#!/bin/bash
# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# 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,

#!/bin/bash
# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# 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,

     * signing and sealing authenticated communication.
     */
    int NTLMSSP_NEGOTIATE_LM_KEY = 0x00000080;

    /**
     * ??? According to spec this is a reserved bit and must be set to zero
     */
    int NTLMSSP_NEGOTIATE_NETWARE = 0x00000100;

    /**
     * Indicates support for NTLM authentication.
     */
    int NTLMSSP_NEGOTIATE_NTLM = 0x00000200;

    /**

import java.util.Set;
import org.jspecify.annotations.Nullable;

/**
 * A class to allow {@link ValueGraph} implementations to be backed by a provided delegate. This is
 * not currently planned to be released as a general-purpose forwarding class.
 *
 * @author James Sexton
 * @author Joshua O'Madadhain
 */
abstract class ForwardingValueGraph<N, V> extends AbstractValueGraph<N, V> {

# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# 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,

        // Write context header
        writeInt4(buffer, offset, getName().length());  // NameOffset
        writeInt4(buffer, offset + 4, getName().length());  // NameLength
        writeInt4(buffer, offset + 8, 16);  // Reserved
        
        int dataOffset = offset + 16 + getName().length();
        dataOffset = (dataOffset + 7) & ~7;  // 8-byte alignment
        
        writeInt4(buffer, offset + 12, dataOffset - offset);  // DataOffset

#!/bin/bash
# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# 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,

# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# 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,

        int actualInfoLevel = SMBUtil.readInt2(buffer, 2);
        assertEquals(0x0101, actualInfoLevel);

        // Check reserved bytes (should be 0)
        int reserved = SMBUtil.readInt2(buffer, 4);
        assertEquals(0, reserved);
    }

    @DisplayName("Test writeParametersWireFormat with different FIDs")
    @ParameterizedTest
    @ValueSource(ints = { 0, 1, 0xFFFF, 0x8000, 0x7FFF })

    input = Arrays.copyOf(input, input.length);
    Bytes.reverse(input);
    assertThat(input).isEqualTo(expectedOutput);
  }

  private static void testReverse(byte[] input, int fromIndex, int toIndex, byte[] expectedOutput) {
    input = Arrays.copyOf(input, input.length);
    Bytes.reverse(input, fromIndex, toIndex);
    assertThat(input).isEqualTo(expectedOutput);
  }