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DEP: Use Cython 3.0 (#55179)
* DEP: Use Cython 3.0 * Cython 3.0.3 * Update to Cython 3.0.4 * Merge pyi updates * fixup * Update pyi files and upgrade to Cython 3.0.5 * Remove debug print * fix typo --------- Co-authored-by: Thomas Li <[email protected]>
1 parent 02e2bae commit e5301a8

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+54
-41
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‎asv_bench/asv.conf.json

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@@ -41,7 +41,7 @@
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// pip (with all the conda available packages installed first,
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// followed by the pip installed packages).
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"matrix": {
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"Cython": ["0.29.33"],
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"Cython": ["3.0.5"],
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"matplotlib": [],
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"sqlalchemy": [],
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"scipy": [],

‎environment.yml

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@@ -8,7 +8,7 @@ dependencies:
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# build dependencies
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- versioneer[toml]
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- cython=0.29.33
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- cython=3.0.5
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- meson[ninja]=1.2.1
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- meson-python=0.13.1
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‎pandas/_libs/arrays.pyi

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@@ -26,7 +26,7 @@ class NDArrayBacked:
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def size(self) -> int: ...
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@property
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def nbytes(self) -> int: ...
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def copy(self): ...
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def copy(self, order=...): ...
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def delete(self, loc, axis=...): ...
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def swapaxes(self, axis1, axis2): ...
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def repeat(self, repeats: int | Sequence[int], axis: int | None = ...): ...

‎pandas/_libs/groupby.pyi

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@@ -44,7 +44,6 @@ def group_fillna_indexer(
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labels: np.ndarray, # ndarray[int64_t]
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sorted_labels: npt.NDArray[np.intp],
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mask: npt.NDArray[np.uint8],
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direction: Literal["ffill", "bfill"],
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limit: int, # int64_t
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dropna: bool,
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) -> None: ...
@@ -55,7 +54,7 @@ def group_any_all(
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mask: np.ndarray, # const uint8_t[::1]
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val_test: Literal["any", "all"],
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skipna: bool,
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nullable: bool,
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result_mask: np.ndarray | None,
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) -> None: ...
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def group_sum(
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out: np.ndarray, # complexfloatingintuint_t[:, ::1]

‎pandas/_libs/hashtable.pyi

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@@ -20,7 +20,6 @@ class Factorizer:
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def factorize(
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self,
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values: np.ndarray,
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sort: bool = ...,
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na_sentinel=...,
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na_value=...,
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mask=...,
@@ -157,9 +156,9 @@ class HashTable:
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def __contains__(self, key: Hashable) -> bool: ...
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def sizeof(self, deep: bool = ...) -> int: ...
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def get_state(self) -> dict[str, int]: ...
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# TODO: `item` type is subclass-specific
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def get_item(self, item): ... # TODO: return type?
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def set_item(self, item, val) -> None: ...
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# TODO: `val/key` type is subclass-specific
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def get_item(self, val): ... # TODO: return type?
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def set_item(self, key, val) -> None: ...
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def get_na(self): ... # TODO: return type?
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def set_na(self, val) -> None: ...
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def map_locations(
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self,
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values: np.ndarray, # np.ndarray[subclass-specific]
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return_inverse: bool = ...,
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mask=...,
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) -> (
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tuple[
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np.ndarray, # np.ndarray[subclass-specific]
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na_sentinel: int = ...,
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na_value: object = ...,
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mask=...,
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ignore_na: bool = True,
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) -> tuple[np.ndarray, npt.NDArray[np.intp]]: ... # np.ndarray[subclass-specific]
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class Complex128HashTable(HashTable): ...

‎pandas/_libs/hashtable_class_helper.pxi.in

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@@ -1239,9 +1239,10 @@ cdef class StringHashTable(HashTable):
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na_value=na_value, ignore_na=ignore_na,
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return_inverse=True)
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# Add unused mask parameter for compat with other signatures
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def get_labels(self, ndarray[object] values, ObjectVector uniques,
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Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
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object na_value=None):
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object na_value=None, object mask=None):
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# -> np.ndarray[np.intp]
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_, labels = self._unique(values, uniques, count_prior=count_prior,
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na_sentinel=na_sentinel, na_value=na_value,
@@ -1496,9 +1497,10 @@ cdef class PyObjectHashTable(HashTable):
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na_value=na_value, ignore_na=ignore_na,
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return_inverse=True)
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# Add unused mask parameter for compat with other signatures
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def get_labels(self, ndarray[object] values, ObjectVector uniques,
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Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
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object na_value=None):
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object na_value=None, object mask=None):
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# -> np.ndarray[np.intp]
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_, labels = self._unique(values, uniques, count_prior=count_prior,
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na_sentinel=na_sentinel, na_value=na_value,

‎pandas/_libs/lib.pyi

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@@ -45,22 +45,24 @@ def is_scalar(val: object) -> bool: ...
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def is_list_like(obj: object, allow_sets: bool = ...) -> bool: ...
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def is_pyarrow_array(obj: object) -> bool: ...
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def is_period(val: object) -> TypeGuard[Period]: ...
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def is_interval(val: object) -> TypeGuard[Interval]: ...
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def is_decimal(val: object) -> TypeGuard[Decimal]: ...
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def is_complex(val: object) -> TypeGuard[complex]: ...
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def is_bool(val: object) -> TypeGuard[bool | np.bool_]: ...
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def is_integer(val: object) -> TypeGuard[int | np.integer]: ...
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def is_interval(obj: object) -> TypeGuard[Interval]: ...
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def is_decimal(obj: object) -> TypeGuard[Decimal]: ...
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def is_complex(obj: object) -> TypeGuard[complex]: ...
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def is_bool(obj: object) -> TypeGuard[bool | np.bool_]: ...
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def is_integer(obj: object) -> TypeGuard[int | np.integer]: ...
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def is_int_or_none(obj) -> bool: ...
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def is_float(val: object) -> TypeGuard[float]: ...
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def is_float(obj: object) -> TypeGuard[float]: ...
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def is_interval_array(values: np.ndarray) -> bool: ...
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def is_datetime64_array(values: np.ndarray) -> bool: ...
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def is_timedelta_or_timedelta64_array(values: np.ndarray) -> bool: ...
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def is_datetime64_array(values: np.ndarray, skipna: bool = True) -> bool: ...
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def is_timedelta_or_timedelta64_array(
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values: np.ndarray, skipna: bool = True
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) -> bool: ...
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def is_datetime_with_singletz_array(values: np.ndarray) -> bool: ...
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def is_time_array(values: np.ndarray, skipna: bool = ...): ...
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def is_date_array(values: np.ndarray, skipna: bool = ...): ...
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def is_datetime_array(values: np.ndarray, skipna: bool = ...): ...
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def is_string_array(values: np.ndarray, skipna: bool = ...): ...
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def is_float_array(values: np.ndarray, skipna: bool = ...): ...
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def is_float_array(values: np.ndarray): ...
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def is_integer_array(values: np.ndarray, skipna: bool = ...): ...
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def is_bool_array(values: np.ndarray, skipna: bool = ...): ...
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def fast_multiget(
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max_bin: int,
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) -> np.ndarray: ... # np.ndarray[np.int64, ndim=2]
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def get_level_sorter(
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label: np.ndarray, # const int64_t[:]
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codes: np.ndarray, # const int64_t[:]
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starts: np.ndarray, # const intp_t[:]
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) -> np.ndarray: ... # np.ndarray[np.intp, ndim=1]
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def generate_bins_dt64(

‎pandas/_libs/ops.pyi

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@@ -37,8 +37,8 @@ def vec_binop(
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@overload
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def maybe_convert_bool(
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arr: npt.NDArray[np.object_],
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true_values: Iterable = ...,
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false_values: Iterable = ...,
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true_values: Iterable | None = None,
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false_values: Iterable | None = None,
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convert_to_masked_nullable: Literal[False] = ...,
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) -> tuple[np.ndarray, None]: ...
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@overload

‎pandas/_libs/sparse.pyi

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@@ -39,6 +39,10 @@ class BlockIndex(SparseIndex):
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self, length: int, blocs: np.ndarray, blengths: np.ndarray
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) -> None: ...
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# Override to have correct parameters
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def intersect(self, other: SparseIndex) -> Self: ...
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def make_union(self, y: SparseIndex) -> Self: ...
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def make_mask_object_ndarray(
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arr: npt.NDArray[np.object_], fill_value
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) -> npt.NDArray[np.bool_]: ...

‎pandas/_libs/tslibs/conversion.pyi

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@@ -9,6 +9,7 @@ DT64NS_DTYPE: np.dtype
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TD64NS_DTYPE: np.dtype
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def precision_from_unit(
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in_reso: int, # NPY_DATETIMEUNIT
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in_reso: int,
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out_reso: int = ...,
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) -> tuple[int, int]: ... # (int64_t, _)
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def localize_pydatetime(dt: datetime, tz: tzinfo | None) -> datetime: ...

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