# Adapted from
# https://github.com/CODAIT/text-extensions-for-pandas/blob/dc03278689fe1c5f131573658ae19815ba25f33e/text_extensions_for_pandas/array/tensor.py
# and
# https://github.com/CODAIT/text-extensions-for-pandas/blob/dc03278689fe1c5f131573658ae19815ba25f33e/text_extensions_for_pandas/array/arrow_conversion.py

#
#  Copyright (c) 2020 IBM Corp.
#  Licensed 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.

# Modifications:
# - Added ArrowTensorType.to_pandas_type()
# - Added ArrowTensorArray.__getitem__()
# - Added ArrowTensorArray.__iter__()
# - Added support for column casts to extension types.
# - Fleshed out docstrings and examples.
# - Fixed TensorArray.isna() so it returns an appropriate ExtensionArray.
# - Added different (more vectorized) TensorArray.take() operation.
# - Added support for more reducers (agg funcs) to TensorArray.
# - Added support for logical operators to TensorArray(Element).
# - Added support for heterogeneously-shaped tensors.
# - Miscellaneous small bug fixes and optimizations.

import numbers
import os
from packaging.version import Version
from typing import Any, Callable, List, Optional, Sequence, Tuple, Union

import numpy as np
import pandas as pd
import pyarrow as pa
from pandas._typing import Dtype
from pandas.compat import set_function_name
from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
from pandas.core.indexers import check_array_indexer, validate_indices
from pandas.io.formats.format import ExtensionArrayFormatter

from ray.air.util.tensor_extensions.utils import (
    _create_possibly_ragged_ndarray,
    _is_ndarray_variable_shaped_tensor,
)
from ray.util.annotations import PublicAPI

try:
    from pandas.core.dtypes.generic import ABCIndex
except ImportError:
    # ABCIndexClass changed to ABCIndex in Pandas 1.3
    from pandas.core.dtypes.generic import ABCIndexClass as ABCIndex


#############################################
# Begin patching of ExtensionArrayFormatter #
#############################################


def _format_strings_patched(self) -> List[str]:
    from pandas.core.construction import extract_array
    from pandas.io.formats.format import format_array

    if not isinstance(self.values, TensorArray):
        return self._format_strings_orig()

    values = extract_array(self.values, extract_numpy=True)
    array = np.asarray(values)

    if array.ndim == 1:
        return self._format_strings_orig()

    def format_array_wrap(array_, formatter_):
        fmt_values = format_array(
            array_,
            formatter_,
            float_format=self.float_format,
            na_rep=self.na_rep,
            digits=self.digits,
            space=self.space,
            justify=self.justify,
            decimal=self.decimal,
            leading_space=self.leading_space,
            quoting=self.quoting,
        )
        return fmt_values

    flat_formatter = self.formatter
    if flat_formatter is None:
        flat_formatter = values._formatter(boxed=True)

    # Flatten array, call function, reshape (use ravel_compat in v1.3.0)
    flat_array = array.ravel("K")
    fmt_flat_array = np.asarray(format_array_wrap(flat_array, flat_formatter))
    order = "F" if array.flags.f_contiguous else "C"
    fmt_array = fmt_flat_array.reshape(array.shape, order=order)

    # Format the array of nested strings, use default formatter
    return format_array_wrap(fmt_array, None)


def _format_strings_patched_v1_0_0(self) -> List[str]:
    from functools import partial

    from pandas.core.construction import extract_array
    from pandas.io.formats.format import format_array
    from pandas.io.formats.printing import pprint_thing

    if not isinstance(self.values, TensorArray):
        return self._format_strings_orig()

    values = extract_array(self.values, extract_numpy=True)
    array = np.asarray(values)

    if array.ndim == 1:
        return self._format_strings_orig()

    def format_array_wrap(array_, formatter_):
        fmt_values = format_array(
            array_,
            formatter_,
            float_format=self.float_format,
            na_rep=self.na_rep,
            digits=self.digits,
            space=self.space,
            justify=self.justify,
            decimal=self.decimal,
            leading_space=self.leading_space,
        )
        return fmt_values

    flat_formatter = self.formatter
    if flat_formatter is None:
        flat_formatter = values._formatter(boxed=True)

    # Flatten array, call function, reshape (use ravel_compat in v1.3.0)
    flat_array = array.ravel("K")
    fmt_flat_array = np.asarray(format_array_wrap(flat_array, flat_formatter))
    order = "F" if array.flags.f_contiguous else "C"
    fmt_array = fmt_flat_array.reshape(array.shape, order=order)

    # Slimmed down version of GenericArrayFormatter due to:
    # https://github.com/pandas-dev/pandas/issues/33770
    def format_strings_slim(array_, leading_space):
        formatter = partial(
            pprint_thing,
            escape_chars=("\t", "\r", "\n"),
        )

        def _format(x):
            return str(formatter(x))

        fmt_values = []
        for v in array_:
            tpl = "{v}" if leading_space is False else " {v}"
            fmt_values.append(tpl.format(v=_format(v)))
        return fmt_values

    return format_strings_slim(fmt_array, self.leading_space)


_FORMATTER_ENABLED_ENV_VAR = "TENSOR_COLUMN_EXTENSION_FORMATTER_ENABLED"

if os.getenv(_FORMATTER_ENABLED_ENV_VAR, "1") == "1":
    ExtensionArrayFormatter._format_strings_orig = (
        ExtensionArrayFormatter._format_strings
    )
    if Version("1.1.0") <= Version(pd.__version__) < Version("1.3.0"):
        ExtensionArrayFormatter._format_strings = _format_strings_patched
    else:
        ExtensionArrayFormatter._format_strings = _format_strings_patched_v1_0_0
    ExtensionArrayFormatter._patched_by_ray_datasets = True

###########################################
# End patching of ExtensionArrayFormatter #
###########################################


@PublicAPI(stability="beta")
@pd.api.extensions.register_extension_dtype
class TensorDtype(pd.api.extensions.ExtensionDtype):
    """
    Pandas extension type for a column of homogeneous-typed tensors.

    This extension supports tensors in which the elements have different shapes.
    However, each tensor element must be non-ragged, i.e. each tensor element must have
    a well-defined, non-ragged shape.

    See:
    https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/base.py
    for up-to-date interface documentation and the subclassing contract. The
    docstrings of the below properties and methods were copied from the base
    ExtensionDtype.

    Examples:
        >>> # Create a DataFrame with a list of ndarrays as a column.
        >>> import pandas as pd
        >>> import numpy as np
        >>> import ray
        >>> df = pd.DataFrame({
        ...     "one": [1, 2, 3],
        ...     "two": list(np.arange(24).reshape((3, 2, 2, 2)))})
        >>> # Note the opaque np.object dtype for this column.
        >>> df.dtypes # doctest: +SKIP
        one     int64
        two    object
        dtype: object
        >>> # Cast column to our TensorDtype extension type.
        >>> from ray.data.extensions import TensorDtype
        >>> df["two"] = df["two"].astype(TensorDtype(np.int64, (3, 2, 2, 2)))
        >>> # Note that the column dtype is now TensorDtype instead of
        >>> # np.object.
        >>> df.dtypes # doctest: +SKIP
        one          int64
        two    TensorDtype(shape=(3, 2, 2, 2), dtype=int64)
        dtype: object
        >>> # Pandas is now aware of this tensor column, and we can do the
        >>> # typical DataFrame operations on this column.
        >>> col = 2 * (df["two"] + 10)
        >>> # The ndarrays underlying the tensor column will be manipulated,
        >>> # but the column itself will continue to be a Pandas type.
        >>> type(col) # doctest: +SKIP
        pandas.core.series.Series
        >>> col # doctest: +SKIP
        0   [[[ 2  4]
              [ 6  8]]
             [[10 12]
               [14 16]]]
        1   [[[18 20]
              [22 24]]
             [[26 28]
              [30 32]]]
        2   [[[34 36]
              [38 40]]
             [[42 44]
              [46 48]]]
        Name: two, dtype: TensorDtype(shape=(3, 2, 2, 2), dtype=int64)
        >>> # Once you do an aggregation on that column that returns a single
        >>> # row's value, you get back our TensorArrayElement type.
        >>> tensor = col.mean()
        >>> type(tensor) # doctest: +SKIP
        ray.data.extensions.tensor_extension.TensorArrayElement
        >>> tensor # doctest: +SKIP
        array([[[18., 20.],
                [22., 24.]],
               [[26., 28.],
                [30., 32.]]])
        >>> # This is a light wrapper around a NumPy ndarray, and can easily
        >>> # be converted to an ndarray.
        >>> type(tensor.to_numpy()) # doctest: +SKIP
        numpy.ndarray
        >>> # In addition to doing Pandas operations on the tensor column,
        >>> # you can now put the DataFrame into a Dataset.
        >>> ds = ray.data.from_pandas(df) # doctest: +SKIP
        >>> # Internally, this column is represented the corresponding
        >>> # Arrow tensor extension type.
        >>> ds.schema() # doctest: +SKIP
        one: int64
        two: extension<arrow.py_extension_type<ArrowTensorType>>
        >>> # You can write the dataset to Parquet.
        >>> ds.write_parquet("/some/path") # doctest: +SKIP
        >>> # And you can read it back.
        >>> read_ds = ray.data.read_parquet("/some/path") # doctest: +SKIP
        >>> read_ds.schema() # doctest: +SKIP
        one: int64
        two: extension<arrow.py_extension_type<ArrowTensorType>>
        >>> read_df = ray.get(read_ds.to_pandas_refs())[0] # doctest: +SKIP
        >>> read_df.dtypes # doctest: +SKIP
        one          int64
        two    TensorDtype(shape=(3, 2, 2, 2), dtype=int64)
        dtype: object
        >>> # The tensor extension type is preserved along the
        >>> # Pandas --> Arrow --> Parquet --> Arrow --> Pandas
        >>> # conversion chain.
        >>> read_df.equals(df) # doctest: +SKIP
        True
    """

    # NOTE(Clark): This is apparently required to prevent integer indexing
    # errors, but is an undocumented ExtensionDtype attribute. See issue:
    # https://github.com/CODAIT/text-extensions-for-pandas/issues/166
    base = None

    def __init__(self, shape: Tuple[Optional[int], ...], dtype: np.dtype):
        self._shape = shape
        self._dtype = dtype

    @property
    def type(self):
        """
        The scalar type for the array, e.g. ``int``
        It's expected ``ExtensionArray[item]`` returns an instance
        of ``ExtensionDtype.type`` for scalar ``item``, assuming
        that value is valid (not NA). NA values do not need to be
        instances of `type`.
        """
        return TensorArrayElement

    @property
    def element_dtype(self):
        """
        The dtype of the underlying tensor elements.
        """
        return self._dtype

    @property
    def element_shape(self):
        """
        The shape of the underlying tensor elements. This will be a tuple of Nones if
        the corresponding TensorArray for this TensorDtype holds variable-shaped tensor
        elements.
        """
        return self._shape

    @property
    def is_variable_shaped(self):
        """
        Whether the corresponding TensorArray for this TensorDtype holds variable-shaped
        tensor elements.
        """
        return all(dim_size is None for dim_size in self.shape)

    @property
    def name(self) -> str:
        """
        A string identifying the data type.
        Will be used for display in, e.g. ``Series.dtype``
        """
        return f"{type(self).__name__}(shape={self._shape}, dtype={self._dtype})"

    @classmethod
    def construct_from_string(cls, string: str):
        r"""
        Construct this type from a string.

        This is useful mainly for data types that accept parameters.
        For example, a period dtype accepts a frequency parameter that
        can be set as ``period[H]`` (where H means hourly frequency).

        By default, in the abstract class, just the name of the type is
        expected. But subclasses can overwrite this method to accept
        parameters.

        Parameters
        ----------
        string : str
            The name of the type, for example ``category``.

        Returns
        -------
        ExtensionDtype
            Instance of the dtype.

        Raises
        ------
        TypeError
            If a class cannot be constructed from this 'string'.

        Examples
        --------
        For extension dtypes with arguments the following may be an
        adequate implementation.

        >>> import re
        >>> @classmethod
        ... def construct_from_string(cls, string):
        ...     pattern = re.compile(r"^my_type\[(?P<arg_name>.+)\]$")
        ...     match = pattern.match(string)
        ...     if match:
        ...         return cls(**match.groupdict())
        ...     else:
        ...         raise TypeError(
        ...             f"Cannot construct a '{cls.__name__}' from '{string}'"
        ...         )
        """
        import ast
        import re

        if not isinstance(string, str):
            raise TypeError(
                f"'construct_from_string' expects a string, got {type(string)}"
            )
        # Upstream code uses exceptions as part of its normal control flow and
        # will pass this method bogus class names.
        regex = r"^TensorDtype\(shape=(\((?:(?:\d+|None),?\s?)*\)), dtype=(\w+)\)$"
        m = re.search(regex, string)
        err_msg = (
            f"Cannot construct a '{cls.__name__}' from '{string}'; expected a string "
            "like 'TensorDtype(shape=(1, 2, 3), dtype=int64)'."
        )
        if m is None:
            raise TypeError(err_msg)
        groups = m.groups()
        if len(groups) != 2:
            raise TypeError(err_msg)
        shape, dtype = groups
        shape = ast.literal_eval(shape)
        dtype = np.dtype(dtype)
        return cls(shape, dtype)

    @classmethod
    def construct_array_type(cls):
        """
        Return the array type associated with this dtype.

        Returns
        -------
        type
        """
        return TensorArray

    def __from_arrow__(self, array: Union[pa.Array, pa.ChunkedArray]):
        """
        Convert a pyarrow (chunked) array to a TensorArray.

        This and TensorArray.__arrow_array__ make up the
        Pandas extension type + array <--> Arrow extension type + array
        interoperability protocol. See
        https://pandas.pydata.org/pandas-docs/stable/development/extending.html#compatibility-with-apache-arrow
        for more information.
        """
        if isinstance(array, pa.ChunkedArray):
            if array.num_chunks > 1:
                # TODO(Clark): Remove concat and construct from list with
                # shape.
                values = np.concatenate(
                    [chunk.to_numpy() for chunk in array.iterchunks()]
                )
            else:
                values = array.chunk(0).to_numpy()
        else:
            values = array.to_numpy()

        return TensorArray(values)

    def __str__(self) -> str:
        return self.name

    def __repr__(self) -> str:
        return str(self)

    @property
    def _is_boolean(self):
        """
        Whether this extension array should be considered boolean.

        By default, ExtensionArrays are assumed to be non-numeric.
        Setting this to True will affect the behavior of several places,
        e.g.

        * is_bool
        * boolean indexing

        Returns
        -------
        bool
        """
        # This is needed to support returning a TensorArray from .isnan().
        from pandas.core.dtypes.common import is_bool_dtype

        return is_bool_dtype(self._dtype)


class _TensorOpsMixin(pd.api.extensions.ExtensionScalarOpsMixin):
    """
    Mixin for TensorArray operator support, applying operations on the
    underlying ndarrays.
    """

    @classmethod
    def _create_method(cls, op, coerce_to_dtype=True, result_dtype=None):
        """
        Add support for binary operators by unwrapping, applying, and
        rewrapping.
        """

        # NOTE(Clark): This overrides, but coerce_to_dtype, result_dtype might
        # not be needed

        def _binop(self, other):
            lvalues = self._tensor

            if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndex)):
                # Rely on Pandas to unbox and dispatch to us.
                return NotImplemented

            # divmod returns a tuple
            if op_name in ["__divmod__", "__rdivmod__"]:
                # TODO(Clark): Add support for divmod and rdivmod.
                # div, mod = result
                raise NotImplementedError

            if isinstance(other, (TensorArray, TensorArrayElement)):
                rvalues = other._tensor
            else:
                rvalues = other

            result = op(lvalues, rvalues)

            # Force a TensorArray if rvalue is not a scalar.
            if isinstance(self, TensorArrayElement) and (
                not isinstance(other, TensorArrayElement) or not np.isscalar(other)
            ):
                result_wrapped = TensorArray(result)
            else:
                result_wrapped = cls(result)

            return result_wrapped

        op_name = f"__{op.__name__}__"
        return set_function_name(_binop, op_name, cls)

    @classmethod
    def _create_logical_method(cls, op):
        return cls._create_method(op)


class _TensorScalarCastMixin:
    """
    Mixin for casting scalar tensors to a particular numeric type.
    """

    def _scalarfunc(self, func: Callable[[Any], Any]):
        return func(self._tensor)

    def __complex__(self):
        return self._scalarfunc(complex)

    def __float__(self):
        return self._scalarfunc(float)

    def __int__(self):
        return self._scalarfunc(int)

    def __hex__(self):
        return self._scalarfunc(hex)

    def __oct__(self):
        return self._scalarfunc(oct)


@PublicAPI(stability="beta")
class TensorArrayElement(_TensorOpsMixin, _TensorScalarCastMixin):
    """
    Single element of a TensorArray, wrapping an underlying ndarray.
    """

    def __init__(self, values: np.ndarray):
        """
        Construct a TensorArrayElement from a NumPy ndarray.

        Args:
            values: ndarray that underlies this TensorArray element.
        """
        self._tensor = values

    def __repr__(self):
        return self._tensor.__repr__()

    def __str__(self):
        return self._tensor.__str__()

    @property
    def numpy_dtype(self):
        """
        Get the dtype of the tensor.
        :return: The numpy dtype of the backing ndarray
        """
        return self._tensor.dtype

    @property
    def numpy_ndim(self):
        """
        Get the number of tensor dimensions.
        :return: integer for the number of dimensions
        """
        return self._tensor.ndim

    @property
    def numpy_shape(self):
        """
        Get the shape of the tensor.
        :return: A tuple of integers for the numpy shape of the backing ndarray
        """
        return self._tensor.shape

    @property
    def numpy_size(self):
        """
        Get the size of the tensor.
        :return: integer for the number of elements in the tensor
        """
        return self._tensor.size

    def to_numpy(self):
        """
        Return the values of this element as a NumPy ndarray.
        """
        return np.asarray(self._tensor)

    def __array__(self, dtype: np.dtype = None, **kwargs) -> np.ndarray:
        return np.asarray(self._tensor, dtype=dtype, **kwargs)


@PublicAPI(stability="beta")
class TensorArray(
    pd.api.extensions.ExtensionArray,
    _TensorOpsMixin,
    _TensorScalarCastMixin,
):
    """
    Pandas `ExtensionArray` representing a tensor column, i.e. a column
    consisting of ndarrays as elements.

    This extension supports tensors in which the elements have different shapes.
    However, each tensor element must be non-ragged, i.e. each tensor element must have
    a well-defined, non-ragged shape.

    Examples:
        >>> # Create a DataFrame with a list of ndarrays as a column.
        >>> import pandas as pd
        >>> import numpy as np
        >>> import ray
        >>> from ray.data.extensions import TensorArray
        >>> df = pd.DataFrame({
        ...     "one": [1, 2, 3],
        ...     "two": TensorArray(np.arange(24).reshape((3, 2, 2, 2)))})
        >>> # Note that the column dtype is TensorDtype.
        >>> df.dtypes # doctest: +SKIP
        one          int64
        two    TensorDtype(shape=(3, 2, 2, 2), dtype=int64)
        dtype: object
        >>> # Pandas is aware of this tensor column, and we can do the
        >>> # typical DataFrame operations on this column.
        >>> col = 2 * (df["two"] + 10)
        >>> # The ndarrays underlying the tensor column will be manipulated,
        >>> # but the column itself will continue to be a Pandas type.
        >>> type(col) # doctest: +SKIP
        pandas.core.series.Series
        >>> col # doctest: +SKIP
        0   [[[ 2  4]
              [ 6  8]]
             [[10 12]
               [14 16]]]
        1   [[[18 20]
              [22 24]]
             [[26 28]
              [30 32]]]
        2   [[[34 36]
              [38 40]]
             [[42 44]
              [46 48]]]
        Name: two, dtype: TensorDtype(shape=(3, 2, 2, 2), dtype=int64)
        >>> # Once you do an aggregation on that column that returns a single
        >>> # row's value, you get back our TensorArrayElement type.
        >>> tensor = col.mean() # doctest: +SKIP
        >>> type(tensor) # doctest: +SKIP
        ray.data.extensions.tensor_extension.TensorArrayElement
        >>> tensor # doctest: +SKIP
        array([[[18., 20.],
                [22., 24.]],
               [[26., 28.],
                [30., 32.]]])
        >>> # This is a light wrapper around a NumPy ndarray, and can easily
        >>> # be converted to an ndarray.
        >>> type(tensor.to_numpy()) # doctest: +SKIP
        numpy.ndarray
        >>> # In addition to doing Pandas operations on the tensor column,
        >>> # you can now put the DataFrame into a Dataset.
        >>> ds = ray.data.from_pandas(df) # doctest: +SKIP
        >>> # Internally, this column is represented the corresponding
        >>> # Arrow tensor extension type.
        >>> ds.schema() # doctest: +SKIP
        one: int64
        two: extension<arrow.py_extension_type<ArrowTensorType>>
        >>> # You can write the dataset to Parquet.
        >>> ds.write_parquet("/some/path") # doctest: +SKIP
        >>> # And you can read it back.
        >>> read_ds = ray.data.read_parquet("/some/path") # doctest: +SKIP
        >>> read_ds.schema() # doctest: +SKIP
        one: int64
        two: extension<arrow.py_extension_type<ArrowTensorType>>

        >>> read_df = ray.get(read_ds.to_pandas_refs())[0] # doctest: +SKIP
        >>> read_df.dtypes # doctest: +SKIP
        one          int64
        two    TensorDtype(shape=(3, 2, 2, 2), dtype=int64)
        dtype: object
        >>> # The tensor extension type is preserved along the
        >>> # Pandas --> Arrow --> Parquet --> Arrow --> Pandas
        >>> # conversion chain.
        >>> read_df.equals(df) # doctest: +SKIP
        True
    """

    SUPPORTED_REDUCERS = {
        "sum": np.sum,
        "all": np.all,
        "any": np.any,
        "min": np.min,
        "max": np.max,
        "mean": np.mean,
        "median": np.median,
        "prod": np.prod,
        "std": np.std,
        "var": np.var,
    }

    # See https://github.com/pandas-dev/pandas/blob/master/pandas/core/arrays/base.py
    # for interface documentation and the subclassing contract.
    def __init__(
        self,
        values: Union[
            np.ndarray,
            ABCSeries,
            Sequence[Union[np.ndarray, TensorArrayElement]],
            TensorArrayElement,
            Any,
        ],
    ):
        """
        Args:
            values: A NumPy ndarray or sequence of NumPy ndarrays of equal
                shape.
        """
        # Try to convert some well-known objects to ndarrays before handing off to
        # ndarray handling logic.
        if isinstance(values, ABCSeries):
            values = _create_possibly_ragged_ndarray(values)
        elif isinstance(values, Sequence):
            values = [
                np.asarray(v) if isinstance(v, TensorArrayElement) else v
                for v in values
            ]
            values = _create_possibly_ragged_ndarray(values)
        elif isinstance(values, TensorArrayElement):
            values = np.array([np.asarray(values)], copy=False)

        if isinstance(values, np.ndarray):
            if values.dtype.type is np.object_:
                if len(values) == 0:
                    # Tensor is empty, pass through to create empty TensorArray.
                    pass
                elif all(
                    isinstance(v, (np.ndarray, TensorArrayElement, Sequence))
                    and not isinstance(v, str)
                    for v in values
                ):
                    values = [np.asarray(v) for v in values]
                    # Try to convert ndarrays of ndarrays/TensorArrayElements with an
                    # opaque object type to a properly typed ndarray of ndarrays.
                    values = _create_possibly_ragged_ndarray(values)
                else:
                    raise TypeError(
                        "Expected a well-typed ndarray or an object-typed ndarray of "
                        "ndarray pointers, but got an object-typed ndarray whose "
                        f"subndarrays are of type {type(values[0])}."
                    )
        elif isinstance(values, TensorArray):
            raise TypeError("Use the copy() method to create a copy of a TensorArray.")
        else:
            raise TypeError(
                "Expected a numpy.ndarray or sequence of numpy.ndarray, "
                f"but received {values} of type {type(values).__name__} instead."
            )
        assert isinstance(values, np.ndarray)
        self._tensor = values
        self._is_variable_shaped = None

    @classmethod
    def _from_sequence(
        cls, scalars, *, dtype: Optional[Dtype] = None, copy: bool = False
    ):
        """
        Construct a new ExtensionArray from a sequence of scalars.

        Parameters
        ----------
        scalars : Sequence
            Each element will be an instance of the scalar type for this
            array, ``cls.dtype.type`` or be converted into this type in this
            method.
        dtype : dtype, optional
            Construct for this particular dtype. This should be a Dtype
            compatible with the ExtensionArray.
        copy : bool, default False
            If True, copy the underlying data.

        Returns
        -------
        ExtensionArray
        """
        if copy and isinstance(scalars, np.ndarray):
            scalars = scalars.copy()
        elif isinstance(scalars, TensorArray):
            scalars = scalars._tensor.copy() if copy else scalars._tensor
        return TensorArray(scalars)

    @classmethod
    def _from_factorized(
        cls, values: np.ndarray, original: pd.api.extensions.ExtensionArray
    ):
        """
        Reconstruct an ExtensionArray after factorization.

        Parameters
        ----------
        values : ndarray
            An integer ndarray with the factorized values.
        original : ExtensionArray
            The original ExtensionArray that factorize was called on.

        See Also
        --------
        factorize : Top-level factorize method that dispatches here.
        ExtensionArray.factorize : Encode the extension array as an enumerated
            type.
        """
        raise NotImplementedError

    def __getitem__(
        self, item: Union[int, slice, np.ndarray]
    ) -> Union["TensorArray", "TensorArrayElement"]:
        """
        Select a subset of self.

        Parameters
        ----------
        item : int, slice, or ndarray
            * int: The position in 'self' to get.
            * slice: A slice object, where 'start', 'stop', and 'step' are
              integers or None
            * ndarray: A 1-d boolean NumPy ndarray the same length as 'self'

        Returns
        -------
        item : scalar or ExtensionArray

        Notes
        -----
        For scalar ``item``, return a scalar value suitable for the array's
        type. This should be an instance of ``self.dtype.type``.
        For slice ``key``, return an instance of ``ExtensionArray``, even
        if the slice is length 0 or 1.
        For a boolean mask, return an instance of ``ExtensionArray``, filtered
        to the values where ``item`` is True.
        """
        # Return scalar if single value is selected, a TensorArrayElement for
        # single array element, or TensorArray for slice.
        if isinstance(item, int):
            value = self._tensor[item]
            if np.isscalar(value):
                return value
            else:
                return TensorArrayElement(value)
        else:
            # BEGIN workaround for Pandas issue #42430
            if isinstance(item, tuple) and len(item) > 1 and item[0] == Ellipsis:
                if len(item) > 2:
                    # Hopefully this case is not possible, but can't be sure
                    raise ValueError(
                        "Workaround Pandas issue #42430 not "
                        "implemented for tuple length > 2"
                    )
                item = item[1]
            # END workaround for issue #42430
            if isinstance(item, TensorArray):
                item = np.asarray(item)
            item = check_array_indexer(self, item)
            return TensorArray(self._tensor[item])

    def __len__(self) -> int:
        """
        Length of this array.

        Returns
        -------
        length : int
        """
        return len(self._tensor)

    @property
    def dtype(self) -> pd.api.extensions.ExtensionDtype:
        """
        An instance of 'ExtensionDtype'.
        """
        if self.is_variable_shaped:
            # A tensor is only considered variable-shaped if it's non-empty, so no
            # non-empty check is needed here.
            dtype = self._tensor[0].dtype
            shape = (None,) * self._tensor[0].ndim
        else:
            dtype = self.numpy_dtype
            shape = self.numpy_shape[1:]
        return TensorDtype(shape, dtype)

    @property
    def is_variable_shaped(self):
        """
        Whether this TensorArray holds variable-shaped tensor elements.
        """
        if self._is_variable_shaped is None:
            self._is_variable_shaped = _is_ndarray_variable_shaped_tensor(self._tensor)
        return self._is_variable_shaped

    @property
    def nbytes(self) -> int:
        """
        The number of bytes needed to store this object in memory.
        """
        return self._tensor.nbytes

    def isna(self) -> "TensorArray":
        """
        A 1-D array indicating if each value is missing.

        Returns
        -------
        na_values : Union[np.ndarray, ExtensionArray]
            In most cases, this should return a NumPy ndarray. For
            exceptional cases like ``SparseArray``, where returning
            an ndarray would be expensive, an ExtensionArray may be
            returned.

        Notes
        -----
        If returning an ExtensionArray, then

        * ``na_values._is_boolean`` should be True
        * `na_values` should implement :func:`ExtensionArray._reduce`
        * ``na_values.any`` and ``na_values.all`` should be implemented
        """
        if self._tensor.dtype.type is np.object_:
            # Avoid comparing with __eq__ because the elements of the tensor
            # may do something funny with that operation.
            return np.array(
                [self._tensor[i] is None for i in range(len(self))], dtype=bool
            )
        elif self._tensor.dtype.type is np.str_:
            return np.all(self._tensor == "", axis=tuple(range(1, self._tensor.ndim)))
        else:
            return np.all(
                np.isnan(self._tensor), axis=tuple(range(1, self._tensor.ndim))
            )

    def take(
        self, indices: Sequence[int], allow_fill: bool = False, fill_value: Any = None
    ) -> "TensorArray":
        """
        Take elements from an array.

        Parameters
        ----------
        indices : sequence of int
            Indices to be taken.
        allow_fill : bool, default False
            How to handle negative values in `indices`.

            * False: negative values in `indices` indicate positional indices
              from the right (the default). This is similar to
              :func:`numpy.take`.

            * True: negative values in `indices` indicate
              missing values. These values are set to `fill_value`. Any other
              other negative values raise a ``ValueError``.

        fill_value : any, optional
            Fill value to use for NA-indices when `allow_fill` is True.
            This may be ``None``, in which case the default NA value for
            the type, ``self.dtype.na_value``, is used.

            For many ExtensionArrays, there will be two representations of
            `fill_value`: a user-facing "boxed" scalar, and a low-level
            physical NA value. `fill_value` should be the user-facing version,
            and the implementation should handle translating that to the
            physical version for processing the take if necessary.

        Returns
        -------
        ExtensionArray

        Raises
        ------
        IndexError
            When the indices are out of bounds for the array.
        ValueError
            When `indices` contains negative values other than ``-1``
            and `allow_fill` is True.

        See Also
        --------
        numpy.take : Take elements from an array along an axis.
        api.extensions.take : Take elements from an array.

        Notes
        -----
        ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``,
        ``iloc``, when `indices` is a sequence of values. Additionally,
        it's called by :meth:`Series.reindex`, or any other method
        that causes realignment, with a `fill_value`.

        Examples
        --------
        Here's an example implementation, which relies on casting the
        extension array to object dtype. This uses the helper method
        :func:`pandas.api.extensions.take`.

        .. code-block:: python

           def take(self, indices, allow_fill=False, fill_value=None):
               from pandas.core.algorithms import take

               # If the ExtensionArray is backed by an ndarray, then
               # just pass that here instead of coercing to object.
               data = self.astype(object)

               if allow_fill and fill_value is None:
                   fill_value = self.dtype.na_value

               # fill value should always be translated from the scalar
               # type for the array, to the physical storage type for
               # the data, before passing to take.

               result = take(data, indices, fill_value=fill_value,
                             allow_fill=allow_fill)
               return self._from_sequence(result, dtype=self.dtype)
        """
        if allow_fill:
            # With allow_fill being True, negative values in `indices` indicate
            # missing values and should be set to `fill_value`.
            indices = np.asarray(indices, dtype=np.intp)
            validate_indices(indices, len(self._tensor))

            # Check if there are missing indices to fill, otherwise we can
            # delegate to NumPy ndarray .take().
            has_missing = np.any(indices < 0)
            if has_missing:
                if fill_value is None:
                    fill_value = np.nan

                # Create an array populated with fill value.
                values = np.full((len(indices),) + self._tensor.shape[1:], fill_value)

                # Put tensors at the given positive indices into array.
                is_nonneg = indices >= 0
                np.put(values, np.where(is_nonneg)[0], self._tensor[indices[is_nonneg]])

                return TensorArray(values)

        # Delegate take to NumPy array.
        values = self._tensor.take(indices, axis=0)

        return TensorArray(values)

    def copy(self) -> "TensorArray":
        """
        Return a copy of the array.

        Returns
        -------
        ExtensionArray
        """
        # TODO(Clark): Copy cached properties.
        return TensorArray(self._tensor.copy())

    @classmethod
    def _concat_same_type(cls, to_concat: Sequence["TensorArray"]) -> "TensorArray":
        """
        Concatenate multiple array of this dtype.

        Parameters
        ----------
        to_concat : sequence of this type

        Returns
        -------
        ExtensionArray
        """
        should_flatten = False
        shape = None
        for a in to_concat:
            if shape is None:
                shape = a.dtype.element_shape
            if a.is_variable_shaped or a.dtype.element_shape != shape:
                should_flatten = True
                break
        if should_flatten:
            concated = TensorArray(
                np.array([e for a in to_concat for e in a._tensor], dtype=object)
            )
        else:
            concated = TensorArray(np.concatenate([a._tensor for a in to_concat]))
        return concated

    def __setitem__(self, key: Union[int, np.ndarray], value: Any) -> None:
        """
        Set one or more values inplace.

        This method is not required to satisfy the pandas extension array
        interface.

        Parameters
        ----------
        key : int, ndarray, or slice
            When called from, e.g. ``Series.__setitem__``, ``key`` will be
            one of

            * scalar int
            * ndarray of integers.
            * boolean ndarray
            * slice object

        value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
            value or values to be set of ``key``.

        Returns
        -------
        None
        """
        key = check_array_indexer(self, key)
        if isinstance(value, TensorArrayElement) or np.isscalar(value):
            value = np.asarray(value)
        if isinstance(value, list):
            value = [
                np.asarray(v) if isinstance(v, TensorArrayElement) else v for v in value
            ]
        if isinstance(value, ABCSeries) and isinstance(value.dtype, TensorDtype):
            value = value.values
        if value is None or isinstance(value, Sequence) and len(value) == 0:
            self._tensor[key] = np.full_like(self._tensor[key], np.nan)
        elif isinstance(key, (int, slice, np.ndarray)):
            self._tensor[key] = value
        else:
            raise NotImplementedError(
                f"__setitem__ with key type '{type(key)}' not implemented"
            )

    def __contains__(self, item) -> bool:
        """
        Return for `item in self`.
        """
        if isinstance(item, TensorArrayElement):
            np_item = np.asarray(item)
            if np_item.size == 1 and np.isnan(np_item).all():
                return self.isna().any()
        return super().__contains__(item)

    def __repr__(self):
        return self._tensor.__repr__()

    def __str__(self):
        return self._tensor.__str__()

    def _values_for_factorize(self) -> Tuple[np.ndarray, Any]:
        # TODO(Clark): return self._tensor, np.nan
        raise NotImplementedError

    def _reduce(self, name: str, skipna: bool = True, **kwargs):
        """
        Return a scalar result of performing the reduction operation.

        Parameters
        ----------
        name : str
            Name of the function, supported values are:
            { any, all, min, max, sum, mean, median, prod,
            std, var, sem, kurt, skew }.
        skipna : bool, default True
            If True, skip NaN values.
        **kwargs
            Additional keyword arguments passed to the reduction function.
            Currently, `ddof` is the only supported kwarg.

        Returns
        -------
        scalar

        Raises
        ------
        TypeError : subclass does not define reductions
        """
        supported_kwargs = ["ddof"]
        reducer_kwargs = {}
        for kw in supported_kwargs:
            try:
                reducer_kwargs[kw] = kwargs[kw]
            except KeyError:
                pass
        try:
            return TensorArrayElement(
                self.SUPPORTED_REDUCERS[name](self._tensor, axis=0, **reducer_kwargs)
            )
        except KeyError:
            raise NotImplementedError(f"'{name}' aggregate not implemented.") from None

    def __array__(self, dtype: np.dtype = None, **kwargs) -> np.ndarray:
        return np.asarray(self._tensor, dtype=dtype, **kwargs)

    def __array_ufunc__(self, ufunc: Callable, method: str, *inputs, **kwargs):
        """
        Supports NumPy ufuncs without requiring sloppy coercion to an
        ndarray.
        """
        out = kwargs.get("out", ())
        for x in inputs + out:
            if not isinstance(x, (TensorArray, np.ndarray, numbers.Number)):
                return NotImplemented

        # Defer to the implementation of the ufunc on unwrapped values.
        inputs = tuple(x._tensor if isinstance(x, TensorArray) else x for x in inputs)
        if out:
            kwargs["out"] = tuple(
                x._tensor if isinstance(x, TensorArray) else x for x in out
            )
        result = getattr(ufunc, method)(*inputs, **kwargs)

        if type(result) is tuple:
            # Multiple return values.
            return tuple(type(self)(x) for x in result)
        elif method == "at":
            # No return value.
            return None
        else:
            # One return value.
            return type(self)(result)

    def to_numpy(
        self,
        dtype: np.dtype = None,
        copy: bool = False,
        na_value: Any = pd.api.extensions.no_default,
    ):
        """
        Convert to a NumPy ndarray.

        .. versionadded:: 1.0.0

        This is similar to :meth:`numpy.asarray`, but may provide additional
        control over how the conversion is done.

        Parameters
        ----------
        dtype : str or numpy.dtype, optional
            The dtype to pass to :meth:`numpy.asarray`.
        copy : bool, default False
            Whether to ensure that the returned value is a not a view on
            another array. Note that ``copy=False`` does not *ensure* that
            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
            a copy is made, even if not strictly necessary.
        na_value : Any, optional
            The value to use for missing values. The default value depends
            on `dtype` and the type of the array.

        Returns
        -------
        numpy.ndarray
        """
        if dtype is not None:
            dtype = pd.api.types.pandas_dtype(dtype)
            if copy:
                values = np.array(self._tensor, dtype=dtype, copy=True)
            else:
                values = self._tensor.astype(dtype)
        elif copy:
            values = self._tensor.copy()
        else:
            values = self._tensor
        return values

    @property
    def numpy_dtype(self):
        """
        Get the dtype of the tensor.
        :return: The numpy dtype of the backing ndarray
        """
        return self._tensor.dtype

    @property
    def numpy_ndim(self):
        """
        Get the number of tensor dimensions.
        :return: integer for the number of dimensions
        """
        return self._tensor.ndim

    @property
    def numpy_shape(self):
        """
        Get the shape of the tensor.
        :return: A tuple of integers for the numpy shape of the backing ndarray
        """
        return self._tensor.shape

    @property
    def numpy_size(self):
        """
        Get the size of the tensor.
        :return: integer for the number of elements in the tensor
        """
        return self._tensor.size

    def astype(self, dtype, copy=True):
        """
        Cast to a NumPy array with 'dtype'.

        Parameters
        ----------
        dtype : str or dtype
            Typecode or data-type to which the array is cast.
        copy : bool, default True
            Whether to copy the data, even if not necessary. If False,
            a copy is made only if the old dtype does not match the
            new dtype.

        Returns
        -------
        array : ndarray
            NumPy ndarray with 'dtype' for its dtype.
        """
        dtype = pd.api.types.pandas_dtype(dtype)

        if isinstance(dtype, TensorDtype):
            values = TensorArray(self._tensor.copy()) if copy else self
        elif not (
            pd.api.types.is_object_dtype(dtype) and pd.api.types.is_string_dtype(dtype)
        ):
            values = np.array([str(t) for t in self._tensor])
            if isinstance(dtype, pd.StringDtype):
                return dtype.construct_array_type()._from_sequence(values, copy=False)
            else:
                return values
        elif pd.api.types.is_object_dtype(dtype):
            # Interpret astype(object) as "cast to an array of numpy arrays"
            values = np.empty(len(self), dtype=object)
            for i in range(len(self)):
                values[i] = self._tensor[i]
        else:
            values = self._tensor.astype(dtype, copy=copy)
        return values

    def any(self, axis=None, out=None, keepdims=False):
        """
        Test whether any array element along a given axis evaluates to True.

        See numpy.any() documentation for more information
        https://numpy.org/doc/stable/reference/generated/numpy.any.html#numpy.any

        :param axis: Axis or axes along which a logical OR reduction is
            performed.
        :param out: Alternate output array in which to place the result.
        :param keepdims: If this is set to True, the axes which are reduced are
            left in the result as dimensions with size one.
        :return: single boolean unless axis is not None else TensorArray
        """
        result = self._tensor.any(axis=axis, out=out, keepdims=keepdims)
        return result if axis is None else TensorArray(result)

    def all(self, axis=None, out=None, keepdims=False):
        """
        Test whether all array elements along a given axis evaluate to True.

        :param axis: Axis or axes along which a logical AND reduction is
            performed.
        :param out: Alternate output array in which to place the result.
        :param keepdims: If this is set to True, the axes which are reduced are
            left in the result as dimensions with size one.
        :return: single boolean unless axis is not None else TensorArray
        """
        result = self._tensor.all(axis=axis, out=out, keepdims=keepdims)
        return result if axis is None else TensorArray(result)

    def __arrow_array__(self, type=None):
        """
        Convert this TensorArray to an ArrowTensorArray extension array.

        This and TensorDtype.__from_arrow__ make up the
        Pandas extension type + array <--> Arrow extension type + array
        interoperability protocol. See
        https://pandas.pydata.org/pandas-docs/stable/development/extending.html#compatibility-with-apache-arrow
        for more information.
        """
        from ray.air.util.tensor_extensions.arrow import (
            ArrowTensorArray,
            ArrowVariableShapedTensorArray,
        )

        if self.is_variable_shaped:
            return ArrowVariableShapedTensorArray.from_numpy(self._tensor)
        else:
            return ArrowTensorArray.from_numpy(self._tensor)

    @property
    def _is_boolean(self):
        """
        Whether this extension array should be considered boolean.

        By default, ExtensionArrays are assumed to be non-numeric.
        Setting this to True will affect the behavior of several places,
        e.g.

        * is_bool
        * boolean indexing

        Returns
        -------
        bool
        """
        # This is needed to support returning a TensorArray from .isnan().
        return self.dtype._is_boolean()


# Add operators from the mixin to the TensorArrayElement and TensorArray
# classes.
TensorArrayElement._add_arithmetic_ops()
TensorArrayElement._add_comparison_ops()
TensorArrayElement._add_logical_ops()
TensorArray._add_arithmetic_ops()
TensorArray._add_comparison_ops()
TensorArray._add_logical_ops()


@PublicAPI(stability="beta")
def column_needs_tensor_extension(s: pd.Series) -> bool:
    """Return whether the provided pandas Series column needs a tensor extension
    representation. This tensor extension representation provides more efficient slicing
    and interop with ML frameworks.

    Args:
        s: The pandas Series column that may need to be represented using the tensor
            extension.

    Returns:
        Whether the provided Series needs a tensor extension representation.
    """
    # NOTE: This is an O(1) check.
    return (
        s.dtype.type is np.object_ and not s.empty and isinstance(s.iloc[0], np.ndarray)
    )
