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""" Array API Inspection namespace This is the namespace for inspection functions as defined by the array API standard. See https://data-apis.org/array-api/latest/API_specification/inspection.html for more details. """ from numpy._core import ( dtype, bool, intp, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float32, float64, complex64, complex128, ) class __array_namespace_info__: """ Get the array API inspection namespace for NumPy. The array API inspection namespace defines the following functions: - capabilities() - default_device() - default_dtypes() - dtypes() - devices() See https://data-apis.org/array-api/latest/API_specification/inspection.html for more details. Returns ------- info : ModuleType The array API inspection namespace for NumPy. Examples -------- >>> info = np.__array_namespace_info__() >>> info.default_dtypes() {'real floating': numpy.float64, 'complex floating': numpy.complex128, 'integral': numpy.int64, 'indexing': numpy.int64} """ __module__ = 'numpy' def capabilities(self): """ Return a dictionary of array API library capabilities. The resulting dictionary has the following keys: - **"boolean indexing"**: boolean indicating whether an array library supports boolean indexing. Always ``True`` for NumPy. - **"data-dependent shapes"**: boolean indicating whether an array library supports data-dependent output shapes. Always ``True`` for NumPy. See https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html for more details. See Also -------- __array_namespace_info__.default_device, __array_namespace_info__.default_dtypes, __array_namespace_info__.dtypes, __array_namespace_info__.devices Returns ------- capabilities : dict A dictionary of array API library capabilities. Examples -------- >>> info = np.__array_namespace_info__() >>> info.capabilities() {'boolean indexing': True, 'data-dependent shapes': True} """ return { "boolean indexing": True, "data-dependent shapes": True, # 'max rank' will be part of the 2024.12 standard # "max rank": 64, } def default_device(self): """ The default device used for new NumPy arrays. For NumPy, this always returns ``'cpu'``. See Also -------- __array_namespace_info__.capabilities, __array_namespace_info__.default_dtypes, __array_namespace_info__.dtypes, __array_namespace_info__.devices Returns ------- device : str The default device used for new NumPy arrays. Examples -------- >>> info = np.__array_namespace_info__() >>> info.default_device() 'cpu' """ return "cpu" def default_dtypes(self, *, device=None): """ The default data types used for new NumPy arrays. For NumPy, this always returns the following dictionary: - **"real floating"**: ``numpy.float64`` - **"complex floating"**: ``numpy.complex128`` - **"integral"**: ``numpy.intp`` - **"indexing"**: ``numpy.intp`` Parameters ---------- device : str, optional The device to get the default data types for. For NumPy, only ``'cpu'`` is allowed. Returns ------- dtypes : dict A dictionary describing the default data types used for new NumPy arrays. See Also -------- __array_namespace_info__.capabilities, __array_namespace_info__.default_device, __array_namespace_info__.dtypes, __array_namespace_info__.devices Examples -------- >>> info = np.__array_namespace_info__() >>> info.default_dtypes() {'real floating': numpy.float64, 'complex floating': numpy.complex128, 'integral': numpy.int64, 'indexing': numpy.int64} """ if device not in ["cpu", None]: raise ValueError( 'Device not understood. Only "cpu" is allowed, but received:' f' {device}' ) return { "real floating": dtype(float64), "complex floating": dtype(complex128), "integral": dtype(intp), "indexing": dtype(intp), } def dtypes(self, *, device=None, kind=None): """ The array API data types supported by NumPy. Note that this function only returns data types that are defined by the array API. Parameters ---------- device : str, optional The device to get the data types for. For NumPy, only ``'cpu'`` is allowed. kind : str or tuple of str, optional The kind of data types to return. If ``None``, all data types are returned. If a string, only data types of that kind are returned. If a tuple, a dictionary containing the union of the given kinds is returned. The following kinds are supported: - ``'bool'``: boolean data types (i.e., ``bool``). - ``'signed integer'``: signed integer data types (i.e., ``int8``, ``int16``, ``int32``, ``int64``). - ``'unsigned integer'``: unsigned integer data types (i.e., ``uint8``, ``uint16``, ``uint32``, ``uint64``). - ``'integral'``: integer data types. Shorthand for ``('signed integer', 'unsigned integer')``. - ``'real floating'``: real-valued floating-point data types (i.e., ``float32``, ``float64``). - ``'complex floating'``: complex floating-point data types (i.e., ``complex64``, ``complex128``). - ``'numeric'``: numeric data types. Shorthand for ``('integral', 'real floating', 'complex floating')``. Returns ------- dtypes : dict A dictionary mapping the names of data types to the corresponding NumPy data types. See Also -------- __array_namespace_info__.capabilities, __array_namespace_info__.default_device, __array_namespace_info__.default_dtypes, __array_namespace_info__.devices Examples -------- >>> info = np.__array_namespace_info__() >>> info.dtypes(kind='signed integer') {'int8': numpy.int8, 'int16': numpy.int16, 'int32': numpy.int32, 'int64': numpy.int64} """ if device not in ["cpu", None]: raise ValueError( 'Device not understood. Only "cpu" is allowed, but received:' f' {device}' ) if kind is None: return { "bool": dtype(bool), "int8": dtype(int8), "int16": dtype(int16), "int32": dtype(int32), "int64": dtype(int64), "uint8": dtype(uint8), "uint16": dtype(uint16), "uint32": dtype(uint32), "uint64": dtype(uint64), "float32": dtype(float32), "float64": dtype(float64), "complex64": dtype(complex64), "complex128": dtype(complex128), } if kind == "bool": return {"bool": bool} if kind == "signed integer": return { "int8": dtype(int8), "int16": dtype(int16), "int32": dtype(int32), "int64": dtype(int64), } if kind == "unsigned integer": return { "uint8": dtype(uint8), "uint16": dtype(uint16), "uint32": dtype(uint32), "uint64": dtype(uint64), } if kind == "integral": return { "int8": dtype(int8), "int16": dtype(int16), "int32": dtype(int32), "int64": dtype(int64), "uint8": dtype(uint8), "uint16": dtype(uint16), "uint32": dtype(uint32), "uint64": dtype(uint64), } if kind == "real floating": return { "float32": dtype(float32), "float64": dtype(float64), } if kind == "complex floating": return { "complex64": dtype(complex64), "complex128": dtype(complex128), } if kind == "numeric": return { "int8": dtype(int8), "int16": dtype(int16), "int32": dtype(int32), "int64": dtype(int64), "uint8": dtype(uint8), "uint16": dtype(uint16), "uint32": dtype(uint32), "uint64": dtype(uint64), "float32": dtype(float32), "float64": dtype(float64), "complex64": dtype(complex64), "complex128": dtype(complex128), } if isinstance(kind, tuple): res = {} for k in kind: res.update(self.dtypes(kind=k)) return res raise ValueError(f"unsupported kind: {kind!r}") def devices(self): """ The devices supported by NumPy. For NumPy, this always returns ``['cpu']``. Returns ------- devices : list of str The devices supported by NumPy. See Also -------- __array_namespace_info__.capabilities, __array_namespace_info__.default_device, __array_namespace_info__.default_dtypes, __array_namespace_info__.dtypes Examples -------- >>> info = np.__array_namespace_info__() >>> info.devices() ['cpu'] """ return ["cpu"]