import pytest
import torch
import platform
import os
import fnmatch

_IS_MAC = platform.system() == 'Darwin'

try:
    from torchvision.models.feature_extraction import create_feature_extractor, get_graph_node_names, NodePathTracer
    has_fx_feature_extraction = True
except ImportError:
    has_fx_feature_extraction = False

import timm
from timm import list_models, create_model, set_scriptable, get_pretrained_cfg_value
from timm.models._features_fx import _leaf_modules, _autowrap_functions

if hasattr(torch._C, '_jit_set_profiling_executor'):
    # legacy executor is too slow to compile large models for unit tests
    # no need for the fusion performance here
    torch._C._jit_set_profiling_executor(True)
    torch._C._jit_set_profiling_mode(False)

# transformer models don't support many of the spatial / feature based model functionalities
NON_STD_FILTERS = [
    'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
    'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit*',
    'poolformer_*', 'volo_*', 'sequencer2d_*', 'swinv2_*', 'pvt_v2*', 'mvitv2*', 'gcvit*', 'efficientformer*',
    'eva_*', 'flexivit*'
]
#'coatnet*', 'coatnext*', 'maxvit*', 'maxxvit*', '
NUM_NON_STD = len(NON_STD_FILTERS)

# exclude models that cause specific test failures
if 'GITHUB_ACTIONS' in os.environ:
    # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
    EXCLUDE_FILTERS = [
        '*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*50x3_bitm',
        '*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*',
        '*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', 'vit_huge*', 'vit_gi*', 'swin*huge*',
        'swin*giant*', 'convnextv2_huge*']
    NON_STD_EXCLUDE_FILTERS = ['vit_huge*', 'vit_gi*', 'swin*giant*', 'eva_giant*']
else:
    EXCLUDE_FILTERS = []
    NON_STD_EXCLUDE_FILTERS = ['vit_gi*']

TARGET_FWD_SIZE = MAX_FWD_SIZE = 384
TARGET_BWD_SIZE = 128
MAX_BWD_SIZE = 320
MAX_FWD_OUT_SIZE = 448
TARGET_JIT_SIZE = 128
MAX_JIT_SIZE = 320
TARGET_FFEAT_SIZE = 96
MAX_FFEAT_SIZE = 256
TARGET_FWD_FX_SIZE = 128
MAX_FWD_FX_SIZE = 256
TARGET_BWD_FX_SIZE = 128
MAX_BWD_FX_SIZE = 224


def _get_input_size(model=None, model_name='', target=None):
    if model is None:
        assert model_name, "One of model or model_name must be provided"
        input_size = get_pretrained_cfg_value(model_name, 'input_size')
        fixed_input_size = get_pretrained_cfg_value(model_name, 'fixed_input_size')
        min_input_size = get_pretrained_cfg_value(model_name, 'min_input_size')
    else:
        default_cfg = model.default_cfg
        input_size = default_cfg['input_size']
        fixed_input_size = default_cfg.get('fixed_input_size', None)
        min_input_size = default_cfg.get('min_input_size', None)
    assert input_size is not None

    if fixed_input_size:
        return input_size

    if min_input_size:
        if target and max(input_size) > target:
            input_size = min_input_size
    else:
        if target and max(input_size) > target:
            input_size = tuple([min(x, target) for x in input_size])
    return input_size


@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward(model_name, batch_size):
    """Run a single forward pass with each model"""
    model = create_model(model_name, pretrained=False)
    model.eval()

    input_size = _get_input_size(model=model, target=TARGET_FWD_SIZE)
    if max(input_size) > MAX_FWD_SIZE:
        pytest.skip("Fixed input size model > limit.")
    inputs = torch.randn((batch_size, *input_size))
    outputs = model(inputs)

    assert outputs.shape[0] == batch_size
    assert not torch.isnan(outputs).any(), 'Output included NaNs'


@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [2])
def test_model_backward(model_name, batch_size):
    """Run a single forward pass with each model"""
    input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_SIZE)
    if max(input_size) > MAX_BWD_SIZE:
        pytest.skip("Fixed input size model > limit.")

    model = create_model(model_name, pretrained=False, num_classes=42)
    num_params = sum([x.numel() for x in model.parameters()])
    model.train()

    inputs = torch.randn((batch_size, *input_size))
    outputs = model(inputs)
    if isinstance(outputs, tuple):
        outputs = torch.cat(outputs)
    outputs.mean().backward()
    for n, x in model.named_parameters():
        assert x.grad is not None, f'No gradient for {n}'
    num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])

    assert outputs.shape[-1] == 42
    assert num_params == num_grad, 'Some parameters are missing gradients'
    assert not torch.isnan(outputs).any(), 'Output included NaNs'


@pytest.mark.timeout(300)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS, include_tags=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_default_cfgs(model_name, batch_size):
    """Run a single forward pass with each model"""
    model = create_model(model_name, pretrained=False)
    model.eval()
    state_dict = model.state_dict()
    cfg = model.default_cfg

    pool_size = cfg['pool_size']
    input_size = model.default_cfg['input_size']

    if all([x <= MAX_FWD_OUT_SIZE for x in input_size]) and \
            not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
        # output sizes only checked if default res <= 448 * 448 to keep resource down
        input_size = tuple([min(x, MAX_FWD_OUT_SIZE) for x in input_size])
        input_tensor = torch.randn((batch_size, *input_size))

        # test forward_features (always unpooled)
        outputs = model.forward_features(input_tensor)
        assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2], 'unpooled feature shape != config'

        # test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
        model.reset_classifier(0)
        outputs = model.forward(input_tensor)
        assert len(outputs.shape) == 2
        assert outputs.shape[-1] == model.num_features

        # test model forward without pooling and classifier
        model.reset_classifier(0, '')  # reset classifier and set global pooling to pass-through
        outputs = model.forward(input_tensor)
        assert len(outputs.shape) == 4
        if not isinstance(model, (timm.models.MobileNetV3, timm.models.GhostNet, timm.models.VGG)):
            # mobilenetv3/ghostnet/vgg forward_features vs removed pooling differ due to location or lack of GAP
            assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]

        if 'pruned' not in model_name:  # FIXME better pruned model handling
            # test classifier + global pool deletion via __init__
            model = create_model(model_name, pretrained=False, num_classes=0, global_pool='').eval()
            outputs = model.forward(input_tensor)
            assert len(outputs.shape) == 4
            if not isinstance(model, (timm.models.MobileNetV3, timm.models.GhostNet, timm.models.VGG)):
                assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]

    # check classifier name matches default_cfg
    if cfg.get('num_classes', None):
        classifier = cfg['classifier']
        if not isinstance(classifier, (tuple, list)):
            classifier = classifier,
        for c in classifier:
            assert c + ".weight" in state_dict.keys(), f'{c} not in model params'

    # check first conv(s) names match default_cfg
    first_conv = cfg['first_conv']
    if isinstance(first_conv, str):
        first_conv = (first_conv,)
    assert isinstance(first_conv, (tuple, list))
    for fc in first_conv:
        assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'


@pytest.mark.timeout(300)
@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS, exclude_filters=NON_STD_EXCLUDE_FILTERS, include_tags=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_default_cfgs_non_std(model_name, batch_size):
    """Run a single forward pass with each model"""
    model = create_model(model_name, pretrained=False)
    model.eval()
    state_dict = model.state_dict()
    cfg = model.default_cfg

    input_size = _get_input_size(model=model)
    if max(input_size) > 320:  # FIXME const
        pytest.skip("Fixed input size model > limit.")

    input_tensor = torch.randn((batch_size, *input_size))
    feat_dim = getattr(model, 'feature_dim', None)

    outputs = model.forward_features(input_tensor)
    if isinstance(outputs, (tuple, list)):
        # cannot currently verify multi-tensor output.
        pass
    else:
        if feat_dim is None:
            feat_dim = -1 if outputs.ndim == 3 else 1
        assert outputs.shape[feat_dim] == model.num_features

    # test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
    model.reset_classifier(0)
    outputs = model.forward(input_tensor)
    if isinstance(outputs,  (tuple, list)):
        outputs = outputs[0]
    if feat_dim is None:
        feat_dim = -1 if outputs.ndim == 3 else 1
    assert outputs.shape[feat_dim] == model.num_features, 'pooled num_features != config'

    model = create_model(model_name, pretrained=False, num_classes=0).eval()
    outputs = model.forward(input_tensor)
    if isinstance(outputs, (tuple, list)):
        outputs = outputs[0]
    if feat_dim is None:
        feat_dim = -1 if outputs.ndim == 3 else 1
    assert outputs.shape[feat_dim] == model.num_features

    # check classifier name matches default_cfg
    if cfg.get('num_classes', None):
        classifier = cfg['classifier']
        if not isinstance(classifier, (tuple, list)):
            classifier = classifier,
        for c in classifier:
            assert c + ".weight" in state_dict.keys(), f'{c} not in model params'

    # check first conv(s) names match default_cfg
    first_conv = cfg['first_conv']
    if isinstance(first_conv, str):
        first_conv = (first_conv,)
    assert isinstance(first_conv, (tuple, list))
    for fc in first_conv:
        assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'


if 'GITHUB_ACTIONS' not in os.environ:
    @pytest.mark.timeout(120)
    @pytest.mark.parametrize('model_name', list_models(pretrained=True))
    @pytest.mark.parametrize('batch_size', [1])
    def test_model_load_pretrained(model_name, batch_size):
        """Create that pretrained weights load, verify support for in_chans != 3 while doing so."""
        in_chans = 3 if 'pruned' in model_name else 1  # pruning not currently supported with in_chans change
        create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5)
        create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=0)

    @pytest.mark.timeout(120)
    @pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS))
    @pytest.mark.parametrize('batch_size', [1])
    def test_model_features_pretrained(model_name, batch_size):
        """Create that pretrained weights load when features_only==True."""
        create_model(model_name, pretrained=True, features_only=True)

EXCLUDE_JIT_FILTERS = [
    '*iabn*', 'tresnet*',  # models using inplace abn unlikely to ever be scriptable
    'dla*', 'hrnet*', 'ghostnet*',  # hopefully fix at some point
    'vit_large_*', 'vit_huge_*', 'vit_gi*',
]


@pytest.mark.timeout(120)
@pytest.mark.parametrize(
    'model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_torchscript(model_name, batch_size):
    """Run a single forward pass with each model"""
    input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
    if max(input_size) > MAX_JIT_SIZE:
        pytest.skip("Fixed input size model > limit.")

    with set_scriptable(True):
        model = create_model(model_name, pretrained=False)
    model.eval()

    model = torch.jit.script(model)
    outputs = model(torch.randn((batch_size, *input_size)))

    assert outputs.shape[0] == batch_size
    assert not torch.isnan(outputs).any(), 'Output included NaNs'


EXCLUDE_FEAT_FILTERS = [
    '*pruned*',  # hopefully fix at some point
] + NON_STD_FILTERS
if 'GITHUB_ACTIONS' in os.environ:  # and 'Linux' in platform.system():
    # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
    EXCLUDE_FEAT_FILTERS += ['*resnext101_32x32d', '*resnext101_32x16d']


@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS, include_tags=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_features(model_name, batch_size):
    """Run a single forward pass with each model in feature extraction mode"""
    model = create_model(model_name, pretrained=False, features_only=True)
    model.eval()
    expected_channels = model.feature_info.channels()
    assert len(expected_channels) >= 4  # all models here should have at least 4 feature levels by default, some 5 or 6

    input_size = _get_input_size(model=model, target=TARGET_FFEAT_SIZE)
    if max(input_size) > MAX_FFEAT_SIZE:
        pytest.skip("Fixed input size model > limit.")

    outputs = model(torch.randn((batch_size, *input_size)))
    assert len(expected_channels) == len(outputs)
    for e, o in zip(expected_channels, outputs):
        assert e == o.shape[1]
        assert o.shape[0] == batch_size
        assert not torch.isnan(o).any()


if not _IS_MAC:
    # MACOS test runners are really slow, only running tests below this point if not on a Darwin runner...

    def _create_fx_model(model, train=False):
        # This block of code does a bit of juggling to handle any case where there are multiple outputs in train mode
        # So we trace once and look at the graph, and get the indices of the nodes that lead into the original fx output
        # node. Then we use those indices to select from train_nodes returned by torchvision get_graph_node_names
        tracer_kwargs = dict(
            leaf_modules=list(_leaf_modules),
            autowrap_functions=list(_autowrap_functions),
            #enable_cpatching=True,
            param_shapes_constant=True
        )
        train_nodes, eval_nodes = get_graph_node_names(model, tracer_kwargs=tracer_kwargs)

        eval_return_nodes = [eval_nodes[-1]]
        train_return_nodes = [train_nodes[-1]]
        if train:
            tracer = NodePathTracer(**tracer_kwargs)
            graph = tracer.trace(model)
            graph_nodes = list(reversed(graph.nodes))
            output_node_names = [n.name for n in graph_nodes[0]._input_nodes.keys()]
            graph_node_names = [n.name for n in graph_nodes]
            output_node_indices = [-graph_node_names.index(node_name) for node_name in output_node_names]
            train_return_nodes = [train_nodes[ix] for ix in output_node_indices]

        fx_model = create_feature_extractor(
            model,
            train_return_nodes=train_return_nodes,
            eval_return_nodes=eval_return_nodes,
            tracer_kwargs=tracer_kwargs,
        )
        return fx_model


    EXCLUDE_FX_FILTERS = ['vit_gi*']
    # not enough memory to run fx on more models than other tests
    if 'GITHUB_ACTIONS' in os.environ:
        EXCLUDE_FX_FILTERS += [
            'beit_large*',
            'mixer_l*',
            '*nfnet_f2*',
            '*resnext101_32x32d',
            'resnetv2_152x2*',
            'resmlp_big*',
            'resnetrs270',
            'swin_large*',
            'vgg*',
            'vit_large*',
            'vit_base_patch8*',
            'xcit_large*',
        ]


    @pytest.mark.timeout(120)
    @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS))
    @pytest.mark.parametrize('batch_size', [1])
    def test_model_forward_fx(model_name, batch_size):
        """
        Symbolically trace each model and run single forward pass through the resulting GraphModule
        Also check that the output of a forward pass through the GraphModule is the same as that from the original Module
        """
        if not has_fx_feature_extraction:
            pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")

        model = create_model(model_name, pretrained=False)
        model.eval()

        input_size = _get_input_size(model=model, target=TARGET_FWD_FX_SIZE)
        if max(input_size) > MAX_FWD_FX_SIZE:
            pytest.skip("Fixed input size model > limit.")
        with torch.no_grad():
            inputs = torch.randn((batch_size, *input_size))
            outputs = model(inputs)
            if isinstance(outputs, tuple):
                outputs = torch.cat(outputs)

            model = _create_fx_model(model)
            fx_outputs = tuple(model(inputs).values())
            if isinstance(fx_outputs, tuple):
                fx_outputs = torch.cat(fx_outputs)

        assert torch.all(fx_outputs == outputs)
        assert outputs.shape[0] == batch_size
        assert not torch.isnan(outputs).any(), 'Output included NaNs'


    @pytest.mark.timeout(120)
    @pytest.mark.parametrize('model_name', list_models(
        exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS, name_matches_cfg=True))
    @pytest.mark.parametrize('batch_size', [2])
    def test_model_backward_fx(model_name, batch_size):
        """Symbolically trace each model and run single backward pass through the resulting GraphModule"""
        if not has_fx_feature_extraction:
            pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")

        input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_FX_SIZE)
        if max(input_size) > MAX_BWD_FX_SIZE:
            pytest.skip("Fixed input size model > limit.")

        model = create_model(model_name, pretrained=False, num_classes=42)
        model.train()
        num_params = sum([x.numel() for x in model.parameters()])
        if 'GITHUB_ACTIONS' in os.environ and num_params > 100e6:
            pytest.skip("Skipping FX backward test on model with more than 100M params.")

        model = _create_fx_model(model, train=True)
        outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
        if isinstance(outputs, tuple):
            outputs = torch.cat(outputs)
        outputs.mean().backward()
        for n, x in model.named_parameters():
            assert x.grad is not None, f'No gradient for {n}'
        num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])

        assert outputs.shape[-1] == 42
        assert num_params == num_grad, 'Some parameters are missing gradients'
        assert not torch.isnan(outputs).any(), 'Output included NaNs'


    if 'GITHUB_ACTIONS' not in os.environ:
        # FIXME this test is causing GitHub actions to run out of RAM and abruptly kill the test process

        # reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
        EXCLUDE_FX_JIT_FILTERS = [
            'deit_*_distilled_patch16_224',
            'levit*',
            'pit_*_distilled_224',
        ] + EXCLUDE_FX_FILTERS


        @pytest.mark.timeout(120)
        @pytest.mark.parametrize(
            'model_name', list_models(
                exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True))
        @pytest.mark.parametrize('batch_size', [1])
        def test_model_forward_fx_torchscript(model_name, batch_size):
            """Symbolically trace each model, script it, and run single forward pass"""
            if not has_fx_feature_extraction:
                pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")

            input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
            if max(input_size) > MAX_JIT_SIZE:
                pytest.skip("Fixed input size model > limit.")

            with set_scriptable(True):
                model = create_model(model_name, pretrained=False)
            model.eval()

            model = torch.jit.script(_create_fx_model(model))
            with torch.no_grad():
                outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
                if isinstance(outputs, tuple):
                    outputs = torch.cat(outputs)

            assert outputs.shape[0] == batch_size
            assert not torch.isnan(outputs).any(), 'Output included NaNs'