From f0d02dcb8fc150735772b52d98fdeb77faac8a77 Mon Sep 17 00:00:00 2001
From: Sam Armstrong <88863522+Sam-Armstrong@users.noreply.github.com>
Date: Wed, 4 Sep 2024 21:10:02 +0100
Subject: [PATCH] fix: torch frontend bernoulli functions and methods (#28815)

---
 ivy/functional/frontends/torch/random_sampling.py | 4 ++--
 ivy/functional/frontends/torch/tensor.py          | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/ivy/functional/frontends/torch/random_sampling.py b/ivy/functional/frontends/torch/random_sampling.py
index 105e983209e64..bc6d381665985 100644
--- a/ivy/functional/frontends/torch/random_sampling.py
+++ b/ivy/functional/frontends/torch/random_sampling.py
@@ -13,9 +13,9 @@
     "torch",
 )
 @to_ivy_arrays_and_back
-def bernoulli(input, p, *, generator=None, out=None):
+def bernoulli(input, *, generator=None, out=None):
     seed = generator.initial_seed() if generator is not None else None
-    return ivy.bernoulli(p, logits=input, seed=seed, out=out)
+    return ivy.bernoulli(input, seed=seed, out=out)
 
 
 @to_ivy_arrays_and_back
diff --git a/ivy/functional/frontends/torch/tensor.py b/ivy/functional/frontends/torch/tensor.py
index 01d465e565549..2bb2e106fa6b7 100644
--- a/ivy/functional/frontends/torch/tensor.py
+++ b/ivy/functional/frontends/torch/tensor.py
@@ -1249,14 +1249,14 @@ def dot(self, tensor):
         return torch_frontend.dot(self, tensor)
 
     @with_supported_dtypes({"2.2 and below": ("float32", "float64")}, "torch")
-    def bernoulli(self, p, *, generator=None, out=None):
+    def bernoulli(self, *, generator=None, out=None):
         return torch_frontend.bernoulli(
-            self._ivy_array, p, generator=generator, out=out
+            self._ivy_array, generator=generator, out=out
         )
 
     @with_supported_dtypes({"2.2 and below": ("float32", "float64")}, "torch")
     def bernoulli_(self, p, *, generator=None, out=None):
-        self.ivy_array = self.bernoulli(p, generator=generator, out=out).ivy_array
+        self.ivy_array = torch_frontend.bernoulli(torch_frontend.full(self.shape, p, dtype=torch_frontend.float64), generator=generator, out=out).ivy_array
         return self
 
     def numel(self):