Removed duplicated words (#10754)

cmake/developer_package/cpplint/cpplint.py

@@ -3592,7 +3592,7 @@ def CheckOperatorSpacing(filename, clean_lines, linenum, error):
     elif not Match(r'#.*include', line):
         # Look for < that is not surrounded by spaces.  This is only
         # triggered if both sides are missing spaces, even though
-        # technically should should flag if at least one side is missing a
+        # technically should flag if at least one side is missing a
         # space.  This is done to avoid some false positives with shifts.
         match = Match(r'^(.*[^\s<])<[^\s=<,]', line)
         if match:

docs/Doxyfile.config

@@ -719,7 +719,7 @@ SHOW_NAMESPACES        = YES
 # The FILE_VERSION_FILTER tag can be used to specify a program or script that
 # doxygen should invoke to get the current version for each file (typically from
 # the version control system). Doxygen will invoke the program by executing (via
-# popen()) the command command input-file, where command is the value of the
+# popen()) the command input-file, where command is the value of the
 # FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
 # by doxygen. Whatever the program writes to standard output is used as the file
 # version. For an example see the documentation.

docs/IE_PLUGIN_DG/Doxyfile

@@ -675,7 +675,7 @@ SHOW_NAMESPACES        = YES
 # The FILE_VERSION_FILTER tag can be used to specify a program or script that
 # doxygen should invoke to get the current version for each file (typically from
 # the version control system). Doxygen will invoke the program by executing (via
-# popen()) the command command input-file, where command is the value of the
+# popen()) the command input-file, where command is the value of the
 # FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
 # by doxygen. Whatever the program writes to standard output is used as the file
 # version. For an example see the documentation.

docs/IE_PLUGIN_DG/InferRequest.md

@@ -54,7 +54,7 @@ Decrements a number of created inference requests:
 
 #### 1. `inferPreprocess`
 
-Below is the code of the the `inferPreprocess` method to demonstrate Inference Engine common preprocessing step handling:
+Below is the code of the `inferPreprocess` method to demonstrate Inference Engine common preprocessing step handling:
 
 @snippet src/template_infer_request.cpp infer_request:infer_preprocess
 

docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md

@@ -637,7 +637,7 @@ To convert the model to the Intermediate Representation (IR), run Model Optimize
 mo --input_model INPUT_MODEL --output_dir <OUTPUT_MODEL_DIR>
 ```
 
-You need to have have write permissions for an output directory.
+You need to have write permissions for an output directory.
 
 > **NOTE**: Some models require using additional arguments to specify conversion parameters, such as `--input_shape`, `--scale`, `--scale_values`, `--mean_values`, `--mean_file`. To learn about when you need to use these parameters, refer to [Converting a Model to Intermediate Representation (IR)](prepare_model/convert_model/Converting_Model.md).
 

docs/MO_DG/prepare_model/Getting_performance_numbers.md

@@ -58,7 +58,7 @@ When comparing the OpenVINO Runtime performance with the framework or another re
 
 ## Using Tools <a name="using-tools"></a>
 
-Whether you are tuning for the first time or doing advanced performance optimization, you need a a tool that provides accurate insights. Intel&reg; VTune&trade; Amplifier gives you the tool to mine it and interpret the profiling data.
+Whether you are tuning for the first time or doing advanced performance optimization, you need a tool that provides accurate insights. Intel&reg; VTune&trade; Amplifier gives you the tool to mine it and interpret the profiling data.
 
 Alternatively, you can gather the raw profiling data that samples report, the second chapter provides example of how to interpret these.
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md

@@ -50,7 +50,7 @@ To convert a Caffe\* model, run Model Optimizer with the path to the input model
 
 Two groups of parameters are available to convert your model:
 
-* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
+* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
 * [Caffe-specific parameters](#caffe_specific_conversion_params) are used to convert only Caffe\* models.
 
 ### Using Caffe\*-Specific Conversion Parameters <a name="caffe_specific_conversion_params"></a>

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md

@@ -50,7 +50,7 @@ mo --input_model <INPUT_MODEL>.nnet --output_dir <OUTPUT_MODEL_DIR>
 
 Two groups of parameters are available to convert your model:
 
-* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
+* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
 * [Kaldi-specific parameters](#kaldi_specific_conversion_params) are used to convert only Kaldi\* models.
 
 ### Using Kaldi\*-Specific Conversion Parameters <a name="kaldi_specific_conversion_params"></a>

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md

@@ -64,7 +64,7 @@ To convert an MXNet\* model, run Model Optimizer with a path to the input model
 
 Two groups of parameters are available to convert your model:
 
-* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
+* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
 * [MXNet-specific parameters](#mxnet_specific_conversion_params) are used to convert only MXNet models.
 
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md

@@ -77,7 +77,7 @@ To convert an ONNX\* model, run Model Optimizer with the path to the input model
 ```sh
  mo --input_model <INPUT_MODEL>.onnx --output_dir <OUTPUT_MODEL_DIR>
 ```
-There are no ONNX\* specific parameters, so only framework-agnostic parameters are available to convert your model. For details, see see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
+There are no ONNX\* specific parameters, so only framework-agnostic parameters are available to convert your model. For details, see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
 
 ## Supported ONNX\* Layers
 Refer to [Supported Framework Layers](../Supported_Frameworks_Layers.md) for the list of supported standard layers.

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md

@@ -285,7 +285,7 @@ To convert a TensorFlow model:
 
 Two groups of parameters are available to convert your model:
 
-* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
+* Framework-agnostic parameters are used to convert a model trained with any supported framework. For details, see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
 * [TensorFlow-specific parameters](#tensorflow_specific_conversion_params): Parameters used to convert only TensorFlow models.
 
 > **NOTE**: The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model to Intermediate Representation (IR)](Converting_Model.md).

docs/install_guides/installing-openvino-docker-linux.md

@@ -50,7 +50,7 @@ You can find prebuilt images on:
 
 ## <a name="prepare-dockerfile"></a>Preparing a Dockerfile
 
-You can use the [available Dockerfiles on GitHub](https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles) or generate a Dockerfile with your settings via [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci) which can generate a Dockerfile, build, test and deploy an image with the the Intel® Distribution of OpenVINO™ toolkit.
+You can use the [available Dockerfiles on GitHub](https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles) or generate a Dockerfile with your settings via [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci) which can generate a Dockerfile, build, test and deploy an image with the Intel® Distribution of OpenVINO™ toolkit.
 You can also try our [Tutorials](https://github.com/openvinotoolkit/docker_ci/tree/master/docs/tutorials) which demonstrate the usage of Docker containers with OpenVINO. 
 
 ## <a name="configure-image"></a>Configuring the Image for Different Devices
@@ -59,7 +59,7 @@ If you want to run inferences on a CPU or Intel® Neural Compute Stick 2, no ext
 
 ### Configuring Docker Image for GPU
 
-By default, the distributed Docker image for OpenVINO has the the recommended version of Intel® Graphics Compute Runtime for oneAPI Level Zero and OpenCL Driver for the operating system installed inside. If you want to build an image with a custom version of OpenCL Runtime included, you need to modify the Dockerfile using the lines below (the 19.41.14441 version is used as an example) and build the image manually:
+By default, the distributed Docker image for OpenVINO has the recommended version of Intel® Graphics Compute Runtime for oneAPI Level Zero and OpenCL Driver for the operating system installed inside. If you want to build an image with a custom version of OpenCL Runtime included, you need to modify the Dockerfile using the lines below (the 19.41.14441 version is used as an example) and build the image manually:
 
 **Ubuntu 18.04/20.04**:
 

docs/install_guides/installing-openvino-docker-windows.md

@@ -52,7 +52,7 @@ You can find prebuilt images on:
 
 ## <a name="prepare-dockerfile"></a>Preparing a Dockerfile
 
-You can use the [available Dockerfiles on GitHub](https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles) or generate a Dockerfile with your settings via [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci) which can generate a Dockerfile, build, test and deploy an image with the the Intel® Distribution of OpenVINO™ toolkit.
+You can use the [available Dockerfiles on GitHub](https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles) or generate a Dockerfile with your settings via [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci) which can generate a Dockerfile, build, test and deploy an image with the Intel® Distribution of OpenVINO™ toolkit.
 
 ## <a name="configure-image"></a>Configuring the Docker Image for Different Devices
 

docs/ops/sequence/RNNCell_3.md

@@ -57,11 +57,11 @@ Formula:
 
 * **2**: `H` - 2D tensor of type *T* `[batch_size, hidden_size]`, initial hidden state. **Required.**
 
-* **3**: `W` - 2D tensor tensor of type *T* `[hidden_size, input_size]`, the weights for matrix multiplication. **Required.**
+* **3**: `W` - 2D tensor of type *T* `[hidden_size, input_size]`, the weights for matrix multiplication. **Required.**
 
-* **4**: `R` - 2D tensor tensor of type *T* `[hidden_size, hidden_size]`, the recurrence weights for matrix multiplication. **Required.**
+* **4**: `R` - 2D tensor of type *T* `[hidden_size, hidden_size]`, the recurrence weights for matrix multiplication. **Required.**
 
-* **5**: `B` 1D tensor tensor of type *T* `[hidden_size]`, the sum of biases (weights and recurrence weights). **Required.**
+* **5**: `B` 1D tensor of type *T* `[hidden_size]`, the sum of biases (weights and recurrence weights). **Required.**
 
 **Outputs**
 

docs/suppress_warnings.txt

@@ -13,7 +13,7 @@ the name \'.+?\' supplied as the argument
 warning: while setting up extension conf.py: csv directory not found
 argument \'.+?\' of command
 no uniquely matching class member found for
-example example was already documented\. ignoring documentation found here\.
+example was already documented\. ignoring documentation found here\.
 documentation for unknown define
 detected potential recursive class relation between class
 no matching file member found for

licensing/onednn_third-party-programs.txt

@@ -6,7 +6,7 @@ terms. This third party software, even if included with the distribution of
 the Intel software, may be governed by separate license terms, including
 without limitation, third party license terms, other Intel software license
 terms, and open source software license terms. These separate license terms
-govern your use of the third party programs as set forth in in the
+govern your use of the third party programs as set forth in the
 "THIRD-PARTY-PROGRAMS" file.
 
 Third party programs and their corresponding required notices and/or license
@@ -554,4 +554,4 @@ LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
 DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
 THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

samples/cpp/benchmark_app/README.md

@@ -166,7 +166,7 @@ This section provides step-by-step instructions on how to run the Benchmark Tool
 
 > **NOTE**: The Internet access is required to execute the following steps successfully. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
 
-1. Download the model. Go to the the Model Downloader directory and run the `downloader.py` script with specifying the model name and directory to download the model to:
+1. Download the model. Go to the Model Downloader directory and run the `downloader.py` script with specifying the model name and directory to download the model to:
    ```sh
    cd <INSTAL_DIR>/extras/open_model_zoo/tools/downloader
    ```

src/core/include/openvino/core/descriptor/input.hpp

@@ -28,12 +28,12 @@ class OPENVINO_API Input {
 
 public:
     /// \param node The node that owns this input
-    /// \param index The position of this this tensor in all input tensors
+    /// \param index The position of this tensor in all input tensors
     /// \param output The output that supplies a value for this input
     Input(Node* node, size_t index, Output& output);
     /// \brief Create an Input that is not connected to an output
     /// \param node The node that owns this input
-    /// \param index The position of this this tensor in all input tensors
+    /// \param index The position of this tensor in all input tensors
     Input(Node* node, size_t index);
     ~Input();
 

src/core/include/openvino/op/softmax.hpp

@@ -47,7 +47,7 @@ private:
 }  // namespace v1
 
 namespace v8 {
-/// \brief Softmax operation with with negative axis values
+/// \brief Softmax operation with negative axis values
 class OPENVINO_API Softmax : public Op {
 public:
     OPENVINO_OP("Softmax", "opset8");

src/inference/include/ie/gna/gna_config.hpp

@@ -38,7 +38,7 @@ namespace GNAConfigParams {
  * @brief Scale factor that is calculated by user, in order to use static quantisation feature
  * This option should be used with floating point value serialized to string with decimal separator equals to . (dot)
  * @details For multiple input case, individual scale factors can be passed, using
- * KEY_GNA_SCALE_FACTOR[_input_layer_name] where input_layer can be obtained from from CNNNetwork::GetInputsInfo
+ * KEY_GNA_SCALE_FACTOR[_input_layer_name] where input_layer can be obtained from CNNNetwork::GetInputsInfo
  */
 DECLARE_GNA_CONFIG_KEY(SCALE_FACTOR);
 

src/tests/README.md

@@ -15,7 +15,7 @@ This is OpenVINO Inference Engine testing framework. OpenVINO Inference Engine t
     files.
     
     > **Example**: We have `ie_reshaper.cpp` within the `src/shape_infer` subfolder of the tested module. In this case
-    new `shape_infer` subfolder should be created within the the root of the Unit Test folder for this module. And new
+    new `shape_infer` subfolder should be created within the root of the Unit Test folder for this module. And new
     `ie_reshaper_test.cpp` file should be created within this newly created subfolder. This test file should cover all
     the classes and methods from the original file.
   
@@ -66,4 +66,4 @@ This is OpenVINO Inference Engine testing framework. OpenVINO Inference Engine t
   Internal namespaces (for example, `CommonTestUtils::`, `FuncTestUtils::` or `UnitTestUtils::`) must be used to
   separate utilities by domains.
   > **NOTE**: All the utilities libraries are added to the developer package and available for closed source
-  development.
+  development.

tools/benchmark_tool/README.md

@@ -229,7 +229,7 @@ This section provides step-by-step instructions on how to run the Benchmark Tool
 
 > **NOTE**: The Internet access is required to execute the following steps successfully. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
 
-1. Download the model. Go to the the Model Downloader directory and run the `downloader.py` script with the model name and directory to download the model to:
+1. Download the model. Go to the Model Downloader directory and run the `downloader.py` script with the model name and directory to download the model to:
    ```sh
    cd <INSTALL_DIR>/extras/open_model_zoo/tools/downloader
    ```