from __future__ import annotations

__copyright__ = """

Copyright (C) 2021 University of Illinois Board of Trustees

"""

__license__ = """

Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in

all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN

THE SOFTWARE.

"""

from functools import partial, reduce

from typing import TYPE_CHECKING, cast, overload

import numpy as np

from typing_extensions import override

from arraycontext.container import NotAnArrayContainerError, serialize_container

from arraycontext.container.traversal import (

rec_map_container,

rec_map_reduce_array_container,

rec_multimap_array_container,

rec_multimap_reduce_array_container,

)

from arraycontext.fake_numpy import (

BaseFakeNumpyLinalgNamespace,

BaseFakeNumpyNamespace,

)

from arraycontext.typing import ArrayOrContainer, OrderCF, is_scalar_like

if TYPE_CHECKING:

from collections.abc import Callable

from numpy.typing import DTypeLike

from pymbolic import Scalar

from arraycontext.typing import (

Array,

ArrayOrContainerOrScalar,

ArrayOrScalar,

)

class NumpyFakeNumpyLinalgNamespace(BaseFakeNumpyLinalgNamespace):

# Everything is implemented in the base class for now.

pass

_NUMPY_UFUNCS = frozenset({"concatenate", "reshape",

"ones_like", "where",

*BaseFakeNumpyNamespace._numpy_math_functions

})

class NumpyFakeNumpyNamespace(BaseFakeNumpyNamespace):

"""

A :mod:`numpy` mimic for :class:`NumpyArrayContext`.

"""

@override

def _get_fake_numpy_linalg_namespace(self):

return NumpyFakeNumpyLinalgNamespace(self._array_context)

@override

def zeros(self, shape: int | tuple[int, ...], dtype: DTypeLike) -> Array:

return cast("Array", cast("object", np.zeros(shape, dtype)))

@override

def _full_like_array(self,

ary: Array,

fill_value: Scalar,

) -> Array:

return cast("Array", cast("object", np.full_like(ary, fill_value)))

def __getattr__(self, name: str):

if name in _NUMPY_UFUNCS:

from functools import partial

return partial(rec_multimap_array_container,

getattr(np, name))

raise AttributeError(name)

@overload

def sum(self,

a: ArrayOrContainer,

axis: int | tuple[int, ...] | None = None,

dtype: DTypeLike = None,

) -> Array: ...

@overload

def sum(self,

a: Scalar,

axis: int | tuple[int, ...] | None = None,

dtype: DTypeLike = None,

) -> Scalar: ...

@override

def sum(self,

a: ArrayOrContainerOrScalar,

axis: int | tuple[int, ...] | None = None,

dtype: DTypeLike = None,

) -> ArrayOrScalar:

return rec_map_reduce_array_container(sum, partial(np.sum,

axis=axis,

dtype=dtype),

a)

@overload

def min(self,

a: ArrayOrContainer,

axis: int | tuple[int, ...] | None = None,

) -> Array: ...

@overload

def min(self,

a: Scalar,

axis: int | tuple[int, ...] | None = None,

) -> Scalar: ...

@override

def min(self,

a: ArrayOrContainerOrScalar,

axis: int | tuple[int, ...] | None = None,

) -> ArrayOrScalar:

return rec_map_reduce_array_container(

partial(reduce, np.minimum), partial(np.amin, axis=axis), a)

@overload

def max(self,

a: ArrayOrContainer,

axis: int | tuple[int, ...] | None = None,

) -> Array: ...

@overload

def max(self,

a: Scalar,

axis: int | tuple[int, ...] | None = None,

) -> Scalar: ...

@override

def max(self,

a: ArrayOrContainerOrScalar,

axis: int | tuple[int, ...] | None = None,

) -> ArrayOrScalar:

return rec_map_reduce_array_container(

partial(reduce, np.maximum), partial(np.amax, axis=axis), a)

def stack(self, arrays, axis=0):

return rec_multimap_array_container(

lambda *args: np.stack(arrays=args, axis=axis),

*arrays)

def broadcast_to(self, array: ArrayOrContainerOrScalar, shape: tuple[int, ...]):

def inner_bcast(ary: ArrayOrScalar) -> ArrayOrScalar:

if is_scalar_like(ary):

return ary

else:

assert isinstance(ary, np.ndarray)

return cast("Array", cast("object", np.broadcast_to(ary, shape)))

return rec_map_container(inner_bcast, array)

# {{{ relational operators

@override

def equal(self, x, y):

return rec_multimap_array_container(np.equal, x, y)

@override

def not_equal(self, x, y):

return rec_multimap_array_container(np.not_equal, x, y)

@override

def greater(self, x, y):

return rec_multimap_array_container(np.greater, x, y)

@override

def greater_equal(self, x, y):

return rec_multimap_array_container(np.greater_equal, x, y)

@override

def less(self, x, y):

return rec_multimap_array_container(np.less, x, y)

@override

def less_equal(self, x, y):

return rec_multimap_array_container(np.less_equal, x, y)

@override

def logical_or(self,

x: ArrayOrContainerOrScalar,

y: ArrayOrContainerOrScalar

) -> Array:

return rec_multimap_array_container(np.logical_or, x, y)

@override

def logical_and(self,

x: ArrayOrContainerOrScalar,

y: ArrayOrContainerOrScalar

) -> Array:

return rec_multimap_array_container(np.logical_and, x, y)

@override

def logical_not(self,

x: ArrayOrContainerOrScalar

) -> ArrayOrContainerOrScalar:

return rec_map_container(

cast("Callable[[ArrayOrScalar], ArrayOrScalar]", np.logical_not), x)

# }}}

@override

def ravel(self, a: ArrayOrContainerOrScalar, order: OrderCF = "C"):

def inner_ravel(ary: ArrayOrScalar) -> ArrayOrScalar:

if is_scalar_like(ary):

return ary

else:

assert isinstance(ary, np.ndarray)

return cast("Array", cast("object", np.ravel(ary, order)))

return rec_map_container(inner_ravel, a)

def vdot(self, x, y):

return rec_multimap_reduce_array_container(sum, np.vdot, x, y)

def any(self, a):

return rec_map_reduce_array_container(partial(reduce, np.logical_or),

lambda subary: np.any(subary), a)

def all(self, a):

return rec_map_reduce_array_container(partial(reduce, np.logical_and),

lambda subary: np.all(subary), a)

@override

def array_equal(self,

a: ArrayOrContainerOrScalar,

b: ArrayOrContainerOrScalar

) -> Array:

false_ary = np.array(False)

true_ary = np.array(True)

if type(a) is not type(b):

return false_ary

try:

serialized_x = serialize_container(a)

serialized_y = serialize_container(b)

except NotAnArrayContainerError:

assert isinstance(a, np.ndarray)

assert isinstance(b, np.ndarray)

return np.array(np.array_equal(a, b))

else:

if len(serialized_x) != len(serialized_y):

return false_ary

return np.logical_and.reduce(

[(true_ary if kx_i == ky_i else false_ary)

and cast("np.ndarray", self.array_equal(x_i, y_i))

for (kx_i, x_i), (ky_i, y_i)

in zip(serialized_x, serialized_y, strict=True)],

initial=true_ary)

@override

def arange(self, *args, **kwargs):

return np.arange(*args, **kwargs)

@override

def linspace(self, *args, **kwargs):

return np.linspace(*args, **kwargs)

# vim: fdm=marker