from __future__ import annotations

import json
from typing import Any
from uuid import UUID

import numpy as np
import psycopg2

from .models import ClusteringAlgorithm, PythonClusteringItem, ReductionMethod, RunMetadata
from .settings import ServiceSettings


def load_run_and_embeddings(run_id: UUID) -> tuple[RunMetadata, list[PythonClusteringItem]]:
    settings = ServiceSettings.from_env()
    with psycopg2.connect(settings.db_dsn) as connection:
        run = _load_run_metadata(connection, run_id)
        items = _load_embeddings(connection, run.selection)
        return run, items


def _load_run_metadata(connection, run_id: UUID) -> RunMetadata:
    with connection.cursor() as cursor:
        cursor.execute(
            """
            select
                id,
                algorithm,
                coalesce(parameters_json::text, '{}'),
                reduction_method,
                reduction_dimensions,
                coalesce(selection_json::text, '{}')
            from doc.doc_embedding_cluster_run
            where id = %s
            """,
            (str(run_id),),
        )
        row = cursor.fetchone()

    if row is None:
        raise ValueError(f"Cluster run not found: {run_id}")

    parameters = _json_to_dict(row[2])
    selection = _json_to_dict(row[5])

    return RunMetadata(
        runId=row[0],
        algorithm=ClusteringAlgorithm(row[1]),
        parameters=parameters,
        reductionMethod=ReductionMethod(row[3]) if row[3] else ReductionMethod.NONE,
        reductionDimensions=row[4],
        selection=selection,
    )


def _load_embeddings(connection, selection: dict[str, Any]) -> list[PythonClusteringItem]:
    sql_parts = [
        """
        select
            e.id as embedding_id,
            e.document_id,
            e.representation_id,
            e.embedding_vector::text as embedding_vector_text
        from doc.doc_embedding e
        join doc.doc_document d on d.id = e.document_id
        join doc.doc_text_representation r on r.id = e.representation_id
        where e.embedding_status = 'COMPLETED'
          and e.embedding_vector is not null
        """
    ]
    params: list[Any] = []

    _apply_selection_filters(selection, sql_parts, params)
    sql_parts.append(" order by e.created_at asc")
    sql = "".join(sql_parts)

    items: list[PythonClusteringItem] = []
    with connection.cursor(name="cluster_embedding_selection") as cursor:
        cursor.itersize = 2000
        cursor.execute(sql, params)
        for embedding_id, document_id, representation_id, vector_text in cursor:
            items.append(
                PythonClusteringItem(
                    embeddingId=embedding_id,
                    documentId=document_id,
                    representationId=representation_id,
                    vector=_parse_vector_text(vector_text),
                )
            )
    return items


def _apply_selection_filters(selection: dict[str, Any], sql_parts: list[str], params: list[Any]) -> None:
    if not selection:
        return

    _append_in_filter(sql_parts, params, "documentTypes", "d.document_type", selection.get("documentTypes"))
    _append_in_filter(sql_parts, params, "documentFamilies", "d.document_family", selection.get("documentFamilies"))
    _append_in_filter(sql_parts, params, "representationTypes", "r.representation_type", selection.get("representationTypes"))
    _append_in_filter(sql_parts, params, "embeddingStatuses", "e.embedding_status", selection.get("embeddingStatuses"))
    _append_in_filter(sql_parts, params, "modelIds", "e.model_id", selection.get("modelIds"))
    _append_in_filter(sql_parts, params, "prefixProfileIds", "e.prefix_profile_id", selection.get("prefixProfileIds"))
    _append_in_filter(sql_parts, params, "builderKeys", "r.builder_key", selection.get("builderKeys"))
    _append_in_filter(sql_parts, params, "languageCodes", "r.language_code", selection.get("languageCodes"))
    _append_in_filter(sql_parts, params, "ownerTenantIds", "d.owner_tenant_id", selection.get("ownerTenantIds"))

    business_key_like = selection.get("businessKeyLike")
    if business_key_like:
        sql_parts.append(" and d.business_key like %s")
        params.append(business_key_like)

    created_from = selection.get("createdFrom")
    if created_from:
        sql_parts.append(" and d.created_at >= %s")
        params.append(created_from)

    created_to = selection.get("createdTo")
    if created_to:
        sql_parts.append(" and d.created_at < %s")
        params.append(created_to)

    if selection.get("primaryRepresentationOnly") is True:
        sql_parts.append(" and r.is_primary = true")


def _append_in_filter(
    sql_parts: list[str],
    params: list[Any],
    _key: str,
    column_name: str,
    values: list[Any] | None,
) -> None:
    if not values:
        return
    placeholders = ", ".join(["%s"] * len(values))
    sql_parts.append(f" and {column_name} in ({placeholders})")
    params.extend(values)


def _parse_vector_text(raw_value: str) -> list[float]:
    if raw_value is None:
        return []

    value = raw_value.strip()
    if value.startswith("[") and value.endswith("]"):
        value = value[1:-1]

    if not value:
        return []

    vector = np.fromstring(value, sep=",", dtype=np.float32)
    return vector.astype(float).tolist()


def _json_to_dict(raw_json: str | dict[str, Any] | None) -> dict[str, Any]:
    if raw_json is None:
        return {}
    if isinstance(raw_json, dict):
        return raw_json
    if not raw_json.strip():
        return {}
    loaded = json.loads(raw_json)
    return loaded if isinstance(loaded, dict) else {}