local and remote branch differences resolved.

Author: ColtAllen

pytensor/scalar/math.py.rej

@@ -1,157 +1,10 @@
 diff a/pytensor/scalar/math.py b/pytensor/scalar/math.py	(rejected hunks)
-@@ -1493,3 +1493,155 @@ def c_code(self, *args, **kwargs):
+@@ -1644,8 +1644,4 @@ def c_code(self, *args, **kwargs):
+         raise NotImplementedError()
 
 
- betainc_der = BetaIncDer(upgrade_to_float_no_complex, name="betainc_der")
-+
-+
-+class Hyp2F1(ScalarOp):
-+    """
-+    Gaussian hypergeometric function ``2F1(a, b; c; z)``.
-+
-+    """
-+
-+    nin = 4
-+    nfunc_spec = ("scipy.special.hyp2f1", 4, 1)
-+
-+    @staticmethod
-+    def st_impl(a, b, c, z):
-+
-+        if abs(z) >= 1:
-+            raise NotImplementedError("hyp2f1 only supported for z < 1.")
-+        else:
-+            return scipy.special.hyp2f1(a, b, c, z)
-+
-+    def impl(self, a, b, c, z):
-+        return Hyp2F1.st_impl(a, b, c, z)
-+
-+    def grad(self, inputs, grads):
-+        a, b, c, z = inputs
-+        (gz,) = grads
-+        return [
-+            gz * hyp2f1_der(a, b, c, z, 0),
-+            gz * hyp2f1_der(a, b, c, z, 1),
-+            gz * hyp2f1_der(a, b, c, z, 2),
-+            gz * hyp2f1_der(a, b, c, z, 3),
-+        ]
-+
-+    def c_code(self, *args, **kwargs):
-+        raise NotImplementedError()
-+
-+
-+hyp2f1 = Hyp2F1(upgrade_to_float, name="hyp2f1")
-+
-+
-+class Hyp2F1Der(ScalarOp):
-+    """
-+    Derivatives of the Gaussian hypergeometric function ``2F1(a, b; c; z)``.
-+
-+    """
-+
-+    nin = 5
-+
-+    def impl(self, a, b, c, z, wrt):
-+        def _infinisum(f):
-+            """
-+            Utility function for infinite summations.
-+            """
-+
-+            n, res = 0, f(0)
-+            while True:
-+                term = f(n + 1)
-+                if RuntimeWarning:
-+                    break
-+                if (res + term) - res == 0:
-+                    break
-+                n, res = n + 1, res + term
-+            return res
-+
-+        def _hyp2f1_da(a, b, c, z):
-+            """
-+            Derivative of hyp2f1 wrt a
-+
-+            """
-+
-+            if abs(z) >= 1:
-+                raise NotImplementedError("Gradient not supported for |z| >= 1")
-+
-+            else:
-+                term1 = _infinisum(
-+                    lambda k: (
-+                        (gamma(a + k) / gamma(a))
-+                        * (gamma(b + k) / gamma(b))
-+                        * psi(a + k)
-+                        * (z**k)
-+                    )
-+                    / (gamma(c + k) / gamma(c))
-+                    * gamma(k + 1)
-+                )
-+                term2 = psi(a) * hyp2f1(a, b, c, z)
-+
-+                return term1 - term2
-+
-+        def _hyp2f1_db(a, b, c, z):
-+            """
-+            Derivative of hyp2f1 wrt b
-+            """
-+
-+            if abs(z) >= 1:
-+                raise NotImplementedError("Gradient not supported for |z| >= 1")
-+
-+            else:
-+                term1 = _infinisum(
-+                    lambda k: (
-+                        (gamma(a + k) / gamma(a))
-+                        * (gamma(b + k) / gamma(b))
-+                        * psi(b + k)
-+                        * (z**k)
-+                    )
-+                    / (gamma(c + k) / gamma(c))
-+                    * gamma(k + 1)
-+                )
-+                term2 = psi(b) * hyp2f1(a, b, c, z)
-+
-+                return term1 - term2
-+
-+        def _hyp2f1_dc(a, b, c, z):
-+            """
-+            Derivative of hyp2f1 wrt c
-+            """
-+            if abs(z) >= 1:
-+                raise NotImplementedError("Gradient not supported for |z| >= 1")
-+
-+            else:
-+                term1 = psi(c) * hyp2f1(a, b, c, z)
-+                term2 = _infinisum(
-+                    lambda k: (
-+                        (gamma(a + k) / gamma(a))
-+                        * (gamma(b + k) / gamma(b))
-+                        * psi(c + k)
-+                        * (z**k)
-+                    )
-+                    / (gamma(c + k) / gamma(c))
-+                    * gamma(k + 1)
-+                )
-+                return term1 - term2
-+
-+        def _hyp2f1_dz(a, b, c, z):
-+            """
-+            Derivative of hyp2f1 wrt z
-+            """
-+
-+            return ((a * b) / c) * hyp2f1(a + 1, b + 1, c + 1, z)
-+
-+        if wrt == 0:
-+            return _hyp2f1_da(a, b, c, z)
-+        elif wrt == 1:
-+            return _hyp2f1_db(a, b, c, z)
-+        elif wrt == 2:
-+            return _hyp2f1_dc(a, b, c, z)
-+        elif wrt == 3:
-+            return _hyp2f1_dz(a, b, c, z)
-+
-+    def c_code(self, *args, **kwargs):
-+        raise NotImplementedError()
-+
-+
-+hyp2f1_der = Hyp2F1Der(upgrade_to_float, name="hyp2f1_der")
+-<<<<<<< HEAD
+ hyp2f1_der = Hyp2F1Der(upgrade_to_float, name="hyp2f1_der")
+-=======
+-hyp2f1_der = Hyp2F1Der(upgrade_to_float, name="hyp2f1_der")
+->>>>>>> db46ad7d8e0497a3e7be10c7a7551c332a6d877e

pytensor/tensor/special.py.rej

@@ -1,14 +1,51 @@
 diff a/pytensor/tensor/special.py b/pytensor/tensor/special.py	(rejected hunks)
-@@ -7,7 +8,11 @@
+@@ -8,19 +8,11 @@
  from pytensor.graph.basic import Apply
  from pytensor.link.c.op import COp
  from pytensor.tensor.basic import as_tensor_variable
--from pytensor.tensor.math import neg, sum
-+from pytensor.tensor.math import gamma, neg, sum
-+
-+
-+if TYPE_CHECKING:
-+    pass
+-<<<<<<< HEAD
+-from pytensor.tensor.math import neg, sum, gamma
+-
+-
+-if TYPE_CHECKING:
+-    from pytensor.tensor import TensorLike, TensorVariable
+-=======
+ from pytensor.tensor.math import gamma, neg, sum
+ 
+ 
+ if TYPE_CHECKING:
+     pass
+->>>>>>> db46ad7d8e0497a3e7be10c7a7551c332a6d877e
 
 
  class SoftmaxGrad(COp):
+@@ -781,21 +773,6 @@ def log_softmax(c, axis=UNSET_AXIS):
+     return LogSoftmax(axis=axis)(c)
+ 
+ 
+-<<<<<<< HEAD
+-def poch(z: "TensorLike", m: "TensorLike") -> "TensorVariable":
+-    """
+-    Pochhammer symbol (rising factorial) function.
+-
+-    """
+-    return gamma(z + m) / gamma(z)
+-
+-
+-def factorial(n: "TensorLike") -> "TensorVariable":
+-    """
+-    Factorial function of a scalar or array of numbers.
+-
+-    """
+-=======
+ def poch(z, m):
+     """Compute the Pochhammer/rising factorial."""
+     return gamma(z + m) / gamma(z)
+@@ -803,7 +780,6 @@ def poch(z, m):
+ 
+ def factorial(n):
+     """Compute the factorial."""
+->>>>>>> db46ad7d8e0497a3e7be10c7a7551c332a6d877e
+     return gamma(n + 1)
+ 
+ 

tests/tensor/test_special.py.rej

@@ -1,28 +1,49 @@
 diff a/tests/tensor/test_special.py b/tests/tensor/test_special.py	(rejected hunks)
-@@ -1,13 +1,25 @@
- import numpy as np
- import pytest
-+from scipy.special import factorial as scipy_factorial
- from scipy.special import log_softmax as scipy_log_softmax
-+from scipy.special import poch as scipy_poch
- from scipy.special import softmax as scipy_softmax
- 
+@@ -8,9 +8,6 @@
  from pytensor.compile.function import function
  from pytensor.configdefaults import config
--from pytensor.tensor.special import LogSoftmax, Softmax, SoftmaxGrad, log_softmax, softmax
-+from pytensor.tensor import scalar, scalars
-+from pytensor.tensor.special import (
-+    LogSoftmax,
-+    Softmax,
-+    SoftmaxGrad,
-+    factorial,
-+    log_softmax,
-+    poch,
-+    softmax,
-+)
+ from pytensor.tensor import scalar, scalars
+-<<<<<<< HEAD
+-from pytensor.tensor.special import LogSoftmax, Softmax, SoftmaxGrad, log_softmax, softmax, poch, factorial
+-=======
+ from pytensor.tensor.special import (
+     LogSoftmax,
+     Softmax,
+@@ -20,7 +17,6 @@
+     poch,
+     softmax,
+ )
+->>>>>>> db46ad7d8e0497a3e7be10c7a7551c332a6d877e
  from pytensor.tensor.type import matrix, tensor3, tensor4, vector
+ from tests.tensor.utils import random_ranged
  from tests import unittest_tools as utt
-+from tests.tensor.utils import random_ranged
+@@ -153,13 +149,7 @@ def test_valid_axis(self):
+             SoftmaxGrad(-4)(*x)
+ 
+ 
+-<<<<<<< HEAD
+[email protected](
+-    "z, m", [random_ranged(0, 5, (2,)), random_ranged(0, 5, (2,))]
+-)
+-=======
+ @pytest.mark.parametrize("z, m", [random_ranged(0, 5, (2,)), random_ranged(0, 5, (2,))])
+->>>>>>> db46ad7d8e0497a3e7be10c7a7551c332a6d877e
+ def test_poch(z, m):
+ 
+     _z, _m = scalars("z", "m")
+@@ -179,15 +169,7 @@ def test_factorial(n):
+ 
+     actual_fn = function([_n], factorial(_n))
+     actual = actual_fn(n)
+-<<<<<<< HEAD
+-    
+-    expected = scipy_factorial(n)
+-
+-    assert np.allclose(actual, expected)
+-    
+-=======
 
+     expected = scipy_factorial(n)
 
- class TestSoftmax(utt.InferShapeTester):
+     assert np.allclose(actual, expected)
+->>>>>>> db46ad7d8e0497a3e7be10c7a7551c332a6d877e