# piker: trading gear for hackers
# Copyright (C) Tyler Goodlet (in stewardship of pikers)

# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.

'''
core task logic for processing chains

'''
from dataclasses import dataclass
from functools import partial
from typing import (
    AsyncIterator,
    Callable,
    Optional,
    Union,
)

import numpy as np
import trio
from trio_typing import TaskStatus
import tractor
from tractor.msg import NamespacePath

from ..log import get_logger, get_console_log
from .. import data
from ..data import attach_shm_array
from ..data.feed import (
    Flume,
    Feed,
)
from ..data._sharedmem import ShmArray
from ..data._sampling import (
    _default_delay_s,
    open_sample_stream,
)
from ..accounting._mktinfo import Symbol
from ._api import (
    Fsp,
    _load_builtins,
    _Token,
)
from .._profile import Profiler

log = get_logger(__name__)


@dataclass
class TaskTracker:
    complete: trio.Event
    cs: trio.CancelScope


async def filter_quotes_by_sym(

    sym: str,
    quote_stream: tractor.MsgStream,

) -> AsyncIterator[dict]:
    '''
    Filter quote stream by target symbol.

    '''
    # TODO: make this the actual first quote from feed
    # XXX: this allows for a single iteration to run for history
    # processing without waiting on the real-time feed for a new quote
    yield {}

    async for quotes in quote_stream:
        quote = quotes.get(sym)
        if quote:
            yield quote


async def fsp_compute(

    symbol: Symbol,
    flume: Flume,
    quote_stream: trio.abc.ReceiveChannel,

    src: ShmArray,
    dst: ShmArray,

    func: Callable,

    # attach_stream: bool = False,
    task_status: TaskStatus[None] = trio.TASK_STATUS_IGNORED,

) -> None:

    profiler = Profiler(
        delayed=False,
        disabled=True
    )

    fqsn = symbol.fqme
    out_stream = func(

        # TODO: do we even need this if we do the feed api right?
        # shouldn't a local stream do this before we get a handle
        # to the async iterable? it's that or we do some kinda
        # async itertools style?
        filter_quotes_by_sym(fqsn, quote_stream),

        # XXX: currently the ``ohlcv`` arg
        flume.rt_shm,
    )

    # HISTORY COMPUTE PHASE
    # conduct a single iteration of fsp with historical bars input
    # and get historical output.
    history_output: Union[
        dict[str, np.ndarray],  # multi-output case
        np.ndarray,  # single output case
    ]
    history_output = await anext(out_stream)

    func_name = func.__name__
    profiler(f'{func_name} generated history')

    # build struct array with an 'index' field to push as history

    # TODO: push using a[['f0', 'f1', .., 'fn']] = .. syntax no?
    # if the output array is multi-field then push
    # each respective field.
    fields = getattr(dst.array.dtype, 'fields', None).copy()
    fields.pop('index')
    history_by_field: Optional[np.ndarray] = None
    src_time = src.array['time']

    if (
        fields and
        len(fields) > 1
    ):
        if not isinstance(history_output, dict):
            raise ValueError(
                f'`{func_name}` is a multi-output FSP and should yield a '
                '`dict[str, np.ndarray]` for history'
            )

        for key in fields.keys():
            if key in history_output:
                output = history_output[key]

                if history_by_field is None:

                    if output is None:
                        length = len(src.array)
                    else:
                        length = len(output)

                    # using the first output, determine
                    # the length of the struct-array that
                    # will be pushed to shm.
                    history_by_field = np.zeros(
                        length,
                        dtype=dst.array.dtype
                    )

                if output is None:
                    continue

                history_by_field[key] = output

    # single-key output stream
    else:
        if not isinstance(history_output, np.ndarray):
            raise ValueError(
                f'`{func_name}` is a single output FSP and should yield an '
                '`np.ndarray` for history'
            )
        history_by_field = np.zeros(
            len(history_output),
            dtype=dst.array.dtype
        )
        history_by_field[func_name] = history_output

    history_by_field['time'] = src_time[-len(history_by_field):]

    history_output['time'] = src.array['time']

    # TODO: XXX:
    # THERE'S A BIG BUG HERE WITH THE `index` field since we're
    # prepending a copy of the first value a few times to make
    # sub-curves align with the parent bar chart.
    # This likely needs to be fixed either by,
    # - manually assigning the index and historical data
    #   seperately to the shm array (i.e. not using .push())
    # - developing some system on top of the shared mem array that
    #   is `index` aware such that historical data can be indexed
    #   relative to the true first datum? Not sure if this is sane
    #   for incremental compuations.
    first = dst._first.value = src._first.value

    # TODO: can we use this `start` flag instead of the manual
    # setting above?
    index = dst.push(
        history_by_field,
        start=first,
    )

    profiler(f'{func_name} pushed history')
    profiler.finish()

    # setup a respawn handle
    with trio.CancelScope() as cs:

        # TODO: might be better to just make a "restart" method where
        # the target task is spawned implicitly and then the event is
        # set via some higher level api? At that poing we might as well
        # be writing a one-cancels-one nursery though right?
        tracker = TaskTracker(trio.Event(), cs)
        task_status.started((tracker, index))

        profiler(f'{func_name} yield last index')

        # import time
        # last = time.time()

        try:

            async for processed in out_stream:

                log.debug(f"{func_name}: {processed}")
                key, output = processed
                # dst.array[-1][key] = output
                dst.array[[key, 'time']][-1] = (
                    output,
                    # TODO: what about pushing ``time.time_ns()``
                    # in which case we'll need to round at the graphics
                    # processing / sampling layer?
                    src.array[-1]['time']
                )

                # NOTE: for now we aren't streaming this to the consumer
                # stream latest array index entry which basically just acts
                # as trigger msg to tell the consumer to read from shm
                # TODO: further this should likely be implemented much
                # like our `Feed` api where there is one background
                # "service" task which computes output and then sends to
                # N-consumers who subscribe for the real-time output,
                # which we'll likely want to implement using local-mem
                # chans for the fan out?
                # index = src.index
                # if attach_stream:
                #     await client_stream.send(index)

                # period = time.time() - last
                # hz = 1/period if period else float('nan')
                # if hz > 60:
                #     log.info(f'FSP quote too fast: {hz}')
                # last = time.time()
        finally:
            tracker.complete.set()


@tractor.context
async def cascade(

    ctx: tractor.Context,

    # data feed key
    fqsn: str,

    src_shm_token: dict,
    dst_shm_token: tuple[str, np.dtype],

    ns_path: NamespacePath,

    shm_registry: dict[str, _Token],

    zero_on_step: bool = False,
    loglevel: Optional[str] = None,

) -> None:
    '''
    Chain streaming signal processors and deliver output to
    destination shm array buffer.

    '''
    profiler = Profiler(
        delayed=False,
        disabled=False
    )

    if loglevel:
        get_console_log(loglevel)

    src = attach_shm_array(token=src_shm_token)
    dst = attach_shm_array(readonly=False, token=dst_shm_token)

    reg = _load_builtins()
    lines = '\n'.join([f'{key.rpartition(":")[2]} => {key}' for key in reg])
    log.info(
        f'Registered FSP set:\n{lines}'
    )

    # update actorlocal flows table which registers
    # readonly "instances" of this fsp for symbol/source
    # so that consumer fsps can look it up by source + fsp.
    # TODO: ugh i hate this wind/unwind to list over the wire
    # but not sure how else to do it.
    for (token, fsp_name, dst_token) in shm_registry:
        Fsp._flow_registry[(
            _Token.from_msg(token),
            fsp_name,
        )] = _Token.from_msg(dst_token), None

    fsp: Fsp = reg.get(
        NamespacePath(ns_path)
    )
    func = fsp.func

    if not func:
        # TODO: assume it's a func target path
        raise ValueError(f'Unknown fsp target: {ns_path}')

    # open a data feed stream with requested broker
    feed: Feed
    async with data.feed.maybe_open_feed(
        [fqsn],

        # TODO throttle tick outputs from *this* daemon since
        # it'll emit tons of ticks due to the throttle only
        # limits quote arrival periods, so the consumer of *this*
        # needs to get throttled the ticks we generate.
        # tick_throttle=60,

    ) as feed:

        flume = feed.flumes[fqsn]
        symbol = flume.symbol
        assert src.token == flume.rt_shm.token
        profiler(f'{func}: feed up')

        func_name = func.__name__
        async with (
            trio.open_nursery() as n,
        ):

            fsp_target = partial(

                fsp_compute,
                symbol=symbol,
                flume=flume,
                quote_stream=flume.stream,

                # shm
                src=src,
                dst=dst,

                # target
                func=func
            )

            tracker, index = await n.start(fsp_target)

            if zero_on_step:
                last = dst.array[-1:]
                zeroed = np.zeros(last.shape, dtype=last.dtype)

            profiler(f'{func_name}: fsp up')

            # sync client
            await ctx.started(index)

            # XXX:  rt stream with client which we MUST
            # open here (and keep it open) in order to make
            # incremental "updates" as history prepends take
            # place.
            async with ctx.open_stream() as client_stream:

                # TODO: these likely should all become
                # methods of this ``TaskLifetime`` or wtv
                # abstraction..
                async def resync(
                    tracker: TaskTracker,

                ) -> tuple[TaskTracker, int]:
                    # TODO: adopt an incremental update engine/approach
                    # where possible here eventually!
                    log.info(f're-syncing fsp {func_name} to source')
                    tracker.cs.cancel()
                    await tracker.complete.wait()
                    tracker, index = await n.start(fsp_target)

                    # always trigger UI refresh after history update,
                    # see ``piker.ui._fsp.FspAdmin.open_chain()`` and
                    # ``piker.ui._display.trigger_update()``.
                    await client_stream.send({
                        'fsp_update': {
                            'key': dst_shm_token,
                            'first': dst._first.value,
                            'last': dst._last.value,
                        }
                    })
                    return tracker, index

                def is_synced(
                    src: ShmArray,
                    dst: ShmArray
                ) -> tuple[bool, int, int]:
                    '''
                    Predicate to dertmine if a destination FSP
                    output array is aligned to its source array.

                    '''
                    step_diff = src.index - dst.index
                    len_diff = abs(len(src.array) - len(dst.array))
                    return not (
                        # the source is likely backfilling and we must
                        # sync history calculations
                        len_diff > 2

                        # we aren't step synced to the source and may be
                        # leading/lagging by a step
                        or step_diff > 1
                        or step_diff < 0
                    ), step_diff, len_diff

                async def poll_and_sync_to_step(
                    tracker: TaskTracker,
                    src: ShmArray,
                    dst: ShmArray,

                ) -> tuple[TaskTracker, int]:

                    synced, step_diff, _ = is_synced(src, dst)
                    while not synced:
                        tracker, index = await resync(tracker)
                        synced, step_diff, _ = is_synced(src, dst)

                    return tracker, step_diff

                s, step, ld = is_synced(src, dst)

                # detect sample period step for subscription to increment
                # signal
                times = src.array['time']
                if len(times) > 1:
                    last_ts = times[-1]
                    delay_s = float(last_ts - times[times != last_ts][-1])
                else:
                    # our default "HFT" sample rate.
                    delay_s = _default_delay_s

                # sub and increment the underlying shared memory buffer
                # on every step msg received from the global `samplerd`
                # service.
                async with open_sample_stream(float(delay_s)) as istream:

                    profiler(f'{func_name}: sample stream up')
                    profiler.finish()

                    async for i in istream:
                        # print(f'FSP incrementing {i}')

                        # respawn the compute task if the source
                        # array has been updated such that we compute
                        # new history from the (prepended) source.
                        synced, step_diff, _ = is_synced(src, dst)
                        if not synced:
                            tracker, step_diff = await poll_and_sync_to_step(
                                tracker,
                                src,
                                dst,
                            )

                            # skip adding a last bar since we should already
                            # be step alinged
                            if step_diff == 0:
                                continue

                        # read out last shm row, copy and write new row
                        array = dst.array

                        # some metrics like vlm should be reset
                        # to zero every step.
                        if zero_on_step:
                            last = zeroed
                        else:
                            last = array[-1:].copy()

                        dst.push(last)

                        # sync with source buffer's time step
                        src_l2 = src.array[-2:]
                        src_li, src_lt = src_l2[-1][['index', 'time']]
                        src_2li, src_2lt = src_l2[-2][['index', 'time']]
                        dst._array['time'][src_li] = src_lt
                        dst._array['time'][src_2li] = src_2lt

                        # last2 = dst.array[-2:]
                        # if (
                        #     last2[-1]['index'] != src_li
                        #     or last2[-2]['index'] != src_2li
                        # ):
                        #     dstl2 = list(last2)
                        #     srcl2 = list(src_l2)
                        #     print(
                        #         # f'{dst.token}\n'
                        #         f'src: {srcl2}\n'
                        #         f'dst: {dstl2}\n'
                        #     )