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fixed

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mcDandy
2026-02-03 14:11:47 +01:00
parent 38e795eb35
commit f5e8c3ba8c
1 changed files with 19 additions and 19 deletions
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more_math/Parser/UnifiedMathVisitor.py
+19 -19
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@@ -179,7 +179,7 @@ class UnifiedMathVisitor(MathExprVisitor):
if var_name in self.variables:
res = self.variables[var_name]
return res
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: Variable '{var_name}' not found")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: Variable '{var_name}' not found")
def visitListExp(self, ctx):
res = []
@@ -249,7 +249,7 @@ class UnifiedMathVisitor(MathExprVisitor):
else:
return val[int(idx + len(val) if idx < 0 else idx)]
else:
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: Indexing only supported on tensors and lists.")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: Indexing only supported on tensors and lists.")
def visitToAtom(self, ctx):
return (yield ctx.atom())
@@ -598,7 +598,7 @@ class UnifiedMathVisitor(MathExprVisitor):
pos_list = yield ctx.expr(1)
if not self._is_tensor(var):
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: get_value expects a tensor as first argument")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: get_value expects a tensor as first argument")
if not self._is_list(pos_list) and not self._is_tensor(pos_list):
pos_list = [pos_list]
@@ -607,7 +607,7 @@ class UnifiedMathVisitor(MathExprVisitor):
pos_list = pos_list.tolist()
if len(pos_list) != var.ndim:
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: Position list length {len(pos_list)} does not match tensor dimensions {var.ndim}")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: Position list length {len(pos_list)} does not match tensor dimensions {var.ndim}")
shape = var.shape
c_strides = [1] * var.ndim
@@ -640,7 +640,7 @@ class UnifiedMathVisitor(MathExprVisitor):
# Check bounds
max_idx = tsr.size(0)
if torch.any(indices < 0) or torch.any(indices >= max_idx):
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: Batch index out of bounds (0-{max_idx-1})")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: Batch index out of bounds (0-{max_idx-1})")
return tsr[indices]
@@ -731,8 +731,8 @@ class UnifiedMathVisitor(MathExprVisitor):
# Let's enforce or just take first N?
# For robustness, we'll assume user provides correct dims or we raise error?
# Given "lists described in get_value" implies stricter checking.
if len(p_l) != inp.ndim: raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: crop: position dim {len(p_l)} != input dim {inp.ndim}")
if len(s_l) != inp.ndim: raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: crop: size dim {len(s_l)} != input dim {inp.ndim}")
if len(p_l) != inp.ndim: raise ValueError(f"{ctx.start.line}:{ctx.start.column}: crop: position dim {len(p_l)} != input dim {inp.ndim}")
if len(s_l) != inp.ndim: raise ValueError(f"{ctx.start.line}:{ctx.start.column}: crop: size dim {len(s_l)} != input dim {inp.ndim}")
out_tensor = torch.zeros(tuple(s_l), dtype=inp.dtype, device=inp.device)
@@ -1169,7 +1169,7 @@ class UnifiedMathVisitor(MathExprVisitor):
y_flat = y.flatten()
if x_flat.numel() != y_flat.numel():
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: x and y must have the same number of elements")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: x and y must have the same number of elements")
n = x_flat.numel()
if n < 2:
@@ -1191,7 +1191,7 @@ class UnifiedMathVisitor(MathExprVisitor):
if num_coords == 0:
return tensor
if num_coords > 3:
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: map() supports max 3 mapping functions.")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: map() supports max 3 mapping functions.")
spatial_in_shape = tensor.shape[-num_coords:]
leading_shape = tensor.shape[:-num_coords]
@@ -1239,13 +1239,13 @@ class UnifiedMathVisitor(MathExprVisitor):
tensor = self._promote_to_tensor(input_raw)
num_args = len(ctx.expr())
if num_args < 3:
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: conv() requires at least 3 arguments")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: conv() requires at least 3 arguments")
kernel_arg_idx = num_args - 1
spatial_dims_count = num_args - 2
if spatial_dims_count not in [1, 2, 3]:
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: conv() supports 1D, 2D, or 3D. Found {spatial_dims_count}")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: conv() supports 1D, 2D, or 3D. Found {spatial_dims_count}")
kernel_sizes = []
for i in range(1, 1 + spatial_dims_count):
@@ -1391,7 +1391,7 @@ class UnifiedMathVisitor(MathExprVisitor):
# Must have at least Channel + Spatial dims
min_dims = spatial_dims_count + 1
if tensor.ndim < min_dims:
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: convolution() input requires at least Channels + Spatial dimensions. Got shape {tensor.shape} for {spatial_dims_count}D conv.")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: convolution() input requires at least Channels + Spatial dimensions. Got shape {tensor.shape} for {spatial_dims_count}D conv.")
spatial_shape = tensor.shape[-spatial_dims_count:]
in_channels = tensor.shape[-(spatial_dims_count + 1)]
@@ -1594,7 +1594,7 @@ class UnifiedMathVisitor(MathExprVisitor):
indices.append((yield expr_list[i]))
if var_name not in self.variables:
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: Variable '{var_name}' not found for indexed assignment.")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: Variable '{var_name}' not found for indexed assignment.")
target = self.variables[var_name]
@@ -1624,7 +1624,7 @@ class UnifiedMathVisitor(MathExprVisitor):
target[idx_tuple] = val_t
return assigned_val
except Exception as e:
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: Indexed assignment to '{var_name}' failed: {str(e)}")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: Indexed assignment to '{var_name}' failed: {str(e)}")
elif self._is_list(target):
# Recurse through nested lists if multiple indices provided
@@ -1635,7 +1635,7 @@ class UnifiedMathVisitor(MathExprVisitor):
curr[last_idx + len(curr) if last_idx < 0 else last_idx] = assigned_val
return assigned_val
else:
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: Indexed assignment not supported for {type(target)}")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: Indexed assignment not supported for {type(target)}")
def visitFunctionDef(self, ctx):
@@ -1665,7 +1665,7 @@ class UnifiedMathVisitor(MathExprVisitor):
args.append((yield e))
if len(args) != len(params):
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: Function '{func_name}' expects {len(params)} arguments, got {len(args)}"
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: Function '{func_name}' expects {len(params)} arguments, got {len(args)}"
)
# Create a new scope for function execution
@@ -1694,7 +1694,7 @@ class UnifiedMathVisitor(MathExprVisitor):
self.variables = self._scope_stack.pop()
self.depth -= 1
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: Unknown function: {func_name}")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: Unknown function: {func_name}")
def visitNoiseFunc(self,ctx):
seed_val = yield ctx.expr()
@@ -1957,7 +1957,7 @@ class UnifiedMathVisitor(MathExprVisitor):
self._ensure_dict_storage()
slot = int((yield ctx.expr()))
if slot not in self._state_storage or not self._state_storage[slot]:
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: Pop from empty slot: {slot}")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: Pop from empty slot: {slot}")
return self._state_storage[slot].pop()
def visitClearFunc(self, ctx):
@@ -1977,7 +1977,7 @@ class UnifiedMathVisitor(MathExprVisitor):
self._ensure_dict_storage()
slot = int((yield ctx.expr()))
if slot not in self._state_storage:
raise ValueError(f"{ctx.VARIABLE().getPayload().line}:{ctx.VARIABLE().getPayload().column}: Get from empty slot: {slot}")
raise ValueError(f"{ctx.start.line}:{ctx.start.column}: Get from empty slot: {slot}")
storage_list = self._state_storage[slot]
return storage_list[-1] if storage_list else None