text: from __future__ import annotations
  
  from typing import (
      Any,
      Hashable,
  )
  import warnings
  
  import numpy as np
  
  from pandas._config import get_option
  
  from pandas._libs import index as libindex
  from pandas._typing import (
      Dtype,
      DtypeObj,
  )
  from pandas.util._decorators import doc
  
  from pandas.core.dtypes.common import (
      is_categorical_dtype,
      is_scalar,
  )
  from pandas.core.dtypes.missing import (
      is_valid_na_for_dtype,
      isna,
      notna,
  )
  
  from pandas.core import accessor
  from pandas.core.arrays.categorical import (
      Categorical,
      contains,
  )
  from pandas.core.construction import extract_array
  import pandas.core.indexes.base as ibase
  from pandas.core.indexes.base import (
      Index,
      maybe_extract_name,
  )
  from pandas.core.indexes.extension import (
      NDArrayBackedExtensionIndex,
      inherit_names,
  )
  
  _index_doc_kwargs: dict[str, str] = dict(ibase._index_doc_kwargs)
  _index_doc_kwargs.update({"target_klass": "CategoricalIndex"})
  
  
  @inherit_names(
      [
          "argsort",
          "_internal_get_values",
          "tolist",
          "codes",
          "categories",
          "ordered",
          "_reverse_indexer",
          "searchsorted",
          "is_dtype_equal",
          "min",
          "max",
      ],
      Categorical,
  )
  @accessor.delegate_names(
      delegate=Categorical,
      accessors=[
          "rename_categories",
          "reorder_categories",
          "add_categories",
          "remove_categories",
          "remove_unused_categories",
          "set_categories",
          "as_ordered",
          "as_unordered",
      ],
      typ="method",
      overwrite=True,
  )
  class CategoricalIndex(NDArrayBackedExtensionIndex, accessor.PandasDelegate):
      """
      Index based on an underlying :class:`Categorical`.
  
      CategoricalIndex, like Categorical, can only take on a limited,
      and usually fixed, number of possible values (`categories`). Also,
      like Categorical, it might have an order, but numerical operations
      (additions, divisions, ...) are not possible.
  
      Parameters
      ----------
      data : array-like (1-dimensional)
          The values of the categorical. If `categories` are given, values not in
          `categories` will be replaced with NaN.
      categories : index-like, optional
          The categories for the categorical. Items need to be unique.
          If the categories are not given here (and also not in `dtype`), they
          will be inferred from the `data`.
      ordered : bool, optional
          Whether or not this categorical is treated as an ordered
          categorical. If not given here or in `dtype`, the resulting
          categorical will be unordered.
      dtype : CategoricalDtype or "category", optional
          If :class:`CategoricalDtype`, cannot be used together with
          `categories` or `ordered`.
      copy : bool, default False
          Make a copy of input ndarray.
      name : object, optional
          Name to be stored in the index.
  
      Attributes
      ----------
      codes
      categories
      ordered
  
      Methods
      -------
      rename_categories
      reorder_categories
      add_categories
      remove_categories
      remove_unused_categories
      set_categories
      as_ordered
      as_unordered
      map
  
      Raises
      ------
      ValueError
          If the categories do not validate.
      TypeError
          If an explicit ``ordered=True`` is given but no `categories` and the
          `values` are not sortable.
  
      See Also
      --------
      Index : The base pandas Index type.
      Categorical : A categorical array.
      CategoricalDtype : Type for categorical data.
  
      Notes
      -----
      See the `user guide
      <https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#categoricalindex>`__
      for more.
  
      Examples
      --------
      >>> pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"])
      CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
                       categories=['a', 'b', 'c'], ordered=False, dtype='category')
  
      ``CategoricalIndex`` can also be instantiated from a ``Categorical``:
  
      >>> c = pd.Categorical(["a", "b", "c", "a", "b", "c"])
      >>> pd.CategoricalIndex(c)
      CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
                       categories=['a', 'b', 'c'], ordered=False, dtype='category')
  
      Ordered ``CategoricalIndex`` can have a min and max value.
  
      >>> ci = pd.CategoricalIndex(
      ...     ["a", "b", "c", "a", "b", "c"], ordered=True, categories=["c", "b", "a"]
      ... )
      >>> ci
      CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
                       categories=['c', 'b', 'a'], ordered=True, dtype='category')
      >>> ci.min()
      'c'
      """
  
      _typ = "categoricalindex"
      _data_cls = Categorical
  
      @property
      def _can_hold_strings(self):
          return self.categories._can_hold_strings
  
      codes: np.ndarray
      categories: Index
      _data: Categorical
      _values: Categorical
  
      @property
      def _engine_type(self):
          # self.codes can have dtype int8, int16, int32 or int64, so we need
          # to return the corresponding engine type (libindex.Int8Engine, etc.).
  
          # error: Invalid index type "Type[generic]" for "Dict[Type[signedinteger[Any]],
          # Any]"; expected type "Type[signedinteger[Any]]"
          return {
              np.int8: libindex.Int8Engine,
              np.int16: libindex.Int16Engine,
              np.int32: libindex.Int32Engine,
              np.int64: libindex.Int64Engine,
          }[
              self.codes.dtype.type  # type: ignore[index]
          ]
  
      _attributes = ["name"]
  
      # --------------------------------------------------------------------
      # Constructors
  
      def __new__(
          cls,
          data=None,
          categories=None,
          ordered=None,
          dtype: Dtype | None = None,
          copy: bool = False,
          name: Hashable = None,
      ) -> CategoricalIndex:
  
          name = maybe_extract_name(name, data, cls)
  
          if data is None:
              # GH#38944
              warnings.warn(
                  "Constructing a CategoricalIndex without passing data is "
                  "deprecated and will raise in a future version. "
                  "Use CategoricalIndex([], ...) instead",
                  FutureWarning,
                  stacklevel=2,
              )
              data = []
  
          if is_scalar(data):
              raise cls._scalar_data_error(data)
  
          data = Categorical(
              data, categories=categories, ordered=ordered, dtype=dtype, copy=copy
          )
  
          return cls._simple_new(data, name=name)
  
      # --------------------------------------------------------------------
  
      def _is_dtype_compat(self, other) -> Categorical:
          """
          *this is an internal non-public method*
  
          provide a comparison between the dtype of self and other (coercing if
          needed)
  
          Parameters
          ----------
          other : Index
  
          Returns
          -------
          Categorical
  
          Raises
          ------
          TypeError if the dtypes are not compatible
          """
          if is_categorical_dtype(other):
              other = extract_array(other)
              if not other._categories_match_up_to_permutation(self):
                  raise TypeError(
                      "categories must match existing categories when appending"
                  )
  
          elif other._is_multi:
              # preempt raising NotImplementedError in isna call
              raise TypeError("MultiIndex is not dtype-compatible with CategoricalIndex")
          else:
              values = other
  
              cat = Categorical(other, dtype=self.dtype)
              other = CategoricalIndex(cat)
              if not other.isin(values).all():
                  raise TypeError(
                      "cannot append a non-category item to a CategoricalIndex"
                  )
              other = other._values
  
              if not ((other == values) | (isna(other) & isna(values))).all():
                  # GH#37667 see test_equals_non_category
                  raise TypeError(
                      "categories must match existing categories when appending"
                  )
  
          return other
  
      def equals(self, other: object) -> bool:
          """
          Determine if two CategoricalIndex objects contain the same elements.
  
          Returns
          -------
          bool
              If two CategoricalIndex objects have equal elements True,
              otherwise False.
          """
          if self.is_(other):
              return True
  
          if not isinstance(other, Index):
              return False
  
          try:
              other = self._is_dtype_compat(other)
          except (TypeError, ValueError):
              return False
  
          return self._data.equals(other)
  
      # --------------------------------------------------------------------
      # Rendering Methods
  
      @property
      def _formatter_func(self):
          return self.categories._formatter_func
  
      def _format_attrs(self):
          """
          Return a list of tuples of the (attr,formatted_value)
          """
          max_categories = (
              10
              if get_option("display.max_categories") == 0
              else get_option("display.max_categories")
          )
          attrs = [
              (
                  "categories",
                  ibase.default_pprint(self.categories, max_seq_items=max_categories),
              ),
              # error: "CategoricalIndex" has no attribute "ordered"
              ("ordered", self.ordered),  # type: ignore[attr-defined]
          ]
          extra = super()._format_attrs()
          return attrs + extra
  
      def _format_with_header(self, header: list[str], na_rep: str = "NaN") -> list[str]:
          from pandas.io.formats.printing import pprint_thing
  
          result = [
              pprint_thing(x, escape_chars=("\t", "\r", "\n")) if notna(x) else na_rep
              for x in self._values
          ]
          return header + result
  
      # --------------------------------------------------------------------
  
      @property
      def inferred_type(self) -> str:
          return "categorical"
  
      @doc(Index.__contains__)
      def __contains__(self, key: Any) -> bool:
          # if key is a NaN, check if any NaN is in self.
          if is_valid_na_for_dtype(key, self.categories.dtype):
              return self.hasnans
  
          return contains(self, key, container=self._engine)
  
      @doc(Index.fillna)
      def fillna(self, value, downcast=None):
          value = self._require_scalar(value)
          try:
              cat = self._data.fillna(value)
          except (ValueError, TypeError):
              # invalid fill_value
              if not self.isna().any():
                  # nothing to fill, we can get away without casting
                  return self.copy()
              return self.astype(object).fillna(value, downcast=downcast)
  
          return type(self)._simple_new(cat, name=self.name)
  
      def reindex(
          self, target, method=None, level=None, limit=None, tolerance=None
      ) -> tuple[Index, np.ndarray | None]:
          """
          Create index with target's values (move/add/delete values as necessary)
  
          Returns
          -------
          new_index : pd.Index
              Resulting index
          indexer : np.ndarray[np.intp] or None
              Indices of output values in original index
  
          """
          if method is not None:
              raise NotImplementedError(
                  "argument method is not implemented for CategoricalIndex.reindex"
              )
          if level is not None:
              raise NotImplementedError(
                  "argument level is not implemented for CategoricalIndex.reindex"
              )
          if limit is not None:
              raise NotImplementedError(
                  "argument limit is not implemented for CategoricalIndex.reindex"
              )
  
          target = ibase.ensure_index(target)
  
          if self.equals(target):
              indexer = None
              missing = np.array([], dtype=np.intp)
          else:
              indexer, missing = self.get_indexer_non_unique(np.array(target))
  
          if len(self.codes) and indexer is not None:
              new_target = self.take(indexer)
          else:
              new_target = target
  
          # filling in missing if needed
          if len(missing):
              cats = self.categories.get_indexer(target)
  
              if not isinstance(cats, CategoricalIndex) or (cats == -1).any():
                  # coerce to a regular index here!
                  result = Index(np.array(self), name=self.name)
                  new_target, indexer, _ = result._reindex_non_unique(target)
              else:
  
                  codes = new_target.codes.copy()
                  codes[indexer == -1] = cats[missing]
                  cat = self._data._from_backing_data(codes)
                  new_target = type(self)._simple_new(cat, name=self.name)
  
          # we always want to return an Index type here
          # to be consistent with .reindex for other index types (e.g. they don't
          # coerce based on the actual values, only on the dtype)
          # unless we had an initial Categorical to begin with
          # in which case we are going to conform to the passed Categorical
          new_target = np.asarray(new_target)
          if is_categorical_dtype(target):
              cat = Categorical(new_target, dtype=target.dtype)
              new_target = type(self)._simple_new(cat, name=self.name)
          else:
              new_target = Index(new_target, name=self.name)
  
          return new_target, indexer
  
      # error: Return type "Tuple[Index, Optional[ndarray], Optional[ndarray]]"
      # of "_reindex_non_unique" incompatible with return type
      # "Tuple[Index, ndarray, Optional[ndarray]]" in supertype "Index"
      def _reindex_non_unique(  # type: ignore[override]
          self, target: Index
      ) -> tuple[Index, np.ndarray | None, np.ndarray | None]:
          """
          reindex from a non-unique; which CategoricalIndex's are almost
          always
          """
          # TODO: rule out `indexer is None` here to make the signature
          #  match the parent class's signature. This should be equivalent
          #  to ruling out `self.equals(target)`
          new_target, indexer = self.reindex(target)
          new_indexer = None
  
          check = indexer == -1
          # error: Item "bool" of "Union[Any, bool]" has no attribute "any"
          if check.any():  # type: ignore[union-attr]
              new_indexer = np.arange(len(self.take(indexer)), dtype=np.intp)
              new_indexer[check] = -1
  
          cats = self.categories.get_indexer(target)
          if not (cats == -1).any():
              # .reindex returns normal Index. Revert to CategoricalIndex if
              # all targets are included in my categories
              cat = Categorical(new_target, dtype=self.dtype)
              new_target = type(self)._simple_new(cat, name=self.name)
  
          return new_target, indexer, new_indexer
  
      # --------------------------------------------------------------------
      # Indexing Methods
  
      def _maybe_cast_indexer(self, key) -> int:
          # GH#41933: we have to do this instead of self._data._validate_scalar
          #  because this will correctly get partial-indexing on Interval categories
          try:
              return self._data._unbox_scalar(key)
          except KeyError:
              if is_valid_na_for_dtype(key, self.categories.dtype):
                  return -1
              raise
  
      def _maybe_cast_listlike_indexer(self, values) -> CategoricalIndex:
          if isinstance(values, CategoricalIndex):
              values = values._data
          if isinstance(values, Categorical):
              # Indexing on codes is more efficient if categories are the same,
              #  so we can apply some optimizations based on the degree of
              #  dtype-matching.
              cat = self._data._encode_with_my_categories(values)
              codes = cat._codes
          else:
              codes = self.categories.get_indexer(values)
              codes = codes.astype(self.codes.dtype, copy=False)
              cat = self._data._from_backing_data(codes)
          return type(self)._simple_new(cat)
  
      # --------------------------------------------------------------------
  
      def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
          return self.categories._is_comparable_dtype(dtype)
  
      def take_nd(self, *args, **kwargs):
          """Alias for `take`"""
          warnings.warn(
              "CategoricalIndex.take_nd is deprecated, use CategoricalIndex.take instead",
              FutureWarning,
              stacklevel=2,
          )
          return self.take(*args, **kwargs)
  
      def map(self, mapper):
          """
          Map values using input correspondence (a dict, Series, or function).
  
          Maps the values (their categories, not the codes) of the index to new
          categories. If the mapping correspondence is one-to-one the result is a
          :class:`~pandas.CategoricalIndex` which has the same order property as
          the original, otherwise an :class:`~pandas.Index` is returned.
  
          If a `dict` or :class:`~pandas.Series` is used any unmapped category is
          mapped to `NaN`. Note that if this happens an :class:`~pandas.Index`
          will be returned.
  
          Parameters
          ----------
          mapper : function, dict, or Series
              Mapping correspondence.
  
          Returns
          -------
          pandas.CategoricalIndex or pandas.Index
              Mapped index.
  
          See Also
          --------
          Index.map : Apply a mapping correspondence on an
              :class:`~pandas.Index`.
          Series.map : Apply a mapping correspondence on a
              :class:`~pandas.Series`.
          Series.apply : Apply more complex functions on a
              :class:`~pandas.Series`.
  
          Examples
          --------
          >>> idx = pd.CategoricalIndex(['a', 'b', 'c'])
          >>> idx
          CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
                            ordered=False, dtype='category')
          >>> idx.map(lambda x: x.upper())
          CategoricalIndex(['A', 'B', 'C'], categories=['A', 'B', 'C'],
                           ordered=False, dtype='category')
          >>> idx.map({'a': 'first', 'b': 'second', 'c': 'third'})
          CategoricalIndex(['first', 'second', 'third'], categories=['first',
                           'second', 'third'], ordered=False, dtype='category')
  
          If the mapping is one-to-one the ordering of the categories is
          preserved:
  
          >>> idx = pd.CategoricalIndex(['a', 'b', 'c'], ordered=True)
          >>> idx
          CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
                           ordered=True, dtype='category')
          >>> idx.map({'a': 3, 'b': 2, 'c': 1})
          CategoricalIndex([3, 2, 1], categories=[3, 2, 1], ordered=True,
                           dtype='category')
  
          If the mapping is not one-to-one an :class:`~pandas.Index` is returned:
  
          >>> idx.map({'a': 'first', 'b': 'second', 'c': 'first'})
          Index(['first', 'second', 'first'], dtype='object')
  
          If a `dict` is used, all unmapped categories are mapped to `NaN` and
          the result is an :class:`~pandas.Index`:
  
          >>> idx.map({'a': 'first', 'b': 'second'})
          Index(['first', 'second', nan], dtype='object')
          """
          mapped = self._values.map(mapper)
          return Index(mapped, name=self.name)
  
      def _concat(self, to_concat: list[Index], name: Hashable) -> Index:
          # if calling index is category, don't check dtype of others
          try:
              codes = np.concatenate([self._is_dtype_compat(c).codes for c in to_concat])
          except TypeError:
              # not all to_concat elements are among our categories (or NA)
              from pandas.core.dtypes.concat import concat_compat
  
              res = concat_compat(to_concat)
              return Index(res, name=name)
          else:
              cat = self._data._from_backing_data(codes)
              return type(self)._simple_new(cat, name=name)
  
      def _delegate_method(self, name: str, *args, **kwargs):
          """method delegation to the ._values"""
          method = getattr(self._values, name)
          if "inplace" in kwargs:
              raise ValueError("cannot use inplace with CategoricalIndex")
          res = method(*args, **kwargs)
          if is_scalar(res):
              return res
          return CategoricalIndex(res, name=self.name)
  