunkify(q: Any, i: int) -> Iterable[Tuple[int, int, Any]]:
        q = q.order_by('id')
        min_id = -1
        while True:
            assert db_chunk_size is not None  # Hint for mypy, but also workaround for mypy bug #3442.
            rows = list(q.filter(id__gt=min_id)[0:db_chunk_size])
            if len(rows) == 0:
                break
            for row in rows:
                yield (row.id, i, row)
            min_id = rows[-1].id

    iterators = [chunkify(q, i) for i, q in enumerate(queries)]
    merged_query = heapq.merge(*iterators)

    while True:
        tup_chunk = list(itertools.islice(merged_query, 0, chunk_size))
        if len(tup_chunk) == 0:
            break

        # Do duplicate-id management here.
        tup_ids = {tup[0] for tup in tup_chunk}
        assert len(tup_ids) == len(tup_chunk)
        assert len(tup_ids.intersection(id_collector)) == 0
        id_collector.update(tup_ids)

        yield [row for row_id, i, row in tup_chunk]

def process_list_in_batches(lst: List[Any],
                            chunk_size: int,
                            process_batch: Callable[[List[Any]], None]) -> None:
    offset = 0

    while True:
        items = lst[offset:offset+chunk_size]
        if not items:
            break
        process_batch(items)
        offset += chunk_size

def split_by(array: List[Any], group_size: int, filler: Any) -> List[List[Any]]:
    """
    Group elements into list of size `group_size` and fill empty cells with
    `filler`. Recipe from https://docs.python.org/3/library/itertools.html
    """
    args = [iter(array)] * group_size
    return list(map(list, zip_longest(*args, fillvalue=filler)))
