#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF 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 sys
from collections import Counter
from typing import List, Optional, Type, Union, no_type_check, overload, TYPE_CHECKING
from warnings import catch_warnings, simplefilter, warn

from pyspark.rdd import _load_from_socket
from pyspark.sql.pandas.serializers import ArrowCollectSerializer
from pyspark.sql.types import (
    IntegralType,
    ByteType,
    ShortType,
    IntegerType,
    LongType,
    FloatType,
    DoubleType,
    BooleanType,
    MapType,
    TimestampType,
    TimestampNTZType,
    DayTimeIntervalType,
    StructType,
    DataType,
)
from pyspark.sql.utils import is_timestamp_ntz_preferred
from pyspark.traceback_utils import SCCallSiteSync

if TYPE_CHECKING:
    import numpy as np
    import pyarrow as pa
    from py4j.java_gateway import JavaObject

    from pyspark.sql.pandas._typing import DataFrameLike as PandasDataFrameLike
    from pyspark.sql import DataFrame


class PandasConversionMixin:
    """
    Mix-in for the conversion from Spark to pandas. Currently, only :class:`DataFrame`
    can use this class.
    """

    def toPandas(self) -> "PandasDataFrameLike":
        """
        Returns the contents of this :class:`DataFrame` as Pandas ``pandas.DataFrame``.

        This is only available if Pandas is installed and available.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Notes
        -----
        This method should only be used if the resulting Pandas ``pandas.DataFrame`` is
        expected to be small, as all the data is loaded into the driver's memory.

        Usage with ``spark.sql.execution.arrow.pyspark.enabled=True`` is experimental.

        Examples
        --------
        >>> df.toPandas()  # doctest: +SKIP
           age   name
        0    2  Alice
        1    5    Bob
        """
        from pyspark.sql.dataframe import DataFrame

        assert isinstance(self, DataFrame)

        from pyspark.sql.pandas.utils import require_minimum_pandas_version

        require_minimum_pandas_version()

        import numpy as np
        import pandas as pd
        from pandas.core.dtypes.common import is_timedelta64_dtype

        jconf = self.sparkSession._jconf
        timezone = jconf.sessionLocalTimeZone()

        if jconf.arrowPySparkEnabled():
            use_arrow = True
            try:
                from pyspark.sql.pandas.types import to_arrow_schema
                from pyspark.sql.pandas.utils import require_minimum_pyarrow_version

                require_minimum_pyarrow_version()
                to_arrow_schema(self.schema)
            except Exception as e:

                if jconf.arrowPySparkFallbackEnabled():
                    msg = (
                        "toPandas attempted Arrow optimization because "
                        "'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, "
                        "failed by the reason below:\n  %s\n"
                        "Attempting non-optimization as "
                        "'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to "
                        "true." % str(e)
                    )
                    warn(msg)
                    use_arrow = False
                else:
                    msg = (
                        "toPandas attempted Arrow optimization because "
                        "'spark.sql.execution.arrow.pyspark.enabled' is set to true, but has "
                        "reached the error below and will not continue because automatic fallback "
                        "with 'spark.sql.execution.arrow.pyspark.fallback.enabled' has been set to "
                        "false.\n  %s" % str(e)
                    )
                    warn(msg)
                    raise

            # Try to use Arrow optimization when the schema is supported and the required version
            # of PyArrow is found, if 'spark.sql.execution.arrow.pyspark.enabled' is enabled.
            if use_arrow:
                try:
                    from pyspark.sql.pandas.types import (
                        _check_series_localize_timestamps,
                        _convert_map_items_to_dict,
                    )
                    import pyarrow

                    # Rename columns to avoid duplicated column names.
                    tmp_column_names = ["col_{}".format(i) for i in range(len(self.columns))]
                    self_destruct = jconf.arrowPySparkSelfDestructEnabled()
                    batches = self.toDF(*tmp_column_names)._collect_as_arrow(
                        split_batches=self_destruct
                    )
                    if len(batches) > 0:
                        table = pyarrow.Table.from_batches(batches)
                        # Ensure only the table has a reference to the batches, so that
                        # self_destruct (if enabled) is effective
                        del batches
                        # Pandas DataFrame created from PyArrow uses datetime64[ns] for date type
                        # values, but we should use datetime.date to match the behavior with when
                        # Arrow optimization is disabled.
                        pandas_options = {"date_as_object": True}
                        if self_destruct:
                            # Configure PyArrow to use as little memory as possible:
                            # self_destruct - free columns as they are converted
                            # split_blocks - create a separate Pandas block for each column
                            # use_threads - convert one column at a time
                            pandas_options.update(
                                {
                                    "self_destruct": True,
                                    "split_blocks": True,
                                    "use_threads": False,
                                }
                            )
                        pdf = table.to_pandas(**pandas_options)
                        # Rename back to the original column names.
                        pdf.columns = self.columns
                        for field in self.schema:
                            if isinstance(field.dataType, TimestampType):
                                pdf[field.name] = _check_series_localize_timestamps(
                                    pdf[field.name], timezone
                                )
                            elif isinstance(field.dataType, MapType):
                                pdf[field.name] = _convert_map_items_to_dict(pdf[field.name])
                        return pdf
                    else:
                        corrected_panda_types = {}
                        for index, field in enumerate(self.schema):
                            pandas_type = PandasConversionMixin._to_corrected_pandas_type(
                                field.dataType
                            )
                            corrected_panda_types[tmp_column_names[index]] = (
                                object if pandas_type is None else pandas_type
                            )

                        pdf = pd.DataFrame(columns=tmp_column_names).astype(
                            dtype=corrected_panda_types
                        )
                        pdf.columns = self.columns
                        return pdf
                except Exception as e:
                    # We might have to allow fallback here as well but multiple Spark jobs can
                    # be executed. So, simply fail in this case for now.
                    msg = (
                        "toPandas attempted Arrow optimization because "
                        "'spark.sql.execution.arrow.pyspark.enabled' is set to true, but has "
                        "reached the error below and can not continue. Note that "
                        "'spark.sql.execution.arrow.pyspark.fallback.enabled' does not have an "
                        "effect on failures in the middle of "
                        "computation.\n  %s" % str(e)
                    )
                    warn(msg)
                    raise

        # Below is toPandas without Arrow optimization.
        pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
        column_counter = Counter(self.columns)

        corrected_dtypes: List[Optional[Type]] = [None] * len(self.schema)
        for index, field in enumerate(self.schema):
            # We use `iloc` to access columns with duplicate column names.
            if column_counter[field.name] > 1:
                pandas_col = pdf.iloc[:, index]
            else:
                pandas_col = pdf[field.name]

            pandas_type = PandasConversionMixin._to_corrected_pandas_type(field.dataType)
            # SPARK-21766: if an integer field is nullable and has null values, it can be
            # inferred by pandas as a float column. If we convert the column with NaN back
            # to integer type e.g., np.int16, we will hit an exception. So we use the
            # pandas-inferred float type, rather than the corrected type from the schema
            # in this case.
            if pandas_type is not None and not (
                isinstance(field.dataType, IntegralType)
                and field.nullable
                and pandas_col.isnull().any()
            ):
                corrected_dtypes[index] = pandas_type
            # Ensure we fall back to nullable numpy types.
            if isinstance(field.dataType, IntegralType) and pandas_col.isnull().any():
                corrected_dtypes[index] = np.float64
            if isinstance(field.dataType, BooleanType) and pandas_col.isnull().any():
                corrected_dtypes[index] = object

        df = pd.DataFrame()
        for index, t in enumerate(corrected_dtypes):
            column_name = self.schema[index].name

            # We use `iloc` to access columns with duplicate column names.
            if column_counter[column_name] > 1:
                series = pdf.iloc[:, index]
            else:
                series = pdf[column_name]

            # No need to cast for non-empty series for timedelta. The type is already correct.
            should_check_timedelta = is_timedelta64_dtype(t) and len(pdf) == 0

            if (t is not None and not is_timedelta64_dtype(t)) or should_check_timedelta:
                series = series.astype(t, copy=False)

            with catch_warnings():
                from pandas.errors import PerformanceWarning

                simplefilter(action="ignore", category=PerformanceWarning)
                # `insert` API makes copy of data,
                # we only do it for Series of duplicate column names.
                # `pdf.iloc[:, index] = pdf.iloc[:, index]...` doesn't always work
                # because `iloc` could return a view or a copy depending by context.
                if column_counter[column_name] > 1:
                    df.insert(index, column_name, series, allow_duplicates=True)
                else:
                    df[column_name] = series

        if timezone is None:
            return df
        else:
            from pyspark.sql.pandas.types import _check_series_convert_timestamps_local_tz

            for field in self.schema:
                # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
                if isinstance(field.dataType, TimestampType):
                    df[field.name] = _check_series_convert_timestamps_local_tz(
                        df[field.name], timezone
                    )
            return df

    @staticmethod
    def _to_corrected_pandas_type(dt: DataType) -> Optional[Type]:
        """
        When converting Spark SQL records to Pandas `pandas.DataFrame`, the inferred data type
        may be wrong. This method gets the corrected data type for Pandas if that type may be
        inferred incorrectly.
        """
        import numpy as np

        if type(dt) == ByteType:
            return np.int8
        elif type(dt) == ShortType:
            return np.int16
        elif type(dt) == IntegerType:
            return np.int32
        elif type(dt) == LongType:
            return np.int64
        elif type(dt) == FloatType:
            return np.float32
        elif type(dt) == DoubleType:
            return np.float64
        elif type(dt) == BooleanType:
            return bool
        elif type(dt) == TimestampType:
            return np.datetime64
        elif type(dt) == TimestampNTZType:
            return np.datetime64
        elif type(dt) == DayTimeIntervalType:
            return np.timedelta64
        else:
            return None

    def _collect_as_arrow(self, split_batches: bool = False) -> List["pa.RecordBatch"]:
        """
        Returns all records as a list of ArrowRecordBatches, pyarrow must be installed
        and available on driver and worker Python environments.
        This is an experimental feature.

        :param split_batches: split batches such that each column is in its own allocation, so
            that the selfDestruct optimization is effective; default False.

        .. note:: Experimental.
        """
        from pyspark.sql.dataframe import DataFrame

        assert isinstance(self, DataFrame)

        with SCCallSiteSync(self._sc):
            (
                port,
                auth_secret,
                jsocket_auth_server,
            ) = self._jdf.collectAsArrowToPython()

        # Collect list of un-ordered batches where last element is a list of correct order indices
        try:
            batch_stream = _load_from_socket((port, auth_secret), ArrowCollectSerializer())
            if split_batches:
                # When spark.sql.execution.arrow.pyspark.selfDestruct.enabled, ensure
                # each column in each record batch is contained in its own allocation.
                # Otherwise, selfDestruct does nothing; it frees each column as its
                # converted, but each column will actually be a list of slices of record
                # batches, and so no memory is actually freed until all columns are
                # converted.
                import pyarrow as pa

                results = []
                for batch_or_indices in batch_stream:
                    if isinstance(batch_or_indices, pa.RecordBatch):
                        batch_or_indices = pa.RecordBatch.from_arrays(
                            [
                                # This call actually reallocates the array
                                pa.concat_arrays([array])
                                for array in batch_or_indices
                            ],
                            schema=batch_or_indices.schema,
                        )
                    results.append(batch_or_indices)
            else:
                results = list(batch_stream)
        finally:
            # Join serving thread and raise any exceptions from collectAsArrowToPython
            jsocket_auth_server.getResult()

        # Separate RecordBatches from batch order indices in results
        batches = results[:-1]
        batch_order = results[-1]

        # Re-order the batch list using the correct order
        return [batches[i] for i in batch_order]


class SparkConversionMixin:
    """
    Min-in for the conversion from pandas to Spark. Currently, only :class:`SparkSession`
    can use this class.
    """

    _jsparkSession: "JavaObject"

    @overload
    def createDataFrame(
        self, data: "PandasDataFrameLike", samplingRatio: Optional[float] = ...
    ) -> "DataFrame":
        ...

    @overload
    def createDataFrame(
        self,
        data: "PandasDataFrameLike",
        schema: Union[StructType, str],
        verifySchema: bool = ...,
    ) -> "DataFrame":
        ...

    def createDataFrame(  # type: ignore[misc]
        self,
        data: "PandasDataFrameLike",
        schema: Optional[Union[StructType, List[str]]] = None,
        samplingRatio: Optional[float] = None,
        verifySchema: bool = True,
    ) -> "DataFrame":
        from pyspark.sql import SparkSession

        assert isinstance(self, SparkSession)

        from pyspark.sql.pandas.utils import require_minimum_pandas_version

        require_minimum_pandas_version()

        timezone = self._jconf.sessionLocalTimeZone()

        # If no schema supplied by user then get the names of columns only
        if schema is None:
            schema = [str(x) if not isinstance(x, str) else x for x in data.columns]

        if self._jconf.arrowPySparkEnabled() and len(data) > 0:
            try:
                return self._create_from_pandas_with_arrow(data, schema, timezone)
            except Exception as e:
                if self._jconf.arrowPySparkFallbackEnabled():
                    msg = (
                        "createDataFrame attempted Arrow optimization because "
                        "'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, "
                        "failed by the reason below:\n  %s\n"
                        "Attempting non-optimization as "
                        "'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to "
                        "true." % str(e)
                    )
                    warn(msg)
                else:
                    msg = (
                        "createDataFrame attempted Arrow optimization because "
                        "'spark.sql.execution.arrow.pyspark.enabled' is set to true, but has "
                        "reached the error below and will not continue because automatic "
                        "fallback with 'spark.sql.execution.arrow.pyspark.fallback.enabled' "
                        "has been set to false.\n  %s" % str(e)
                    )
                    warn(msg)
                    raise
        converted_data = self._convert_from_pandas(data, schema, timezone)
        return self._create_dataframe(converted_data, schema, samplingRatio, verifySchema)

    def _convert_from_pandas(
        self, pdf: "PandasDataFrameLike", schema: Union[StructType, str, List[str]], timezone: str
    ) -> List:
        """
        Convert a pandas.DataFrame to list of records that can be used to make a DataFrame

        Returns
        -------
        list
            list of records
        """
        import pandas as pd
        from pyspark.sql import SparkSession

        assert isinstance(self, SparkSession)

        if timezone is not None:
            from pyspark.sql.pandas.types import _check_series_convert_timestamps_tz_local
            from pandas.core.dtypes.common import is_datetime64tz_dtype, is_timedelta64_dtype

            copied = False
            if isinstance(schema, StructType):
                for field in schema:
                    # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
                    if isinstance(field.dataType, TimestampType):
                        s = _check_series_convert_timestamps_tz_local(pdf[field.name], timezone)
                        if s is not pdf[field.name]:
                            if not copied:
                                # Copy once if the series is modified to prevent the original
                                # Pandas DataFrame from being updated
                                pdf = pdf.copy()
                                copied = True
                            pdf[field.name] = s
            else:
                should_localize = not is_timestamp_ntz_preferred()
                for column, series in pdf.items():
                    s = series
                    if should_localize and is_datetime64tz_dtype(s.dtype) and s.dt.tz is not None:
                        s = _check_series_convert_timestamps_tz_local(series, timezone)
                    if s is not series:
                        if not copied:
                            # Copy once if the series is modified to prevent the original
                            # Pandas DataFrame from being updated
                            pdf = pdf.copy()
                            copied = True
                        pdf[column] = s

            for column, series in pdf.items():
                if is_timedelta64_dtype(series):
                    if not copied:
                        pdf = pdf.copy()
                        copied = True
                    # Explicitly set the timedelta as object so the output of numpy records can
                    # hold the timedelta instances as are. Otherwise, it converts to the internal
                    # numeric values.
                    ser = pdf[column]
                    pdf[column] = pd.Series(
                        ser.dt.to_pytimedelta(), index=ser.index, dtype="object", name=ser.name
                    )

        # Convert pandas.DataFrame to list of numpy records
        np_records = pdf.to_records(index=False)

        # Check if any columns need to be fixed for Spark to infer properly
        if len(np_records) > 0:
            record_dtype = self._get_numpy_record_dtype(np_records[0])
            if record_dtype is not None:
                return [r.astype(record_dtype).tolist() for r in np_records]

        # Convert list of numpy records to python lists
        return [r.tolist() for r in np_records]

    def _get_numpy_record_dtype(self, rec: "np.recarray") -> Optional["np.dtype"]:
        """
        Used when converting a pandas.DataFrame to Spark using to_records(), this will correct
        the dtypes of fields in a record so they can be properly loaded into Spark.

        Parameters
        ----------
        rec : numpy.record
            a numpy record to check field dtypes

        Returns
        -------
        numpy.dtype
            corrected dtype for a numpy.record or None if no correction needed
        """
        import numpy as np

        cur_dtypes = rec.dtype
        col_names = cur_dtypes.names
        record_type_list = []
        has_rec_fix = False
        for i in range(len(cur_dtypes)):
            curr_type = cur_dtypes[i]
            # If type is a datetime64 timestamp, convert to microseconds
            # NOTE: if dtype is datetime[ns] then np.record.tolist() will output values as longs,
            # conversion from [us] or lower will lead to py datetime objects, see SPARK-22417
            if curr_type == np.dtype("datetime64[ns]"):
                curr_type = "datetime64[us]"
                has_rec_fix = True
            record_type_list.append((str(col_names[i]), curr_type))
        return np.dtype(record_type_list) if has_rec_fix else None

    def _create_from_pandas_with_arrow(
        self, pdf: "PandasDataFrameLike", schema: Union[StructType, List[str]], timezone: str
    ) -> "DataFrame":
        """
        Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
        to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
        data types will be used to coerce the data in Pandas to Arrow conversion.
        """
        from pyspark.sql import SparkSession
        from pyspark.sql.dataframe import DataFrame

        assert isinstance(self, SparkSession)

        from pyspark.sql.pandas.serializers import ArrowStreamPandasSerializer
        from pyspark.sql.types import TimestampType
        from pyspark.sql.pandas.types import from_arrow_type, to_arrow_type
        from pyspark.sql.pandas.utils import (
            require_minimum_pandas_version,
            require_minimum_pyarrow_version,
        )

        require_minimum_pandas_version()
        require_minimum_pyarrow_version()

        from pandas.api.types import (  # type: ignore[attr-defined]
            is_datetime64_dtype,
            is_datetime64tz_dtype,
        )
        import pyarrow as pa

        # Create the Spark schema from list of names passed in with Arrow types
        if isinstance(schema, (list, tuple)):
            arrow_schema = pa.Schema.from_pandas(pdf, preserve_index=False)
            struct = StructType()
            prefer_timestamp_ntz = is_timestamp_ntz_preferred()
            for name, field in zip(schema, arrow_schema):
                struct.add(
                    name, from_arrow_type(field.type, prefer_timestamp_ntz), nullable=field.nullable
                )
            schema = struct

        # Determine arrow types to coerce data when creating batches
        if isinstance(schema, StructType):
            arrow_types = [to_arrow_type(f.dataType) for f in schema.fields]
        elif isinstance(schema, DataType):
            raise ValueError("Single data type %s is not supported with Arrow" % str(schema))
        else:
            # Any timestamps must be coerced to be compatible with Spark
            arrow_types = [
                to_arrow_type(TimestampType())
                if is_datetime64_dtype(t) or is_datetime64tz_dtype(t)
                else None
                for t in pdf.dtypes
            ]

        # Slice the DataFrame to be batched
        step = self._jconf.arrowMaxRecordsPerBatch()
        step = step if step > 0 else len(pdf)
        pdf_slices = (pdf.iloc[start : start + step] for start in range(0, len(pdf), step))

        # Create list of Arrow (columns, type) for serializer dump_stream
        arrow_data = [
            [(c, t) for (_, c), t in zip(pdf_slice.items(), arrow_types)]
            for pdf_slice in pdf_slices
        ]

        jsparkSession = self._jsparkSession

        safecheck = self._jconf.arrowSafeTypeConversion()
        col_by_name = True  # col by name only applies to StructType columns, can't happen here
        ser = ArrowStreamPandasSerializer(timezone, safecheck, col_by_name)

        @no_type_check
        def reader_func(temp_filename):
            return self._jvm.PythonSQLUtils.readArrowStreamFromFile(temp_filename)

        @no_type_check
        def create_iter_server():
            return self._jvm.ArrowIteratorServer()

        # Create Spark DataFrame from Arrow stream file, using one batch per partition
        jiter = self._sc._serialize_to_jvm(arrow_data, ser, reader_func, create_iter_server)
        assert self._jvm is not None
        jdf = self._jvm.PythonSQLUtils.toDataFrame(jiter, schema.json(), jsparkSession)
        df = DataFrame(jdf, self)
        df._schema = schema
        return df


def _test() -> None:
    import doctest
    from pyspark.sql import SparkSession
    import pyspark.sql.pandas.conversion

    globs = pyspark.sql.pandas.conversion.__dict__.copy()
    spark = (
        SparkSession.builder.master("local[4]").appName("sql.pandas.conversion tests").getOrCreate()
    )
    globs["spark"] = spark
    (failure_count, test_count) = doctest.testmod(
        pyspark.sql.pandas.conversion,
        globs=globs,
        optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF,
    )
    spark.stop()
    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()