examples/gradient_pruning/probabilistic_gradient_pruning.ipynb

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        "\n",
        "# Probabilistic gradient pruning\n",
        "<p align=\"left\">\n",
        "<img src=\"https://github.com/mit-han-lab/torchquantum/blob/master/torchquantum_logo.jpg?raw=true\" alt=\"torchquantum Logo\" width=\"250\">\n",
        "</p>\n",
        "\n",
        "Tutorial Author: Zirui Li, Hanrui Wang\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "bugs:\n",
        " - Directly run all. In the first epoch, the process will get stuck in the inference phase.\n",
        " - modify `torchquantum/plugins/qiskit_processor.py:228 parallel=True` to `parallel=False` to fix the bug.\n"
      ],
      "metadata": {
        "id": "7ZB2E7IfzHGQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Outline\n",
        " - Introduction to probabilistic gradient pruning.\n",
        " - Train a model with gradient pruning."
      ],
      "metadata": {
        "id": "mWygf3uG6rOj"
      }
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      "source": [
        "## Introduction to probabilistic gradient pruning\n",
        "\n",
        "By carefully investigating the on-chip training process, we observe that small gradients tend to have large relative variations\n",
        "or even wrong directions under quantum noises. Also, not all gradient computations are necessary for the\n",
        "training process, especially for small-magnitude gradients. \n",
        "\n",
        "The observations provide great opportunities for us to boost the robustness and efficiency of QNN on-chip learning. Inspired by that, we\n",
        "propose a **probabilistic gradient pruning** method to predict and only\n",
        "compute gradients of high reliability. Hence we can reduce noise impact and also save the required number of circuit runs on real\n",
        "quantum machines.\n",
        "\n",
        "### Accumulation Window and Pruning Window\n",
        "\n",
        "We separate all the training epochs into a repeat of an accumulation window followed by a pruning window. There are three important hyper-parameters in our probabilistic gradient pruning method:\n",
        "  - accumulation window width 𝑤𝑎,\n",
        "  - pruning ratio 𝑟,\n",
        "  - pruning window width 𝑤𝑝 .\n",
        "\n",
        "In the accumulation window, we collect the information of gradients in each training step. In each step of the pruning window, we probabilistically exempt the calculations of some gradients based on the information collected from the accumulation window and pruning ratio.\n",
        "\n",
        "<div align=\"center\">\n",
        "<img src=\"https://github.com/mit-han-lab/torchquantum/blob/master/figs/probabilistic_gradient_pruning.png?raw=true\" alt=\"conv-full-layer\" width=\"800\">\n",
        "</div>\n",
        "\n",
        "The accumulation window width\n",
        "and pruning window width decide the reliability of the gradient\n",
        "trend evaluation and our confidence in it, respectively. The pruning\n",
        "ratio can be tuned to balance the gradient variances caused by noise\n",
        "perturbation and pruning. Thus, the percentage of the time saved\n",
        "by our probabilistic gradient pruning method is $𝑟\\frac{𝑤𝑝}{𝑤𝑎+𝑤𝑝}× 100%$.\n",
        "In\n",
        "our experiments, we find that the setting (𝑤𝑎=1, 𝑤𝑝=2∼3, 𝑟=0.3∼0.5)\n",
        "usually works well in all cases"
      ],
      "metadata": {
        "id": "_RVTE9myO0m-"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-42mdL8CG5Vi"
      },
      "source": [
        "##Train a model with probabilistic gradient pruning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pfwd2SNaOA4z"
      },
      "source": [
        "###Installation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ELpIlt-3HG8"
      },
      "source": [
        ""
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      "source": [
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    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/zhijian-liu/torchpack.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nu5fogBIrj19",
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      },
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      "outputs": [
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          "name": "stdout",
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    {
      "cell_type": "code",
      "source": [
        "%cd torchpack"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ILB0aparryz",
        "outputId": "ad2e18b2-baa3-4923-ee47-bf3054cff807"
      },
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/torchpack\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4kuK88QIruTL",
        "outputId": "e162a7a9-aa8e-4686-9e3f-9ebc0cacb0e2"
      },
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing /content/torchpack\n",
            "\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
            "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
            "Collecting loguru\n",
            "  Downloading loguru-0.6.0-py3-none-any.whl (58 kB)\n",
            "\u001b[K     |████████████████████████████████| 58 kB 3.3 MB/s \n",
            "\u001b[?25hCollecting multimethod\n",
            "  Downloading multimethod-1.7-py3-none-any.whl (9.5 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchpack==0.3.1) (1.21.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from torchpack==0.3.1) (3.13)\n",
            "Requirement already satisfied: torch>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from torchpack==0.3.1) (1.10.0+cu111)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from torchpack==0.3.1) (0.11.1+cu111)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from torchpack==0.3.1) (4.63.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.5.0->torchpack==0.3.1) (3.10.0.2)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->torchpack==0.3.1) (7.1.2)\n",
            "Building wheels for collected packages: torchpack\n",
            "  Building wheel for torchpack (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torchpack: filename=torchpack-0.3.1-py3-none-any.whl size=34632 sha256=6e05974cb0c985fbc1e541043be80ee7da98c12608ff883943ee70843656c42a\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-njdff8xi/wheels/4f/c9/1a/b8f5a127071f807be5ee14d3d364b428a891ad020a962af415\n",
            "Successfully built torchpack\n",
            "Installing collected packages: multimethod, loguru, torchpack\n",
            "Successfully installed loguru-0.6.0 multimethod-1.7 torchpack-0.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUfZPto0sBmT",
        "outputId": "10f6d93b-49f6-4c05-a8bc-236601e2655e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F2-0UpluOIQl"
      },
      "source": [
        "Download and cd to the repo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6sNqLl9tjjAf",
        "outputId": "4ea47dc0-5146-471b-e3e6-769f887a2358"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'torchquantum'...\n",
            "remote: Enumerating objects: 10990, done.\u001b[K\n",
            "remote: Counting objects: 100% (7782/7782), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3963/3963), done.\u001b[K\n",
            "remote: Total 10990 (delta 3911), reused 7243 (delta 3410), pack-reused 3208\u001b[K\n",
            "Receiving objects: 100% (10990/10990), 6.24 MiB | 18.09 MiB/s, done.\n",
            "Resolving deltas: 100% (5878/5878), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/mit-han-lab/torchquantum.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0c2MCqFXjxkD",
        "outputId": "ba3c94cc-d7a6-4430-da0c-abb3f81ea0c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/torchquantum\n"
          ]
        }
      ],
      "source": [
        "%cd torchquantum"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PO1xLbaOOxWk"
      },
      "source": [
        "Install torch-quantum."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c6nfq3KkVXG",
        "outputId": "9b305639-c241-47a8-9341-f7ed2cd651ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Obtaining file:///content/torchquantum\n",
            "Requirement already satisfied: numpy>=1.19.2 in /usr/local/lib/python3.7/dist-packages (from torchquantum==0.1.0) (1.21.5)\n",
            "Requirement already satisfied: torchvision>=0.9.0.dev20210130 in /usr/local/lib/python3.7/dist-packages (from torchquantum==0.1.0) (0.11.1+cu111)\n",
            "Requirement already satisfied: tqdm>=4.56.0 in /usr/local/lib/python3.7/dist-packages (from torchquantum==0.1.0) (4.63.0)\n",
            "Requirement already satisfied: setuptools>=52.0.0 in /usr/local/lib/python3.7/dist-packages (from torchquantum==0.1.0) (57.4.0)\n",
            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.7/dist-packages (from torchquantum==0.1.0) (1.10.0+cu111)\n",
            "Requirement already satisfied: torchpack>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from torchquantum==0.1.0) (0.3.1)\n",
            "Requirement already satisfied: qiskit>=0.32.0 in /usr/local/lib/python3.7/dist-packages (from torchquantum==0.1.0) (0.32.1)\n",
            "Collecting matplotlib>=3.3.2\n",
            "  Downloading matplotlib-3.5.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (11.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 11.2 MB 5.1 MB/s \n",
            "\u001b[?25hCollecting pathos>=0.2.7\n",
            "  Downloading pathos-0.2.8-py2.py3-none-any.whl (81 kB)\n",
            "\u001b[K     |████████████████████████████████| 81 kB 11.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.3.2->torchquantum==0.1.0) (2.8.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.3.2->torchquantum==0.1.0) (7.1.2)\n",
            "Collecting fonttools>=4.22.0\n",
            "  Downloading fonttools-4.31.2-py3-none-any.whl (899 kB)\n",
            "\u001b[K     |████████████████████████████████| 899 kB 54.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.3.2->torchquantum==0.1.0) (0.11.0)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.3.2->torchquantum==0.1.0) (3.0.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.3.2->torchquantum==0.1.0) (21.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.3.2->torchquantum==0.1.0) (1.4.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.3.2->torchquantum==0.1.0) (3.10.0.2)\n",
            "Collecting ppft>=1.6.6.4\n",
            "  Downloading ppft-1.6.6.4-py3-none-any.whl (65 kB)\n",
            "\u001b[K     |████████████████████████████████| 65 kB 4.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: multiprocess>=0.70.12 in /usr/local/lib/python3.7/dist-packages (from pathos>=0.2.7->torchquantum==0.1.0) (0.70.12.2)\n",
            "Requirement already satisfied: dill>=0.3.4 in /usr/local/lib/python3.7/dist-packages (from pathos>=0.2.7->torchquantum==0.1.0) (0.3.4)\n",
            "Collecting pox>=0.3.0\n",
            "  Downloading pox-0.3.0-py2.py3-none-any.whl (30 kB)\n",
            "Requirement already satisfied: six>=1.7.3 in /usr/local/lib/python3.7/dist-packages (from ppft>=1.6.6.4->pathos>=0.2.7->torchquantum==0.1.0) (1.15.0)\n",
            "Requirement already satisfied: qiskit-aqua==0.9.5 in /usr/local/lib/python3.7/dist-packages (from qiskit>=0.32.0->torchquantum==0.1.0) (0.9.5)\n",
            "Requirement already satisfied: qiskit-ignis==0.6.0 in /usr/local/lib/python3.7/dist-packages (from qiskit>=0.32.0->torchquantum==0.1.0) (0.6.0)\n",
            "Requirement already satisfied: qiskit-terra==0.18.3 in /usr/local/lib/python3.7/dist-packages (from qiskit>=0.32.0->torchquantum==0.1.0) (0.18.3)\n",
            "Requirement already satisfied: qiskit-aer==0.9.1 in /usr/local/lib/python3.7/dist-packages (from qiskit>=0.32.0->torchquantum==0.1.0) (0.9.1)\n",
            "Requirement already satisfied: qiskit-ibmq-provider==0.18.1 in /usr/local/lib/python3.7/dist-packages (from qiskit>=0.32.0->torchquantum==0.1.0) (0.18.1)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from qiskit-aer==0.9.1->qiskit>=0.32.0->torchquantum==0.1.0) (1.4.1)\n",
            "Requirement already satisfied: h5py<3.3.0 in /usr/local/lib/python3.7/dist-packages (from qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (3.1.0)\n",
            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.7/dist-packages (from qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (1.7.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (1.3.5)\n",
            "Requirement already satisfied: fastdtw<=0.3.4 in /usr/local/lib/python3.7/dist-packages (from qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (0.3.4)\n",
            "Requirement already satisfied: dlx<=1.0.4 in /usr/local/lib/python3.7/dist-packages (from qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (1.0.4)\n",
            "Requirement already satisfied: psutil>=5 in /usr/local/lib/python3.7/dist-packages (from qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (5.4.8)\n",
            "Requirement already satisfied: quandl in /usr/local/lib/python3.7/dist-packages (from qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (3.7.0)\n",
            "Requirement already satisfied: yfinance>=0.1.62 in /usr/local/lib/python3.7/dist-packages (from qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (0.1.70)\n",
            "Requirement already satisfied: docplex>=2.21.207 in /usr/local/lib/python3.7/dist-packages (from qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (2.23.222)\n",
            "Requirement already satisfied: retworkx>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (0.11.0)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.7/dist-packages (from qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (1.0.2)\n",
            "Requirement already satisfied: requests>=2.19 in /usr/local/lib/python3.7/dist-packages (from qiskit-ibmq-provider==0.18.1->qiskit>=0.32.0->torchquantum==0.1.0) (2.27.1)\n",
            "Requirement already satisfied: websocket-client>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from qiskit-ibmq-provider==0.18.1->qiskit>=0.32.0->torchquantum==0.1.0) (1.3.1)\n",
            "Requirement already satisfied: urllib3>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from qiskit-ibmq-provider==0.18.1->qiskit>=0.32.0->torchquantum==0.1.0) (1.24.3)\n",
            "Requirement already satisfied: requests-ntlm>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from qiskit-ibmq-provider==0.18.1->qiskit>=0.32.0->torchquantum==0.1.0) (1.1.0)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.7/dist-packages (from qiskit-terra==0.18.3->qiskit>=0.32.0->torchquantum==0.1.0) (4.3.3)\n",
            "Requirement already satisfied: symengine>0.7 in /usr/local/lib/python3.7/dist-packages (from qiskit-terra==0.18.3->qiskit>=0.32.0->torchquantum==0.1.0) (0.9.2)\n",
            "Requirement already satisfied: tweedledum<2.0,>=1.1 in /usr/local/lib/python3.7/dist-packages (from qiskit-terra==0.18.3->qiskit>=0.32.0->torchquantum==0.1.0) (1.1.1)\n",
            "Requirement already satisfied: ply>=3.10 in /usr/local/lib/python3.7/dist-packages (from qiskit-terra==0.18.3->qiskit>=0.32.0->torchquantum==0.1.0) (3.11)\n",
            "Requirement already satisfied: python-constraint>=1.4 in /usr/local/lib/python3.7/dist-packages (from qiskit-terra==0.18.3->qiskit>=0.32.0->torchquantum==0.1.0) (1.4.0)\n",
            "Requirement already satisfied: fastjsonschema>=2.10 in /usr/local/lib/python3.7/dist-packages (from qiskit-terra==0.18.3->qiskit>=0.32.0->torchquantum==0.1.0) (2.15.3)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py<3.3.0->qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (1.5.2)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->qiskit-terra==0.18.3->qiskit>=0.32.0->torchquantum==0.1.0) (0.18.1)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->qiskit-terra==0.18.3->qiskit>=0.32.0->torchquantum==0.1.0) (21.4.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->qiskit-terra==0.18.3->qiskit>=0.32.0->torchquantum==0.1.0) (4.11.3)\n",
            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->qiskit-terra==0.18.3->qiskit>=0.32.0->torchquantum==0.1.0) (5.4.0)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources>=1.4.0->jsonschema>=2.6->qiskit-terra==0.18.3->qiskit>=0.32.0->torchquantum==0.1.0) (3.7.0)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19->qiskit-ibmq-provider==0.18.1->qiskit>=0.32.0->torchquantum==0.1.0) (2.0.12)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19->qiskit-ibmq-provider==0.18.1->qiskit>=0.32.0->torchquantum==0.1.0) (2021.10.8)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19->qiskit-ibmq-provider==0.18.1->qiskit>=0.32.0->torchquantum==0.1.0) (2.10)\n",
            "Requirement already satisfied: ntlm-auth>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from requests-ntlm>=1.1.0->qiskit-ibmq-provider==0.18.1->qiskit>=0.32.0->torchquantum==0.1.0) (1.5.0)\n",
            "Requirement already satisfied: cryptography>=1.3 in /usr/local/lib/python3.7/dist-packages (from requests-ntlm>=1.1.0->qiskit-ibmq-provider==0.18.1->qiskit>=0.32.0->torchquantum==0.1.0) (36.0.2)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.7/dist-packages (from cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibmq-provider==0.18.1->qiskit>=0.32.0->torchquantum==0.1.0) (1.15.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.12->cryptography>=1.3->requests-ntlm>=1.1.0->qiskit-ibmq-provider==0.18.1->qiskit>=0.32.0->torchquantum==0.1.0) (2.21)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (3.1.0)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.7/dist-packages (from sympy>=1.3->qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (1.2.1)\n",
            "Requirement already satisfied: multimethod in /usr/local/lib/python3.7/dist-packages (from torchpack>=0.3.0->torchquantum==0.1.0) (1.7)\n",
            "Requirement already satisfied: loguru in /usr/local/lib/python3.7/dist-packages (from torchpack>=0.3.0->torchquantum==0.1.0) (0.6.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from torchpack>=0.3.0->torchquantum==0.1.0) (3.13)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance>=0.1.62->qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (0.0.10)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance>=0.1.62->qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (4.8.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (2018.9)\n",
            "Requirement already satisfied: inflection>=0.3.1 in /usr/local/lib/python3.7/dist-packages (from quandl->qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (0.5.1)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.7/dist-packages (from quandl->qiskit-aqua==0.9.5->qiskit>=0.32.0->torchquantum==0.1.0) (8.12.0)\n",
            "Installing collected packages: ppft, pox, fonttools, pathos, matplotlib, torchquantum\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Running setup.py develop for torchquantum\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed fonttools-4.31.2 matplotlib-3.5.1 pathos-0.2.8 pox-0.3.0 ppft-1.6.6.4 torchquantum-0.1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install --editable ."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18tqleq4O3cX"
      },
      "source": [
        "Change PYTHONPATH and install other packages."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7ZPp7FikrZz",
        "outputId": "48292375-0261-49a6-d2d8-4bc46a74725b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: PYTHONPATH=.\n"
          ]
        }
      ],
      "source": [
        "%env PYTHONPATH=."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhsBhV23PD4g"
      },
      "source": [
        "Run the following code to store a qiskit token. You can replace it with your own token from your IBMQ account if you like.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Iw9rQ6dcnrhe"
      },
      "outputs": [],
      "source": [
        "from qiskit import IBMQ\n",
        "IBMQ.save_account('0238b0afc0dc515fe7987b02706791d1719cb89b68befedc125eded0607e6e9e9f26d3eed482f66fdc45fdfceca3aab2edb9519d96b39e9c78040194b86e7858', overwrite=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nvn9EkRH5fTs",
        "outputId": "a3089d81-7781-4c78-b24f-8b4b6697e041"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting matplotlib==3.1.3\n",
            "  Downloading matplotlib-3.1.3-cp37-cp37m-manylinux1_x86_64.whl (13.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 13.1 MB 5.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (3.0.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (1.4.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (1.21.5)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib==3.1.3) (3.10.0.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib==3.1.3) (1.15.0)\n",
            "Installing collected packages: matplotlib\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.5.1\n",
            "    Uninstalling matplotlib-3.5.1:\n",
            "      Successfully uninstalled matplotlib-3.5.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchquantum 0.1.0 requires matplotlib>=3.3.2, but you have matplotlib 3.1.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed matplotlib-3.1.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install matplotlib==3.1.3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTwecaw_YyCX",
        "outputId": "44c397df-cf6f-4664-9332-fe740caad57c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "aerbackend.py  example2  example4  example6  README.md\n",
            "example1       example3  example5  example7\n"
          ]
        }
      ],
      "source": [
        "!ls artifact"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SNWF7ZlbVwVZ"
      },
      "outputs": [],
      "source": [
        "!cp artifact/aerbackend.py ../../usr/local/lib/python3.7/dist-packages/qiskit/providers/aer/backends/ -r"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QiOV-xIGKXVK"
      },
      "source": [
        "### Import modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5acspJ8G1n3",
        "outputId": "a4c4d79d-8c7f-4a3b-9e7f-61be5033326d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            " WARNING: The qiskit parameterization bug is not fixed!\n",
            "\n",
            "run python fix_qiskit_parameterization.py to fix it!\n"
          ]
        }
      ],
      "source": [
        "import argparse\n",
        "import os\n",
        "import sys\n",
        "import pdb\n",
        "import json\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.backends.cudnn\n",
        "import torch.cuda\n",
        "import torch.nn\n",
        "import torch.utils.data\n",
        "import torchquantum as tq\n",
        "\n",
        "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
        "\n",
        "from torchquantum.datasets import MNIST\n",
        "from examples.gradient_pruning.q_models import *\n",
        "from torchpack.callbacks import (InferenceRunner, MeanAbsoluteError,\n",
        "                                 MaxSaver, MinSaver,\n",
        "                                 Saver, SaverRestore, CategoricalAccuracy)\n",
        "from examples.gradient_pruning.callbacks import LegalInferenceRunner, SubnetInferenceRunner, \\\n",
        "    NLLError, TrainerRestore, AddNoiseInferenceRunner, GradRestore\n",
        "\n",
        "# from torchpack import distributed as dist\n",
        "from torchpack.environ import set_run_dir\n",
        "from torchpack.utils.config import configs\n",
        "from torchpack.utils.logging import logger\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Function to build the callbacks"
      ],
      "metadata": {
        "id": "RVT-qeHDiqPz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_subcallbacks(config):\n",
        "    subcallbacks = []\n",
        "    for subcallback in config:\n",
        "        if subcallback['metrics'] == 'CategoricalAccuracy':\n",
        "            subcallbacks.append(\n",
        "                CategoricalAccuracy(name=subcallback['name'])\n",
        "            )\n",
        "        elif subcallback['metrics'] == 'MeanAbsoluteError':\n",
        "            subcallbacks.append(\n",
        "                MeanAbsoluteError(name=subcallback['name'])\n",
        "            )\n",
        "        elif subcallback['metrics'] == 'NLLError':\n",
        "            subcallbacks.append(\n",
        "                NLLError(name=subcallback['name'])\n",
        "            )\n",
        "        else:\n",
        "            raise NotImplementedError(subcallback['metrics'])\n",
        "    return subcallbacks\n",
        "\n",
        "\n",
        "def make_callbacks(dataflow):\n",
        "    callbacks = []\n",
        "    for config in configs['callbacks']:\n",
        "        if config['callback'] == 'InferenceRunner':\n",
        "            callback = InferenceRunner(\n",
        "                dataflow=dataflow[config['split']],\n",
        "                callbacks=get_subcallbacks(config['subcallbacks'])\n",
        "            )\n",
        "        elif config['callback'] == 'LegalInferenceRunner':\n",
        "            callback = LegalInferenceRunner(\n",
        "                dataflow=dataflow[config['split']],\n",
        "                callbacks=get_subcallbacks(config['subcallbacks'])\n",
        "            )\n",
        "        elif config['callback'] == 'SubnetInferenceRunner':\n",
        "            callback = SubnetInferenceRunner(\n",
        "                dataflow=dataflow[config['split']],\n",
        "                callbacks=get_subcallbacks(config['subcallbacks']),\n",
        "                subnet=config['subnet']\n",
        "            )\n",
        "        elif config['callback'] == 'AddNoiseInferenceRunner':\n",
        "            callback = AddNoiseInferenceRunner(\n",
        "                dataflow=dataflow[config['split']],\n",
        "                callbacks=get_subcallbacks(config['subcallbacks']),\n",
        "                noise_total_prob=config['noise_total_prob']\n",
        "            )\n",
        "        elif config['callback'] == 'SaverRestore':\n",
        "            callback = SaverRestore()\n",
        "        elif config['callback'] == 'Saver':\n",
        "            callback = Saver(max_to_keep=config['max_to_keep'])\n",
        "        elif config['callback'] == 'MaxSaver':\n",
        "            callback = MaxSaver(config['name'])\n",
        "        elif config['callback'] == 'MinSaver':\n",
        "            callback = MinSaver(config['name'])\n",
        "        elif config['callback'] == 'GradRestore':\n",
        "            callback = GradRestore()\n",
        "        else:\n",
        "            raise NotImplementedError(config['callback'])\n",
        "        callbacks.append(callback)\n",
        "\n",
        "    return callbacks\n"
      ],
      "metadata": {
        "id": "CR2gWhh2imuz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Load configs\n",
        "The config file describes everything about the model structure and the hyper-parameters of the training process, including batch size, learning rate, number of epochs, and whether to use qiskit's processor(we use qiskit's noise processor to train and test our quantum circuit in the following example)."
      ],
      "metadata": {
        "id": "p-1_Yfa8z9HM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iBnWI5yqKfMB"
      },
      "outputs": [],
      "source": [
        "configs.load('examples/gradient_pruning/configs.yml')\n",
        "if configs.debug.set_seed:\n",
        "    torch.manual_seed(configs.debug.seed)\n",
        "    np.random.seed(configs.debug.seed)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Function to train\n",
        "In this function, we create the dataset and the model according to configs. And we train our quantum model using qiskit's noise processor. The function will return a list of model accuracy after each epoch with respect to the number of inferences."
      ],
      "metadata": {
        "id": "DgyvEq-QCIKC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_with_configs(configs):\n",
        "  if os.path.exists(\"runs/probabilistic_gradient_pruning/summary/scalars.jsonl\"):\n",
        "    os.remove(\"runs/probabilistic_gradient_pruning/summary/scalars.jsonl\")\n",
        "  else:\n",
        "    print(\"runs/probabilistic_gradient_pruning/summary/scalars.jsonl does not exist\")\n",
        "\n",
        "  device = torch.device('cuda')\n",
        "  if isinstance(configs.optimizer.lr, str):\n",
        "    configs.optimizer.lr = eval(configs.optimizer.lr)\n",
        "  dataset = MNIST(\n",
        "      root='./mnist_data',\n",
        "      train_valid_split_ratio=[0.9, 0.1],\n",
        "      digits_of_interest=[0, 1, 2, 3],\n",
        "      n_test_samples=30,\n",
        "      n_train_samples=50,\n",
        "      n_valid_samples=30,\n",
        "  )\n",
        "  dataflow = dict()\n",
        "  for split in dataset:\n",
        "      sampler = torch.utils.data.RandomSampler(dataset[split])\n",
        "      dataflow[split] = torch.utils.data.DataLoader(\n",
        "          dataset[split],\n",
        "          batch_size=configs.run.bsz,\n",
        "          sampler=sampler,\n",
        "          num_workers=configs.run.workers_per_gpu,\n",
        "          pin_memory=True)\n",
        "\n",
        "  model = QMultiFCModel0(configs.model.arch)\n",
        "\n",
        "  if configs.qiskit.use_qiskit_train or configs.qiskit.use_qiskit_valid:\n",
        "      from torchquantum.plugin import QiskitProcessor\n",
        "      processor = QiskitProcessor(use_real_qc=configs.qiskit.use_real_qc, n_shots=configs.qiskit.n_shots, backend_name=configs.qiskit.backend_name)\n",
        "      model.set_qiskit_processor(processor)\n",
        "\n",
        "  model.to(device)\n",
        "\n",
        "  total_params = sum(p.numel() for p in model.parameters())\n",
        "  logger.info(f'Model Size: {total_params}')\n",
        "\n",
        "  criterion = torch.nn.NLLLoss()\n",
        "  optimizer = torch.optim.Adam(\n",
        "      model.parameters(),\n",
        "      lr=configs.optimizer.lr,\n",
        "      weight_decay=configs.optimizer.weight_decay)\n",
        "  scheduler = CosineAnnealingLR(optimizer, T_max=configs.run.n_epochs)\n",
        "\n",
        "  from examples.gradient_pruning.trainers import ParamsShiftTrainer\n",
        "  trainer = ParamsShiftTrainer(model=model,\n",
        "                    criterion=criterion,\n",
        "                    optimizer=optimizer,\n",
        "                    scheduler=scheduler)\n",
        "\n",
        "  trainer.set_use_qiskit(configs)\n",
        "  run_dir = 'runs/probabilistic_gradient_pruning/'\n",
        "  set_run_dir(run_dir)\n",
        "\n",
        "  logger.info(' '.join([sys.executable] + sys.argv))\n",
        "\n",
        "  logger.info(f'Training started: \"{run_dir}\".' + '\\n' +\n",
        "      f'{configs}')\n",
        "\n",
        "  callbacks = make_callbacks(dataflow)\n",
        "\n",
        "  trainer.train_with_defaults(\n",
        "      dataflow['train'],\n",
        "      num_epochs=configs.run.n_epochs,\n",
        "      callbacks=callbacks)\n",
        "  \n",
        "  num_forward = []\n",
        "  accu = []\n",
        "  with open('runs/probabilistic_gradient_pruning/summary/scalars.jsonl', 'r') as json_file:\n",
        "      json_list = list(json_file)\n",
        "\n",
        "  for json_str in json_list:\n",
        "      result = json.loads(json_str)\n",
        "      if 'acc/test' in result.keys():\n",
        "        num_forward.append(result['global_step'])\n",
        "        accu.append(result['acc/test'])\n",
        "  \n",
        "\n",
        "  return num_forward, accu"
      ],
      "metadata": {
        "id": "bQ-PuL-Yl2iX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_forward1, accu1 = train_with_configs(configs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "id": "9RKs52mWousi",
        "outputId": "bbd2024b-dcf9-4144-e8f3-3d24b574fb0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "runs/probabilistic_gradient_pruning/summary/scalars.jsonl does not exist\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./mnist_data/MNIST/raw/train-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./mnist_data/MNIST/raw/train-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./mnist_data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./mnist_data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw\n",
            "\n",
            "[2022-03-23 12:51:09.924] Only use the front 500 images as TRAIN set.\n",
            "[2022-03-23 12:51:09.983] Only use the front 30 images as VALID set.\n",
            "[2022-03-23 12:51:09.996] Only use the front 300 images as TEST set.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2022-03-23 12:51:12.594] Cannot use MIT backend, roll back to open\n",
            "[2022-03-23 12:51:22.494] Model Size: 36\n",
            "[2022-03-23 12:51:22.554] /usr/bin/python3 /usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py -f /root/.local/share/jupyter/runtime/kernel-16eed591-b4a6-4540-88ba-fa352954d776.json\n",
            "[2022-03-23 12:51:22.558] Training started: \"runs/probabilistic_gradient_pruning/\".\n",
            "model:\n",
            "  arch:\n",
            "    n_nodes: 1\n",
            "    n_wires: 4\n",
            "    node_archs: [{'n_wires': 4, 'encoder_op_list_name': '4x4_ryzxy', 'n_blocks': 3, 'n_layers_per_block': 3, 'q_layer_name': 'barren_0'}]\n",
            "    down_sample_kernel_size: 6\n",
            "    act_norm: None\n",
            "    pruning_method: gradient_based_pruning\n",
            "    accumulation_window_size: 1\n",
            "    pruning_window_size: 2\n",
            "    pruning_ratio: 0.5\n",
            "  name: q_multifc0\n",
            "  transpile_before_run: False\n",
            "  load_op_list: False\n",
            "dataset:\n",
            "  input_name: image\n",
            "  target_name: digit\n",
            "optimizer:\n",
            "  name: adam\n",
            "  lr: 0.05\n",
            "  weight_decay: 0.0001\n",
            "  lambda_lr: 0.01\n",
            "run:\n",
            "  n_epochs: 15\n",
            "  bsz: 25\n",
            "  workers_per_gpu: 8\n",
            "  device: gpu\n",
            "debug:\n",
            "  pdb: False\n",
            "  set_seed: True\n",
            "  seed: 42\n",
            "callbacks: [{'callback': 'InferenceRunner', 'split': 'valid', 'subcallbacks': [{'metrics': 'CategoricalAccuracy', 'name': 'acc/valid'}, {'metrics': 'NLLError', 'name': 'loss/valid'}]}, {'callback': 'InferenceRunner', 'split': 'test', 'subcallbacks': [{'metrics': 'CategoricalAccuracy', 'name': 'acc/test'}, {'metrics': 'NLLError', 'name': 'loss/test'}]}, {'callback': 'MaxSaver', 'name': 'acc/valid'}, {'callback': 'Saver', 'max_to_keep': 10}]\n",
            "qiskit:\n",
            "  use_qiskit: True\n",
            "  use_real_qc: False\n",
            "  use_qiskit_train: True\n",
            "  use_qiskit_valid: True\n",
            "  backend_name: None\n",
            "  noise_model_name: None\n",
            "  basis_gates_name: None\n",
            "  n_shots: 8192\n",
            "  initial_layout: None\n",
            "  seed_transpiler: 42\n",
            "  seed_simulator: 42\n",
            "  optimization_level: 0\n",
            "  est_success_rate: False\n",
            "  max_jobs: 1\n",
            "[2022-03-23 12:51:25.368] Epoch 1/15 started.\n",
            "  0%|          | 0/20 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/qiskit/circuit/quantumcircuit.py:933: DeprecationWarning: The QuantumCircuit.combine() method is being deprecated. Use the compose() method which is more flexible w.r.t circuit register compatibility.\n",
            "  return self.combine(rhs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
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            "[loss] = 1.23, [lr/lr_group0] = 0.05, [nll_loss] = 1.23: 100%|██████████| 20/20 [13:28<00:00, 40.44s/it]\n",
            "[2022-03-23 13:04:54.195] Training finished in 13 minutes 28 seconds.\n",
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        },
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          "name": "stderr",
          "text": [
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            "  cpuset_checked))\n"
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            "[loss] = 1.12, [lr/lr_group0] = 0.0478, [nll_loss] = 1.12: 100%|██████████| 20/20 [13:06<00:00, 39.33s/it]\n",
            "[2022-03-23 13:32:07.545] Training finished in 13 minutes 6 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 13:32:07.906] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:12<00:04,  1.37s/it][2022-03-23 13:32:23.753] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 13:32:23.814] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:13<00:02,  1.29s/it][2022-03-23 13:32:24.871] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 13:32:24.940] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:15<00:01,  1.24s/it][2022-03-23 13:32:26.002] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 13:32:26.076] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:16<00:00,  1.37s/it]\n",
            "[2022-03-23 13:32:27.380] Inference finished in 16.5 seconds.\n",
            "[2022-03-23 13:32:27.394] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 13:32:27.407] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-2940.pt\".\n",
            "[2022-03-23 13:32:27.408] \n",
            "+ [acc/test] = 61\n",
            "+ [acc/valid] = 56.667\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 1.1162\n",
            "+ [loss/test] = 1.0443\n",
            "+ [loss/valid] = 1.1153\n",
            "+ [lr/lr_group0] = 0.047839\n",
            "+ [nll_loss] = 1.1162\n",
            "[2022-03-23 13:32:27.410] Estimated time left: 2 hours 44 minutes 8 seconds.\n",
            "[2022-03-23 13:32:27.418] Epoch finished in 13 minutes 26 seconds.\n",
            "[2022-03-23 13:32:27.420] Epoch 4/15 started.\n",
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            "[loss] = 1.02, [lr/lr_group0] = 0.0452, [nll_loss] = 1.02: 100%|██████████| 20/20 [14:01<00:00, 42.07s/it]\n",
            "[2022-03-23 13:46:28.859] Training finished in 14 minutes 1 second.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 13:46:29.232] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:14<00:04,  1.55s/it][2022-03-23 13:46:46.665] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 13:46:46.738] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:16<00:03,  1.54s/it][2022-03-23 13:46:48.194] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 13:46:48.266] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:17<00:01,  1.54s/it][2022-03-23 13:46:49.739] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 13:46:49.798] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:19<00:00,  1.63s/it]\n",
            "[2022-03-23 13:46:51.669] Inference finished in 19.5 seconds.\n",
            "[2022-03-23 13:46:51.677] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 13:46:51.701] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-3932.pt\".\n",
            "[2022-03-23 13:46:51.702] \n",
            "+ [acc/test] = 65\n",
            "+ [acc/valid] = 63.333\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 1.0202\n",
            "+ [loss/test] = 1.0286\n",
            "+ [loss/valid] = 1.0648\n",
            "+ [lr/lr_group0] = 0.045225\n",
            "+ [nll_loss] = 1.0202\n",
            "[2022-03-23 13:46:51.704] Estimated time left: 2 hours 32 minutes 27 seconds.\n",
            "[2022-03-23 13:46:51.706] Epoch finished in 14 minutes 24 seconds.\n",
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            "[loss] = 1.07, [lr/lr_group0] = 0.0417, [nll_loss] = 1.07: 100%|██████████| 20/20 [13:29<00:00, 40.50s/it]\n",
            "[2022-03-23 14:00:21.712] Training finished in 13 minutes 30 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 14:00:22.078] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:00:36.787] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:13<00:04,  1.44s/it][2022-03-23 14:00:38.227] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:00:38.296] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:14<00:02,  1.34s/it][2022-03-23 14:00:39.353] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:00:39.439] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:16<00:01,  1.40s/it][2022-03-23 14:00:40.876] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:00:40.946] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:17<00:00,  1.47s/it]\n",
            "[2022-03-23 14:00:42.326] Inference finished in 17.7 seconds.\n",
            "[2022-03-23 14:00:42.340] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 14:00:42.369] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-4924.pt\".\n",
            "[2022-03-23 14:00:42.371] \n",
            "+ [acc/test] = 66.667\n",
            "+ [acc/valid] = 63.333\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 1.0742\n",
            "+ [loss/test] = 1.0121\n",
            "+ [loss/valid] = 1.0645\n",
            "+ [lr/lr_group0] = 0.041728\n",
            "+ [nll_loss] = 1.0742\n",
            "[2022-03-23 14:00:42.374] Estimated time left: 2 hours 18 minutes 34 seconds.\n",
            "[2022-03-23 14:00:42.378] Epoch finished in 13 minutes 50 seconds.\n",
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            "[loss] = 1.02, [lr/lr_group0] = 0.0375, [nll_loss] = 1.02: 100%|██████████| 20/20 [13:07<00:00, 39.37s/it]\n",
            "[2022-03-23 14:13:49.708] Training finished in 13 minutes 7 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 14:13:50.104] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:13:54.747] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:14:00.826] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:13<00:04,  1.48s/it][2022-03-23 14:14:06.444] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:14:06.510] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:15<00:02,  1.49s/it][2022-03-23 14:14:07.953] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:14:08.264] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:17<00:01,  1.57s/it][2022-03-23 14:14:09.709] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:14:09.771] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:18<00:00,  1.57s/it]\n",
            "[2022-03-23 14:14:11.469] Inference finished in 18.8 seconds.\n",
            "[2022-03-23 14:14:11.474] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 14:14:11.489] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-5880.pt\".\n",
            "[2022-03-23 14:14:11.491] \n",
            "+ [acc/test] = 66.333\n",
            "+ [acc/valid] = 60\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 1.0234\n",
            "+ [loss/test] = 1.0198\n",
            "+ [loss/valid] = 1.0777\n",
            "+ [lr/lr_group0] = 0.0375\n",
            "+ [nll_loss] = 1.0234\n",
            "[2022-03-23 14:14:11.493] Estimated time left: 2 hours 4 minutes 9 seconds.\n",
            "[2022-03-23 14:14:11.495] Epoch finished in 13 minutes 29 seconds.\n",
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            "[loss] = 0.934, [lr/lr_group0] = 0.0327, [nll_loss] = 0.934: 100%|██████████| 20/20 [13:25<00:00, 40.27s/it]\n",
            "[2022-03-23 14:27:36.866] Training finished in 13 minutes 25 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 14:27:37.265] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:27:37.336] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:27:41.862] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:27:42.980] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:27:51.890] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:13<00:04,  1.51s/it][2022-03-23 14:27:53.344] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:27:53.410] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:14<00:03,  1.51s/it][2022-03-23 14:27:54.855] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:27:54.924] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:16<00:01,  1.52s/it][2022-03-23 14:27:56.399] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:27:56.464] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:17<00:00,  1.49s/it]\n",
            "[2022-03-23 14:27:58.139] Inference finished in 17.9 seconds.\n",
            "[2022-03-23 14:27:58.156] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 14:27:58.171] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-6872.pt\".\n",
            "[2022-03-23 14:27:58.173] \n",
            "+ [acc/test] = 68.667\n",
            "+ [acc/valid] = 60\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 0.9337\n",
            "+ [loss/test] = 0.997\n",
            "+ [loss/valid] = 1.0683\n",
            "+ [lr/lr_group0] = 0.032725\n",
            "+ [nll_loss] = 0.9337\n",
            "[2022-03-23 14:27:58.176] Estimated time left: 1 hour 50 minutes 20 seconds.\n",
            "[2022-03-23 14:27:58.178] Epoch finished in 13 minutes 46 seconds.\n",
            "[2022-03-23 14:27:58.181] Epoch 8/15 started.\n",
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            "[loss] = 0.957, [lr/lr_group0] = 0.0276, [nll_loss] = 0.957: 100%|██████████| 20/20 [13:17<00:00, 39.89s/it]\n",
            "[2022-03-23 14:41:15.898] Training finished in 13 minutes 17 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 14:41:16.308] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:41:16.381] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:41:19.264] Inference finished in 3.36 seconds.\n",
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            "[2022-03-23 14:41:27.064] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 67%|██████▋   | 8/12 [00:12<00:06,  1.52s/it][2022-03-23 14:41:31.541] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:41:31.612] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:13<00:04,  1.47s/it][2022-03-23 14:41:32.895] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:41:32.976] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:14<00:02,  1.37s/it][2022-03-23 14:41:34.034] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:41:34.101] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:16<00:01,  1.41s/it][2022-03-23 14:41:35.550] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:41:35.622] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:18<00:00,  1.50s/it]\n",
            "[2022-03-23 14:41:37.326] Inference finished in 18 seconds.\n",
            "[2022-03-23 14:41:37.330] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 14:41:37.344] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-7864.pt\".\n",
            "[2022-03-23 14:41:37.346] \n",
            "+ [acc/test] = 67.667\n",
            "+ [acc/valid] = 60\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 0.95734\n",
            "+ [loss/test] = 0.99281\n",
            "+ [loss/valid] = 1.0654\n",
            "+ [lr/lr_group0] = 0.027613\n",
            "+ [nll_loss] = 0.95734\n",
            "[2022-03-23 14:41:37.348] Estimated time left: 1 hour 36 minutes 25 seconds.\n",
            "[2022-03-23 14:41:37.350] Epoch finished in 13 minutes 39 seconds.\n",
            "[2022-03-23 14:41:37.352] Epoch 9/15 started.\n",
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            "[loss] = 0.961, [lr/lr_group0] = 0.0224, [nll_loss] = 0.961: 100%|██████████| 20/20 [12:58<00:00, 38.91s/it]\n",
            "[2022-03-23 14:54:35.493] Training finished in 12 minutes 58 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 14:54:35.877] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:54:35.954] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:54:37.096] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:54:38.446] Inference finished in 2.95 seconds.\n",
            "  0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 14:54:38.911] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 14:54:38.989] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:54:40.531] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:54:41.889] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:54:43.419] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:54:46.058] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:54:47.179] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:54:48.702] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:54:50.211] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:54:51.727] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 14:54:56.051] Inference finished in 17.6 seconds.\n",
            "[2022-03-23 14:54:56.057] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 14:54:56.072] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-8820.pt\".\n",
            "[2022-03-23 14:54:56.074] \n",
            "+ [acc/test] = 67.667\n",
            "+ [acc/valid] = 60\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 0.96125\n",
            "+ [loss/test] = 0.994\n",
            "+ [loss/valid] = 1.0689\n",
            "+ [lr/lr_group0] = 0.022387\n",
            "+ [nll_loss] = 0.96125\n",
            "[2022-03-23 14:54:56.076] Estimated time left: 1 hour 22 minutes 14 seconds.\n",
            "[2022-03-23 14:54:56.078] Epoch finished in 13 minutes 18 seconds.\n",
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            "[loss] = 1.03, [lr/lr_group0] = 0.0173, [nll_loss] = 1.03: 100%|██████████| 20/20 [13:15<00:00, 39.76s/it]\n",
            "[2022-03-23 15:08:11.385] Training finished in 13 minutes 15 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 15:08:11.774] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 15:08:14.756] Inference finished in 3.36 seconds.\n",
            "  0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 15:08:15.197] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 15:08:30.723] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:17<00:00,  1.47s/it]\n",
            "[2022-03-23 15:08:32.419] Inference finished in 17.7 seconds.\n",
            "[2022-03-23 15:08:32.435] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 15:08:32.450] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-9812.pt\".\n",
            "[2022-03-23 15:08:32.453] \n",
            "+ [acc/test] = 68.333\n",
            "+ [acc/valid] = 56.667\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 1.0284\n",
            "+ [loss/test] = 0.98941\n",
            "+ [loss/valid] = 1.0661\n",
            "+ [lr/lr_group0] = 0.017275\n",
            "+ [nll_loss] = 1.0284\n",
            "[2022-03-23 15:08:32.456] Estimated time left: 1 hour 8 minutes 27 seconds.\n",
            "[2022-03-23 15:08:32.459] Epoch finished in 13 minutes 36 seconds.\n",
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            "[loss] = 0.942, [lr/lr_group0] = 0.0125, [nll_loss] = 0.942: 100%|██████████| 20/20 [13:27<00:00, 40.38s/it]\n",
            "[2022-03-23 15:22:00.038] Training finished in 13 minutes 27 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 15:22:00.434] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 15:22:00.517] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 15:22:02.057] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 15:22:03.415] Inference finished in 3.37 seconds.\n",
            "  0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 15:22:03.892] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 15:22:03.982] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 15:22:05.134] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 15:22:06.649] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 25%|██▌       | 3/12 [00:04<00:13,  1.55s/it][2022-03-23 15:22:08.111] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 15:22:18.782] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:16<00:01,  1.47s/it][2022-03-23 15:22:20.229] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 15:22:20.307] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:18<00:00,  1.55s/it]\n",
            "[2022-03-23 15:22:22.018] Inference finished in 18.6 seconds.\n",
            "[2022-03-23 15:22:22.021] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 15:22:22.039] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-10804.pt\".\n",
            "[2022-03-23 15:22:22.045] \n",
            "+ [acc/test] = 70\n",
            "+ [acc/valid] = 56.667\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 0.94174\n",
            "+ [loss/test] = 0.98321\n",
            "+ [loss/valid] = 1.059\n",
            "+ [lr/lr_group0] = 0.0125\n",
            "+ [nll_loss] = 0.94174\n",
            "[2022-03-23 15:22:22.047] Estimated time left: 54 minutes 57 seconds.\n",
            "[2022-03-23 15:22:22.052] Epoch finished in 13 minutes 49 seconds.\n",
            "[2022-03-23 15:22:22.053] Epoch 12/15 started.\n",
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            "[loss] = 0.871, [lr/lr_group0] = 0.00827, [nll_loss] = 0.871: 100%|██████████| 20/20 [13:05<00:00, 39.29s/it]\n",
            "[2022-03-23 15:35:27.839] Training finished in 13 minutes 5 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 15:35:28.256] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 15:35:28.343] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "  0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 15:35:31.405] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 15:35:37.982] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 15:35:45.354] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:15<00:03,  1.52s/it][2022-03-23 15:35:46.807] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 15:35:46.893] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:17<00:01,  1.53s/it][2022-03-23 15:35:48.356] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 15:35:48.430] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:19<00:00,  1.60s/it]\n",
            "[2022-03-23 15:35:50.131] Inference finished in 19.2 seconds.\n",
            "[2022-03-23 15:35:50.144] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 15:35:50.160] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-11760.pt\".\n",
            "[2022-03-23 15:35:50.162] \n",
            "+ [acc/test] = 67.667\n",
            "+ [acc/valid] = 56.667\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 0.87124\n",
            "+ [loss/test] = 0.98594\n",
            "+ [loss/valid] = 1.0628\n",
            "+ [lr/lr_group0] = 0.0082717\n",
            "+ [nll_loss] = 0.87124\n",
            "[2022-03-23 15:35:50.165] Estimated time left: 40 minutes 51 seconds.\n",
            "[2022-03-23 15:35:50.167] Epoch finished in 13 minutes 28 seconds.\n",
            "[2022-03-23 15:35:50.169] Epoch 13/15 started.\n",
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            "[loss] = 0.971, [lr/lr_group0] = 0.00477, [nll_loss] = 0.971: 100%|██████████| 20/20 [13:15<00:00, 39.77s/it]\n",
            "[2022-03-23 15:49:05.507] Training finished in 13 minutes 15 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 15:49:05.913] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:13<00:04,  1.44s/it][2022-03-23 15:49:22.555] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 15:49:22.634] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:15<00:02,  1.46s/it][2022-03-23 15:49:24.072] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 15:49:24.152] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:16<00:01,  1.36s/it][2022-03-23 15:49:25.205] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 15:49:25.274] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:17<00:00,  1.47s/it]\n",
            "[2022-03-23 15:49:26.580] Inference finished in 17.7 seconds.\n",
            "[2022-03-23 15:49:26.595] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 15:49:26.620] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-12752.pt\".\n",
            "[2022-03-23 15:49:26.621] \n",
            "+ [acc/test] = 68.667\n",
            "+ [acc/valid] = 56.667\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 0.97094\n",
            "+ [loss/test] = 0.98713\n",
            "+ [loss/valid] = 1.0617\n",
            "+ [lr/lr_group0] = 0.0047746\n",
            "+ [nll_loss] = 0.97094\n",
            "[2022-03-23 15:49:26.624] Estimated time left: 27 minutes 11 seconds.\n",
            "[2022-03-23 15:49:26.625] Epoch finished in 13 minutes 36 seconds.\n",
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            "[loss] = 1.04, [lr/lr_group0] = 0.00216, [nll_loss] = 1.04: 100%|██████████| 20/20 [13:21<00:00, 40.07s/it]\n",
            "[2022-03-23 16:02:47.994] Training finished in 13 minutes 21 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 16:02:48.400] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 16:03:03.566] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:13<00:04,  1.43s/it][2022-03-23 16:03:05.001] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 16:03:05.076] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:15<00:02,  1.46s/it][2022-03-23 16:03:06.516] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 16:03:06.598] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:16<00:01,  1.48s/it][2022-03-23 16:03:08.053] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 16:03:08.130] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:18<00:00,  1.54s/it]\n",
            "[2022-03-23 16:03:09.839] Inference finished in 18.4 seconds.\n",
            "[2022-03-23 16:03:09.847] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 16:03:09.861] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-13744.pt\".\n",
            "[2022-03-23 16:03:09.863] \n",
            "+ [acc/test] = 69.333\n",
            "+ [acc/valid] = 56.667\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 1.0415\n",
            "+ [loss/test] = 0.98499\n",
            "+ [loss/valid] = 1.0606\n",
            "+ [lr/lr_group0] = 0.0021614\n",
            "+ [nll_loss] = 1.0415\n",
            "[2022-03-23 16:03:09.865] Estimated time left: 13 minutes 37 seconds.\n",
            "[2022-03-23 16:03:09.867] Epoch finished in 13 minutes 43 seconds.\n",
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            "[loss] = 0.894, [lr/lr_group0] = 0.000546, [nll_loss] = 0.894: 100%|██████████| 20/20 [12:51<00:00, 38.56s/it]\n",
            "[2022-03-23 16:16:01.019] Training finished in 12 minutes 51 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 16:16:01.424] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 16:16:01.504] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 16:16:04.407] Inference finished in 3.38 seconds.\n",
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            "[2022-03-23 16:16:12.232] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 16:16:15.282] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 16:16:16.804] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:13<00:04,  1.40s/it][2022-03-23 16:16:17.860] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 16:16:17.943] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:14<00:02,  1.32s/it][2022-03-23 16:16:19.001] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 16:16:19.075] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:16<00:01,  1.46s/it][2022-03-23 16:16:20.768] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 16:16:20.847] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
            "Job Status: job has successfully run\n",
            "100%|██████████| 12/12 [00:18<00:00,  1.51s/it]\n",
            "[2022-03-23 16:16:22.536] Inference finished in 18.1 seconds.\n",
            "[2022-03-23 16:16:22.550] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 16:16:22.564] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-14700.pt\".\n",
            "[2022-03-23 16:16:22.566] \n",
            "+ [acc/test] = 69\n",
            "+ [acc/valid] = 56.667\n",
            "+ [acc/valid/max] = 76.667\n",
            "+ [loss] = 0.89366\n",
            "+ [loss/test] = 0.98533\n",
            "+ [loss/valid] = 1.0605\n",
            "+ [lr/lr_group0] = 0.00054631\n",
            "+ [nll_loss] = 0.89366\n",
            "[2022-03-23 16:16:22.567] Epoch finished in 13 minutes 12 seconds.\n",
            "[2022-03-23 16:16:22.569] 15 epochs of training finished in 3 hours 24 minutes 57 seconds.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "configs.model.arch.pruning_method=None\n",
        "num_forward2, accu2 = train_with_configs(configs)"
      ],
      "metadata": {
        "id": "hJAHzIOxrj5p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d12a8e4f-734d-45c7-89a9-562e197f8003"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2022-03-23 16:16:39.545] Only use the front 500 images as TRAIN set.\n",
            "[2022-03-23 16:16:39.608] Only use the front 30 images as VALID set.\n",
            "[2022-03-23 16:16:39.622] Only use the front 300 images as TEST set.\n",
            "[2022-03-23 16:16:39.634] Not use any pruning\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "ibmqfactory.load_account:WARNING:2022-03-23 16:16:40,153: Credentials are already in use. The existing account in the session will be replaced.\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2022-03-23 16:16:42.263] Cannot use MIT backend, roll back to open\n",
            "[2022-03-23 16:16:42.269] Model Size: 36\n",
            "[2022-03-23 16:16:42.275] /usr/bin/python3 /usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py -f /root/.local/share/jupyter/runtime/kernel-16eed591-b4a6-4540-88ba-fa352954d776.json\n",
            "[2022-03-23 16:16:42.277] Training started: \"runs/probabilistic_gradient_pruning/\".\n",
            "model:\n",
            "  arch:\n",
            "    n_nodes: 1\n",
            "    n_wires: 4\n",
            "    node_archs: [{'n_wires': 4, 'encoder_op_list_name': '4x4_ryzxy', 'n_blocks': 3, 'n_layers_per_block': 3, 'q_layer_name': 'barren_0'}]\n",
            "    down_sample_kernel_size: 6\n",
            "    act_norm: None\n",
            "    pruning_method: None\n",
            "    accumulation_window_size: 1\n",
            "    pruning_window_size: 2\n",
            "    pruning_ratio: 0.5\n",
            "  name: q_multifc0\n",
            "  transpile_before_run: False\n",
            "  load_op_list: False\n",
            "dataset:\n",
            "  input_name: image\n",
            "  target_name: digit\n",
            "optimizer:\n",
            "  name: adam\n",
            "  lr: 0.05\n",
            "  weight_decay: 0.0001\n",
            "  lambda_lr: 0.01\n",
            "run:\n",
            "  n_epochs: 15\n",
            "  bsz: 25\n",
            "  workers_per_gpu: 8\n",
            "  device: gpu\n",
            "debug:\n",
            "  pdb: False\n",
            "  set_seed: True\n",
            "  seed: 42\n",
            "callbacks: [{'callback': 'InferenceRunner', 'split': 'valid', 'subcallbacks': [{'metrics': 'CategoricalAccuracy', 'name': 'acc/valid'}, {'metrics': 'NLLError', 'name': 'loss/valid'}]}, {'callback': 'InferenceRunner', 'split': 'test', 'subcallbacks': [{'metrics': 'CategoricalAccuracy', 'name': 'acc/test'}, {'metrics': 'NLLError', 'name': 'loss/test'}]}, {'callback': 'MaxSaver', 'name': 'acc/valid'}, {'callback': 'Saver', 'max_to_keep': 10}]\n",
            "qiskit:\n",
            "  use_qiskit: True\n",
            "  use_real_qc: False\n",
            "  use_qiskit_train: True\n",
            "  use_qiskit_valid: True\n",
            "  backend_name: None\n",
            "  noise_model_name: None\n",
            "  basis_gates_name: None\n",
            "  n_shots: 8192\n",
            "  initial_layout: None\n",
            "  seed_transpiler: 42\n",
            "  seed_simulator: 42\n",
            "  optimization_level: 0\n",
            "  est_success_rate: False\n",
            "  max_jobs: 1\n",
            "[2022-03-23 16:16:43.012] Epoch 1/15 started.\n",
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            "[loss] = 1.04, [lr/lr_group0] = 0.05, [nll_loss] = 1.04: 100%|██████████| 20/20 [19:46<00:00, 59.32s/it]\n",
            "[2022-03-23 16:36:29.500] Training finished in 19 minutes 46 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[loss] = 1.04, [lr/lr_group0] = 0.0327, [nll_loss] = 1.04: 100%|██████████| 20/20 [19:10<00:00, 57.51s/it]\n",
            "[2022-03-23 18:34:29.230] Training finished in 19 minutes 10 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 18:34:29.645] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 18:34:29.719] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 18:34:31.234] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 18:34:32.583] Inference finished in 3.35 seconds.\n",
            "  0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 18:34:33.072] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 18:34:35.818] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 18:34:41.905] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 18:34:45.268] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:15<00:01,  1.43s/it][2022-03-23 18:34:48.450] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 18:34:48.513] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:17<00:00,  1.43s/it]\n",
            "[2022-03-23 18:34:49.818] Inference finished in 17.2 seconds.\n",
            "[2022-03-23 18:34:49.833] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 18:34:49.853] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-10220.pt\".\n",
            "[2022-03-23 18:34:49.858] \n",
            "+ [acc/test] = 67.333\n",
            "+ [acc/valid] = 60\n",
            "+ [acc/valid/max] = 60\n",
            "+ [loss] = 1.0416\n",
            "+ [loss/test] = 0.98833\n",
            "+ [loss/valid] = 1.0702\n",
            "+ [lr/lr_group0] = 0.032725\n",
            "+ [nll_loss] = 1.0416\n",
            "[2022-03-23 18:34:49.862] Estimated time left: 2 hours 37 minutes 50 seconds.\n",
            "[2022-03-23 18:34:49.867] Epoch finished in 19 minutes 30 seconds.\n",
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            "[loss] = 0.97, [lr/lr_group0] = 0.0276, [nll_loss] = 0.97: 100%|██████████| 20/20 [19:36<00:00, 58.82s/it]\n",
            "[2022-03-23 18:54:26.370] Training finished in 19 minutes 36 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 18:54:26.809] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            "  0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 18:54:30.587] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 18:54:31.835] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 18:54:37.946] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 18:54:42.526] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:13<00:04,  1.53s/it][2022-03-23 18:54:43.986] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 18:54:44.059] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:15<00:03,  1.53s/it][2022-03-23 18:54:45.508] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 18:54:45.577] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:16<00:01,  1.53s/it][2022-03-23 18:54:47.037] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 18:54:47.105] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:19<00:00,  1.59s/it]\n",
            "[2022-03-23 18:54:49.109] Inference finished in 19 seconds.\n",
            "[2022-03-23 18:54:49.119] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 18:54:49.135] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/max-acc-valid.pt\" (63.333).\n",
            "[2022-03-23 18:54:49.138] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 18:54:49.150] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-11680.pt\".\n",
            "[2022-03-23 18:54:49.153] \n",
            "+ [acc/test] = 67.333\n",
            "+ [acc/valid] = 63.333\n",
            "+ [acc/valid/max] = 63.333\n",
            "+ [loss] = 0.96974\n",
            "+ [loss/test] = 0.98923\n",
            "+ [loss/valid] = 1.0744\n",
            "+ [lr/lr_group0] = 0.027613\n",
            "+ [nll_loss] = 0.96974\n",
            "[2022-03-23 18:54:49.155] Estimated time left: 2 hours 18 minutes 20 seconds.\n",
            "[2022-03-23 18:54:49.157] Epoch finished in 19 minutes 59 seconds.\n",
            "[2022-03-23 18:54:49.159] Epoch 9/15 started.\n",
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            "[loss] = 0.982, [lr/lr_group0] = 0.0224, [nll_loss] = 0.982: 100%|██████████| 20/20 [19:21<00:00, 58.06s/it]\n",
            "[2022-03-23 19:14:10.434] Training finished in 19 minutes 21 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 19:14:10.869] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:14:10.951] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 19:14:13.856] Inference finished in 3.42 seconds.\n",
            "  0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 19:14:14.360] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 19:14:17.524] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 19:14:26.917] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:14<00:04,  1.54s/it][2022-03-23 19:14:28.382] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:14:28.455] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:16<00:03,  1.54s/it][2022-03-23 19:14:29.905] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:14:29.976] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:17<00:01,  1.54s/it][2022-03-23 19:14:31.439] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:14:31.514] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:19<00:00,  1.61s/it]\n",
            "[2022-03-23 19:14:33.242] Inference finished in 19.4 seconds.\n",
            "[2022-03-23 19:14:33.251] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 19:14:33.274] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-13140.pt\".\n",
            "[2022-03-23 19:14:33.280] \n",
            "+ [acc/test] = 68.333\n",
            "+ [acc/valid] = 63.333\n",
            "+ [acc/valid/max] = 63.333\n",
            "+ [loss] = 0.98214\n",
            "+ [loss/test] = 0.983\n",
            "+ [loss/valid] = 1.0687\n",
            "+ [lr/lr_group0] = 0.022387\n",
            "+ [nll_loss] = 0.98214\n",
            "[2022-03-23 19:14:33.282] Estimated time left: 1 hour 58 minutes 18 seconds.\n",
            "[2022-03-23 19:14:33.287] Epoch finished in 19 minutes 44 seconds.\n",
            "[2022-03-23 19:14:33.289] Epoch 10/15 started.\n",
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            "[loss] = 0.944, [lr/lr_group0] = 0.0173, [nll_loss] = 0.944: 100%|██████████| 20/20 [19:50<00:00, 59.51s/it]\n",
            "[2022-03-23 19:34:23.474] Training finished in 19 minutes 50 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 19:34:23.911] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:24.002] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 19:34:25.559] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 2/2 [00:03<00:00,  1.71s/it]\n",
            "[2022-03-23 19:34:26.914] Inference finished in 3.43 seconds.\n",
            "  0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 19:34:27.421] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:27.515] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "  8%|▊         | 1/12 [00:02<00:22,  2.03s/it][2022-03-23 19:34:28.976] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:29.054] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 19:34:30.587] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 25%|██▌       | 3/12 [00:05<00:14,  1.64s/it][2022-03-23 19:34:32.038] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:32.111] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 33%|███▎      | 4/12 [00:06<00:11,  1.44s/it][2022-03-23 19:34:33.172] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:33.242] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 42%|████▏     | 5/12 [00:07<00:10,  1.47s/it][2022-03-23 19:34:34.691] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:34.771] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 50%|█████     | 6/12 [00:09<00:08,  1.49s/it][2022-03-23 19:34:36.212] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:36.283] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 58%|█████▊    | 7/12 [00:10<00:06,  1.37s/it][2022-03-23 19:34:37.341] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:37.414] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 67%|██████▋   | 8/12 [00:11<00:05,  1.29s/it][2022-03-23 19:34:38.470] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:38.545] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:13<00:04,  1.37s/it][2022-03-23 19:34:39.998] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:40.075] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:14<00:02,  1.41s/it][2022-03-23 19:34:41.519] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:41.593] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:15<00:01,  1.40s/it][2022-03-23 19:34:42.899] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:34:42.972] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:17<00:00,  1.48s/it]\n",
            "[2022-03-23 19:34:44.669] Inference finished in 17.7 seconds.\n",
            "[2022-03-23 19:34:44.680] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 19:34:44.702] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-14600.pt\".\n",
            "[2022-03-23 19:34:44.707] \n",
            "+ [acc/test] = 67.333\n",
            "+ [acc/valid] = 63.333\n",
            "+ [acc/valid/max] = 63.333\n",
            "+ [loss] = 0.94449\n",
            "+ [loss/test] = 0.98667\n",
            "+ [loss/valid] = 1.0679\n",
            "+ [lr/lr_group0] = 0.017275\n",
            "+ [nll_loss] = 0.94449\n",
            "[2022-03-23 19:34:44.708] Estimated time left: 1 hour 38 minutes 51 seconds.\n",
            "[2022-03-23 19:34:44.709] Epoch finished in 20 minutes 11 seconds.\n",
            "[2022-03-23 19:34:44.711] Epoch 11/15 started.\n",
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            "[loss] = 1.07, [lr/lr_group0] = 0.0125, [nll_loss] = 1.07: 100%|██████████| 20/20 [19:30<00:00, 58.51s/it]\n",
            "[2022-03-23 19:54:14.921] Training finished in 19 minutes 30 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 19:54:15.360] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:13<00:04,  1.46s/it][2022-03-23 19:54:31.627] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:54:31.694] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:14<00:02,  1.36s/it][2022-03-23 19:54:32.750] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:54:32.825] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:15<00:01,  1.29s/it][2022-03-23 19:54:33.896] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 19:54:33.973] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:17<00:00,  1.44s/it]\n",
            "[2022-03-23 19:54:35.658] Inference finished in 17.3 seconds.\n",
            "[2022-03-23 19:54:35.674] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 19:54:35.692] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-16060.pt\".\n",
            "[2022-03-23 19:54:35.697] \n",
            "+ [acc/test] = 67\n",
            "+ [acc/valid] = 63.333\n",
            "+ [acc/valid/max] = 63.333\n",
            "+ [loss] = 1.0701\n",
            "+ [loss/test] = 0.98661\n",
            "+ [loss/valid] = 1.0738\n",
            "+ [lr/lr_group0] = 0.0125\n",
            "+ [nll_loss] = 1.0701\n",
            "[2022-03-23 19:54:35.702] Estimated time left: 1 hour 19 minutes 14 seconds.\n",
            "[2022-03-23 19:54:35.703] Epoch finished in 19 minutes 50 seconds.\n",
            "[2022-03-23 19:54:35.708] Epoch 12/15 started.\n",
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            "[loss] = 0.98, [lr/lr_group0] = 0.00827, [nll_loss] = 0.98: 100%|██████████| 20/20 [19:29<00:00, 58.48s/it]\n",
            "[2022-03-23 20:14:05.419] Training finished in 19 minutes 29 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 20:14:05.853] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:14:05.938] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 20:14:08.434] Inference finished in 3.01 seconds.\n",
            "  0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 20:14:08.935] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:12<00:03,  1.26s/it][2022-03-23 20:14:20.746] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:14:20.826] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:14<00:02,  1.42s/it][2022-03-23 20:14:22.525] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:14:22.599] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:15<00:01,  1.45s/it][2022-03-23 20:14:24.045] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:14:24.113] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:17<00:00,  1.45s/it]\n",
            "[2022-03-23 20:14:25.808] Inference finished in 17.4 seconds.\n",
            "[2022-03-23 20:14:25.821] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 20:14:25.837] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-17520.pt\".\n",
            "[2022-03-23 20:14:25.838] \n",
            "+ [acc/test] = 68.333\n",
            "+ [acc/valid] = 60\n",
            "+ [acc/valid/max] = 63.333\n",
            "+ [loss] = 0.97979\n",
            "+ [loss/test] = 0.98503\n",
            "+ [loss/valid] = 1.0691\n",
            "+ [lr/lr_group0] = 0.0082717\n",
            "+ [nll_loss] = 0.97979\n",
            "[2022-03-23 20:14:25.847] Estimated time left: 59 minutes 30 seconds.\n",
            "[2022-03-23 20:14:25.851] Epoch finished in 19 minutes 50 seconds.\n",
            "[2022-03-23 20:14:25.852] Epoch 13/15 started.\n",
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            "[loss] = 0.984, [lr/lr_group0] = 0.00477, [nll_loss] = 0.984: 100%|██████████| 20/20 [19:10<00:00, 57.52s/it]\n",
            "[2022-03-23 20:33:36.341] Training finished in 19 minutes 10 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 20:33:36.794] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:33:36.870] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 50%|█████     | 1/2 [00:01<00:01,  1.57s/it][2022-03-23 20:33:37.941] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:33:38.007] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 2/2 [00:03<00:00,  1.52s/it]\n",
            "[2022-03-23 20:33:39.397] Inference finished in 3.05 seconds.\n",
            "  0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 20:33:39.902] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:33:39.995] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "  8%|▊         | 1/12 [00:01<00:18,  1.64s/it][2022-03-23 20:33:41.066] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:33:41.135] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 17%|█▋        | 2/12 [00:02<00:13,  1.34s/it][2022-03-23 20:33:42.191] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:33:42.270] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 20:33:50.980] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:13<00:02,  1.46s/it][2022-03-23 20:33:52.660] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:33:52.747] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:14<00:01,  1.48s/it][2022-03-23 20:33:54.204] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:33:54.279] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:16<00:00,  1.35s/it]\n",
            "[2022-03-23 20:33:55.586] Inference finished in 16.2 seconds.\n",
            "[2022-03-23 20:33:55.595] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 20:33:55.611] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-18980.pt\".\n",
            "[2022-03-23 20:33:55.613] \n",
            "+ [acc/test] = 67.667\n",
            "+ [acc/valid] = 63.333\n",
            "+ [acc/valid/max] = 63.333\n",
            "+ [loss] = 0.98411\n",
            "+ [loss/test] = 0.98492\n",
            "+ [loss/valid] = 1.0692\n",
            "+ [lr/lr_group0] = 0.0047746\n",
            "+ [nll_loss] = 0.98411\n",
            "[2022-03-23 20:33:55.616] Estimated time left: 39 minutes 38 seconds.\n",
            "[2022-03-23 20:33:55.618] Epoch finished in 19 minutes 29 seconds.\n",
            "[2022-03-23 20:33:55.620] Epoch 14/15 started.\n",
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            "[loss] = 1, [lr/lr_group0] = 0.00216, [nll_loss] = 1: 100%|██████████| 20/20 [19:05<00:00, 57.28s/it]\n",
            "[2022-03-23 20:53:01.141] Training finished in 19 minutes 5 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 20:53:01.574] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
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            " 75%|███████▌  | 9/12 [00:13<00:04,  1.41s/it][2022-03-23 20:53:18.116] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:53:18.191] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 83%|████████▎ | 10/12 [00:15<00:03,  1.52s/it][2022-03-23 20:53:19.876] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:53:19.952] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            " 92%|█████████▏| 11/12 [00:16<00:01,  1.40s/it][2022-03-23 20:53:21.019] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 20:53:21.091] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 12/12 [00:17<00:00,  1.48s/it]\n",
            "[2022-03-23 20:53:22.405] Inference finished in 17.8 seconds.\n",
            "[2022-03-23 20:53:22.410] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 20:53:22.427] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-20440.pt\".\n",
            "[2022-03-23 20:53:22.429] \n",
            "+ [acc/test] = 67.667\n",
            "+ [acc/valid] = 63.333\n",
            "+ [acc/valid/max] = 63.333\n",
            "+ [loss] = 1.0041\n",
            "+ [loss/test] = 0.98459\n",
            "+ [loss/valid] = 1.0707\n",
            "+ [lr/lr_group0] = 0.0021614\n",
            "+ [nll_loss] = 1.0041\n",
            "[2022-03-23 20:53:22.431] Estimated time left: 19 minutes 45 seconds.\n",
            "[2022-03-23 20:53:22.433] Epoch finished in 19 minutes 26 seconds.\n",
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            "[loss] = 0.915, [lr/lr_group0] = 0.000546, [nll_loss] = 0.915: 100%|██████████| 20/20 [19:30<00:00, 58.53s/it]\n",
            "[2022-03-23 21:12:53.004] Training finished in 19 minutes 30 seconds.\n",
            "  0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 21:12:53.440] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 21:12:53.770] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 21:12:55.293] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "100%|██████████| 2/2 [00:03<00:00,  1.82s/it]\n",
            "[2022-03-23 21:12:56.670] Inference finished in 3.66 seconds.\n",
            "  0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 21:12:57.178] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 21:12:57.267] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 21:12:58.404] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 21:12:59.538] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 21:13:01.054] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 21:13:04.086] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 21:13:05.594] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 21:13:07.109] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 21:13:08.613] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
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            "[2022-03-23 21:13:09.734] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
            "Job Status: job has successfully run\n",
            " 83%|████████▎ | 10/12 [00:14<00:02,  1.43s/it][2022-03-23 21:13:11.196] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 21:13:11.263] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
            "Job Status: job has successfully run\n",
            " 92%|█████████▏| 11/12 [00:16<00:01,  1.45s/it][2022-03-23 21:13:12.702] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}\n",
            "[2022-03-23 21:13:12.780] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}\n",
            "Job Status: job has successfully run\n",
            "100%|██████████| 12/12 [00:18<00:00,  1.50s/it]\n",
            "[2022-03-23 21:13:14.722] Inference finished in 18.1 seconds.\n",
            "[2022-03-23 21:13:14.729] No q_layer_op_list found, will not save it.\n",
            "[2022-03-23 21:13:14.747] Checkpoint saved: \"runs/probabilistic_gradient_pruning/checkpoints/step-21900.pt\".\n",
            "[2022-03-23 21:13:14.752] \n",
            "+ [acc/test] = 68\n",
            "+ [acc/valid] = 63.333\n",
            "+ [acc/valid/max] = 63.333\n",
            "+ [loss] = 0.91464\n",
            "+ [loss/test] = 0.98475\n",
            "+ [loss/valid] = 1.0677\n",
            "+ [lr/lr_group0] = 0.00054631\n",
            "+ [nll_loss] = 0.91464\n",
            "[2022-03-23 21:13:14.756] Epoch finished in 19 minutes 52 seconds.\n",
            "[2022-03-23 21:13:14.757] 15 epochs of training finished in 4 hours 56 minutes 32 seconds.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plot and compare the accuracy curves"
      ],
      "metadata": {
        "id": "1MPk61-oTsji"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(num_forward1, accu1, marker='o', label=\"with gradient pruning\")\n",
        "plt.plot(num_forward2, accu2, marker='o', label=\"without gradient pruning\")\n",
        "plt.ylabel(\"test accuracy\")\n",
        "plt.xlabel(\"num of forwards\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ZRpRBc-FG2WP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e2ec1dc-ffb3-4cdf-f53d-cdcf730791a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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vGvj8Dsg5BSJQWGpkrG8gRWNm85ddF7EkPoWZk3px/cXVCGwVOHjyDA9+Gk/8gXRuvKQj/zumBwFpe+CzWyB1N1z+OFz+mNUQXIfSTudx8T9XcsugcP4+pgdrdh/jjvmxDOzcjPdvHYC/T6ny7Pza6sFkgAlvQvextTv47mWw5G4wRTD+DceqSeqDvNNW0D/wsxX0nVVldSgeYmbCb8utNqVB98MlfwT/IOfsvyaKCq1fJknrrKv6/T9DXqb1XIuuZ6tCwy+DJq3K1/GD1dlh7Oxq1fHXeVWP7aBrgTuMMbtF5GmgMTAGuMcYEyMiVwIvGWP6VbYfDfzu8dEv+/nbkm18cd9g+pTuhVJUaGtcewladoWp861GSTsNcfmFRdw+P5Yff0ulWWM/0k7nVauhtXSu+qaN/TiTm4+vtzczJ/dmdO9Sv0LyTsM3j8CvH0PE5VYgaWKnEdWFxr6+lu2HMij+SIWFBrD8T0MJCrB1TSzIs67oNrwJbaOsqp1mERXtrnrS9sOi26yryEvuhhHPgY/9X2L1Qt5p+GgaHPgJJr0LvaY4/xgpcVYV0J4V1hfApQ9A45bW+7Zsg3FtbFlY/r3fc7L1mUhaZ/3t/wlybb8+mnexOjaEXwadLoOg1o7vt5pldVfgjwLeA/yAROA2IBKrt48PkAPca4yJq2w/GvjdY/wb68gtKGLZQ0POVlNkHoXFd8C+H6HPDTD6ZfBrXOl+Fmw8wIwlWyn9Vgvw8WLGtd0YFdmmwu1WbD/CzG93kVNQVLJMBP5+bXduH9LZ/kbx/4Vv/mLV9U6ea33A6sDS+BQeW7SFvMKzZQ3w8WLm5N7WF1z6AatOPiUWLr4LRv7D6p7oTAV5sPIp2PAWhEVbXyxNazf3gEvknYGPp8H+9VbPrN5TXXu85DiIeQF+/778cz4BcNWz0H1Mzfa982tY+SQUlBq7Il7g5Q+Ftqv15hfaruZtwT6o4ve8s7kl8DuLBv66t+tIBle/tvbcnir7frRV7WRYAb/vTQ7tq6L0CTVVZdqFo9utXj8n98Kwv8KQv9S+J1EVKk0RMTEPlvzR+qU0/vWa1WNXx86vYOl9VveJCW9Dt9GuPV51nBP0/+2UrokOm9UFTlc90M4pfBvD2H9ZgT64gvaxOlBR4NcZuJRdCzYdxM/WR52iIlj7snXV1OwC+J+l0LqHw/uqLE3CzEl22g5sZthm96rO/gBoHQl3xcDXf4I1/zhbndDYRVlEKyiTDwXcnDUXPvka2vSyqsSa12yu32rpPhZa97TGBXx6g1XHfdXTlY+GrQt5Z+CT66ygP+Gdug36AKdTK35u7Oya7bO4b31Z+Wdc/0umFjTwq3JyCwpZEp/CiMjWNCUDProT9q6GXlNhzGtW2oRqCAsNrPBquLIG39dX/253O4fSLvg3sYJ9p8Gw7HF45zKY8j50urRaZXdU2dfYhhO84fc6/b1+g/7TYdQLddvdslkE3P6d1V305zfg4C8w5QMIrWAQnqvlZ8Mn11tdFSe+A32uq/syhLSvOPFbv1tqts8fZ1WwT+f0vnEVzdWjyvl+x1HSz+RzZ8cjVsBMWm8F/EnvVjvoAzw6qiuBZaY7dCR9Qk23KyEC/W+DO1ZaPSLmjbFGvf66wOlD4V/rsYf1/g+S6H8Dsf53s9L/EbrLATb1mwVjXnVPH3sffyv1xdR5cGyXNdp5xd/rPsd9SdD/0ap66nO9649pz5VPlk8D4htoLa9P+6wDWsevyrn5vZ8ZdPi/3F30CdK0k1VF0bZ3rfZZundOTXv11CrtQk6G9bN8+xKr8c2cbYStSTe5c9jpeleEsL3HX+g17X9rtk9nO7EX5o+DjDKjQWv72quSnw2f/MEaYzHhLYiqdJC+6zmhp0yd7NNJtHFXOeTw4WR2vnUjw70TrEbIsbM9awRkZYyBF8Mhx87IR28/qydMTRzafO4YhmL1LXf8/0WWD/xgBauHtzv/ePk5VhvD3tXW+AIHOwMo59HGXVW1A7/Q5L//w2CvE6QNe4Gml99jVZecL0SswWb2FObVvHulvaAP9S93fEYFGURPJcOHE6weKBFDrQyYtW0Izs+BBTdaqQfGadCvbzTwK+tK+KfXMaueIcM05+3Wr/GPYf/j7lK5RmUNfLd8WbN9VjhbVD1r4Kvotfs1gaxjsPo567FvY+h4ia3f+RAIi6reF0F+Diy4CX5fCeNeh+jz9L3kwTTwN0Sl6ySDw6BRcziyhRMdRnH1nqk8P3iwu0voOlc+aX8ofG0b+Jy9T1eoqJxjXrXqpE8ft7paJq2zet+sesZax68JdBxoG4Q01MqN410mdJR+T/n4WwOaxs6G6Jvr7vUph2ngb2jKNkRmpFh/va/jmZy78Qo8zsgeFQwhPx9UlOe/No1xrtinK1RVzsYtrDw/xbl+slJtXwRrrS+DlU9by/2CoNOgs7llUvfAN386+54qyLF+IZTt7aLqDW3cbWgqqJYoDG5P95Mvc8PFHXl6XKQbCqbqvaxjZ3PPJK2D47ttTwj20nLXu8btBkgbd5WlggZHr4wU8gqKmNq/ntVLq/qjSSvoOcn6Aytv0/51sGi6/fXrW+O2KqEDuBqaClLTHvNqQc92wUSGnQezF6m6EdTaykIZUsFo4PrWuK1KaOBvSH6cZaWGlXNHwxb5BPJ8zlSuq2hOXaUq46GjVxsyrerxYNUa1frjy7D6H9D7erjgCuu+rYHv85DprEjsznNRzp2IXDUQntK4rUpo4PdQS+NTeGLxVrLzCwFISc/mCVs2y3LBf+0rVh/t3tdZw+a9vEvypeTkF/Ls8yu5pmcrQgLdnL1Rea7e0zTQexCt6vFQs1bsLgn6xbLzC3lp+a5zV1z3qnUl1mualSCrzJSEy7cdITOngGkDtJpHqYZCA7+Hqign/aFTOTz95XZr0u91r1l9r3tNtVLh2pmHdsGmg3RoFsjAiOYuLrFSqr7QwO+hKspJH+jrxce/HGDxG4/DyqfY1+ZqTo163W7Q33/iND8nnmBavw54eZ1HOXmUUpXSwO+h7h9efianQF9vXpjUm4QRu/mb78f84DuEq5Ju5OKZMTz0aTzrfz9OUZFhaXwKg2eu5vJZMQAEBWpTj1INiX7iPVRymlXV0zLIn+OZuWd79WQvgZinIHIiQye9yxdHzrAw9iBL41P4IuEQoYG+ZOUWUFB0dqTli8t2ExroV7M890opj6MpGzzQ0YwcLp+1hpE92jD7D33PPvHzm7Dir9BjAkyee04irZz8QlZsP8Jji7aQW1BUbp9VTmCulPI4FaVs0KoeD/Tayt8oLDI8MrLUFIQb3rYF/fEw+b1y2RMDfL0ZH9WOPDtBHxyYwFwpdd7QwO9hfj+WxYJNB7nxkk50bN7IWrjhHVg+A7qPs13pV9wfv6JGYYcmMFdKnRdcGvhFJFREFonILhHZKSKDbMsfsC3bLiIvueLYxQ2YETO+YfDM1SyNr2D2IQ/z0vJdNPLz4YHhF1oLfvk3LH8cuo+FKe9XOWFGrScwV0p5PFc37v4LWG6MmSIifkAjEbkCGA/0McbkikgrZx+0WqNaPUjc/pN8t+MofxlxEc2b+MPGd2HZY9BtDEz5wKFZkopfv1MmMFdKeSSXBX4RCQGGArcCGGPygDwRuQeYaYzJtS0/5uxjVzSqddaK3R4b4IwxzFy2i5ZB/tw+JMIK+t8+Al1HOxz0i03o285jz4NSqvZcWdUTAaQCH4hIvIi8JyKNgYuAISLyi4j8ICID7G0sIneJSKyIxKamplbrwBWOavXgBsyVO4+xKSmNP13VhUa/zjsb9KfOAx8/dxdPKeVBXBn4fYBo4G1jTF/gNDDDtrwZMBB4FFgoIuWGjRpj5hhj+htj+rds2bJaBz7fGjALCot4afkuOrdozPWyEr75C1x0jQZ9pVSNuDLwJwPJxphfbI8XYX0RJAOLjWUjUAS0cOaBz7cGzMWbU9hzLIvZXRLw/vbPcNHVMG2+Bn2lVI1UWccvIt7GmMKq1ivLGHNERA6KSFdjzG7gSmAHsBe4AlgjIhcBfsDx6u6/MsX11y+t2MWh9Bya+Pvwjwk9Pa9ee8tCilY+w5SMFK4ObEJwfCZ0GQXTPgQff3eXTinloRxp3N0jIp8DHxhjdlRz/w8AH9l69CQCt2FV+bwvItuAPOAW44Lhw8UNmGNfX0dIoK9HBn2+ehCvfKtdIthkgnhB5AQN+kqpWnEk8PcBrgfeExEv4H3gU2NMRlUbGmMSgHLDhYGbqlXKWogMC2b59iMYY7DTlFB/rXoW8ss0RpsiWPNPiLrBPWVSSp0XqqzjN8ZkGmPeNcZcCjwOPAUcFpH5InKhy0tYS5FhwaSfyefQqRx3F6V6TiVXb7lSSjmoysAvIt4iMk5ElgCvAa8AnYGvgG9dXL5ai2wXAsD2lFNuLkn1FARVUDUV0r5uC6KUOu840qtnD9ZI21nGmL7GmP8zxhw1xiwClru2eLXXvU0wXgLbDlVZM1WvfBp0KwWmzL/HN9CaxFoppWrBkTr+3saYLHtPGGMedHJ5nC7Qz5sLWjZhxyHPueLfeTiDWfs6cr2/N/gGWHX9Ie2toK8TWiulasmRwP+miDxkjEkHEJGmwCvGmOmuLZrzRIYFsyHxpLuL4bCXlu/iVv8f8CEfbl8DbXq5u0hKqfOII1U9vYuDPoAxJg3oW8n69U7PdiEcycjhRFauu4tSpZ/3nmDt7sPc6b8Swodo0FdKOZ0jgd/LdpUPgIg0w8OmbOwRFgzA9npez28lYtvJH5ok0CT3KAy6z91FUkqdhxwJ4K8AP4vIZ4AAU4DnXVoqJ4tsa/Xs2XboFEMvql7en7qwND6FWSt2k5KeDRjeaLoMgi+wRukqpZSTVRn4jTEfikgcVpoFgEk1GMHrViGNfOnQLLBeXvGXnTsgWvbQIXsnv3b5X/p46QRpSinnc6jKxhizXURSgQAAEelojDng0pI5WWTbkHrZl7/s3AG3+3xLumnMn3f3YJUby6WUOn85MoBrnIjsAfYBPwBJwDIXl8vperYLJunEGTJz8t1dlHOUniOgvRzjaq9NfFI4nMRTTk9fpJRSgGONu89h5c7/zRgTgZVlc4NLS+UCkWFWPf+OelbdU3qOgFu9V1CEF/MLRnrs3AFKqfrPkcCfb4w5gdW7x8sYswb7idfqtch29bNnz6OjuuLn7UUTznCddwzfFl3CKd9WHjt3gFKq/nOkjj9dRJoAP2KlWD6GlVrZo7QKCqBlkH+9C/wT+rZj2dZDtNv9DUGSzZeBE3jhml6el0ZaKeUxHAn844Fs4GHgRiAEeNaVhXKVyLBgttfD1A2nc/O40/87aD+IudP/6O7iKKXOc5UGfhHxBr42xlyBNUXi/DoplYv0DAth7Z7j5OQXElBmakZ3KSwyND+4krZeR2Hgy+4ujlKqAai0jt825WKRiITUUXlcKjIsmMIiw+4jme4uSondRzK5ia853ag9dBvt7uIopRoAR6p6soCtIvI9per2PSEzZ1k9i3PzH8qgT4dQN5fGkrR1Ldd6/UZav2do7FU/foUopc5vjgT+xbY/j9e+aSDBAT5sq0f1/G22zyWLQEIH3+buoiilGghHUjZ4dL1+aSJCj7Dg+tOz51QKvTNiiAmZwFUB50VtmlLKA1QZ+EVkH1BuGKkxprNLSuRiPcNC+M+G/RQUFuHj7d5cOGfWv42/KeJod73aV0rVHUeqekoP1goApgLNXFMc14tsF0xuQRF7U0/TtU2Q+wqSm4Vv/HyWFw2ga7dI95VDKdXgVHnJa4w5UeovxRjzGuCx3U96hhU38Lq5nv/XT/DNz2C+GV3S6KyUUnXBkSRt0aX++ovI3TiY1VNEQkVkkYjsEpGdIjKo1HN/EREjIi1qUf5q69yyCQG+XmxLcWM9f1ERbHibPb5dyWvTv96MKVBKNQyOTsRSrAArS6ejM37/C1hujJkiIn5AIwAR6QCMBOo8tbO3l9C9rZtH8O5ZASf38lbhg/Tr47G1ZkopD+VIr54rqlrHHtugr6HArbb95AF5tqdfBR4DvqjJvmsrMiyYL+IPUVRk8PKSui/Az2+S17gtX53oz+xOTateXymlnMiRqp5/ikhoqcdNReQfDuw7AkgFPhOzbqQAACAASURBVBCReBF5T0Qai8h4IMUY82sVx71LRGJFJDY1NdWBwzkuMiyEzNwCDqadcep+HXJ4CyStJaHtNArwIbqjBn6lVN1ypD/jNcaY9OIHxpg04FoHtvMBooG3jTF9sUb9Pg38FXiyqo2NMXOMMf2NMf1btnTuPLlnG3jdUM+/4W3wbcRCcyXtQgNpExJQ92VQSjVojgR+bxHxL34gIoGAfyXrF0sGko0xv9geL8L6IogAfhWRJKA9sFlE2lSr1LV0UZsm+HgJ2+p6KsbMI7D1M+h7E+uTC4jWah6llBs4Evg/AlaJyO0icjvwPQ5k6TTGHAEOikjxjCJXApuNMa2MMeHGmHCsL4do27p1xt/Hmy6tg+r+in/Te1BUwNEet3H4VA7RHetHviClVMPiSOPuiyLyK3CVbdFzxpgVDu7/AazJW/yARKDeDFGNDAsmZvcxjDGI1EEDb342xL4PXa9hU4YV8PvpFb9Syg0cSdkQAcQYY5bbHgeKSLgxJqmqbY0xCVQyTaPtqt8teoYFsygumWOZubQOroN69i0L4MwJGHgvcdvSCPD1onvbYNcfVymlynCkquczrElYihXalnm0SNto2Tqp5zfGatRt0wvCL2Pz/jR6tw/F1825gpRSDZMjkcfH1gcfKOmP7+e6ItWN7m2DEamjnj17V0HqLhh4HzkFRWw/lKHVPEopt3Ek8KeKyLjiB7Z++MddV6S60cTfh4jmjetmBO/Pb0GT1tBzMluST1FQZOin/feVUm7iSMqGu7EaaN8ABDgI3OzSUtWRHmHBxB9Ir3rF2ji207riH/538PEjbn8aAH21R49Syk0c6dWzFxgoIk1sj7NcXqo60rNdCF9vOUz6mTxCG7mo9mrDW+ATAP2mA7D5QBoRLRrTvIkjQyGUUsr5HM2yORqIBAKKuz4aY551YbnqRGSY1atm+6EMBl/ogiShp4/Drwsg6g/QuDnGGDbvT2NY11bOP5ZSSjnIkVw97wDXYfXJF6yJWDq5uFx1ItLVuflj34fCXBh4LwD7T5zhxOk8ojtpNY9Syn0cady91BhzM5BmjHkGGARc5Npi1Y1mjf0ICwlwTW7+glzY+C5ceBW0tAYvbz5g1e9rjx6llDs5EvizbbdnRCQMyAfauq5IdatHWIhrrvi3fQ6nj5Vc7QPE7U8jyN+HLq3cOOWjUqrBcyTwf21LyzwL2AwkAR+7slB1qWe7YBKPn+ZMXoHzdmoM/PwmtOwOFwwvWRy3P42ojqF4u2MOAKWUsnFkzt3njDHpxpjPser2uxljqkyr7Ckiw0IwBnYedmJ1z74f4eg2GHQv2BrDM3Py+e1opubfV0q5XbVyBhhjco0xbp6l3Ll6tjvbs8dpNrwFjVpAr7MzVP568BRFRuv3lVLu1+CTxbQJDqBZYz/n5ew5/jv8thwG3A6+Z5O/xe1PQwSidOCWUsrNHOrHfz4TESLDgmt/xb9lIax6Fk4dtB43Prev/uYDaVzUKojgAN/aHUcppWrJkX78qxxZ5skiw0L47WgmeQVFVa9sz5aF8NWDZ4M+wPd/t5YDRUWGzQfSdMYtpVS9UGHgF5EAEWkGtLBNsN7M9hcOtKurAtaFyLBg8gsNvx3NrNkOVj1rTbRSWn62tRz4PTWLzJwCnXFLKVUvVFbV80fgT0AYEIc1ahcgA3jDxeWqUz1tufl3HMoouV8tp5IrXb55vw7cUkrVHxVe8Rtj/mWMiQAeMcZ0NsZE2P76GGPOq8DfqVkjmvj7sK2mA7lC2le6PG5/Gk0b+RLRonENS6iUUs7jSK+eIyISBCAifxeRxSIS7eJy1SkvL6FH21o08F75JHiXybbpG2gtB+IOpBHdsWndzO2rlFJVcCTw/68xJlNELsOacH0u8LZri1X3eoQFs+NQBoVFpvob954Gna+wPRAI6QBjZ0PvaaSdziMx9bQ27Cql6g1HunMW2m5HA3OMMd+IyD9cWCa3iAwLJju/kH3HT3NhqybV30HWEeh4KUxfds7i+INav6+Uql8cueJPEZF/Y6Vm/lZE/B3czqMUN+rWKGHbmZNw+FfoPKzcU3H70/D2Enq3r0GjsVJKuYAjAXwasAIYZYxJB5oBj7q0VG5wYasm+Pl41ayef9+PgLEb+DfvT6dH22Aa+TX4sXJKqXrCkSRtZ4BjwGW2RQXAHkd2LiKhIrJIRHaJyE4RGSQis2yPt4jIElvmT7fz9faiW5ugml3xJ8aAXxC0O7fNu6CwiISD6VrNo5SqVxwZufsU8DjwhG2RL/BfB/f/L2C5MaYb0AfYCXwP9DTG9AZ+K7Vft4sMC2ZbSgbGVLOBNzEGwi8D73PTMew6kkl2fqFOrK6UqlccqeqZCIwDTgMYYw4BVc4kIiIhwFCsXkAYY/Js6Z2/M8YUJ7/fAFTQCb7uRYaFcCo7n5T07KpXLpaWBGn77Ffz6IxbSql6yJHAn2esS2ADICKOjkKKAFKBD0QkXkTes7PtdGBZ+U1BRO4SkVgRiU1NTXXwkLVTPPl6taZiTPzBuu08rNxTcfvTaB3sT7vQwNoXTimlnMSRwL/Q1qsnVETuBFYC7zmwnQ8QDbxtjOmL9YthRvGTIvI3rPaCj+xtbIyZY4zpb4zp37JlSwcOV3vd2gTjJbCjOvX8+36AJm1K5tUtLW6/DtxSStU/jjTuvgwsAj4HugJPGmNmO7DvZCDZGPOL7fEirC8CRORWYAxwo6l2hbrrBPp5c2GrJmxztGdPUZF1xd/58pKZtoody8ghOS1bq3mUUvVOlX0MReRFY8zjWI2yZZdVyBhzREQOikhXY8xu4Epgh4hcDTwGXG7rMVSvRIaF8NPe446tfGw7nDleaf2+jthVStU3jlT1jLCz7BoH9/8A8JGIbAGigH9iZfYMAr4XkQQRecfBfdWJyLBgjmbkkpqZW/XKiTHWbcTl5Z6K25+Gn7dXSbuBUkrVFxVe8YvIPcC9QGdb4C4WBKx3ZOfGmASgf5nFF1a3kHUpMuzsCN5hXVtVvnJiDLS4CELKT0+w+UA6vdqH4O/j7YJSKqVUzVV2xf8xMBb40nZb/NfPGHNTHZTNLXqEOTj5ekEu7P/JbjVPbkEhW5NPaf2+UqpeqvCK3xhzCjgF/KHuiuN+IYG+dGzWqOoRvMmbIP+M3cC/LSWDvMIinXFLKVUvnXfJ1pwhtJEPK7YfJWLGNwyeuZql8SnlV0qMAfGyRuyWEV/csNtRr/iVUvWPZg4rY2l8CjsOZZbk5U9Jz+aJxVsBmNC3VF1+Ygy06wcB5bNuxu1Po0OzQFoFB9RFkZVSqlr0ir+MWSt2U1BmMpbs/EJmrdh9dkHOKUjZbLeaxxjDZtuMW0opVR9p4C/jUAV5es5ZnrQeTKHdwJ+Sns3RjFxt2FVK1Vsa+MsIqyCvjgEmvrWeTzYeIG/PavAJhPYDyq0Xt1/r95VS9ZsG/jIeHdWVQN9z+94H+HoxISqMrJwCnli8lQOx37LLvxe/HMg6J4Xz0vgU/rrEag+46z+x9huFlVLKzbRxt4ziBtxZK3ZzKD2bsNBAHh3VlQl922GMYcfu3Vz4aQovZ13BG3M2EN68EVP7d6CxvzcvLttNdr41RfGh9Bz7jcJKKeVmGvjtmNC3nd1gLSJE5sQDcP8dd9I5tQULNh08t+G3lOJGYQ38Sqn6RAN/dSXGQKPmBLTrzaQOXkyKbk/S8dMMeznG7uoVNRYrpZS7aB1/dRhjBf6Iy8Hr7KkLb9G4wslWKmosVkopd9HAXx2puyHriN1unPYahQN9vXl0VPkJWpRSyp20qqc69lU8zWJljcJKKVWfaOCvjsQYaBoBTTvZfbqiRmGllKpPtKrHUYUFsG+t3at9pZTyJBr4HXVoM+RlWvPrKqWUB9PA76jEGEAgfKi7S6KUUrWigd9RiTHQtjc0bu7ukiilVK1o4HdEbhYc3Kj1+0qp84IGfkcc+BmK8jXwK6XOCxr4HZEYA97+0HGQu0uilFK1poHfEYkx0PES8NX0C0opz+fSwC8ioSKySER2ichOERkkIs1E5HsR2WO7rd8zlmSlwtFtWs2jlDpvuPqK/1/AcmNMN6APsBOYAawyxnQBVtke11+VpGlQSilP5LLALyIhwFBgLoAxJs8Ykw6MB+bbVpsPTHBVGZwiMQYCQqBtlLtLopRSTuHKK/4IIBX4QETiReQ9EWkMtDbGHLatcwRo7cIy1E5JGuah4OVd5epKKeUJXBn4fYBo4G1jTF/gNGWqdYw1Ya2xsy0icpeIxIpIbGpqqguLWYmTiXDqoJV/XymlzhOuDPzJQLIx5hfb40VYXwRHRaQtgO32mL2NjTFzjDH9jTH9W7Zs6cJiViIxxrrtfIV7jq+UUi7gssBvjDkCHBSR4plIrgR2AF8Ct9iW3QJ84aoy1FpiDAS3h+YXuLskSinlNK7Ox/8A8JGI+AGJwG1YXzYLReR2YD8wzcVlqJmiQtj3I3QbAyLuLo1SSjmNSwO/MSYB6G/nqStdeVynOPwr5KRrN06l1HlHR+5WpKT/vjbsKqXOLxr4K5IYA60ioUkrd5dEKaWcSgO/PfnZsP9nreZRSp2XNPDbc/AXKMzVwK+UOi+5ulePZ0qMAS8f6HSpu0uiPER+fj7Jycnk5OS4uyiqAQoICKB9+/b4+vo6tL4GfnsSY6D9APBv4u6SKA+RnJxMUFAQ4eHhiHb/VXXIGMOJEydITk4mIiLCoW20qqesMyfhUIJW86hqycnJoXnz5hr0VZ0TEZo3b16tX5sa+MtKWgsYDfyq2jToK3ep7ntPA39ZiTHg1wTa9XN3SZRSyiU08JeV+AOEXwbejjWSKFUTS+NTGDxzNREzvmHwzNUsjU9x+TGvvfZa0tPTSU9P56233ipZHhMTw5gxY1x+/LKefvppXn75ZQCefPJJVq5cWaP9JCQk8O233zqzaA45dOgQU6ZMqfPjOoMG/tLSD8DJvVrNo1xqaXwKTyzeSkp6NgZISc/micVbXR78v/32W0JDQ8sFfmcqKCio0XbPPvssV111VY22rW3gN8ZQVFRU7e3CwsJYtGhRjY/rTtqrp7REnWZR1d4zX21nx6GMCp+PP5BOXuG5gSY7v5DHFm3hk40H7G7TIyyYp8ZGVrjPWbNm4e/vz4MPPsjDDz/Mr7/+yurVq1m9ejVz587lo48+Ijw8nNjYWGbMmMHevXuJiopixIgRjB49mqysLKZMmcK2bdvo168f//3vf8vVG2/atInbb78dLy8vRowYwbJly9i2bRvz5s1j8eLFZGVlUVhYyDfffMP48eNJS0sjPz+ff/zjH4wfPx6A559/nvnz59OqVSs6dOhAv35Wleqtt97KmDFjmDJlCnFxcfz5z38mKyuLFi1aMG/ePNq2bcuwYcO45JJLWLNmDenp6cydO5dLLrmEJ598kuzsbNatW8cTTzzBddddV1LmefPmsWTJEk6dOkVKSgo33XQTTz31FElJSYwaNYpLLrmEuLg4vv32WyIjI8nKygJg0aJFfP3118ybN49bb72V4OBgYmNjOXLkCC+99BJTpkwhKSmJMWPGlJyDL7/8kjNnzrB3714mTpzISy+9BMDcuXN58cUXCQ0NpU+fPvj7+/PGG29U+L+sCxr4S0uMgSatoWU3d5dEncfKBv2qljtiyJAhvPLKKzz44IPExsaSm5tLfn4+a9euZejQoeesO3PmTLZt20ZCQgJgVfXEx8ezfft2wsLCGDx4MOvXr+eyyy47Z7vbbruNd999l0GDBjFjxrlTZW/evJktW7bQrFkzCgoKWLJkCcHBwRw/fpyBAwcybtw4Nm/ezKeffkpCQgIFBQVER0eXBP5i+fn5PPDAA3zxxRe0bNmSBQsW8Le//Y33338fsH5RbNy4kW+//ZZnnnmGlStX8uyzzxIbG1thMN24cSPbtm2jUaNGDBgwgNGjR9OiRQv27NnD/PnzGThwYJXn9/Dhw6xbt45du3Yxbtw4u1U8CQkJxMfH4+/vT9euXXnggQfw9vbmueeeY/PmzQQFBTF8+HD69OlT5fFcTQN/saIiK/BfeKWmYVa1UtmVOcDgmatJSc8ut7xdaCAL/jioRsfs168fcXFxZGRk4O/vT3R0NLGxsaxdu5bZs2dXuf3FF19M+/btAYiKiiIpKemcwJ+enk5mZiaDBlnlu+GGG/j6669Lnh8xYgTNmjUDrKqTv/71r/z44494eXmRkpLC0aNHWbt2LRMnTqRRo0YAjBs3rlw5du/ezbZt2xgxYgQAhYWFtG3btuT5SZMmlbzepKQkh87NiBEjaN68ecn269atY8KECXTq1MmhoA8wYcIEvLy86NGjB0ePHrW7zpVXXklISAgAPXr0YP/+/Rw/fpzLL7+85NxMnTqV3377zaFjupIG/mLHdsCZ41rNo1zu0VFdeWLxVrLzC0uWBfp68+iorpVsVTlfX18iIiKYN28el156Kb1792bNmjX8/vvvdO/evcrt/f39S+57e3tXu66+cePGJfc/+ugjUlNTiYuLw9fXl/DwcIf7mBtjiIyM5Oeff660nNUpY9kqq+LHpctcdr2y5S19fqwZYysuW3XL5w7auFuseJpFnV9XudiEvu14YVIv2oUGIlhX+i9M6sWEvu1qtd8hQ4bw8ssvM3ToUIYMGcI777xD3759ywW+oKAgMjMzq7Xv0NBQgoKC+OUXaybVTz/9tMJ1T506RatWrfD19WXNmjXs378fgKFDh7J06VKys7PJzMzkq6++Krdt165dSU1NLQn8+fn5bN++vdKyVfV6vv/+e06ePEl2djZLly5l8ODBdtdr3bo1O3fupKioiCVLllR6TEcNGDCAH374gbS0NAoKCvj888+dst/a0sBfLDEGmneBkNp9+JRyxIS+7Vg/Yzj7Zo5m/YzhtQ76YAX+w4cPM2jQIFq3bk1AQABDhgwpt17z5s0ZPHgwPXv25NFHH3V4/3PnzuXOO+8kKiqK06dPl1RrlHXjjTcSGxtLr169+PDDD+nWzWozi46O5rrrrqNPnz5cc801DBgwoNy2fn5+LFq0iMcff5w+ffoQFRXFTz/9VGm5rrjiCnbs2EFUVBQLFiwo9/zFF1/M5MmT6d27N5MnT6Z/f3tzQ1ltH2PGjOHSSy89p3qpNtq1a8df//pXLr74YgYPHkx4eHiF560uSUU/W+qT/v37m9jYWNcdoCAPXuwEUTfC6Jdddxx13tq5c6dDVSqeLCsriyZNrPxVM2fO5PDhw/zrX/9yc6kqN2/evEobfutC8XkrKChg4sSJTJ8+nYkTJzr9OPbegyISZ4wp902ndfwAyZsg/4zW7ytViW+++YYXXniBgoICOnXqxLx589xdJI/w9NNPs3LlSnJychg5ciQTJkxwd5H0ih+ANf+EH2fBY/sgMNR1x1HnrYZwxa/qt+pc8WsdP1j1+2HRGvSVUg2CBv6cDEiO1WoepVSDoYF//3owhRr4lVINhksbd0UkCcgECoECY0x/EYkC3gECgALgXmPMRleWo1KJMeATCB0udlsRlFKqLtXFFf8VxpioUg0MLwHPGGOigCdtj90nMcaaW9fHv8pVlXKaLQvh1Z7wdKh1u2Whyw9ZF2mZY2Jiqux37yyly/3ll18yc+bMGu3HldlKq1L8P6lr7qjqMUCw7X4IcMgNZbBkHIbUXVrNo+rWloXw1YNw6iBgrNuvHnR58K+LtMy1Dfw1TZE8bty4conjHOWM81FYWFj1SnYU/0/qmqsDvwG+E5E4EbnLtuxPwCwROQi8DDzh4jJUbF9xGmZN06CcaNkM+GB0xX9f3A/5ZZK05WdbyyvaZlnlQW3WrFklydgefvhhhg8fDsDq1au58cYbAQgPD+f48ePnpGUuHrlbnJa5W7du3HjjjSX5aFatWkXfvn3p1asX06dPJzc395x9AcTGxjJs2DCSkpJ45513ePXVV4mKimLt2rXnlDE1NZURI0YQGRnJHXfcQadOnTh+/DhJSUl07dqVm2++mZ49e3Lw4EHuuece+vfvT2RkJE899VTJPpYvX063bt2Ijo5m8eLFJcvnzZvH/fffX3KcyZMnM2DAAAYMGMD69esBqz/99OnTGTZsGJ07dy45X/bOR7GkpKSSc9K9e3emTJnCmTNnSs7B448/TnR0NJ999hnDhg2juNv58ePHCQ8PLynbpEmTuPrqq+nSpQuPPfZYyf6Lz2NSUhLdu3fnzjvvJDIykpEjR5Kdbb1HNm3aRO/evUvK17Nnz0rfC45wdeC/zBgTDVwD3CciQ4F7gIeNMR2Ah4G59jYUkbtEJFZEYlNTU11TusQYCGwGrXu5Zv9K2VOYW73lDhgyZEhJoI2NjSUrK6vStMwXXHABCQkJzJo1C4D4+Hhee+01duzYQWJiIuvXrycnJ4dbb72VBQsWsHXrVgoKCnj77bcrLEN4eDh33303Dz/8MAkJCeXSRTzzzDMMHz6c7du3M2XKFA4cODv3wJ49e7j33nvZvn07nTp14vnnnyc2NpYtW7bwww8/sGXLFnJycrjzzjv56quviIuL48iRI3bL8dBDD/Hwww+zadMmPv/8c+64446S53bt2sWKFSvYuHEjzzzzDPn5+XbPR2m7d+/m3nvvZefOnQQHB5/z66B58+Zs3ryZ66+/vsLzAlbK5uLzuGDBAg4ePFhunT179nDfffexfft2QkNDS/L63Hbbbfz73/8mISEBb2/vSo/jKJc27hpjUmy3x0RkCXAxcAvwkG2Vz4D3Kth2DjAHrAFcLiicNfFK58vBSzs3KSe6poq65ld72qp5ygjpALd9U6NDuiItc1BQEBEREVx00UUA3HLLLbz55pv86U9/qlEZ161bV5L87Oqrr6Zp06Ylz5VNkbxw4ULmzJlDQUEBhw8fZseOHRQ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