249 lines
10 KiB
Python
249 lines
10 KiB
Python
from inspect import cleandoc
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from comfy_api.latest import io
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from .helper_functions import (
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generate_dim_variables,
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getIndexTensorAlongDim,
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parse_expr,
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as_tensor,
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normalize_to_common_shape,
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make_zero_like,
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get_v_variable,
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get_f_variable,
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checkLazyNew
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)
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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import torch
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from .Stack import MrmthStack
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from .ParseTree import MrmthParseTree
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import copy
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class LatentMathNode(io.ComfyNode):
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"""
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This node enables the use of math expressions on Latents using Autogrow inputs.
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"""
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def __init__(self):
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pass
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@classmethod
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def define_schema(cls) -> io.Schema:
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""" """
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return io.Schema(
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node_id="mrmth_ag_LatentMathNode",
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display_name="Latent math",
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category="More math",
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inputs=[
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io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Latent.Input("values"), prefix="V", min=1, max=50)),
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io.Autogrow.Input(id="F", template=io.Autogrow.TemplatePrefix(io.Float.Input("float", default=0.0, optional=True, lazy=True, force_input=True), prefix="F", min=1, max=50)),
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io.MultiType.Input(
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io.String.Input("Expression", default="I0*(1-F0)+I1*F0", multiline=False),
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types=[io.String,MrmthParseTree],
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tooltip="Expression to apply on input latents",
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),
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io.Combo.Input(
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id="length_mismatch",
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options=["do nothing","error","tile", "pad"],
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display_name="on size mismatch",
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default="error",
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tooltip="How to handle mismatched latent batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
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),
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io.Int.Input(id="batching"),
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io.Boolean.Input(
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id="remember_stack",
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default=False,
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display_name="Remember stack across batch",
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tooltip=(
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"If enabled, stack is copied at output leading to changes being remembered during batch operations (node runs multiple times in sucession). If disabled each batch gets it's own copy of the stack."
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),
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),
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MrmthStack.Input(id="stack", tooltip="Access stack between nodes",optional=True)
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],
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outputs=[
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io.Latent.Output(is_output_list=True),
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MrmthStack.Output(),
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],
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)
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tooltip = cleandoc(__doc__)
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@classmethod
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def check_lazy_status(cls, Expression, V, F,batching, length_mismatch="tile",remember_stack=False,stack={}):
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return checkLazyNew(Expression,V,F)
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@classmethod
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def execute(cls, V, F, Expression,batching, length_mismatch="tile",remember_stack=False,stack={}) -> io.NodeOutput:
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# Determine reference latent
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ref_latent = None
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for lat in V.values():
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if lat is not None:
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ref_latent = lat
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break
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if ref_latent is None:
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raise ValueError("At least one input is required.")
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stack = stack if remember_stack else (copy.deepcopy(stack) if stack is not None else {})
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# Identify all present tensors and their keys.
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# NestedTensor inputs keep their original latent dict under the base key (V0, V1, ...)
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# so downstream nodes receive the original NestedTensor, while individual components
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# are also exposed as V0_0, V0_1, ... for math expressions.
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tensor_keys = []
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V_norm_samples = {}
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nested_component_keys = {} # maps original key -> list of expanded component variable names
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for k, v in V.items():
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if v is None:
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continue
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samples = v.get("samples")
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if getattr(samples, "is_nested", False):
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component_names = []
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for idx, t in enumerate(samples.tensors):
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comp_key = f"{k}_{idx}"
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V_norm_samples[comp_key] = t
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component_names.append(comp_key)
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nested_component_keys[k] = component_names
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# Keep the original NestedTensor under the base key so V0 returns it unchanged
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tensor_keys.append(k)
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V_norm_samples[k] = samples
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else:
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tensor_keys.append(k)
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V_norm_samples[k] = samples
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# Normalize all together; NestedTensor base entries are skipped but other non-tensor values are still supported
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at_list = [V_norm_samples[k] for k in tensor_keys if torch.is_tensor(V_norm_samples[k])]
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if at_list:
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normalized_samples = normalize_to_common_shape(*at_list, mode=length_mismatch)
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norm_iter = iter(normalized_samples)
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for key in tensor_keys:
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if not torch.is_tensor(V_norm_samples[key]):
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continue
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V_norm_samples[key] = next(norm_iter)
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# Add nested component variables to the list of available inputs for variable binding
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tensor_keys.extend([c for components in nested_component_keys.values() for c in components])
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def _resolve_alias(base):
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# Aliases a/b/c/d should point to the first tensor component of a NestedTensor input
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components = nested_component_keys.get(base)
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if components:
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return V_norm_samples[components[0]]
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return V_norm_samples.get(base)
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first_sample = next(iter(V_norm_samples.values()))
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if not torch.is_tensor(first_sample):
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# If the first value is a NestedTensor, grab its first component for metadata
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first_sample = first_sample.tensors[0]
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ae_res = _resolve_alias("V0")
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ae = ae_res if ae_res is not None else make_zero_like(first_sample)
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be_res = _resolve_alias("V1")
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be = be_res if be_res is not None else make_zero_like(ae)
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ce_res = _resolve_alias("V2")
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ce = ce_res if ce_res is not None else make_zero_like(ae)
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de_res = _resolve_alias("V3")
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de = de_res if de_res is not None else make_zero_like(ae)
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# Ensure legacy are normalized
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ae, be, ce, de = normalize_to_common_shape(ae, be, ce, de, mode=length_mismatch)
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if length_mismatch == "error":
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for name in tensor_keys:
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sample = V_norm_samples.get(name)
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if sample is None:
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continue
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if not torch.is_tensor(sample):
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continue
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if sample.shape[0] != ae.shape[0]:
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raise ValueError(f"Input '{name}' has shape {sample.shape[0]}, expected {ae.shape[0]} to match input.")
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# parse expression once
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tree = None
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if isinstance(Expression,str):
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tree = parse_expr(Expression)
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else:
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tree = Expression
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ndim = ae.ndim
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batch_dim = 0
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channel_dim = -3
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height_dim = -2
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width_dim = -1
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time_dim = None
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if ndim >= 5:
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time_dim = -4
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frame_count = ae.shape[time_dim] if time_dim is not None else ae.shape[batch_dim]
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variables = {
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"a": ae, "b": be, "c": ce, "d": de,
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"w": F.get("F0", 0.0) if F.get("F0") is not None else 0.0,
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"x": F.get("F1", 0.0) if F.get("F1") is not None else 0.0,
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"y": F.get("F2", 0.0) if F.get("F2") is not None else 0.0,
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"z": F.get("F3", 0.0) if F.get("F3") is not None else 0.0,
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"X": getIndexTensorAlongDim(ae, width_dim),
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"Y": getIndexTensorAlongDim(ae, height_dim),
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"B": getIndexTensorAlongDim(ae, batch_dim),
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"batch": getIndexTensorAlongDim(ae, batch_dim),
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"C": getIndexTensorAlongDim(ae, channel_dim),
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"channel": getIndexTensorAlongDim(ae, channel_dim),
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"W": float(ae.shape[width_dim]),
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"width": float(ae.shape[width_dim]),
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"H": float(ae.shape[height_dim]),
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"height": float(ae.shape[height_dim]),
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"T": float(frame_count),
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"batch_count": float(ae.shape[batch_dim]),
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"N": float(ae.shape[channel_dim]),
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"channel_count": float(ae.shape[channel_dim]),
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} | generate_dim_variables(ae)
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if time_dim is not None:
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F_idx = getIndexTensorAlongDim(ae, time_dim)
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variables.update({"frame_idx": F_idx, "frame": F_idx, "frame_count": frame_count})
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# Add all dynamic inputs
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variables.update(V_norm_samples)
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v_stacked, v_cnt = get_v_variable(V_norm_samples, length_mismatch=length_mismatch)
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if v_stacked is not None:
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variables["V"] = v_stacked
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variables["Vcnt"] = float(v_cnt)
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variables["V_count"] = float(v_cnt)
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f_stacked, f_cnt = get_f_variable(F)
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if f_stacked is not None:
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variables["F"] = f_stacked
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variables["Fcnt"] = float(f_cnt)
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variables["F_count"] = float(f_cnt)
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for k, v in F.items():
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variables[k] = v if v is not None else 0.0
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visitor = UnifiedMathVisitor(variables, ae.shape,ae.device,state_storage=stack)
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raw_result = visitor.visit(tree)
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result_t = as_tensor(raw_result, ae.shape)
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result_latent = ref_latent.copy()
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# If result is a list of tensors (e.g. user wrote just V1 for a NestedTensor),
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# return each component as a separate latent output.
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if isinstance(result_t, (list, tuple)) and result_t and isinstance(result_t[0], torch.Tensor):
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results = []
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for comp in result_t:
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rl = result_latent.copy()
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rl["samples"] = comp
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results.append(rl)
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stack = stack if remember_stack else copy.deepcopy(stack)
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return (results, stack)
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if batching > 0:
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res = torch.split(result_t, batching)
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results = []
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for result_tensor in res:
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rl = result_latent.copy()
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rl["samples"] = result_tensor
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results.append(rl)
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stack = stack if remember_stack else copy.deepcopy(stack)
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return (results, stack)
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rl = result_latent.copy()
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rl["samples"] = result_t
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stack = stack if remember_stack else copy.deepcopy(stack)
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return ([rl], stack)
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