# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership.  The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License.  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific language
# governing permissions and limitations under the License.

import inspect
import pathlib
from typing import Union, IO, AnyStr

import pandas

from . import DataFrame
from modin.config import IsExperimental, Engine
from modin.data_management.factories.dispatcher import EngineDispatcher
from ...pandas import _update_engine


def read_sql(
    sql,
    con,
    index_col=None,
    coerce_float=True,
    params=None,
    parse_dates=None,
    columns=None,
    chunksize=None,
    partition_column=None,
    lower_bound=None,
    upper_bound=None,
    max_sessions=None,
):
    """Read SQL query or database table into a DataFrame.

    Args:
        sql: string or SQLAlchemy Selectable (select or text object) SQL query to be executed or a table name.
        con: SQLAlchemy connectable (engine/connection) or database string URI or DBAPI2 connection (fallback mode)
        index_col: Column(s) to set as index(MultiIndex).
        coerce_float: Attempts to convert values of non-string, non-numeric objects (like decimal.Decimal) to
                      floating point, useful for SQL result sets.
        params: List of parameters to pass to execute method. The syntax used
                to pass parameters is database driver dependent. Check your
                database driver documentation for which of the five syntax styles,
                described in PEP 249's paramstyle, is supported.
        parse_dates:
                     - List of column names to parse as dates.
                     - Dict of ``{column_name: format string}`` where format string is
                       strftime compatible in case of parsing string times, or is one of
                       (D, s, ns, ms, us) in case of parsing integer timestamps.
                     - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
                       to the keyword arguments of :func:`pandas.to_datetime`
                       Especially useful with databases without native Datetime support,
                       such as SQLite.
        columns: List of column names to select from SQL table (only used when reading a table).
        chunksize: If specified, return an iterator where `chunksize` is the number of rows to include in each chunk.
        partition_column: column used to share the data between the workers (MUST be a INTEGER column)
        lower_bound: the minimum value to be requested from the partition_column
        upper_bound: the maximum value to be requested from the partition_column
        max_sessions: the maximum number of simultaneous connections allowed to use

    Returns:
        Pandas Dataframe
    """
    Engine.subscribe(_update_engine)
    assert IsExperimental.get(), "This only works in experimental mode"
    _, _, _, kwargs = inspect.getargvalues(inspect.currentframe())
    return DataFrame(query_compiler=EngineDispatcher.read_sql(**kwargs))


# CSV and table
def _make_parser_func(sep):
    """
    Create a parser function from the given sep.

    Parameters
    ----------
    sep: str
        The separator default to use for the parser.

    Returns
    -------
    A function object.
    """

    def parser_func(
        filepath_or_buffer: Union[str, pathlib.Path, IO[AnyStr]],
        sep=sep,
        delimiter=None,
        header="infer",
        names=None,
        index_col=None,
        usecols=None,
        squeeze=False,
        prefix=None,
        mangle_dupe_cols=True,
        dtype=None,
        engine=None,
        converters=None,
        true_values=None,
        false_values=None,
        skipinitialspace=False,
        skiprows=None,
        nrows=None,
        na_values=None,
        keep_default_na=True,
        na_filter=True,
        verbose=False,
        skip_blank_lines=True,
        parse_dates=False,
        infer_datetime_format=False,
        keep_date_col=False,
        date_parser=None,
        dayfirst=False,
        cache_dates=True,
        iterator=False,
        chunksize=None,
        compression="infer",
        thousands=None,
        decimal: str = ".",
        lineterminator=None,
        quotechar='"',
        quoting=0,
        escapechar=None,
        comment=None,
        encoding=None,
        dialect=None,
        error_bad_lines=True,
        warn_bad_lines=True,
        skipfooter=0,
        doublequote=True,
        delim_whitespace=False,
        low_memory=True,
        memory_map=False,
        float_precision=None,
    ):
        # ISSUE #2408: parse parameter shared with pandas read_csv and read_table and update with provided args
        _pd_read_csv_signature = {
            val.name for val in inspect.signature(pandas.read_csv).parameters.values()
        }
        _, _, _, f_locals = inspect.getargvalues(inspect.currentframe())
        if f_locals.get("sep", sep) is False:
            f_locals["sep"] = "\t"

        kwargs = {k: v for k, v in f_locals.items() if k in _pd_read_csv_signature}
        return _read(**kwargs)

    return parser_func


def _read(**kwargs):
    """
    Read csv file from local disk.

    Parameters
    ----------
    filepath_or_buffer:
        The filepath of the csv file.
        We only support local files for now.
    kwargs: Keyword arguments in pandas.read_csv
    """
    from modin.data_management.factories.dispatcher import EngineDispatcher

    Engine.subscribe(_update_engine)

    try:
        pd_obj = EngineDispatcher.read_csv_glob(**kwargs)
    except AttributeError:
        raise AttributeError("read_csv_glob() is only implemented for pandas on Ray.")

    # This happens when `read_csv` returns a TextFileReader object for iterating through
    if isinstance(pd_obj, pandas.io.parsers.TextFileReader):
        reader = pd_obj.read
        pd_obj.read = lambda *args, **kwargs: DataFrame(
            query_compiler=reader(*args, **kwargs)
        )
        return pd_obj

    return DataFrame(query_compiler=pd_obj)


read_csv_glob = _make_parser_func(sep=",")

# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership.  The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License.  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific language
# governing permissions and limitations under the License.

import inspect
import pathlib
from typing import Union, IO, AnyStr

import pandas

from . import DataFrame
from modin.config import IsExperimental, Engine
from modin.data_management.factories.dispatcher import EngineDispatcher
from ...pandas import _update_engine


def read_sql(
    sql,
    con,
    index_col=None,
    coerce_float=True,
    params=None,
    parse_dates=None,
    columns=None,
    chunksize=None,
    partition_column=None,
    lower_bound=None,
    upper_bound=None,
    max_sessions=None,
):
    """Read SQL query or database table into a DataFrame.

    Args:
        sql: string or SQLAlchemy Selectable (select or text object) SQL query to be executed or a table name.
        con: SQLAlchemy connectable (engine/connection) or database string URI or DBAPI2 connection (fallback mode)
        index_col: Column(s) to set as index(MultiIndex).
        coerce_float: Attempts to convert values of non-string, non-numeric objects (like decimal.Decimal) to
                      floating point, useful for SQL result sets.
        params: List of parameters to pass to execute method. The syntax used
                to pass parameters is database driver dependent. Check your
                database driver documentation for which of the five syntax styles,
                described in PEP 249's paramstyle, is supported.
        parse_dates:
                     - List of column names to parse as dates.
                     - Dict of ``{column_name: format string}`` where format string is
                       strftime compatible in case of parsing string times, or is one of
                       (D, s, ns, ms, us) in case of parsing integer timestamps.
                     - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
                       to the keyword arguments of :func:`pandas.to_datetime`
                       Especially useful with databases without native Datetime support,
                       such as SQLite.
        columns: List of column names to select from SQL table (only used when reading a table).
        chunksize: If specified, return an iterator where `chunksize` is the number of rows to include in each chunk.
        partition_column: column used to share the data between the workers (MUST be a INTEGER column)
        lower_bound: the minimum value to be requested from the partition_column
        upper_bound: the maximum value to be requested from the partition_column
        max_sessions: the maximum number of simultaneous connections allowed to use

    Returns:
        Pandas Dataframe
    """
    Engine.subscribe(_update_engine)
    assert IsExperimental.get(), "This only works in experimental mode"
    _, _, _, kwargs = inspect.getargvalues(inspect.currentframe())
    return DataFrame(query_compiler=EngineDispatcher.read_sql(**kwargs))


# CSV and table
def _make_parser_func(sep):
    """
    Create a parser function from the given sep.

    Parameters
    ----------
    sep: str
        The separator default to use for the parser.

    Returns
    -------
    A function object.
    """

    def parser_func(
        filepath_or_buffer: Union[str, pathlib.Path, IO[AnyStr]],
        sep=sep,
        delimiter=None,
        header="infer",
        names=None,
        index_col=None,
        usecols=None,
        squeeze=False,
        prefix=None,
        mangle_dupe_cols=True,
        dtype=None,
        engine=None,
        converters=None,
        true_values=None,
        false_values=None,
        skipinitialspace=False,
        skiprows=None,
        nrows=None,
        na_values=None,
        keep_default_na=True,
        na_filter=True,
        verbose=False,
        skip_blank_lines=True,
        parse_dates=False,
        infer_datetime_format=False,
        keep_date_col=False,
        date_parser=None,
        dayfirst=False,
        cache_dates=True,
        iterator=False,
        chunksize=None,
        compression="infer",
        thousands=None,
        decimal: str = ".",
        lineterminator=None,
        quotechar='"',
        quoting=0,
        escapechar=None,
        comment=None,
        encoding=None,
        dialect=None,
        error_bad_lines=True,
        warn_bad_lines=True,
        skipfooter=0,
        doublequote=True,
        delim_whitespace=False,
        low_memory=True,
        memory_map=False,
        float_precision=None,
    ):
        # ISSUE #2408: parse parameter shared with pandas read_csv and read_table and update with provided args
        _pd_read_csv_signature = {
            val.name for val in inspect.signature(pandas.read_csv).parameters.values()
        }
        _, _, _, f_locals = inspect.getargvalues(inspect.currentframe())
        if f_locals.get("sep", sep) is False:
            f_locals["sep"] = "\t"

        kwargs = {k: v for k, v in f_locals.items() if k in _pd_read_csv_signature}
        return _read(**kwargs)

    return parser_func


def _read(**kwargs):
    """
    Read csv file from local disk.

    Parameters
    ----------
    filepath_or_buffer:
        The filepath of the csv file.
        We only support local files for now.
    kwargs: Keyword arguments in pandas.read_csv
    """
    from modin.data_management.factories.dispatcher import EngineDispatcher

    Engine.subscribe(_update_engine)

    try:
        pd_obj = EngineDispatcher.read_csv_glob(**kwargs)
    except AttributeError:
        raise AttributeError("read_csv_glob() is only implemented for pandas on Ray.")

    # This happens when `read_csv` returns a TextFileReader object for iterating through
    if isinstance(pd_obj, pandas.io.parsers.TextFileReader):
        reader = pd_obj.read
        pd_obj.read = lambda *args, **kwargs: DataFrame(
            query_compiler=reader(*args, **kwargs)
        )
        return pd_obj

    return DataFrame(query_compiler=pd_obj)


read_csv_glob = _make_parser_func(sep=",")

# Licensed to Modin Development Team under one or more contributor license agreements. # See the NOTICE file distributed with this work for additional information regarding # copyright ownership.  The Modin Development Team licenses this file to you under the # Apache License, Version 2.0 (the "License"); you may not use this file except in # compliance with the License.  You may obtain a copy of the License at # #     http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software distributed under # the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific language # governing permissions and limitations under the License. Read SQL query or database table into a DataFrame.      Args:         sql: string or SQLAlchemy Selectable (select or text object) SQL query to be executed or a table name.         con: SQLAlchemy connectable (engine/connection) or database string URI or DBAPI2 connection (fallback mode)         index_col: Column(s) to set as index(MultiIndex).         coerce_float: Attempts to convert values of non-string, non-numeric objects (like decimal.Decimal) to                       floating point, useful for SQL result sets.         params: List of parameters to pass to execute method. The syntax used                 to pass parameters is database driver dependent. Check your                 database driver documentation for which of the five syntax styles,                 described in PEP 249's paramstyle, is supported.         parse_dates:                      - List of column names to parse as dates.                      - Dict of ``{column_name: format string}`` where format string is                        strftime compatible in case of parsing string times, or is one of                        (D, s, ns, ms, us) in case of parsing integer timestamps.                      - Dict of ``{column_name: arg dict}``, where the arg dict corresponds                        to the keyword arguments of :func:`pandas.to_datetime`                        Especially useful with databases without native Datetime support,                        such as SQLite.         columns: List of column names to select from SQL table (only used when reading a table).         chunksize: If specified, return an iterator where `chunksize` is the number of rows to include in each chunk.         partition_column: column used to share the data between the workers (MUST be a INTEGER column)         lower_bound: the minimum value to be requested from the partition_column         upper_bound: the maximum value to be requested from the partition_column         max_sessions: the maximum number of simultaneous connections allowed to use      Returns:         Pandas Dataframe # CSV and table Create a parser function from the given sep.      Parameters     ----------     sep: str         The separator default to use for the parser.      Returns     -------     A function object. # ISSUE #2408: parse parameter shared with pandas read_csv and read_table and update with provided args Read csv file from local disk.      Parameters     ----------     filepath_or_buffer:         The filepath of the csv file.         We only support local files for now.     kwargs: Keyword arguments in pandas.read_csv # This happens when `read_csv` returns a TextFileReader object for iterating through