--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mupdf-source/thirdparty/tesseract/src/lstm/static_shape.h	Mon Sep 15 11:43:07 2025 +0200
@@ -0,0 +1,115 @@
+///////////////////////////////////////////////////////////////////////
+// File:        static_shape.h
+// Description: Defines the size of the 4-d tensor input/output from a network.
+// Author:      Ray Smith
+// Created:     Fri Oct 14 09:07:31 PST 2016
+//
+// (C) Copyright 2016, 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.
+///////////////////////////////////////////////////////////////////////
+
+#ifndef TESSERACT_LSTM_STATIC_SHAPE_H_
+#define TESSERACT_LSTM_STATIC_SHAPE_H_
+
+#include "serialis.h" // for TFile
+#include "tprintf.h"  // for tprintf
+
+namespace tesseract {
+
+// Enum describing the loss function to apply during training and/or the
+// decoding method to apply at runtime.
+enum LossType {
+  LT_NONE,     // Undefined.
+  LT_CTC,      // Softmax with standard CTC for training/decoding.
+  LT_SOFTMAX,  // Outputs sum to 1 in fixed positions.
+  LT_LOGISTIC, // Logistic outputs with independent values.
+};
+
+// Simple class to hold the tensor shape that is known at network build time
+// and the LossType of the loss function.
+class StaticShape {
+public:
+  StaticShape() : batch_(0), height_(0), width_(0), depth_(0), loss_type_(LT_NONE) {}
+  int batch() const {
+    return batch_;
+  }
+  void set_batch(int value) {
+    batch_ = value;
+  }
+  int height() const {
+    return height_;
+  }
+  void set_height(int value) {
+    height_ = value;
+  }
+  int width() const {
+    return width_;
+  }
+  void set_width(int value) {
+    width_ = value;
+  }
+  int depth() const {
+    return depth_;
+  }
+  void set_depth(int value) {
+    depth_ = value;
+  }
+  LossType loss_type() const {
+    return loss_type_;
+  }
+  void set_loss_type(LossType value) {
+    loss_type_ = value;
+  }
+  void SetShape(int batch, int height, int width, int depth) {
+    batch_ = batch;
+    height_ = height;
+    width_ = width;
+    depth_ = depth;
+  }
+
+  void Print() const {
+    tprintf("Batch=%d, Height=%d, Width=%d, Depth=%d, loss=%d\n", batch_, height_, width_, depth_,
+            loss_type_);
+  }
+
+  bool DeSerialize(TFile *fp) {
+    int32_t tmp = LT_NONE;
+    bool result = fp->DeSerialize(&batch_) && fp->DeSerialize(&height_) &&
+                  fp->DeSerialize(&width_) && fp->DeSerialize(&depth_) && fp->DeSerialize(&tmp);
+    loss_type_ = static_cast<LossType>(tmp);
+    return result;
+  }
+
+  bool Serialize(TFile *fp) const {
+    int32_t tmp = loss_type_;
+    return fp->Serialize(&batch_) && fp->Serialize(&height_) && fp->Serialize(&width_) &&
+           fp->Serialize(&depth_) && fp->Serialize(&tmp);
+  }
+
+private:
+  // Size of the 4-D tensor input/output to a network. A value of zero is
+  // allowed for all except depth_ and means to be determined at runtime, and
+  // regarded as variable.
+  // Number of elements in a batch, or number of frames in a video stream.
+  int32_t batch_;
+  // Height of the image.
+  int32_t height_;
+  // Width of the image.
+  int32_t width_;
+  // Depth of the image. (Number of "nodes").
+  int32_t depth_;
+  // How to train/interpret the output.
+  LossType loss_type_;
+};
+
+} // namespace tesseract
+
+#endif // TESSERACT_LSTM_STATIC_SHAPE_H_