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# Source code for astropy.io.misc.pyarrow.csv

    # Licensed under a 3-clause BSD style license - see LICENSE.rst
    """
    This module provides functionality to read CSV files into Astropy Tables using PyArrow.
    """

    import datetime
    import io
    import os
    from typing import TYPE_CHECKING, BinaryIO, Literal, Union

    import numpy as np
    from numpy.typing import DTypeLike, NDArray

    from astropy.utils.compat.optional_deps import HAS_PYARROW
    from astropy.utils.data import get_readable_fileobj

    if TYPE_CHECKING:
        import pyarrow
        import pyarrow.csv

        from astropy.table import Table

    __all__ = ["convert_pa_table_to_astropy_table", "read_csv"]

    def read_csv(
        input_file: os.PathLike[str] | str | BinaryIO,
        *,
        delimiter: str = ",",
        quotechar: str | Literal[False] = '"',
        doublequote: bool = True,
        escapechar: str | bool = False,
        header_start: int | None = 0,
        data_start: int | None = None,
        names: list[str] | None = None,
        include_names: list[str] | None = None,
        dtypes: dict[str, DTypeLike] | None = None,
        comment: str | None = None,
        null_values: list[str] | None = None,
        encoding: str = "utf-8",
        newlines_in_values: bool = False,
        timestamp_parsers: list[str] | None = None,
    ) -> "Table":
        """Read a CSV file into an astropy Table using PyArrow.

        This function allows highly performant reading of text CSV files into an astropy
        ``Table`` using `PyArrow <https://arrow.apache.org/docs/python/csv.html>`_. The
        best performance is achieved for files with only numeric data types, but even for
        files with mixed data types, the performance is still better than the standard
        ``astropy.io.ascii`` fast CSV reader.

        By default, empty values (zero-length string "") in the CSV file are read as masked
        values in the Table. This can be changed by using the ``null_values`` parameter to
        specify a list of strings to interpret as null (masked) values.

        Entirely empty lines in the CSV file are ignored.

        Columns consisting of only string values ``True`` and ``False`` are parsed as
        boolean data.

        Columns with ISO 8601 date/time strings are parsed as shown below:
        - ``12:13:14.123456``: ``object[datetime.time]``
        - ``2025-01-01``: ``np.datetime64[D]``
        - ``2025-01-01T01:02:03``: ``np.datetime64[s]``
        - ``2025-01-01T01:02:03.123456``: ``np.datetime64[ns]``

        Support for ignoring comment lines in the CSV file is provided by the ``comment``
        parameter. If this is set to a string, any line starting with optional whitespace
        and then this string is ignored. This is done by reading the entire file and
        scanning for comment lines. If the comment lines are all at the beginning of the
        file and both ``header_start`` and ``data_start`` are not specified, then the file
        is read efficiently by setting ``header_start`` to the first line after the
        comments. Otherwise the entire file is read into memory and the comment lines are
        removed before passing to the PyArrow CSV reader. Any values of ``header_start`` and
        ``data_start`` apply to the lines counts *after* the comment lines have been
        removed.

        Parameters
        ----------
        input_file : str, path-like, or file-like
            File path or binary file-like object to read from.
        delimiter : 1-character str, optional (default ",")
            Character delimiting individual cells in the CSV data.
        quotechar : 1-character str or False, optional (default '"')
            Character used optionally for quoting CSV values (`False` if quoting is not
            allowed).
        doublequote : bool, optional (default `True`)
            Whether two quotes in a quoted CSV value denote a single quote in the data.
        escapechar : 1-character str or `False`, optional (default `False`)
            Character used optionally for escaping special characters (`False` if escaping
            is not allowed).
        header_start : int, None, optional (default 0)
            Line index for the header line with column names. If `None` this implies that
            there is no header line and the column names are taken from ``names`` or
            generated automatically ("f0", "f1", ...).
        data_start : int, None, optional (default None)
            Line index for the start of data. If `None`, then data starts one line after the
            header line, or on the first line if there is no header.
        names : list, None, optional (default None)
            List of names for input data columns when there is no header line. If supplied,
            then ``header_start`` must be `None`.
        include_names : list, None, optional (default None)
            List of column names to include in output. If `None`, all columns are included.
        dtypes : dict[str, Any], None, optional (default None)
            If provided, this is a dictionary of data types for output columns. Each key is
            a column name and the value is either a PyArrow data type or a data type
            specifier that is accepted as an argument to `numpy.dtype`. Examples include
            ``pyarrow.Int32()``, ``pyarrow.time32("s")``, ``int``, ``np.float32``,
            ``np.dtype('f4')`` or ``"float32"``. Default is to infer the data types.
        comment : 1-character str or None, optional (default None)
            Character used to indicate the start of a comment. Any line starting with
            optional whitespace and then this character is ignored. Using this option will
            cause the parser to be slower and potentially use more memory as it uses Python
            code to strip comments.

        Other Parameters
        ----------------
        null_values : list, optional (default None)
            List of strings to interpret as null values. By default, only empty strings are
            considered as null values (equivalent to ``null_values=[""]``). Set to ``[]`` to
            disable null value handling.
        encoding: str, optional (default 'utf-8')
            Encoding of the input data.
        newlines_in_values: bool, optional (default False)
            Whether newline characters are allowed in CSV values. Setting this to True
            reduces the performance of multi-threaded CSV reading.
        timestamp_parsers: list, optional
            A sequence of strptime()-compatible format strings, tried in order when
            attempting to infer or convert timestamp values. The default is the special
            value ``pyarrow.csv.ISO8601`` uses the optimized internal ISO8601 parser.

        Returns
        -------
        astropy.table.Table
            An astropy Table containing the data from the CSV file.
        """
        check_has_pyarrow()
        from pyarrow import csv

        parse_options = csv.ParseOptions(
            delimiter=delimiter,
            quote_char=quotechar,
            double_quote=doublequote,
            escape_char=escapechar,
            newlines_in_values=newlines_in_values,
        )

        if comment is not None:
            input_file_stripped, header_start_new = strip_comment_lines(
                input_file, comment, encoding, header_start, data_start
            )
            if header_start_new is not None:
                header_start = header_start_new
            if input_file_stripped is not None:
                input_file = input_file_stripped

        read_options = get_read_options(header_start, data_start, names, encoding)
        convert_options = get_convert_options(
            include_names, dtypes, null_values, timestamp_parsers
        )

        with get_readable_fileobj(input_file, encoding="binary") as f:
            table_pa = csv.read_csv(
                f,
                parse_options=parse_options,
                read_options=read_options,
                convert_options=convert_options,
            )
        return convert_pa_table_to_astropy_table(table_pa)

    def check_has_pyarrow():
        """Check for pyarrow and raise an informative exception if not available.

        Raises
        ------
        ModuleNotFoundError
            If pyarrow is not installed.
        """
        if not HAS_PYARROW:
            raise ModuleNotFoundError(
                "pyarrow is required for astropy.io.misc.pyarrow.csv functions"
            )

    def convert_pa_string_array_to_numpy(arr: "pyarrow.Array") -> NDArray:
        """
        Convert a PyArrow string array to a NumPy array.

        Parameters
        ----------
        arr : pyarrow.Array
            The PyArrow string array to be converted.

        Returns
        -------
        np.ndarray
            The converted NumPy array.
        """
        check_has_pyarrow()
        import pyarrow as pa
        import pyarrow.compute as pc

        # This implementation is faster than these alternatives:
        # >>> arr.to_numpy().astype(str)
        # >>> np.array(arr.to_pylist(), dtype='str')
        max_length = pa.compute.max(pc.utf8_length(arr))
        out = np.empty(len(arr), dtype=f"U{max_length}")
        out[:] = arr.to_numpy()
        return out

    def pyarrow_zero(data_type: "pyarrow.DataType"):
        """
        Return a "zero" value for the given PyArrow data type.

        This function provides a default value corresponding to the specified
        PyArrow data type. The returned value is intended to represent a
        neutral or "zero" equivalent for the type.

        Parameters
        ----------
        data_type : pa.DataType
            The PyArrow data type for which to determine the zero value.

        Returns
        -------
        Any
            A zero-equivalent value for the specified data type. The type of the
            returned value depends on the input data type:
            - Integer or floating types: 0
            - Boolean type: False
            - String or large string types: ""
            - Binary or large binary types: b""
            - Date type: datetime.date(1970, 1, 1)
            - Time type: datetime.time(0, 0, 0)
            - Timestamp type: datetime.datetime(1970, 1, 1)
            - Null type: None
            - Decimal type: decimal.Decimal(0)

        Raises
        ------
        NotImplementedError
            If no zero value is defined for the given data type.
        """
        check_has_pyarrow()
        import pyarrow as pa

        if pa.types.is_integer(data_type) or pa.types.is_floating(data_type):
            return 0
        elif pa.types.is_boolean(data_type):
            return False
        elif pa.types.is_string(data_type) or pa.types.is_large_string(data_type):
            return ""
        elif pa.types.is_binary(data_type) or pa.types.is_large_binary(data_type):
            return b""
        elif pa.types.is_date(data_type):
            return datetime.date(1970, 1, 1)
        elif pa.types.is_time(data_type):
            return datetime.time(0, 0, 0)
        elif pa.types.is_timestamp(data_type):
            return datetime.datetime(1970, 1, 1)
        elif pa.types.is_null(data_type):
            return None
        elif pa.types.is_decimal(data_type):
            import decimal

            return decimal.Decimal(0)
        else:
            raise NotImplementedError(f"No zero value defined for type: {data_type}")

    def convert_pa_array_to_numpy(
        arr: Union["pyarrow.Array", "pyarrow.ChunkedArray"],
    ) -> NDArray:
        """
        Convert a PyArrow array to a NumPy array.

        Parameters
        ----------
        arr : pyarrow.Array, pyarrow.ChunkedArray
            The PyArrow array to be converted.

        Returns
        -------
        np.ndarray or np.ma.MaskedArray
            The converted NumPy array. If the input array contains null values, a masked
            array is returned with nulls masked out.

        Notes
        -----
        - If the input array does not contain null values, the result is an ndarray.
        - If the input array contains null values, they are replaced with a fill value
          (equivalent to ``np.zeros((), dtype)``)  before conversion, and a masked array is
          returned with the null positions masked.
        - The current implementation supports only int, float, bool, and string types.
        """
        check_has_pyarrow()
        import pyarrow as pa

        # Validate input type and set the fill value
        is_string = pa.types.is_string(arr.type)

        if arr.null_count == 0:
            # No nulls, just return an ndarray view of the pyarray
            out = convert_pa_string_array_to_numpy(arr) if is_string else arr.to_numpy()
        else:
            mask = arr.is_null().to_numpy()
            # Fill nulls in `arr` with zero. We do not know of a zero-copy fill for
            # pyarrow arrays with nulls, so we need to copy the data.
            arr = arr.fill_null(pyarrow_zero(arr.type))
            data = convert_pa_string_array_to_numpy(arr) if is_string else arr.to_numpy()
            out = np.ma.array(data, mask=mask, copy=False)

        return out

    def convert_pa_table_to_astropy_table(table_pa) -> "Table":
        """
        Convert a PyArrow Table to an Astropy Table.

        Parameters
        ----------
        table_pa : ``pyarrow.Table``
            The PyArrow Table to be converted.

        Returns
        -------
        astropy.table.Table
            Converted astropy Table.
        """
        from astropy.table import Table

        columns = {
            name: convert_pa_array_to_numpy(col)
            for name, col in zip(table_pa.column_names, table_pa.itercolumns())
        }
        return Table(columns, copy=False)

    def strip_comment_lines(
        input_file: os.PathLike | str | BinaryIO,
        comment: str,
        encoding: str,
        header_start: int | None,
        data_start: int | None,
    ) -> tuple[io.BytesIO | None, int | None]:
        """
        Handle specified comment string when reading ``input_file``.

        This function has two modes of operation:

        1. If ``header_start`` is `None` or 0, then read the file line by line, looking for
           lines that start with the specified comment string. If all comment lines are at
           the beginning of the file (a common case), then the lines can be efficiently
           skipped within the pyarrow CSV reader. In this case the return value is (`None`,
           header_start after comments).
        2. Otherwise, the function reads the entire file into memory in a BytesIO object,
           removing all lines that start with the specified comment string along the way. In
           this case the return value is (BytesIO object, `None`).

        Checking for the comment string ignores leading whitespace.

        If ``input_file`` is a file path, it will be opened in binary read mode. The
        ``comment`` string is encoded to bytes using the provided encoding for comparison
        with the file content.

        Parameters
        ----------
        input_file : os.PathLike, str, or BinaryIO
            The input file path or file-like object to read from.
        comment : str
            The comment string that identifies lines to be removed.
        encoding : str
            The encoding to use for the input file.
        header_start : int or None
            The line index where the header starts. See ``read_csv`` for details.
        data_start : int or None
            The line index where the data starts. See ``read_csv`` for details.

        Returns
        -------
        output_file : io.BytesIO | None
            A BytesIO object containing the filtered content with comment lines removed, or
            `None` if all comment lines are at the beginning of the file.
        header_start : int | None
            The line index after the last comment line, or `None` if not all comment lines
            are at the beginning of the file.
        """
        comment_encode = comment.encode(encoding)

        with get_readable_fileobj(input_file, encoding="binary") as f:
            if header_start in (None, 0) and data_start is None:
                idx_last_comment = -1
                for idx, line in enumerate(f):
                    if line.lstrip().startswith(comment_encode):
                        if idx - idx_last_comment == 1:
                            idx_last_comment = idx
                        else:
                            # Gap between comment lines, need to reset input file handle and
                            # break out to the logic below.
                            f.seek(0)
                            break
                else:
                    f.seek(0)
                    return None, idx_last_comment + 1

            # If we get here, we need to read the whole file and remove comment lines and
            # write into an output BytesIO file.
            output = io.BytesIO()
            for idx, line in enumerate(f):
                if not line.lstrip().startswith(comment_encode):
                    output.write(line)

            # Set up for the consumer to read it.
            output.seek(0)

        return output, None

    def get_convert_options(
        include_names: list[str] | None,
        dtypes: dict[str, DTypeLike] | None,
        null_values: list[str] | None,
        timestamp_parsers: list[str] | None,
    ) -> "pyarrow.csv.ConvertOptions":
        """
        Generate PyArrow CSV convert options.

        Parameters
        ----------
        include_names : list or None
            List of column names to include in the conversion. If None, all columns are
            included.
        dtypes : dict or None
            Dictionary mapping column names to their respective data types. If None, default
            data types are used.
        null_values : list or None, optional (default None)
            List of strings to interpret as null values. By default, only empty strings are
            considered as null values.
        timestamp_parsers : list or None
            A sequence of strptime()-compatible format strings, tried in order when
            attempting to infer or convert timestamp values. The default is the special
            value ``pyarrow.csv.ISO8601`` uses the optimized internal ISO8601 parser.

        Returns
        -------
        pyarrow.csv.ConvertOptions
            PyArrow CSV ConvertOptions object configured with the specified column names and
            data types.
        """
        check_has_pyarrow()
        import pyarrow as pa
        from pyarrow import csv

        convert_options = csv.ConvertOptions()
        convert_options.strings_can_be_null = True
        convert_options.null_values = [""] if null_values is None else null_values

        if include_names is not None:
            convert_options.include_columns = include_names
        if dtypes is not None:
            convert_options.column_types = {
                colname: (
                    dtype if isinstance(dtype, pa.DataType) else pa.from_numpy_dtype(dtype)
                )
                for colname, dtype in dtypes.items()
            }
        if timestamp_parsers is not None:
            convert_options.timestamp_parsers = timestamp_parsers
        return convert_options

    def get_read_options(
        header_start: int | None,
        data_start: int | None,
        names: list[str] | None,
        encoding: str | None,
    ) -> "pyarrow.csv.ReadOptions":
        """
        Generate PyArrow CSV read options.

        Parameters
        ----------
        header_start : int or None
            The row index where the header starts. If None, no header is present.
        data_start : int or None
            The row index where the data starts. If None, data starts immediately after the
            header.
        names : list or None
            A list of column names. If None, column names will be autogenerated if no header
            is present.
        encoding : str or None
            The encoding of the CSV file. If None, the default encoding is used.

        Returns
        -------
        pyarrow.csv.ReadOptions
            The configured read options for reading the CSV file.
        """
        check_has_pyarrow()
        from pyarrow import csv

        if header_start is not None and names is not None:
            raise ValueError("cannot specify `names` unless `header_start=None`")

        read_options = csv.ReadOptions()

        # No header: header_start = None, data_start = None or int
        #   Set column_names to an empty list and autogenerate_column_names to True.
        if header_start is None:
            read_options.skip_rows_after_names = 0 if data_start is None else data_start
            if names is None:
                # With these two in combination, column names are generated automatically
                # as "f0", "f1", ...
                read_options.column_names = []
                read_options.autogenerate_column_names = True
            else:
                # Explicitly set column names.
                read_options.column_names = names
                read_options.autogenerate_column_names = False
        else:
            # Header present, header_start is not None
            if data_start is None:
                data_start = header_start + 1
            read_options.skip_rows = header_start
            read_options.skip_rows_after_names = data_start - header_start - 1

        if encoding is not None:
            read_options.encoding = encoding

        return read_options

    def register_pyarrow_csv_table():
        """
        Register pyarrow.csv with Unified I/O as a Table reader.
        """
        from astropy.io import registry as io_registry
        from astropy.table import Table

        io_registry.register_reader("pyarrow.csv", Table, read_csv)