// Copyright (C) 2018-2026 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#pragma once

#include "openvino/pass/matcher_pass.hpp"
#include "transformations_visibility.hpp"

namespace ov {
namespace pass {
class TRANSFORMATIONS_API DeReshapeMatMul;
class TRANSFORMATIONS_API DeReshapeFullyConnected;
}  // namespace pass
}  // namespace ov

/**
 * @ingroup ov_transformation_common_api
 * @brief Transformation uses symbol information to optimize out Reshape operations surrounding MatMul.
 * It checks that surrounding Reshapes are only manipulating with batch dimensions of tensor in a do-undo kind of way.
 *
 * Example:
 *   Before:
 *     [A,B,C,D] -> Reshape -> [A*B,C,D]
 *                                       MatMul [A*B,C,E] -> Reshape -> [A,B,C,E]
 *     [A,B,D,E] -> Reshape -> [A*B,D,E]
 *
 *   After:
 *     [A,B,C,D]  ->
 *                   MatMul -> [A,B,C,E]
 *     [A,B,D,E]  ->
 *
 *  Transformation allows slightly different variations of the pattern on inputs of MatMul.
 *    - Simplest pattern contains only Reshape operation on MatMul input:
 *        Reshape -> MatMul
 *
 *    - The next acceptable variation is Concat of two inputs on MatMul input:
 *        Reshape -[-> Concat -]-> MatMul
 *      This variation would be transformed with realignment of the other input of Concat and the other outputs of
 *      Concat with the help of Reshape operations
 *
 *    - The most complex variation on the MatMul input pattern is with Binary Elementwise Operation with scalar second
 *      input: Reshape -[-> Concat -]-[-> BEA (scalar) -]-> MatMul
 *
 *  Additionally, transformation supports variation of the pattern on output of MatMul. It allows for
 *  Binary Elementwise Arithmetic operation without second input scalar restriction.
 *        MatMul -[-> BEA -]-> Reshape
 *  this pattern variation is only applicable for the case when input reshapes are 4D -> 3D and output reshape is 3D ->
 *  4D. Additionally, shape symbols on output of MatMul should be equal to the input shape symbols of the last Reshape,
 *  meaning that this Binary Elementwise Arithmetic doesn't perform any broadcasting of input coming from MatMul -- only
 *  other input may be broadcasted to the MatMul input of this BEA. This effect (equality of MatMul output shape symbols
 *  and output shape of BEA) is being handled by LabelResolvingThroughSelect transformation in the particular models
 *  that this variation targets.
 *
 *  Full pattern this transformation searches for:
 *     -> Reshape -[-> Concat -]-[-> BEA (scalar) -]->
 *                                                     MatMul -[-> BEA -]-> Reshape ->
 *     -> Reshape -[-> Concat -]-[-> BEA (scalar) -]->
 *
 *   NOTE: input branches could be (and in observed model cases are) asymmetrical, meaning that the presence of Concat
 *         on one input of MatMul doesn't require the other input to also have Concat
 */
class ov::pass::DeReshapeMatMul : public ov::pass::MatcherPass {
public:
    OPENVINO_MATCHER_PASS_RTTI("DeReshapeMatMul");
    DeReshapeMatMul();
};

/**
 * @ingroup ov_transformation_common_api
 * @brief Transformation uses symbol information to optimize out Reshape operations surrounding special cases of
 * MatMul. It checks that surrounding Reshapes are only manipulating with batch dimensions of tensor in a do-undo kind
 * of way. The difference with previous optimization is that this case has Reshape only on one input of MatMul and the
 * other input is strictly 2D. Such MatMuls are also called FullyConnected
 *
 * Example:
 *   Before:
 *     [A,B,4096] -> Reshape -> [A*B,4096]
 *                                       MatMul [A*B,4608] -> Reshape -> [A,B,4608]
 *                             [4096,4608]
 *
 *   After:
 *     [A,B,4096]  ->
 *                   MatMul -> [A,B,4608]
 *    [4096,4608]  ->
 *
 */
class ov::pass::DeReshapeFullyConnected : public ov::pass::MatcherPass {
public:
    OPENVINO_MATCHER_PASS_RTTI("DeReshapeFullyConnected");
    DeReshapeFullyConnected();
};
