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https://github.com/comfyanonymous/ComfyUI.git
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7 Commits
curve-node
...
feature/nu
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2a6e3dc7a8 | ||
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dae107e430 | ||
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82cf5d88c2 | ||
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b941913f1d | ||
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cad24ce262 | ||
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68d542cc06 | ||
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735a0465e5 |
@@ -136,16 +136,7 @@ class ResBlock(nn.Module):
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ops.Linear(c_hidden, c),
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)
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self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True)
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# Init weights
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def _basic_init(module):
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if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
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torch.nn.init.xavier_uniform_(module.weight)
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if module.bias is not None:
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nn.init.constant_(module.bias, 0)
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self.apply(_basic_init)
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self.gammas = nn.Parameter(torch.zeros(6), requires_grad=False)
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def _norm(self, x, norm):
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return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
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@@ -1003,7 +1003,7 @@ def text_encoder_offload_device():
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def text_encoder_device():
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if args.gpu_only:
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return get_torch_device()
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elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM) or comfy.memory_management.aimdo_enabled:
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elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM, VRAMState.SHARED) or comfy.memory_management.aimdo_enabled:
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if should_use_fp16(prioritize_performance=False):
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return get_torch_device()
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else:
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@@ -455,7 +455,7 @@ class VAE:
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self.output_channels = 3
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self.pad_channel_value = None
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self.process_input = lambda image: image * 2.0 - 1.0
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self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
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self.process_output = lambda image: image.add_(1.0).div_(2.0).clamp_(0.0, 1.0)
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self.working_dtypes = [torch.bfloat16, torch.float32]
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self.disable_offload = False
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self.not_video = False
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@@ -952,8 +952,8 @@ class VAE:
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batch_number = max(1, batch_number)
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for x in range(0, samples_in.shape[0], batch_number):
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samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
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out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).to(dtype=self.vae_output_dtype()))
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samples = samples_in[x:x + batch_number].to(device=self.device, dtype=self.vae_dtype)
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out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True))
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if pixel_samples is None:
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pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
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pixel_samples[x:x+batch_number] = out
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79
comfy_extras/nodes_number_convert.py
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79
comfy_extras/nodes_number_convert.py
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@@ -0,0 +1,79 @@
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"""Number Convert node for unified numeric type conversion.
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Provides a single node that converts INT, FLOAT, STRING, and BOOL
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inputs into FLOAT and INT outputs.
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"""
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from __future__ import annotations
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import math
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, io
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class NumberConvertNode(io.ComfyNode):
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"""Converts various types to numeric FLOAT and INT outputs."""
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="ComfyNumberConvert",
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display_name="Number Convert",
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category="math",
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search_aliases=[
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"int to float", "float to int", "number convert",
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"int2float", "float2int", "cast", "parse number",
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"string to number", "bool to int",
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],
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inputs=[
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io.MultiType.Input(
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"value",
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[io.Int, io.Float, io.String, io.Boolean],
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display_name="value",
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),
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],
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outputs=[
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io.Float.Output(display_name="FLOAT"),
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io.Int.Output(display_name="INT"),
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],
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)
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@classmethod
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def execute(cls, value) -> io.NodeOutput:
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if isinstance(value, bool):
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float_val = 1.0 if value else 0.0
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elif isinstance(value, (int, float)):
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float_val = float(value)
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elif isinstance(value, str):
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text = value.strip()
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if not text:
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raise ValueError("Cannot convert empty string to number.")
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try:
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float_val = float(text)
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except ValueError:
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raise ValueError(
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f"Cannot convert string to number: {value!r}"
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) from None
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else:
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raise TypeError(
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f"Unsupported input type: {type(value).__name__}"
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)
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if not math.isfinite(float_val):
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raise ValueError(
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f"Cannot convert non-finite value to number: {float_val}"
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)
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return io.NodeOutput(float_val, int(float_val))
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class NumberConvertExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [NumberConvertNode]
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async def comfy_entrypoint() -> NumberConvertExtension:
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return NumberConvertExtension()
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1
nodes.py
1
nodes.py
@@ -2452,6 +2452,7 @@ async def init_builtin_extra_nodes():
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"nodes_nag.py",
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"nodes_sdpose.py",
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"nodes_math.py",
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"nodes_number_convert.py",
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"nodes_painter.py",
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]
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123
tests-unit/comfy_extras_test/nodes_number_convert_test.py
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123
tests-unit/comfy_extras_test/nodes_number_convert_test.py
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@@ -0,0 +1,123 @@
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import pytest
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from unittest.mock import patch, MagicMock
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mock_nodes = MagicMock()
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mock_nodes.MAX_RESOLUTION = 16384
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mock_server = MagicMock()
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with patch.dict("sys.modules", {"nodes": mock_nodes, "server": mock_server}):
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from comfy_extras.nodes_number_convert import NumberConvertNode
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class TestNumberConvertExecute:
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@staticmethod
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def _exec(value) -> object:
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return NumberConvertNode.execute(value)
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# --- INT input ---
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def test_int_input(self):
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result = self._exec(42)
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assert result[0] == 42.0
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assert result[1] == 42
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def test_int_zero(self):
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result = self._exec(0)
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assert result[0] == 0.0
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assert result[1] == 0
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def test_int_negative(self):
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result = self._exec(-7)
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assert result[0] == -7.0
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assert result[1] == -7
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# --- FLOAT input ---
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def test_float_input(self):
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result = self._exec(3.14)
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assert result[0] == 3.14
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assert result[1] == 3
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def test_float_truncation_toward_zero(self):
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result = self._exec(-2.9)
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assert result[0] == -2.9
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assert result[1] == -2 # int() truncates toward zero, not floor
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def test_float_output_type(self):
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result = self._exec(5)
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assert isinstance(result[0], float)
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def test_int_output_type(self):
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result = self._exec(5.7)
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assert isinstance(result[1], int)
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# --- BOOL input ---
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def test_bool_true(self):
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result = self._exec(True)
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assert result[0] == 1.0
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assert result[1] == 1
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def test_bool_false(self):
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result = self._exec(False)
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assert result[0] == 0.0
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assert result[1] == 0
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# --- STRING input ---
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def test_string_integer(self):
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result = self._exec("42")
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assert result[0] == 42.0
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assert result[1] == 42
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def test_string_float(self):
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result = self._exec("3.14")
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assert result[0] == 3.14
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assert result[1] == 3
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def test_string_negative(self):
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result = self._exec("-5.5")
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assert result[0] == -5.5
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assert result[1] == -5
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def test_string_with_whitespace(self):
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result = self._exec(" 7.0 ")
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assert result[0] == 7.0
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assert result[1] == 7
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def test_string_scientific_notation(self):
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result = self._exec("1e3")
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assert result[0] == 1000.0
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assert result[1] == 1000
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# --- STRING error paths ---
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def test_empty_string_raises(self):
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with pytest.raises(ValueError, match="Cannot convert empty string"):
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self._exec("")
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def test_whitespace_only_string_raises(self):
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with pytest.raises(ValueError, match="Cannot convert empty string"):
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self._exec(" ")
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def test_non_numeric_string_raises(self):
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with pytest.raises(ValueError, match="Cannot convert string to number"):
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self._exec("abc")
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def test_string_inf_raises(self):
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with pytest.raises(ValueError, match="non-finite"):
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self._exec("inf")
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def test_string_nan_raises(self):
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with pytest.raises(ValueError, match="non-finite"):
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self._exec("nan")
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def test_string_negative_inf_raises(self):
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with pytest.raises(ValueError, match="non-finite"):
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self._exec("-inf")
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# --- Unsupported type ---
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def test_unsupported_type_raises(self):
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with pytest.raises(TypeError, match="Unsupported input type"):
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self._exec([1, 2, 3])
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