From 97aaa50f3a23e4b5569dd11edb99003cd69716d0 Mon Sep 17 00:00:00 2001
From: chenyu <chenyu@fastmail.com>
Date: Wed, 11 Dec 2024 22:59:35 -0500
Subject: [PATCH] remove duplicated UOp in Tensor init types [pr] (#8177)

and a small comment
---
 tinygrad/engine/realize.py | 3 +--
 tinygrad/tensor.py         | 4 ++--
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/tinygrad/engine/realize.py b/tinygrad/engine/realize.py
index fa7d02be851d5..8ba3451467ad6 100644
--- a/tinygrad/engine/realize.py
+++ b/tinygrad/engine/realize.py
@@ -12,8 +12,7 @@
 
 logkerns, logkerns_level = open(getenv("LOGKERNS", ""), "a") if getenv("LOGKERNS", "") else None, getenv("LOGKERNS_LEVEL", 1)
 def get_kernel(renderer:Renderer, ast:UOp) -> Kernel:
-  if DEBUG >= 5:
-    print(ast)
+  if DEBUG >= 5: print(ast)
   k = Kernel(ast, opts=renderer).required_optimizations()
   if not NOOPT:
     if not (used_tensor_cores:=k.apply_tensor_cores(getenv("TC", 1))): k.hand_coded_optimizations()
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index ba5eef5e2fb99..22f1fa68b7f14 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -122,7 +122,7 @@ class Tensor(SimpleMathTrait):
   training: ClassVar[bool] = False
   no_grad: ClassVar[bool] = False
 
-  def __init__(self, data:Union[None, ConstType, UOp, bytes, List, Tuple, UOp, MultiLazyBuffer, 'np.ndarray', pathlib.Path],  # type: ignore [name-defined] # noqa: F821
+  def __init__(self, data:Union[None, ConstType, bytes, List, Tuple, UOp, MultiLazyBuffer, 'np.ndarray', pathlib.Path],  # type: ignore [name-defined] # noqa: F821
                device:Optional[Union[str, tuple, list]]=None, dtype:Optional[DTypeLike]=None, requires_grad:Optional[bool]=None):
     if dtype is not None: dtype = to_dtype(dtype)
     assert dtype is None or isinstance(dtype, DType), f"invalid dtype {dtype}"
@@ -150,7 +150,7 @@ def __init__(self, data:Union[None, ConstType, UOp, bytes, List, Tuple, UOp, Mul
     elif isinstance(data, (list, tuple)):
       if dtype is None:
         if (d := fully_flatten(data)) and all(isinstance(s, bool) for s in d): dtype = dtypes.bool
-        else: dtype = dtypes.default_int if d and all_int(d) else dtypes.default_float
+        else: dtype = dtypes.default_int if d and all_int(d) else dtypes.default_float  # NOTE: this works because all_int([True, False]) is True
       if dtype == dtypes.bfloat16: data = Tensor(_frompy(data, dtypes.float32), device=device).cast(dtypes.bfloat16).lazydata
       else: data = _frompy(data, dtype)
     elif str(type(data)) == "<class 'numpy.ndarray'>":