missed a few

__init__.py

@@ -1 +1 @@
-from .more_math.nodes import comfy_entrypoint
+from .more_math.nodes import comfy_entrypoint as comfy_entrypoint

tests/reproduce_conv_issues.py

@@ -11,8 +11,6 @@ if _comfy_root not in sys.path:
     sys.path.insert(0, _comfy_root)
 
 import torch
-import pytest
-from more_math.Parser.UnifiedMathVisitor import UnifiedMathVisitor
 
 from more_math.LatentMathNode import LatentMathNode
 
@@ -139,7 +137,7 @@ def test_conv_audio():
         print(f"Audio Result Shape: {res_tensor.shape}")
 
         if res_tensor.shape != shape:
-            print(f"Likely interpreted as Channels Last [B, L, C] where C is small? No.")
+            print("Likely interpreted as Channels Last [B, L, C] where C is small? No.")
             # If interpreted as Channels last [..., C].
             # [1, 2, 100]. Spatial=[2]. Channel=100.
             # Output [1, 2, 100] (but confusing channels).
@@ -262,7 +260,7 @@ if __name__ == "__main__":
         test_conv_complex_padding()
         test_conv_3d_asymmetric()
         print("All Conv tests passed!")
-    except Exception as e:
+    except Exception:
         import traceback
 
         traceback.print_exc()

tests/test_grammar_coverage.py

@@ -122,7 +122,7 @@ def test_all_functions():
 
     # FFT/IFFT
     # We need a shape for FFT usually
-    res_fft = eval_tensor_expr("fft(ta)", variables, (3,))
+    eval_tensor_expr("fft(ta)", variables, (3,))
     res_ifft = eval_tensor_expr("ifft(fft(ta))", variables, (3,))
     assert torch.allclose(res_ifft, tensor_a, atol=1e-4)
 

tests/test_model_math.py

@@ -112,7 +112,6 @@ def test_model_math_device_mismatch():
     # Since we might only have CPU, we can't fully reproduce 'cpu vs cuda' crash without cuda.
     # But we can verify that the scalar created by visitor.visitNumberExp has the same device as 'a'.
 
-    from more_math.Parser.TensorEvalVisitor import TensorEvalVisitor
     from antlr4 import InputStream, CommonTokenStream
     from more_math.Parser.MathExprLexer import MathExprLexer
     from more_math.Parser.MathExprParser import MathExprParser
@@ -125,7 +124,7 @@ def test_model_math_device_mismatch():
     # Let's try to pass a dummy device string if create_tensor allows, or just check the code path.
     # Better: Inspect the created tensor from visitor.
 
-    tsr = torch.zeros((1,))
+    torch.zeros((1,))
     # We interpret "device mismatch" as: created scalars didn't pick up the device of 'tsr'.
     # We can force 'tsr' to be on a specific device if available, but likely only 'cpu' is available.
     # Use a mock object for 'a' that claims to be on 'cuda:0', even if it isn't real tensor?
@@ -139,9 +138,9 @@ def test_model_math_device_mismatch():
     lexer = MathExprLexer(input_stream)
     stream = CommonTokenStream(lexer)
     parser = MathExprParser(stream)
-    tree = parser.expr()
+    parser.expr()
 
-    variables = {"a": torch.zeros(1)}
+    {"a": torch.zeros(1)}
     # We want to ensure that if we had a non-cpu device, it would use it.
     # Since we can't really test this without a GPU, we will write the fix and verify it analytically
     # or use a mock that wraps a tensor but intercepts .device?

tests/test_more_math.py

@@ -405,7 +405,6 @@ def test_pow_log_functions():
 
 
 def test_min_max_functions():
-    import sys
 
     node = FloatMathNode()
     print("Testing tmin...", flush=True)
@@ -466,7 +465,7 @@ if __name__ == "__main__":
         test_basic_utilities()
         test_advanced_activations()
         print("All tests passed!")
-    except Exception as e:
+    except Exception:
         import traceback
 
         traceback.print_exc()

tests/test_unified_math.py

@@ -2,7 +2,6 @@ import sys
 import os
 import torch
 import math
-import pytest
 
 # Ensure we can import the module
 _here = os.path.abspath(os.path.dirname(__file__))
@@ -187,7 +186,7 @@ if __name__ == "__main__":
         test_kernel_coords()
         test_bool_ops()
         print("All UnifiedMathVisitor tests passed!")
-    except Exception as e:
+    except Exception:
         import traceback
 
         traceback.print_exc()