import threading
from contextlib import contextmanager
from typing import Any, Iterator, List, Optional, Union

from langchain_core.runnables import RunnableConfig
from psycopg import Connection, Cursor, Pipeline
from psycopg.errors import UndefinedTable
from psycopg.rows import DictRow, dict_row
from psycopg.types.json import Jsonb
from psycopg_pool import ConnectionPool

from langgraph.checkpoint.base import (
    WRITES_IDX_MAP,
    ChannelVersions,
    Checkpoint,
    CheckpointMetadata,
    CheckpointTuple,
    get_checkpoint_id,
)
from langgraph.checkpoint.postgres.base import (
    BasePostgresSaver,
)
from langgraph.checkpoint.serde.base import SerializerProtocol

Conn = Union[Connection[DictRow], ConnectionPool[Connection[DictRow]]]


@contextmanager
def _get_connection(conn: Conn) -> Iterator[Connection[DictRow]]:
    if isinstance(conn, Connection):
        yield conn
    elif isinstance(conn, ConnectionPool):
        with conn.connection() as conn:
            yield conn
    else:
        raise TypeError(f"Invalid connection type: {type(conn)}")


class PostgresSaver(BasePostgresSaver):
    lock: threading.Lock

    def __init__(
        self,
        conn: Conn,
        pipe: Optional[Pipeline] = None,
        serde: Optional[SerializerProtocol] = None,
    ) -> None:
        super().__init__(serde=serde)
        if isinstance(conn, ConnectionPool) and pipe is not None:
            raise ValueError(
                "Pipeline should be used only with a single Connection, not ConnectionPool."
            )

        self.conn = conn
        self.pipe = pipe
        self.lock = threading.Lock()

    @classmethod
    @contextmanager
    def from_conn_string(
        cls, conn_string: str, *, pipeline: bool = False
    ) -> Iterator["PostgresSaver"]:
        """Create a new PostgresSaver instance from a connection string.

        Args:
            conn_string (str): The Postgres connection info string.
            pipeline (bool): whether to use Pipeline

        Returns:
            PostgresSaver: A new PostgresSaver instance.
        """
        with Connection.connect(
            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
        ) as conn:
            if pipeline:
                with conn.pipeline() as pipe:
                    yield PostgresSaver(conn, pipe)
            else:
                yield PostgresSaver(conn)

    def setup(self) -> None:
        """Set up the checkpoint database asynchronously.

        This method creates the necessary tables in the Postgres database if they don't
        already exist and runs database migrations. It MUST be called directly by the user
        the first time checkpointer is used.
        """
        with self._cursor() as cur:
            try:
                row = cur.execute(
                    "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
                ).fetchone()
                if row is None:
                    version = -1
                else:
                    version = row["v"]
            except UndefinedTable:
                version = -1
            for v, migration in zip(
                range(version + 1, len(self.MIGRATIONS)),
                self.MIGRATIONS[version + 1 :],
            ):
                cur.execute(migration)
                cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
        if self.pipe:
            self.pipe.sync()

    def list(
        self,
        config: Optional[RunnableConfig],
        *,
        filter: Optional[dict[str, Any]] = None,
        before: Optional[RunnableConfig] = None,
        limit: Optional[int] = None,
    ) -> Iterator[CheckpointTuple]:
        """List checkpoints from the database.

        This method retrieves a list of checkpoint tuples from the Postgres database based
        on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).

        Args:
            config (RunnableConfig): The config to use for listing the checkpoints.
            filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. Defaults to None.
            before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.
            limit (Optional[int]): The maximum number of checkpoints to return. Defaults to None.

        Yields:
            Iterator[CheckpointTuple]: An iterator of checkpoint tuples.

        Examples:
            >>> from langgraph.checkpoint.postgres import PostgresSaver
            >>> DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
            >>> with PostgresSaver.from_conn_string(DB_URI) as memory:
            ... # Run a graph, then list the checkpoints
            >>>     config = {"configurable": {"thread_id": "1"}}
            >>>     checkpoints = list(memory.list(config, limit=2))
            >>> print(checkpoints)
            [CheckpointTuple(...), CheckpointTuple(...)]

            >>> config = {"configurable": {"thread_id": "1"}}
            >>> before = {"configurable": {"checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875"}}
            >>> with PostgresSaver.from_conn_string(DB_URI) as memory:
            ... # Run a graph, then list the checkpoints
            >>>     checkpoints = list(memory.list(config, before=before))
            >>> print(checkpoints)
            [CheckpointTuple(...), ...]
        """
        where, args = self._search_where(config, filter, before)
        query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
        if limit:
            query += f" LIMIT {limit}"
        # if we change this to use .stream() we need to make sure to close the cursor
        with self._cursor() as cur:
            cur.execute(query, args, binary=True)
            for value in cur:
                yield CheckpointTuple(
                    {
                        "configurable": {
                            "thread_id": value["thread_id"],
                            "checkpoint_ns": value["checkpoint_ns"],
                            "checkpoint_id": value["checkpoint_id"],
                        }
                    },
                    self._load_checkpoint(
                        value["checkpoint"],
                        value["channel_values"],
                        value["pending_sends"],
                    ),
                    self._load_metadata(value["metadata"]),
                    {
                        "configurable": {
                            "thread_id": value["thread_id"],
                            "checkpoint_ns": value["checkpoint_ns"],
                            "checkpoint_id": value["parent_checkpoint_id"],
                        }
                    }
                    if value["parent_checkpoint_id"]
                    else None,
                    self._load_writes(value["pending_writes"]),
                )

    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
        """Get a checkpoint tuple from the database.

        This method retrieves a checkpoint tuple from the Postgres database based on the
        provided config. If the config contains a "checkpoint_id" key, the checkpoint with
        the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint
        for the given thread ID is retrieved.

        Args:
            config (RunnableConfig): The config to use for retrieving the checkpoint.

        Returns:
            Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.

        Examples:

            Basic:
            >>> config = {"configurable": {"thread_id": "1"}}
            >>> checkpoint_tuple = memory.get_tuple(config)
            >>> print(checkpoint_tuple)
            CheckpointTuple(...)

            With timestamp:

            >>> config = {
            ...    "configurable": {
            ...        "thread_id": "1",
            ...        "checkpoint_ns": "",
            ...        "checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875",
            ...    }
            ... }
            >>> checkpoint_tuple = memory.get_tuple(config)
            >>> print(checkpoint_tuple)
            CheckpointTuple(...)
        """  # noqa
        thread_id = config["configurable"]["thread_id"]
        checkpoint_id = get_checkpoint_id(config)
        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
        if checkpoint_id:
            args: tuple[Any, ...] = (thread_id, checkpoint_ns, checkpoint_id)
            where = "WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id = %s"
        else:
            args = (thread_id, checkpoint_ns)
            where = "WHERE thread_id = %s AND checkpoint_ns = %s ORDER BY checkpoint_id DESC LIMIT 1"

        with self._cursor() as cur:
            cur.execute(
                self.SELECT_SQL + where,
                args,
                binary=True,
            )

            for value in cur:
                return CheckpointTuple(
                    {
                        "configurable": {
                            "thread_id": thread_id,
                            "checkpoint_ns": checkpoint_ns,
                            "checkpoint_id": value["checkpoint_id"],
                        }
                    },
                    self._load_checkpoint(
                        value["checkpoint"],
                        value["channel_values"],
                        value["pending_sends"],
                    ),
                    self._load_metadata(value["metadata"]),
                    {
                        "configurable": {
                            "thread_id": thread_id,
                            "checkpoint_ns": checkpoint_ns,
                            "checkpoint_id": value["parent_checkpoint_id"],
                        }
                    }
                    if value["parent_checkpoint_id"]
                    else None,
                    self._load_writes(value["pending_writes"]),
                )

    def put(
        self,
        config: RunnableConfig,
        checkpoint: Checkpoint,
        metadata: CheckpointMetadata,
        new_versions: ChannelVersions,
    ) -> RunnableConfig:
        """Save a checkpoint to the database.

        This method saves a checkpoint to the Postgres database. The checkpoint is associated
        with the provided config and its parent config (if any).

        Args:
            config (RunnableConfig): The config to associate with the checkpoint.
            checkpoint (Checkpoint): The checkpoint to save.
            metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
            new_versions (ChannelVersions): New channel versions as of this write.

        Returns:
            RunnableConfig: Updated configuration after storing the checkpoint.

        Examples:

            >>> from langgraph.checkpoint.postgres import PostgresSaver
            >>> DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
            >>> with PostgresSaver.from_conn_string(DB_URI) as memory:
            >>>     config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
            >>>     checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "id": "1ef4f797-8335-6428-8001-8a1503f9b875", "data": {"key": "value"}}
            >>>     saved_config = memory.put(config, checkpoint, {"source": "input", "step": 1, "writes": {"key": "value"}}, {})
            >>> print(saved_config)
            {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef4f797-8335-6428-8001-8a1503f9b875'}}
        """
        configurable = config["configurable"].copy()
        thread_id = configurable.pop("thread_id")
        checkpoint_ns = configurable.pop("checkpoint_ns")
        checkpoint_id = configurable.pop(
            "checkpoint_id", configurable.pop("thread_ts", None)
        )

        copy = checkpoint.copy()
        next_config = {
            "configurable": {
                "thread_id": thread_id,
                "checkpoint_ns": checkpoint_ns,
                "checkpoint_id": checkpoint["id"],
            }
        }

        with self._cursor(pipeline=True) as cur:
            cur.executemany(
                self.UPSERT_CHECKPOINT_BLOBS_SQL,
                self._dump_blobs(
                    thread_id,
                    checkpoint_ns,
                    copy.pop("channel_values"),  # type: ignore[misc]
                    new_versions,
                ),
            )
            cur.execute(
                self.UPSERT_CHECKPOINTS_SQL,
                (
                    thread_id,
                    checkpoint_ns,
                    checkpoint["id"],
                    checkpoint_id,
                    Jsonb(self._dump_checkpoint(copy)),
                    self._dump_metadata(metadata),
                ),
            )
        return next_config

    def put_writes(
        self,
        config: RunnableConfig,
        writes: List[tuple[str, Any]],
        task_id: str,
    ) -> None:
        """Store intermediate writes linked to a checkpoint.

        This method saves intermediate writes associated with a checkpoint to the Postgres database.

        Args:
            config (RunnableConfig): Configuration of the related checkpoint.
            writes (List[Tuple[str, Any]]): List of writes to store.
            task_id (str): Identifier for the task creating the writes.
        """
        query = (
            self.UPSERT_CHECKPOINT_WRITES_SQL
            if all(w[0] in WRITES_IDX_MAP for w in writes)
            else self.INSERT_CHECKPOINT_WRITES_SQL
        )
        with self._cursor(pipeline=True) as cur:
            cur.executemany(
                query,
                self._dump_writes(
                    config["configurable"]["thread_id"],
                    config["configurable"]["checkpoint_ns"],
                    config["configurable"]["checkpoint_id"],
                    task_id,
                    writes,
                ),
            )

    @contextmanager
    def _cursor(self, *, pipeline: bool = False) -> Iterator[Cursor[DictRow]]:
        with _get_connection(self.conn) as conn:
            if self.pipe:
                # a connection in pipeline mode can be used concurrently
                # in multiple threads/coroutines, but only one cursor can be
                # used at a time
                try:
                    with conn.cursor(binary=True, row_factory=dict_row) as cur:
                        yield cur
                finally:
                    if pipeline:
                        self.pipe.sync()
            elif pipeline:
                # a connection not in pipeline mode can only be used by one
                # thread/coroutine at a time, so we acquire a lock
                with self.lock, conn.pipeline(), conn.cursor(
                    binary=True, row_factory=dict_row
                ) as cur:
                    yield cur
            else:
                with self.lock, conn.cursor(binary=True, row_factory=dict_row) as cur:
                    yield cur