--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mupdf-source/thirdparty/tesseract/src/lstm/plumbing.cpp	Mon Sep 15 11:44:09 2025 +0200
@@ -0,0 +1,267 @@
+///////////////////////////////////////////////////////////////////////
+// File:        plumbing.cpp
+// Description: Base class for networks that organize other networks
+//              eg series or parallel.
+// Author:      Ray Smith
+//
+// (C) Copyright 2014, Google Inc.
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// http://www.apache.org/licenses/LICENSE-2.0
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+///////////////////////////////////////////////////////////////////////
+
+#include "plumbing.h"
+
+namespace tesseract {
+
+// ni_ and no_ will be set by AddToStack.
+Plumbing::Plumbing(const std::string &name) : Network(NT_PARALLEL, name, 0, 0) {}
+
+// Suspends/Enables training by setting the training_ flag. Serialize and
+// DeSerialize only operate on the run-time data if state is false.
+void Plumbing::SetEnableTraining(TrainingState state) {
+  Network::SetEnableTraining(state);
+  for (auto &i : stack_) {
+    i->SetEnableTraining(state);
+  }
+}
+
+// Sets flags that control the action of the network. See NetworkFlags enum
+// for bit values.
+void Plumbing::SetNetworkFlags(uint32_t flags) {
+  Network::SetNetworkFlags(flags);
+  for (auto &i : stack_) {
+    i->SetNetworkFlags(flags);
+  }
+}
+
+// Sets up the network for training. Initializes weights using weights of
+// scale `range` picked according to the random number generator `randomizer`.
+// Note that randomizer is a borrowed pointer that should outlive the network
+// and should not be deleted by any of the networks.
+// Returns the number of weights initialized.
+int Plumbing::InitWeights(float range, TRand *randomizer) {
+  num_weights_ = 0;
+  for (auto &i : stack_) {
+    num_weights_ += i->InitWeights(range, randomizer);
+  }
+  return num_weights_;
+}
+
+// Recursively searches the network for softmaxes with old_no outputs,
+// and remaps their outputs according to code_map. See network.h for details.
+int Plumbing::RemapOutputs(int old_no, const std::vector<int> &code_map) {
+  num_weights_ = 0;
+  for (auto &i : stack_) {
+    num_weights_ += i->RemapOutputs(old_no, code_map);
+  }
+  return num_weights_;
+}
+
+// Converts a float network to an int network.
+void Plumbing::ConvertToInt() {
+  for (auto &i : stack_) {
+    i->ConvertToInt();
+  }
+}
+
+// Provides a pointer to a TRand for any networks that care to use it.
+// Note that randomizer is a borrowed pointer that should outlive the network
+// and should not be deleted by any of the networks.
+void Plumbing::SetRandomizer(TRand *randomizer) {
+  for (auto &i : stack_) {
+    i->SetRandomizer(randomizer);
+  }
+}
+
+// Adds the given network to the stack.
+void Plumbing::AddToStack(Network *network) {
+  if (stack_.empty()) {
+    ni_ = network->NumInputs();
+    no_ = network->NumOutputs();
+  } else if (type_ == NT_SERIES) {
+    // ni is input of first, no output of last, others match output to input.
+    ASSERT_HOST(no_ == network->NumInputs());
+    no_ = network->NumOutputs();
+  } else {
+    // All parallel types. Output is sum of outputs, inputs all match.
+    ASSERT_HOST(ni_ == network->NumInputs());
+    no_ += network->NumOutputs();
+  }
+  stack_.push_back(network);
+}
+
+// Sets needs_to_backprop_ to needs_backprop and calls on sub-network
+// according to needs_backprop || any weights in this network.
+bool Plumbing::SetupNeedsBackprop(bool needs_backprop) {
+  if (IsTraining()) {
+    needs_to_backprop_ = needs_backprop;
+    bool retval = needs_backprop;
+    for (auto &i : stack_) {
+      if (i->SetupNeedsBackprop(needs_backprop)) {
+        retval = true;
+      }
+    }
+    return retval;
+  }
+  // Frozen networks don't do backprop.
+  needs_to_backprop_ = false;
+  return false;
+}
+
+// Returns an integer reduction factor that the network applies to the
+// time sequence. Assumes that any 2-d is already eliminated. Used for
+// scaling bounding boxes of truth data.
+// WARNING: if GlobalMinimax is used to vary the scale, this will return
+// the last used scale factor. Call it before any forward, and it will return
+// the minimum scale factor of the paths through the GlobalMinimax.
+int Plumbing::XScaleFactor() const {
+  return stack_[0]->XScaleFactor();
+}
+
+// Provides the (minimum) x scale factor to the network (of interest only to
+// input units) so they can determine how to scale bounding boxes.
+void Plumbing::CacheXScaleFactor(int factor) {
+  for (auto &i : stack_) {
+    i->CacheXScaleFactor(factor);
+  }
+}
+
+// Provides debug output on the weights.
+void Plumbing::DebugWeights() {
+  for (auto &i : stack_) {
+    i->DebugWeights();
+  }
+}
+
+// Returns a set of strings representing the layer-ids of all layers below.
+void Plumbing::EnumerateLayers(const std::string *prefix, std::vector<std::string> &layers) const {
+  for (size_t i = 0; i < stack_.size(); ++i) {
+    std::string layer_name;
+    if (prefix) {
+      layer_name = *prefix;
+    }
+    layer_name += ":" + std::to_string(i);
+    if (stack_[i]->IsPlumbingType()) {
+      auto *plumbing = static_cast<Plumbing *>(stack_[i]);
+      plumbing->EnumerateLayers(&layer_name, layers);
+    } else {
+      layers.push_back(layer_name);
+    }
+  }
+}
+
+// Returns a pointer to the network layer corresponding to the given id.
+Network *Plumbing::GetLayer(const char *id) const {
+  char *next_id;
+  int index = strtol(id, &next_id, 10);
+  if (index < 0 || static_cast<unsigned>(index) >= stack_.size()) {
+    return nullptr;
+  }
+  if (stack_[index]->IsPlumbingType()) {
+    auto *plumbing = static_cast<Plumbing *>(stack_[index]);
+    ASSERT_HOST(*next_id == ':');
+    return plumbing->GetLayer(next_id + 1);
+  }
+  return stack_[index];
+}
+
+// Returns a pointer to the learning rate for the given layer id.
+float *Plumbing::LayerLearningRatePtr(const char *id) {
+  char *next_id;
+  int index = strtol(id, &next_id, 10);
+  if (index < 0 || static_cast<unsigned>(index) >= stack_.size()) {
+    return nullptr;
+  }
+  if (stack_[index]->IsPlumbingType()) {
+    auto *plumbing = static_cast<Plumbing *>(stack_[index]);
+    ASSERT_HOST(*next_id == ':');
+    return plumbing->LayerLearningRatePtr(next_id + 1);
+  }
+  if (static_cast<unsigned>(index) >= learning_rates_.size()) {
+    return nullptr;
+  }
+  return &learning_rates_[index];
+}
+
+// Writes to the given file. Returns false in case of error.
+bool Plumbing::Serialize(TFile *fp) const {
+  if (!Network::Serialize(fp)) {
+    return false;
+  }
+  uint32_t size = stack_.size();
+  // Can't use PointerVector::Serialize here as we need a special DeSerialize.
+  if (!fp->Serialize(&size)) {
+    return false;
+  }
+  for (uint32_t i = 0; i < size; ++i) {
+    if (!stack_[i]->Serialize(fp)) {
+      return false;
+    }
+  }
+  if ((network_flags_ & NF_LAYER_SPECIFIC_LR) && !fp->Serialize(learning_rates_)) {
+    return false;
+  }
+  return true;
+}
+
+// Reads from the given file. Returns false in case of error.
+bool Plumbing::DeSerialize(TFile *fp) {
+  for (auto data : stack_) {
+    delete data;
+  }
+  stack_.clear();
+  no_ = 0; // We will be modifying this as we AddToStack.
+  uint32_t size;
+  if (!fp->DeSerialize(&size)) {
+    return false;
+  }
+  for (uint32_t i = 0; i < size; ++i) {
+    Network *network = CreateFromFile(fp);
+    if (network == nullptr) {
+      return false;
+    }
+    AddToStack(network);
+  }
+  if ((network_flags_ & NF_LAYER_SPECIFIC_LR) && !fp->DeSerialize(learning_rates_)) {
+    return false;
+  }
+  return true;
+}
+
+// Updates the weights using the given learning rate, momentum and adam_beta.
+// num_samples is used in the adam computation iff use_adam_ is true.
+void Plumbing::Update(float learning_rate, float momentum, float adam_beta, int num_samples) {
+  for (size_t i = 0; i < stack_.size(); ++i) {
+    if (network_flags_ & NF_LAYER_SPECIFIC_LR) {
+      if (i < learning_rates_.size()) {
+        learning_rate = learning_rates_[i];
+      } else {
+        learning_rates_.push_back(learning_rate);
+      }
+    }
+    if (stack_[i]->IsTraining()) {
+      stack_[i]->Update(learning_rate, momentum, adam_beta, num_samples);
+    }
+  }
+}
+
+// Sums the products of weight updates in *this and other, splitting into
+// positive (same direction) in *same and negative (different direction) in
+// *changed.
+void Plumbing::CountAlternators(const Network &other, TFloat *same, TFloat *changed) const {
+  ASSERT_HOST(other.type() == type_);
+  const auto *plumbing = static_cast<const Plumbing *>(&other);
+  ASSERT_HOST(plumbing->stack_.size() == stack_.size());
+  for (size_t i = 0; i < stack_.size(); ++i) {
+    stack_[i]->CountAlternators(*plumbing->stack_[i], same, changed);
+  }
+}
+
+} // namespace tesseract.