import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import tqdm as hf_tqdm
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
    import sqlite3

    import sqlalchemy


class SqlDatasetReader(AbstractDatasetInputStream):
    def __init__(
        self,
        sql: Union[str, "sqlalchemy.sql.Selectable"],
        con: Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"],
        features: Optional[Features] = None,
        cache_dir: str = None,
        keep_in_memory: bool = False,
        **kwargs,
    ):
        super().__init__(features=features, cache_dir=cache_dir, keep_in_memory=keep_in_memory, **kwargs)
        self.builder = Sql(
            cache_dir=cache_dir,
            features=features,
            sql=sql,
            con=con,
            **kwargs,
        )

    def read(self):
        download_config = None
        download_mode = None
        verification_mode = None
        base_path = None

        self.builder.download_and_prepare(
            download_config=download_config,
            download_mode=download_mode,
            verification_mode=verification_mode,
            # try_from_hf_gcs=try_from_hf_gcs,
            base_path=base_path,
        )

        # Build dataset for splits
        dataset = self.builder.as_dataset(
            split="train", verification_mode=verification_mode, in_memory=self.keep_in_memory
        )
        return dataset


class SqlDatasetWriter:
    def __init__(
        self,
        dataset: Dataset,
        name: str,
        con: Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"],
        batch_size: Optional[int] = None,
        num_proc: Optional[int] = None,
        **to_sql_kwargs,
    ):
        if num_proc is not None and num_proc <= 0:
            raise ValueError(f"num_proc {num_proc} must be an integer > 0.")

        self.dataset = dataset
        self.name = name
        self.con = con
        self.batch_size = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        self.num_proc = num_proc
        self.to_sql_kwargs = to_sql_kwargs

    def write(self) -> int:
        _ = self.to_sql_kwargs.pop("sql", None)
        _ = self.to_sql_kwargs.pop("con", None)
        index = self.to_sql_kwargs.pop("index", False)

        written = self._write(index=index, **self.to_sql_kwargs)
        return written

    def _batch_sql(self, args):
        offset, index, to_sql_kwargs = args
        to_sql_kwargs = {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
        batch = query_table(
            table=self.dataset.data,
            key=slice(offset, offset + self.batch_size),
            indices=self.dataset._indices,
        )
        df = batch.to_pandas()
        num_rows = df.to_sql(self.name, self.con, index=index, **to_sql_kwargs)
        return num_rows or len(df)

    def _write(self, index, **to_sql_kwargs) -> int:
        """Writes the pyarrow table as SQL to a database.

        Caller is responsible for opening and closing the SQL connection.
        """
        written = 0

        if self.num_proc is None or self.num_proc == 1:
            for offset in hf_tqdm(
                range(0, len(self.dataset), self.batch_size),
                unit="ba",
                desc="Creating SQL from Arrow format",
            ):
                written += self._batch_sql((offset, index, to_sql_kwargs))
        else:
            num_rows, batch_size = len(self.dataset), self.batch_size
            with multiprocessing.Pool(self.num_proc) as pool:
                for num_rows in hf_tqdm(
                    pool.imap(
                        self._batch_sql,
                        [(offset, index, to_sql_kwargs) for offset in range(0, num_rows, batch_size)],
                    ),
                    total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,
                    unit="ba",
                    desc="Creating SQL from Arrow format",
                ):
                    written += num_rows

        return written

import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
    import sqlite3

    import sqlalchemy


class SqlDatasetReader(AbstractDatasetInputStream):
    def __init__(
        self,
        sql: Union[str, "sqlalchemy.sql.Selectable"],
        con: Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"],
        features: Optional[Features] = None,
        cache_dir: str = None,
        keep_in_memory: bool = False,
        **kwargs,
    ):
        super().__init__(features=features, cache_dir=cache_dir, keep_in_memory=keep_in_memory, **kwargs)
        self.builder = Sql(
            cache_dir=cache_dir,
            features=features,
            sql=sql,
            con=con,
            **kwargs,
        )

    def read(self):
        download_config = None
        download_mode = None
        verification_mode = None
        base_path = None

        self.builder.download_and_prepare(
            download_config=download_config,
            download_mode=download_mode,
            verification_mode=verification_mode,
            # try_from_hf_gcs=try_from_hf_gcs,
            base_path=base_path,
        )

        # Build dataset for splits
        dataset = self.builder.as_dataset(
            split="train", verification_mode=verification_mode, in_memory=self.keep_in_memory
        )
        return dataset


class SqlDatasetWriter:
    def __init__(
        self,
        dataset: Dataset,
        name: str,
        con: Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"],
        batch_size: Optional[int] = None,
        num_proc: Optional[int] = None,
        **to_sql_kwargs,
    ):
        if num_proc is not None and num_proc <= 0:
            raise ValueError(f"num_proc {num_proc} must be an integer > 0.")

        self.dataset = dataset
        self.name = name
        self.con = con
        self.batch_size = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        self.num_proc = num_proc
        self.to_sql_kwargs = to_sql_kwargs

    def write(self) -> int:
        _ = self.to_sql_kwargs.pop("sql", None)
        _ = self.to_sql_kwargs.pop("con", None)
        index = self.to_sql_kwargs.pop("index", False)

        written = self._write(index=index, **self.to_sql_kwargs)
        return written

    def _batch_sql(self, args):
        offset, index, to_sql_kwargs = args
        to_sql_kwargs = {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
        batch = query_table(
            table=self.dataset.data,
            key=slice(offset, offset + self.batch_size),
            indices=self.dataset._indices,
        )
        df = batch.to_pandas()
        num_rows = df.to_sql(self.name, self.con, index=index, **to_sql_kwargs)
        return num_rows or len(df)

    def _write(self, index, **to_sql_kwargs) -> int:
        """Writes the pyarrow table as SQL to a database.

        Caller is responsible for opening and closing the SQL connection.
        """
        written = 0

        if self.num_proc is None or self.num_proc == 1:
            for offset in logging.tqdm(
                range(0, len(self.dataset), self.batch_size),
                unit="ba",
                disable=not logging.is_progress_bar_enabled(),
                desc="Creating SQL from Arrow format",
            ):
                written += self._batch_sql((offset, index, to_sql_kwargs))
        else:
            num_rows, batch_size = len(self.dataset), self.batch_size
            with multiprocessing.Pool(self.num_proc) as pool:
                for num_rows in logging.tqdm(
                    pool.imap(
                        self._batch_sql,
                        [(offset, index, to_sql_kwargs) for offset in range(0, num_rows, batch_size)],
                    ),
                    total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,
                    unit="ba",
                    disable=not logging.is_progress_bar_enabled(),
                    desc="Creating SQL from Arrow format",
                ):
                    written += num_rows

        return written
