chore: import upstream snapshot with attribution
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This commit is contained in:
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# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
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from typing import Literal
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import numpy as np
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from pydantic import ValidationInfo, field_validator
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from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
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from invokeai.app.invocations.fields import FieldDescriptions, InputField
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from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
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from invokeai.app.services.shared.invocation_context import InvocationContext
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@invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.1")
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class AddInvocation(BaseInvocation):
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"""Adds two numbers"""
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a: int = InputField(default=0, description=FieldDescriptions.num_1)
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b: int = InputField(default=0, description=FieldDescriptions.num_2)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
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return IntegerOutput(value=self.a + self.b)
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@invocation("sub", title="Subtract Integers", tags=["math", "subtract"], category="math", version="1.0.1")
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class SubtractInvocation(BaseInvocation):
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"""Subtracts two numbers"""
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a: int = InputField(default=0, description=FieldDescriptions.num_1)
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b: int = InputField(default=0, description=FieldDescriptions.num_2)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
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return IntegerOutput(value=self.a - self.b)
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@invocation("mul", title="Multiply Integers", tags=["math", "multiply"], category="math", version="1.0.1")
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class MultiplyInvocation(BaseInvocation):
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"""Multiplies two numbers"""
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a: int = InputField(default=0, description=FieldDescriptions.num_1)
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b: int = InputField(default=0, description=FieldDescriptions.num_2)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
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return IntegerOutput(value=self.a * self.b)
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@invocation("div", title="Divide Integers", tags=["math", "divide"], category="math", version="1.0.1")
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class DivideInvocation(BaseInvocation):
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"""Divides two numbers"""
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a: int = InputField(default=0, description=FieldDescriptions.num_1)
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b: int = InputField(default=0, description=FieldDescriptions.num_2)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
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return IntegerOutput(value=int(self.a / self.b))
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@invocation(
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"rand_int",
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title="Random Integer",
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tags=["math", "random"],
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category="math",
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version="1.0.1",
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use_cache=False,
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)
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class RandomIntInvocation(BaseInvocation):
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"""Outputs a single random integer."""
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low: int = InputField(default=0, description=FieldDescriptions.inclusive_low)
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high: int = InputField(default=np.iinfo(np.int32).max, description=FieldDescriptions.exclusive_high)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
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return IntegerOutput(value=np.random.randint(self.low, self.high))
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@invocation(
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"rand_float",
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title="Random Float",
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tags=["math", "float", "random"],
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category="math",
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version="1.0.1",
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use_cache=False,
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)
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class RandomFloatInvocation(BaseInvocation):
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"""Outputs a single random float"""
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low: float = InputField(default=0.0, description=FieldDescriptions.inclusive_low)
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high: float = InputField(default=1.0, description=FieldDescriptions.exclusive_high)
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decimals: int = InputField(default=2, description=FieldDescriptions.decimal_places)
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def invoke(self, context: InvocationContext) -> FloatOutput:
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random_float = np.random.uniform(self.low, self.high)
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rounded_float = round(random_float, self.decimals)
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return FloatOutput(value=rounded_float)
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@invocation(
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"float_to_int",
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title="Float To Integer",
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tags=["math", "round", "integer", "float", "convert"],
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category="math",
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version="1.0.1",
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)
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class FloatToIntegerInvocation(BaseInvocation):
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"""Rounds a float number to (a multiple of) an integer."""
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value: float = InputField(default=0, description="The value to round")
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multiple: int = InputField(default=1, ge=1, title="Multiple of", description="The multiple to round to")
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method: Literal["Nearest", "Floor", "Ceiling", "Truncate"] = InputField(
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default="Nearest", description="The method to use for rounding"
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)
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def invoke(self, context: InvocationContext) -> IntegerOutput:
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if self.method == "Nearest":
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return IntegerOutput(value=round(self.value / self.multiple) * self.multiple)
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elif self.method == "Floor":
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return IntegerOutput(value=np.floor(self.value / self.multiple) * self.multiple)
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elif self.method == "Ceiling":
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return IntegerOutput(value=np.ceil(self.value / self.multiple) * self.multiple)
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else: # self.method == "Truncate"
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return IntegerOutput(value=int(self.value / self.multiple) * self.multiple)
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@invocation("round_float", title="Round Float", tags=["math", "round"], category="math", version="1.0.1")
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class RoundInvocation(BaseInvocation):
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"""Rounds a float to a specified number of decimal places."""
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value: float = InputField(default=0, description="The float value")
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decimals: int = InputField(default=0, description="The number of decimal places")
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def invoke(self, context: InvocationContext) -> FloatOutput:
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return FloatOutput(value=round(self.value, self.decimals))
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INTEGER_OPERATIONS = Literal[
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"ADD",
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"SUB",
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"MUL",
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"DIV",
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"EXP",
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"MOD",
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"ABS",
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"MIN",
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"MAX",
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]
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INTEGER_OPERATIONS_LABELS = {
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"ADD": "Add A+B",
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"SUB": "Subtract A-B",
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"MUL": "Multiply A*B",
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"DIV": "Divide A/B",
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"EXP": "Exponentiate A^B",
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"MOD": "Modulus A%B",
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"ABS": "Absolute Value of A",
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"MIN": "Minimum(A,B)",
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"MAX": "Maximum(A,B)",
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}
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@invocation(
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"integer_math",
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title="Integer Math",
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tags=[
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"math",
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"integer",
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"add",
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"subtract",
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"multiply",
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"divide",
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"modulus",
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"power",
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"absolute value",
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"min",
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"max",
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],
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category="math",
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version="1.0.1",
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)
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class IntegerMathInvocation(BaseInvocation):
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"""Performs integer math."""
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operation: INTEGER_OPERATIONS = InputField(
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default="ADD", description="The operation to perform", ui_choice_labels=INTEGER_OPERATIONS_LABELS
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)
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a: int = InputField(default=1, description=FieldDescriptions.num_1)
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b: int = InputField(default=1, description=FieldDescriptions.num_2)
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@field_validator("b")
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def no_unrepresentable_results(cls, v: int, info: ValidationInfo):
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if info.data["operation"] == "DIV" and v == 0:
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raise ValueError("Cannot divide by zero")
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elif info.data["operation"] == "MOD" and v == 0:
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raise ValueError("Cannot divide by zero")
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elif info.data["operation"] == "EXP" and v < 0:
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raise ValueError("Result of exponentiation is not an integer")
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return v
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def invoke(self, context: InvocationContext) -> IntegerOutput:
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# Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
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if self.operation == "ADD":
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return IntegerOutput(value=self.a + self.b)
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elif self.operation == "SUB":
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return IntegerOutput(value=self.a - self.b)
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elif self.operation == "MUL":
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return IntegerOutput(value=self.a * self.b)
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elif self.operation == "DIV":
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return IntegerOutput(value=int(self.a / self.b))
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elif self.operation == "EXP":
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return IntegerOutput(value=self.a**self.b)
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elif self.operation == "MOD":
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return IntegerOutput(value=self.a % self.b)
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elif self.operation == "ABS":
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return IntegerOutput(value=abs(self.a))
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elif self.operation == "MIN":
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return IntegerOutput(value=min(self.a, self.b))
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else: # self.operation == "MAX":
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return IntegerOutput(value=max(self.a, self.b))
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FLOAT_OPERATIONS = Literal[
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"ADD",
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"SUB",
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"MUL",
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"DIV",
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"EXP",
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"ABS",
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"SQRT",
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"MIN",
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"MAX",
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]
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FLOAT_OPERATIONS_LABELS = {
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"ADD": "Add A+B",
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"SUB": "Subtract A-B",
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"MUL": "Multiply A*B",
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"DIV": "Divide A/B",
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"EXP": "Exponentiate A^B",
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"ABS": "Absolute Value of A",
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"SQRT": "Square Root of A",
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"MIN": "Minimum(A,B)",
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"MAX": "Maximum(A,B)",
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}
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@invocation(
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"float_math",
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title="Float Math",
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tags=["math", "float", "add", "subtract", "multiply", "divide", "power", "root", "absolute value", "min", "max"],
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category="math",
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version="1.0.1",
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)
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class FloatMathInvocation(BaseInvocation):
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"""Performs floating point math."""
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operation: FLOAT_OPERATIONS = InputField(
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default="ADD", description="The operation to perform", ui_choice_labels=FLOAT_OPERATIONS_LABELS
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)
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a: float = InputField(default=1, description=FieldDescriptions.num_1)
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b: float = InputField(default=1, description=FieldDescriptions.num_2)
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@field_validator("b")
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def no_unrepresentable_results(cls, v: float, info: ValidationInfo):
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if info.data["operation"] == "DIV" and v == 0:
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raise ValueError("Cannot divide by zero")
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elif info.data["operation"] == "EXP" and info.data["a"] == 0 and v < 0:
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raise ValueError("Cannot raise zero to a negative power")
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elif info.data["operation"] == "EXP" and isinstance(info.data["a"] ** v, complex):
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raise ValueError("Root operation resulted in a complex number")
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return v
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def invoke(self, context: InvocationContext) -> FloatOutput:
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# Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
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if self.operation == "ADD":
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return FloatOutput(value=self.a + self.b)
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elif self.operation == "SUB":
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return FloatOutput(value=self.a - self.b)
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elif self.operation == "MUL":
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return FloatOutput(value=self.a * self.b)
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elif self.operation == "DIV":
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return FloatOutput(value=self.a / self.b)
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elif self.operation == "EXP":
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return FloatOutput(value=self.a**self.b)
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elif self.operation == "SQRT":
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return FloatOutput(value=np.sqrt(self.a))
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elif self.operation == "ABS":
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return FloatOutput(value=abs(self.a))
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elif self.operation == "MIN":
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return FloatOutput(value=min(self.a, self.b))
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else: # self.operation == "MAX":
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return FloatOutput(value=max(self.a, self.b))
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