from collections import defaultdict
from datetime import datetime
from typing import Any, Optional

import numpy as np
from aioitertools.itertools import groupby
from cachetools import LFUCache, TTLCache
from sqlalchemy import Select, select
from strawberry.dataloader import AbstractCache, DataLoader
from typing_extensions import TypeAlias

from phoenix.db import models
from phoenix.db.helpers import SupportedSQLDialect
from phoenix.metrics.retrieval_metrics import RetrievalMetrics
from phoenix.server.api.dataloaders.cache import TwoTierCache
from phoenix.server.api.input_types.TimeRange import TimeRange
from phoenix.server.api.types.DocumentEvaluationSummary import DocumentEvaluationSummary
from phoenix.server.types import DbSessionFactory
from phoenix.trace.dsl import SpanFilter

ProjectRowId: TypeAlias = int
TimeInterval: TypeAlias = tuple[Optional[datetime], Optional[datetime]]
FilterCondition: TypeAlias = Optional[str]
EvalName: TypeAlias = str

Segment: TypeAlias = tuple[ProjectRowId, TimeInterval, FilterCondition]
Param: TypeAlias = EvalName

Key: TypeAlias = tuple[ProjectRowId, Optional[TimeRange], FilterCondition, EvalName]
Result: TypeAlias = Optional[DocumentEvaluationSummary]
ResultPosition: TypeAlias = int
DEFAULT_VALUE: Result = None


def _cache_key_fn(key: Key) -> tuple[Segment, Param]:
    project_rowid, time_range, filter_condition, eval_name = key
    interval = (
        (time_range.start, time_range.end) if isinstance(time_range, TimeRange) else (None, None)
    )
    return (project_rowid, interval, filter_condition), eval_name


_Section: TypeAlias = tuple[ProjectRowId, EvalName]
_SubKey: TypeAlias = tuple[TimeInterval, FilterCondition]


class DocumentEvaluationSummaryCache(
    TwoTierCache[Key, Result, _Section, _SubKey],
):
    def __init__(self) -> None:
        super().__init__(
            # TTL=3600 (1-hour) because time intervals are always moving forward, but
            # interval endpoints are rounded down to the hour by the UI, so anything
            # older than an hour most likely won't be a cache-hit anyway.
            main_cache=TTLCache(maxsize=64 * 32, ttl=3600),
            sub_cache_factory=lambda: LFUCache(maxsize=2 * 2),
        )

    def invalidate_project(self, project_rowid: ProjectRowId) -> None:
        for section in self._cache.keys():
            if section[0] == project_rowid:
                del self._cache[section]

    def _cache_key(self, key: Key) -> tuple[_Section, _SubKey]:
        (project_rowid, interval, filter_condition), eval_name = _cache_key_fn(key)
        return (project_rowid, eval_name), (interval, filter_condition)


class DocumentEvaluationSummaryDataLoader(DataLoader[Key, Result]):
    def __init__(
        self,
        db: DbSessionFactory,
        cache_map: Optional[AbstractCache[Key, Result]] = None,
    ) -> None:
        super().__init__(
            load_fn=self._load_fn,
            cache_key_fn=_cache_key_fn,
            cache_map=cache_map,
        )
        self._db = db

    async def _load_fn(self, keys: list[Key]) -> list[Result]:
        results: list[Result] = [DEFAULT_VALUE] * len(keys)
        arguments: defaultdict[
            Segment,
            defaultdict[Param, list[ResultPosition]],
        ] = defaultdict(lambda: defaultdict(list))
        for position, key in enumerate(keys):
            segment, param = _cache_key_fn(key)
            arguments[segment][param].append(position)
        for segment, params in arguments.items():
            async with self._db.read() as session:
                dialect = SupportedSQLDialect(session.bind.dialect.name)
                stmt = _get_stmt(dialect, segment, *params.keys())
                data = await session.stream(stmt)
                async for eval_name, group in groupby(data, lambda d: d.name):
                    metrics_collection = []
                    async for (_, num_docs), subgroup in groupby(
                        group, lambda g: (g.id, g.num_docs)
                    ):
                        scores = [np.nan] * num_docs
                        for row in subgroup:
                            scores[row.document_position] = row.score
                        metrics_collection.append(RetrievalMetrics(scores))
                    summary = DocumentEvaluationSummary(
                        evaluation_name=eval_name,
                        metrics_collection=metrics_collection,
                    )
                    for position in params[eval_name]:
                        results[position] = summary
        return results


def _get_stmt(
    dialect: SupportedSQLDialect,
    segment: Segment,
    *eval_names: Param,
) -> Select[Any]:
    project_rowid, (start_time, end_time), filter_condition = segment
    mda = models.DocumentAnnotation
    stmt = (
        select(
            mda.name,
            models.Span.id,
            models.Span.num_documents.label("num_docs"),
            mda.score,
            mda.document_position,
        )
        .join(models.Trace)
        .where(models.Trace.project_rowid == project_rowid)
        .join(mda)
        .where(mda.name.in_(eval_names))
        .where(mda.annotator_kind == "LLM")
        .where(mda.score.is_not(None))
        .order_by(mda.name, models.Span.id)
    )
    if start_time:
        stmt = stmt.where(start_time <= models.Span.start_time)
    if end_time:
        stmt = stmt.where(models.Span.start_time < end_time)
    if filter_condition:
        span_filter = SpanFilter(condition=filter_condition)
        stmt = span_filter(stmt)
    return stmt
