import io
import numpy as np
import os
import shutil
import sys
import unittest
import uuid

TEST_TENSORBOARD = True
try:
    import tensorboard.summary.writer.event_file_writer  # noqa F401
    from tensorboard.compat.proto.summary_pb2 import Summary
except ImportError:
    TEST_TENSORBOARD = False

HAS_TORCHVISION = True
try:
    import torchvision
except ImportError:
    HAS_TORCHVISION = False
skipIfNoTorchVision = unittest.skipIf(not HAS_TORCHVISION, "no torchvision")

TEST_CAFFE2 = True
try:
    from caffe2.python import brew, cnn, core, workspace
    from caffe2.python.model_helper import ModelHelper
except ImportError:
    TEST_CAFFE2 = False
skipIfNoCaffe2 = unittest.skipIf(not TEST_CAFFE2, "no caffe2")

TEST_MATPLOTLIB = True
try:
    import matplotlib
    if os.environ.get('DISPLAY', '') == '':
        matplotlib.use('Agg')
    import matplotlib.pyplot as plt
except ImportError:
    TEST_MATPLOTLIB = False
skipIfNoMatplotlib = unittest.skipIf(not TEST_MATPLOTLIB, "no matplotlib")

import torch
from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_ASAN

def tensor_N(shape, dtype=float):
    numel = np.prod(shape)
    x = (np.arange(numel, dtype=dtype)).reshape(shape)
    return x

class BaseTestCase(TestCase):
    """ Base class used for all TensorBoard tests """
    def setUp(self):
        if not TEST_TENSORBOARD:
            return self.skipTest("Skip the test since TensorBoard is not installed")
        self.temp_dirs = []

    def createSummaryWriter(self):
        temp_dir = str(uuid.uuid4())
        self.temp_dirs.append(temp_dir)
        return SummaryWriter(temp_dir)

    def tearDown(self):
        super(BaseTestCase, self).tearDown()
        # Remove directories created by SummaryWriter
        for temp_dir in self.temp_dirs:
            if os.path.exists(temp_dir):
                shutil.rmtree(temp_dir)


if TEST_TENSORBOARD:
    from tensorboard.compat.proto.graph_pb2 import GraphDef
    from torch.utils.tensorboard import summary, SummaryWriter
    from torch.utils.tensorboard._utils import _prepare_video, convert_to_HWC
    from torch.utils.tensorboard._convert_np import make_np
    from torch.utils.tensorboard import _caffe2_graph as c2_graph
    from torch.utils.tensorboard._pytorch_graph import graph
    from google.protobuf import text_format
    from PIL import Image

class TestTensorBoardPyTorchNumpy(BaseTestCase):
    def test_pytorch_np(self):
        tensors = [torch.rand(3, 10, 10), torch.rand(1), torch.rand(1, 2, 3, 4, 5)]
        for tensor in tensors:
            # regular tensor
            self.assertIsInstance(make_np(tensor), np.ndarray)

            # CUDA tensor
            if torch.cuda.device_count() > 0:
                self.assertIsInstance(make_np(tensor.cuda()), np.ndarray)

            # regular variable
            self.assertIsInstance(make_np(torch.autograd.Variable(tensor)), np.ndarray)

            # CUDA variable
            if torch.cuda.device_count() > 0:
                self.assertIsInstance(make_np(torch.autograd.Variable(tensor).cuda()), np.ndarray)

        # python primitive type
        self.assertIsInstance(make_np(0), np.ndarray)
        self.assertIsInstance(make_np(0.1), np.ndarray)

    def test_pytorch_autograd_np(self):
        x = torch.autograd.Variable(torch.Tensor(1))
        self.assertIsInstance(make_np(x), np.ndarray)

    def test_pytorch_write(self):
        with self.createSummaryWriter() as w:
            w.add_scalar('scalar', torch.autograd.Variable(torch.rand(1)), 0)

    def test_pytorch_histogram(self):
        with self.createSummaryWriter() as w:
            w.add_histogram('float histogram', torch.rand((50,)))
            w.add_histogram('int histogram', torch.randint(0, 100, (50,)))

    def test_pytorch_histogram_raw(self):
        with self.createSummaryWriter() as w:
            num = 50
            floats = make_np(torch.rand((num,)))
            bins = [0.0, 0.25, 0.5, 0.75, 1.0]
            counts, limits = np.histogram(floats, bins)
            sum_sq = floats.dot(floats).item()
            w.add_histogram_raw('float histogram raw',
                                min=floats.min().item(),
                                max=floats.max().item(),
                                num=num,
                                sum=floats.sum().item(),
                                sum_squares=sum_sq,
                                bucket_limits=limits[1:].tolist(),
                                bucket_counts=counts.tolist())

            ints = make_np(torch.randint(0, 100, (num,)))
            bins = [0, 25, 50, 75, 100]
            counts, limits = np.histogram(ints, bins)
            sum_sq = ints.dot(ints).item()
            w.add_histogram_raw('int histogram raw',
                                min=ints.min().item(),
                                max=ints.max().item(),
                                num=num,
                                sum=ints.sum().item(),
                                sum_squares=sum_sq,
                                bucket_limits=limits[1:].tolist(),
                                bucket_counts=counts.tolist())

            ints = torch.tensor(range(0, 100)).float()
            nbins = 100
            counts = torch.histc(ints, bins=nbins, min=0, max=99)
            limits = torch.tensor(range(nbins))
            sum_sq = ints.dot(ints).item()
            w.add_histogram_raw('int histogram raw',
                                min=ints.min().item(),
                                max=ints.max().item(),
                                num=num,
                                sum=ints.sum().item(),
                                sum_squares=sum_sq,
                                bucket_limits=limits.tolist(),
                                bucket_counts=counts.tolist())

class TestTensorBoardUtils(BaseTestCase):
    def test_to_HWC(self):
        test_image = np.random.randint(0, 256, size=(3, 32, 32), dtype=np.uint8)
        converted = convert_to_HWC(test_image, 'chw')
        self.assertEqual(converted.shape, (32, 32, 3))
        test_image = np.random.randint(0, 256, size=(16, 3, 32, 32), dtype=np.uint8)
        converted = convert_to_HWC(test_image, 'nchw')
        self.assertEqual(converted.shape, (64, 256, 3))
        test_image = np.random.randint(0, 256, size=(32, 32), dtype=np.uint8)
        converted = convert_to_HWC(test_image, 'hw')
        self.assertEqual(converted.shape, (32, 32, 3))

    def test_convert_to_HWC_dtype_remains_same(self):
        # test to ensure convert_to_HWC restores the dtype of input np array and
        # thus the scale_factor calculated for the image is 1
        test_image = torch.tensor([[[[1, 2, 3], [4, 5, 6]]]], dtype=torch.uint8)
        tensor = make_np(test_image)
        tensor = convert_to_HWC(tensor, 'NCHW')
        scale_factor = summary._calc_scale_factor(tensor)
        self.assertEqual(scale_factor, 1, msg='Values are already in [0, 255], scale factor should be 1')


    def test_prepare_video(self):
        # At each timeframe, the sum over all other
        # dimensions of the video should be the same.
        shapes = [
            (16, 30, 3, 28, 28),
            (36, 30, 3, 28, 28),
            (19, 29, 3, 23, 19),
            (3, 3, 3, 3, 3)
        ]
        for s in shapes:
            V_input = np.random.random(s)
            V_after = _prepare_video(np.copy(V_input))
            total_frame = s[1]
            V_input = np.swapaxes(V_input, 0, 1)
            for f in range(total_frame):
                x = np.reshape(V_input[f], newshape=(-1))
                y = np.reshape(V_after[f], newshape=(-1))
                np.testing.assert_array_almost_equal(np.sum(x), np.sum(y))

    def test_numpy_vid_uint8(self):
        V_input = np.random.randint(0, 256, (16, 30, 3, 28, 28)).astype(np.uint8)
        V_after = _prepare_video(np.copy(V_input)) * 255
        total_frame = V_input.shape[1]
        V_input = np.swapaxes(V_input, 0, 1)
        for f in range(total_frame):
            x = np.reshape(V_input[f], newshape=(-1))
            y = np.reshape(V_after[f], newshape=(-1))
            np.testing.assert_array_almost_equal(np.sum(x), np.sum(y))

freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]

true_positive_counts = [75, 64, 21, 5, 0]
false_positive_counts = [150, 105, 18, 0, 0]
true_negative_counts = [0, 45, 132, 150, 150]
false_negative_counts = [0, 11, 54, 70, 75]
precision = [0.3333333, 0.3786982, 0.5384616, 1.0, 0.0]
recall = [1.0, 0.8533334, 0.28, 0.0666667, 0.0]

class TestTensorBoardWriter(BaseTestCase):
    def test_writer(self):
        with self.createSummaryWriter() as writer:
            sample_rate = 44100

            n_iter = 0
            writer.add_hparams(
                {'lr': 0.1, 'bsize': 1},
                {'hparam/accuracy': 10, 'hparam/loss': 10}
            )
            writer.add_scalar('data/scalar_systemtime', 0.1, n_iter)
            writer.add_scalar('data/scalar_customtime', 0.2, n_iter, walltime=n_iter)
            writer.add_scalars('data/scalar_group', {
                "xsinx": n_iter * np.sin(n_iter),
                "xcosx": n_iter * np.cos(n_iter),
                "arctanx": np.arctan(n_iter)
            }, n_iter)
            x = np.zeros((32, 3, 64, 64))  # output from network
            writer.add_images('Image', x, n_iter)  # Tensor
            writer.add_image_with_boxes('imagebox',
                                        np.zeros((3, 64, 64)),
                                        np.array([[10, 10, 40, 40], [40, 40, 60, 60]]),
                                        n_iter)
            x = np.zeros(sample_rate * 2)

            writer.add_audio('myAudio', x, n_iter)
            writer.add_video('myVideo', np.random.rand(16, 48, 1, 28, 28).astype(np.float32), n_iter)
            writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter)
            writer.add_text('markdown Text', '''a|b\n-|-\nc|d''', n_iter)
            writer.add_histogram('hist', np.random.rand(100, 100), n_iter)
            writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(
                100), n_iter)  # needs tensorboard 0.4RC or later
            writer.add_pr_curve_raw('prcurve with raw data', true_positive_counts,
                                    false_positive_counts,
                                    true_negative_counts,
                                    false_negative_counts,
                                    precision,
                                    recall, n_iter)

            v = np.array([[[1, 1, 1], [-1, -1, 1], [1, -1, -1], [-1, 1, -1]]], dtype=float)
            c = np.array([[[255, 0, 0], [0, 255, 0], [0, 0, 255], [255, 0, 255]]], dtype=int)
            f = np.array([[[0, 2, 3], [0, 3, 1], [0, 1, 2], [1, 3, 2]]], dtype=int)
            writer.add_mesh('my_mesh', vertices=v, colors=c, faces=f)

class TestTensorBoardSummaryWriter(BaseTestCase):
    def test_summary_writer_ctx(self):
        # after using a SummaryWriter as a ctx it should be closed
        with self.createSummaryWriter() as writer:
            writer.add_scalar('test', 1)
        self.assertIs(writer.file_writer, None)

    def test_summary_writer_close(self):
        # Opening and closing SummaryWriter a lot should not run into
        # OSError: [Errno 24] Too many open files
        passed = True
        try:
            writer = self.createSummaryWriter()
            writer.close()
        except OSError:
            passed = False

        self.assertTrue(passed)

    def test_pathlib(self):
        import pathlib
        p = pathlib.Path('./pathlibtest' + str(uuid.uuid4()))
        with SummaryWriter(p) as writer:
            writer.add_scalar('test', 1)
        import shutil
        shutil.rmtree(str(p))

class TestTensorBoardEmbedding(BaseTestCase):
    def test_embedding(self):
        w = self.createSummaryWriter()
        all_features = torch.Tensor([[1, 2, 3], [5, 4, 1], [3, 7, 7]])
        all_labels = torch.Tensor([33, 44, 55])
        all_images = torch.zeros(3, 3, 5, 5)

        w.add_embedding(all_features,
                        metadata=all_labels,
                        label_img=all_images,
                        global_step=2)

        dataset_label = ['test'] * 2 + ['train'] * 2
        all_labels = list(zip(all_labels, dataset_label))
        w.add_embedding(all_features,
                        metadata=all_labels,
                        label_img=all_images,
                        metadata_header=['digit', 'dataset'],
                        global_step=2)
        # assert...

    def test_embedding_64(self):
        w = self.createSummaryWriter()
        all_features = torch.Tensor([[1, 2, 3], [5, 4, 1], [3, 7, 7]])
        all_labels = torch.Tensor([33, 44, 55])
        all_images = torch.zeros((3, 3, 5, 5), dtype=torch.float64)

        w.add_embedding(all_features,
                        metadata=all_labels,
                        label_img=all_images,
                        global_step=2)

        dataset_label = ['test'] * 2 + ['train'] * 2
        all_labels = list(zip(all_labels, dataset_label))
        w.add_embedding(all_features,
                        metadata=all_labels,
                        label_img=all_images,
                        metadata_header=['digit', 'dataset'],
                        global_step=2)

class TestTensorBoardSummary(BaseTestCase):
    def test_uint8_image(self):
        '''
        Tests that uint8 image (pixel values in [0, 255]) is not changed
        '''
        test_image = np.random.randint(0, 256, size=(3, 32, 32), dtype=np.uint8)
        scale_factor = summary._calc_scale_factor(test_image)
        self.assertEqual(scale_factor, 1, msg='Values are already in [0, 255], scale factor should be 1')

    def test_float32_image(self):
        '''
        Tests that float32 image (pixel values in [0, 1]) are scaled correctly
        to [0, 255]
        '''
        test_image = np.random.rand(3, 32, 32).astype(np.float32)
        scale_factor = summary._calc_scale_factor(test_image)
        self.assertEqual(scale_factor, 255, msg='Values are in [0, 1], scale factor should be 255')

    def test_list_input(self):
        with self.assertRaises(Exception) as e_info:
            summary.histogram('dummy', [1, 3, 4, 5, 6], 'tensorflow')

    def test_empty_input(self):
        with self.assertRaises(Exception) as e_info:
            summary.histogram('dummy', np.ndarray(0), 'tensorflow')

    def test_image_with_boxes(self):
        self.assertTrue(compare_image_proto(summary.image_boxes('dummy',
                                            tensor_N(shape=(3, 32, 32)),
                                            np.array([[10, 10, 40, 40]])),
                                            self))

    def test_image_with_one_channel(self):
        self.assertTrue(compare_image_proto(
            summary.image('dummy',
                          tensor_N(shape=(1, 8, 8)),
                          dataformats='CHW'),
                          self))  # noqa E127

    def test_image_with_one_channel_batched(self):
        self.assertTrue(compare_image_proto(
            summary.image('dummy',
                          tensor_N(shape=(2, 1, 8, 8)),
                          dataformats='NCHW'),
                          self))  # noqa E127

    def test_image_with_3_channel_batched(self):
        self.assertTrue(compare_image_proto(
            summary.image('dummy',
                          tensor_N(shape=(2, 3, 8, 8)),
                          dataformats='NCHW'),
                          self))  # noqa E127

    def test_image_without_channel(self):
        self.assertTrue(compare_image_proto(
            summary.image('dummy',
                          tensor_N(shape=(8, 8)),
                          dataformats='HW'),
                          self))  # noqa E127

    def test_video(self):
        try:
            import moviepy  # noqa F401
        except ImportError:
            return
        self.assertTrue(compare_proto(summary.video('dummy', tensor_N(shape=(4, 3, 1, 8, 8))), self))
        summary.video('dummy', np.random.rand(16, 48, 1, 28, 28))
        summary.video('dummy', np.random.rand(20, 7, 1, 8, 8))

    def test_audio(self):
        self.assertTrue(compare_proto(summary.audio('dummy', tensor_N(shape=(42,))), self))

    def test_text(self):
        self.assertTrue(compare_proto(summary.text('dummy', 'text 123'), self))

    def test_histogram_auto(self):
        self.assertTrue(compare_proto(summary.histogram('dummy', tensor_N(shape=(1024,)), bins='auto', max_bins=5), self))

    def test_histogram_fd(self):
        self.assertTrue(compare_proto(summary.histogram('dummy', tensor_N(shape=(1024,)), bins='fd', max_bins=5), self))

    def test_histogram_doane(self):
        self.assertTrue(compare_proto(summary.histogram('dummy', tensor_N(shape=(1024,)), bins='doane', max_bins=5), self))

    def test_custom_scalars(self):
        layout = {
            'Taiwan': {
                'twse': ['Multiline', ['twse/0050', 'twse/2330']]
            },
            'USA': {
                'dow': ['Margin', ['dow/aaa', 'dow/bbb', 'dow/ccc']],
                'nasdaq': ['Margin', ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]
            }
        }
        summary.custom_scalars(layout)  # only smoke test. Because protobuf in python2/3 serialize dictionary differently.

    def test_hparams_smoke(self):
        hp = {'lr': 0.1, 'bsize': 4}
        mt = {'accuracy': 0.1, 'loss': 10}
        summary.hparams(hp, mt)  # only smoke test. Because protobuf in python2/3 serialize dictionary differently.

        hp = {'use_magic': True, 'init_string': "42"}
        mt = {'accuracy': 0.1, 'loss': 10}
        summary.hparams(hp, mt)

        mt = {'accuracy': torch.zeros(1), 'loss': torch.zeros(1)}
        summary.hparams(hp, mt)

    def test_hparams_wrong_parameter(self):
        with self.assertRaises(TypeError):
            summary.hparams([], {})
        with self.assertRaises(TypeError):
            summary.hparams({}, [])
        with self.assertRaises(ValueError):
            res = summary.hparams({'pytorch': [1, 2]}, {'accuracy': 2.0})
        # metric data is used in writer.py so the code path is different, which leads to different exception type.
        with self.assertRaises(NotImplementedError):
            with self.createSummaryWriter() as writer:
                writer.add_hparams({'pytorch': 1.0}, {'accuracy': [1, 2]})

    def test_hparams_number(self):
        hp = {'lr': 0.1}
        mt = {'accuracy': 0.1}
        self.assertTrue(compare_proto(summary.hparams(hp, mt), self))

    def test_hparams_bool(self):
        hp = {'bool_var': True}
        mt = {'accuracy': 0.1}
        self.assertTrue(compare_proto(summary.hparams(hp, mt), self))

    def test_hparams_string(self):
        hp = {'string_var': "hi"}
        mt = {'accuracy': 0.1}
        self.assertTrue(compare_proto(summary.hparams(hp, mt), self))

    def test_hparams_domain_discrete(self):
        hp = {"lr": 0.1, "bool_var": True, "string_var": "hi"}
        mt = {"accuracy": 0.1}
        hp_domain = {"lr": [0.1], "bool_var": [True], "string_var": ["hi"]}

        # hparam_domain_discrete keys needs to be subset of hparam_dict keys
        with self.assertRaises(TypeError):
            summary.hparams(hp, mt, hparam_domain_discrete={"wrong_key": []})

        # hparam_domain_discrete values needs to be same type as hparam_dict values
        with self.assertRaises(TypeError):
            summary.hparams(hp, mt, hparam_domain_discrete={"lr": [True]})

        # only smoke test. Because protobuf map serialization is nondeterministic.
        summary.hparams(hp, mt, hparam_domain_discrete=hp_domain)

    def test_mesh(self):
        v = np.array([[[1, 1, 1], [-1, -1, 1], [1, -1, -1], [-1, 1, -1]]], dtype=float)
        c = np.array([[[255, 0, 0], [0, 255, 0], [0, 0, 255], [255, 0, 255]]], dtype=int)
        f = np.array([[[0, 2, 3], [0, 3, 1], [0, 1, 2], [1, 3, 2]]], dtype=int)
        mesh = summary.mesh('my_mesh', vertices=v, colors=c, faces=f, config_dict=None)
        self.assertTrue(compare_proto(mesh, self))

def remove_whitespace(string):
    return string.replace(' ', '').replace('\t', '').replace('\n', '')

def get_expected_file(function_ptr):
    module_id = function_ptr.__class__.__module__
    test_file = sys.modules[module_id].__file__
    # Look for the .py file (since __file__ could be pyc).
    test_file = ".".join(test_file.split('.')[:-1]) + '.py'

    # Use realpath to follow symlinks appropriately.
    test_dir = os.path.dirname(os.path.realpath(test_file))
    functionName = function_ptr.id().split('.')[-1]
    return os.path.join(test_dir,
                        "expect",
                        'TestTensorBoard.' + functionName + ".expect")

def read_expected_content(function_ptr):
    expected_file = get_expected_file(function_ptr)
    assert os.path.exists(expected_file)
    with open(expected_file, "r") as f:
        return f.read()

def compare_image_proto(actual_proto, function_ptr):
    expected_str = read_expected_content(function_ptr)
    expected_proto = Summary()
    text_format.Parse(expected_str, expected_proto)

    [actual, expected] = [actual_proto.value[0], expected_proto.value[0]]
    actual_img = Image.open(io.BytesIO(actual.image.encoded_image_string))
    expected_img = Image.open(io.BytesIO(expected.image.encoded_image_string))

    return (
        actual.tag == expected.tag and
        actual.image.height == expected.image.height and
        actual.image.width == expected.image.width and
        actual.image.colorspace == expected.image.colorspace and
        actual_img == expected_img
    )

def compare_proto(str_to_compare, function_ptr):
    expected = read_expected_content(function_ptr)
    str_to_compare = str(str_to_compare)
    return remove_whitespace(str_to_compare) == remove_whitespace(expected)

def write_proto(str_to_compare, function_ptr):
    expected_file = get_expected_file(function_ptr)
    with open(expected_file, 'w') as f:
        f.write(str(str_to_compare))

class TestTensorBoardPytorchGraph(BaseTestCase):
    def test_pytorch_graph(self):
        dummy_input = (torch.zeros(1, 3),)

        class myLinear(torch.nn.Module):
            def __init__(self):
                super(myLinear, self).__init__()
                self.l = torch.nn.Linear(3, 5)

            def forward(self, x):
                return self.l(x)

        with self.createSummaryWriter() as w:
            w.add_graph(myLinear(), dummy_input)

        actual_proto, _ = graph(myLinear(), dummy_input)

        expected_str = read_expected_content(self)
        expected_proto = GraphDef()
        text_format.Parse(expected_str, expected_proto)

        self.assertEquals(len(expected_proto.node), len(actual_proto.node))
        for i in range(len(expected_proto.node)):
            expected_node = expected_proto.node[i]
            actual_node = actual_proto.node[i]
            self.assertEquals(expected_node.name, actual_node.name)
            self.assertEquals(expected_node.op, actual_node.op)
            self.assertEquals(expected_node.input, actual_node.input)
            self.assertEquals(expected_node.device, actual_node.device)
            self.assertEquals(
                sorted(expected_node.attr.keys()), sorted(actual_node.attr.keys()))

    def test_mlp_graph(self):
        dummy_input = (torch.zeros(2, 1, 28, 28),)

        # This MLP class with the above input is expected
        # to fail JIT optimizations as seen at
        # https://github.com/pytorch/pytorch/issues/18903
        #
        # However, it should not raise an error during
        # the add_graph call and still continue.
        class myMLP(torch.nn.Module):
            def __init__(self):
                super(myMLP, self).__init__()
                self.input_len = 1 * 28 * 28
                self.fc1 = torch.nn.Linear(self.input_len, 1200)
                self.fc2 = torch.nn.Linear(1200, 1200)
                self.fc3 = torch.nn.Linear(1200, 10)

            def forward(self, x, update_batch_stats=True):
                h = torch.nn.functional.relu(
                    self.fc1(x.view(-1, self.input_len)))
                h = self.fc2(h)
                h = torch.nn.functional.relu(h)
                h = self.fc3(h)
                return h

        with self.createSummaryWriter() as w:
            w.add_graph(myMLP(), dummy_input)

    def test_wrong_input_size(self):
        with self.assertRaises(RuntimeError) as e_info:
            dummy_input = torch.rand(1, 9)
            model = torch.nn.Linear(3, 5)
            with self.createSummaryWriter() as w:
                w.add_graph(model, dummy_input)  # error

    @skipIfNoTorchVision
    def test_torchvision_smoke(self):
        model_input_shapes = {
            'alexnet': (2, 3, 224, 224),
            'resnet34': (2, 3, 224, 224),
            'resnet152': (2, 3, 224, 224),
            'densenet121': (2, 3, 224, 224),
            'vgg16': (2, 3, 224, 224),
            'vgg19': (2, 3, 224, 224),
            'vgg16_bn': (2, 3, 224, 224),
            'vgg19_bn': (2, 3, 224, 224),
            'mobilenet_v2': (2, 3, 224, 224),
        }
        for model_name, input_shape in model_input_shapes.items():
            with self.createSummaryWriter() as w:
                model = getattr(torchvision.models, model_name)()
                w.add_graph(model, torch.zeros(input_shape))

class TestTensorBoardFigure(BaseTestCase):
    @skipIfNoMatplotlib
    def test_figure(self):
        writer = self.createSummaryWriter()

        figure, axes = plt.figure(), plt.gca()
        circle1 = plt.Circle((0.2, 0.5), 0.2, color='r')
        circle2 = plt.Circle((0.8, 0.5), 0.2, color='g')
        axes.add_patch(circle1)
        axes.add_patch(circle2)
        plt.axis('scaled')
        plt.tight_layout()

        writer.add_figure("add_figure/figure", figure, 0, close=False)
        self.assertTrue(plt.fignum_exists(figure.number))

        writer.add_figure("add_figure/figure", figure, 1)
        if matplotlib.__version__ != '3.3.0':
            self.assertFalse(plt.fignum_exists(figure.number))
        else:
            print("Skipping fignum_exists, see https://github.com/matplotlib/matplotlib/issues/18163")

        writer.close()

    @skipIfNoMatplotlib
    def test_figure_list(self):
        writer = self.createSummaryWriter()

        figures = []
        for i in range(5):
            figure = plt.figure()
            plt.plot([i * 1, i * 2, i * 3], label="Plot " + str(i))
            plt.xlabel("X")
            plt.xlabel("Y")
            plt.legend()
            plt.tight_layout()
            figures.append(figure)

        writer.add_figure("add_figure/figure_list", figures, 0, close=False)
        self.assertTrue(all([plt.fignum_exists(figure.number) is True for figure in figures]))  # noqa F812

        writer.add_figure("add_figure/figure_list", figures, 1)
        if matplotlib.__version__ != '3.3.0':
            self.assertTrue(all([plt.fignum_exists(figure.number) is False for figure in figures]))  # noqa F812
        else:
            print("Skipping fignum_exists, see https://github.com/matplotlib/matplotlib/issues/18163")

        writer.close()

class TestTensorBoardNumpy(BaseTestCase):
    def test_scalar(self):
        res = make_np(1.1)
        self.assertIsInstance(res, np.ndarray) and self.assertEqual(res.shape, (1,))
        res = make_np(1 << 64 - 1)  # uint64_max
        self.assertIsInstance(res, np.ndarray) and self.assertEqual(res.shape, (1,))
        res = make_np(np.float16(1.00000087))
        self.assertIsInstance(res, np.ndarray) and self.assertEqual(res.shape, (1,))
        res = make_np(np.float128(1.00008 + 9))
        self.assertIsInstance(res, np.ndarray) and self.assertEqual(res.shape, (1,))
        res = make_np(np.int64(100000000000))
        self.assertIsInstance(res, np.ndarray) and self.assertEqual(res.shape, (1,))

    @skipIfNoCaffe2
    def test_caffe2_np(self):
        workspace.FeedBlob("testBlob", tensor_N(shape=(1, 3, 64, 64)))
        self.assertIsInstance(make_np('testBlob'), np.ndarray)

    @skipIfNoCaffe2
    def test_caffe2_np_expect_fail(self):
        with self.assertRaises(RuntimeError):
            res = make_np('This_blob_does_not_exist')

    def test_pytorch_np_expect_fail(self):
        with self.assertRaises(NotImplementedError):
            res = make_np({'pytorch': 1.0})

    @skipIfNoCaffe2
    @unittest.skipIf(TEST_WITH_ASAN, "Caffe2 failure with ASAN")
    def test_caffe2_simple_model(self):
        model = ModelHelper(name="mnist")
        # how come those inputs don't break the forward pass =.=a
        workspace.FeedBlob("data", np.random.randn(1, 3, 64, 64).astype(np.float32))
        workspace.FeedBlob("label", np.random.randn(1, 1000).astype(np.int))

        with core.NameScope("conv1"):
            conv1 = brew.conv(model, "data", 'conv1', dim_in=1, dim_out=20, kernel=5)
            # Image size: 24 x 24 -> 12 x 12
            pool1 = brew.max_pool(model, conv1, 'pool1', kernel=2, stride=2)
            # Image size: 12 x 12 -> 8 x 8
            conv2 = brew.conv(model, pool1, 'conv2', dim_in=20, dim_out=100, kernel=5)
            # Image size: 8 x 8 -> 4 x 4
            pool2 = brew.max_pool(model, conv2, 'pool2', kernel=2, stride=2)
        with core.NameScope("classifier"):
            # 50 * 4 * 4 stands for dim_out from previous layer multiplied by the image size
            fc3 = brew.fc(model, pool2, 'fc3', dim_in=100 * 4 * 4, dim_out=500)
            relu = brew.relu(model, fc3, fc3)
            pred = brew.fc(model, relu, 'pred', 500, 10)
            softmax = brew.softmax(model, pred, 'softmax')
            xent = model.LabelCrossEntropy([softmax, "label"], 'xent')
            # compute the expected loss
            loss = model.AveragedLoss(xent, "loss")
        model.net.RunAllOnMKL()
        model.param_init_net.RunAllOnMKL()
        model.AddGradientOperators([loss], skip=1)
        blob_name_tracker = {}
        graph = c2_graph.model_to_graph_def(
            model,
            blob_name_tracker=blob_name_tracker,
            shapes={},
            show_simplified=False,
        )
        compare_proto(graph, self)

    @skipIfNoCaffe2
    def test_caffe2_simple_cnnmodel(self):
        model = cnn.CNNModelHelper("NCHW", name="overfeat")
        workspace.FeedBlob("data", np.random.randn(1, 3, 64, 64).astype(np.float32))
        workspace.FeedBlob("label", np.random.randn(1, 1000).astype(np.int))
        with core.NameScope("conv1"):
            conv1 = model.Conv("data", "conv1", 3, 96, 11, stride=4)
            relu1 = model.Relu(conv1, conv1)
            pool1 = model.MaxPool(relu1, "pool1", kernel=2, stride=2)
        with core.NameScope("classifier"):
            fc = model.FC(pool1, "fc", 4096, 1000)
            pred = model.Softmax(fc, "pred")
            xent = model.LabelCrossEntropy([pred, "label"], "xent")
            loss = model.AveragedLoss(xent, "loss")

        blob_name_tracker = {}
        graph = c2_graph.model_to_graph_def(
            model,
            blob_name_tracker=blob_name_tracker,
            shapes={},
            show_simplified=False,
        )
        compare_proto(graph, self)

if __name__ == '__main__':
    run_tests()