# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
from contextlib import nullcontext
from copy import deepcopy
from types import SimpleNamespace

import pytest
import torch

from megatron.core import parallel_state as ps
from megatron.core.inference.contexts import StaticInferenceContext
from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec
from megatron.core.models.multimodal import context_parallel
from megatron.core.models.multimodal.llava_model import LLaVAModel
from megatron.core.models.vision.vit_layer_specs import get_vit_layer_with_transformer_engine_spec
from megatron.core.packed_seq_params import PackedSeqParams
from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.utils import is_te_min_version
from megatron.training.global_vars import set_args
from tests.unit_tests.test_utilities import Utils


class TestLLaVAModel:
    @pytest.mark.internal  # The model is under active development and its methods may change.
    def setup_method(self, method):
        Utils.initialize_model_parallel(1, 1)
        model_parallel_cuda_manual_seed(123)

        self.language_hidden_size = 64
        self.language_num_attention_heads = 4

        language_config = TransformerConfig(
            num_layers=3,
            hidden_size=self.language_hidden_size,
            num_attention_heads=self.language_num_attention_heads,
            use_cpu_initialization=False,
        )
        vision_config = TransformerConfig(
            num_layers=2, hidden_size=16, num_attention_heads=2, use_cpu_initialization=False
        )
        vision_projection_config = TransformerConfig(
            num_layers=2,
            hidden_size=self.language_hidden_size,
            ffn_hidden_size=32,
            num_attention_heads=1,
            use_cpu_initialization=False,
        )

        language_layer_spec = get_gpt_layer_with_transformer_engine_spec()
        vision_layer_spec = deepcopy(language_layer_spec)
        vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules)

        language_config.language_model_type = "dummy"
        vision_config.vision_model_type = "clip"
        self.model = LLaVAModel(
            language_transformer_config=language_config,
            language_transformer_layer_spec=language_layer_spec,
            language_vocab_size=8192,
            language_max_sequence_length=4096,
            vision_transformer_config=vision_config,
            vision_transformer_layer_spec=vision_layer_spec,
            drop_vision_class_token=False,
            vision_projection_config=vision_projection_config,
            vision_projection_layer_spec=vision_projection_spec,
            img_h=336,
            img_w=336,
            patch_dim=14,
        )

    @pytest.mark.internal
    def teardown_method(self, method):
        Utils.destroy_model_parallel()

    @pytest.mark.internal
    def test_constructor(self):
        assert isinstance(self.model, LLaVAModel)

        num_weights = sum([p.numel() for p in self.model.parameters()])
        assert num_weights == 1488736

    @pytest.mark.internal
    def test_set_input_tensor(self):
        expected_shape = (1, 2, 3, 4)
        input_tensor = torch.zeros(expected_shape)
        self.model.set_input_tensor(input_tensor)
        assert self.model.vision_model.decoder.input_tensor.shape == expected_shape

    @pytest.mark.internal
    def test_preprocess_data(self):
        self.model.cuda()

        hidden_size = 72

        # 3 images with 1 tile and 2 image with 2 tiles = 7 tiles.
        image_embeddings = (
            torch.arange(577 * 7 * hidden_size, dtype=torch.float)
            .reshape(577, 7, hidden_size)
            .cuda()
        )

        image_token_index = self.model.image_token_index
        input_ids = torch.arange(1024).expand(5, 1024).cuda()
        input_ids[0, 0] = image_token_index  # image before text
        input_ids[1, 100] = image_token_index  # image in between
        input_ids[2, -1] = image_token_index  # image at the end
        # input_ids[3] - no image
        input_ids[4, 50] = image_token_index  # two images in between
        input_ids[4, 150] = image_token_index

        # Using negative sign to distinguish from image embeddings.
        language_embeddings = (
            -torch.arange(5 * 1024 * hidden_size, dtype=torch.float)
            .reshape(5, 1024, hidden_size)
            .cuda()
        )

        # Labels are input_ids shifted to left by one.
        labels = torch.arange(1, 1025, dtype=torch.int).expand(5, 1024).cuda()
        # labels[0] - image token got dropped by shift to left by one.
        labels[1, 99] = image_token_index
        labels[2, -2] = image_token_index
        # labels[3] - no image.
        labels[4, 49] = image_token_index
        labels[4, 149] = image_token_index

        loss_mask = torch.ones((5, 1024), dtype=torch.float).cuda()
        # Mask some text inputs (the text mask should carry over)
        loss_mask[:2, :10] = 0.0
        loss_mask[:2, 110:120] = 0.0

        # Number of tiles for each image in the batch.
        num_image_tiles = torch.tensor([1, 2, 1, 2, 1], dtype=torch.int).cuda()

        use_inference_kv_cache = False
        inference_context = None

        embeddings, labels, loss_mask = self.model._preprocess_data(
            image_embeddings,
            language_embeddings,
            input_ids,
            loss_mask,
            labels,
            use_inference_kv_cache,
            inference_context,
            image_token_index,
            num_image_tiles,
        )

        img_seq_len = 577
        # The fifth sample has 2 images with 3 tiles and 1024 text tokens.
        max_seq_len = 3 * img_seq_len - 2 + 1024

        assert embeddings.shape == torch.Size((max_seq_len, 5, hidden_size))
        assert labels.shape == torch.Size((5, max_seq_len))
        assert loss_mask.shape == labels.shape

        # First sample where image is before text (index 0).
        expected_embeddings = torch.empty(max_seq_len, hidden_size).cuda()
        expected_embeddings[:577] = image_embeddings[:, 0]
        expected_embeddings[577:1600] = language_embeddings[0, 1:]
        expected_embeddings[1600:] = 0  # padding

        expected_labels = torch.empty(max_seq_len, dtype=torch.int).cuda()
        expected_labels[:576] = -100  # image
        expected_labels[576:1600] = torch.arange(1, 1025, dtype=torch.int)
        expected_labels[1600:] = -100  # padding

        expected_loss_mask = torch.empty(max_seq_len, dtype=torch.float).cuda()
        expected_loss_mask[:577] = 0
        expected_loss_mask[577:586] = 0
        expected_loss_mask[586:686] = 1
        expected_loss_mask[686:696] = 0
        expected_loss_mask[696:1600] = 1
        expected_loss_mask[1600:] = 0

        assert torch.allclose(embeddings[:, 0], expected_embeddings)
        assert torch.allclose(labels[0], expected_labels)
        assert torch.allclose(loss_mask[0], expected_loss_mask)

        # Second sample where image is in between (index 100). The image has 2 tiles.
        expected_embeddings = torch.empty(max_seq_len, hidden_size).cuda()
        expected_embeddings[:100] = language_embeddings[1, :100]
        expected_embeddings[100:677] = image_embeddings[:, 1]
        expected_embeddings[677:1254] = image_embeddings[:, 2]
        expected_embeddings[1254:2177] = language_embeddings[1, 101:]
        expected_embeddings[2177:] = 0  # padding

        expected_labels = torch.empty(max_seq_len, dtype=torch.int).cuda()
        expected_labels[:99] = torch.arange(1, 100)
        expected_labels[99:1253] = -100  # image
        expected_labels[1253:2177] = torch.arange(101, 1025)
        expected_labels[2177:] = -100  # padding

        expected_loss_mask = torch.empty(max_seq_len, dtype=torch.float).cuda()
        expected_loss_mask[:10] = 0
        expected_loss_mask[10:99] = 1
        # Last text position before the image is not required to predict the first image embedding.
        expected_loss_mask[99] = 0
        expected_loss_mask[100:1254] = 0
        expected_loss_mask[1254:1263] = 1
        expected_loss_mask[1263:1273] = 0
        expected_loss_mask[1273:2177] = 1
        expected_loss_mask[2177:] = 0  # padding

        assert torch.allclose(embeddings[:, 1], expected_embeddings)
        assert torch.allclose(labels[1], expected_labels)
        assert torch.allclose(loss_mask[1], expected_loss_mask)

        # Third sample where image is at the end.
        expected_embeddings = torch.empty(max_seq_len, hidden_size).cuda()
        expected_embeddings[:1023] = language_embeddings[2, :1023]
        expected_embeddings[1023:1600] = image_embeddings[:, 3]
        expected_embeddings[1600:] = 0  # padding

        expected_labels = torch.empty(max_seq_len, dtype=torch.int).cuda()
        expected_labels[:1022] = torch.arange(1, 1023)
        expected_labels[1022:1599] = -100
        expected_labels[1599] = 1024
        expected_labels[1600:] = -100  # padding

        expected_loss_mask = torch.empty(max_seq_len, dtype=torch.float).cuda()
        expected_loss_mask[:1022] = 1
        # Last text position before the image is not required to predict the first image embedding.
        expected_loss_mask[1022] = 0
        expected_loss_mask[1023:1600] = 0
        expected_loss_mask[1600:] = 0  # padding

        assert torch.allclose(embeddings[:, 2], expected_embeddings)
        assert torch.allclose(labels[2], expected_labels)
        assert torch.allclose(loss_mask[2], expected_loss_mask)

        # Fourth sample where there is no image.
        expected_embeddings = torch.empty(max_seq_len, hidden_size).cuda()
        expected_embeddings[:1024] = language_embeddings[3]
        expected_embeddings[1024:] = 0  # padding

        expected_labels = torch.empty(max_seq_len, dtype=torch.int).cuda()
        expected_labels[:1024] = torch.arange(1, 1025)
        expected_labels[1024:] = -100  # padding

        expected_loss_mask = torch.empty(max_seq_len, dtype=torch.float).cuda()
        expected_loss_mask[:1024] = 1
        expected_loss_mask[1024:] = 0  # padding

        assert torch.allclose(embeddings[:, 3], expected_embeddings)
        assert torch.allclose(labels[3], expected_labels)
        assert torch.allclose(loss_mask[3], expected_loss_mask)

        # Fifth sample has two images in between (indices 50 and 150). The first image has two tiles.
        expected_embeddings = torch.empty(max_seq_len, hidden_size).cuda()
        expected_embeddings[:50] = language_embeddings[4, :50]
        expected_embeddings[50:627] = image_embeddings[:, 4]  # two tiles
        expected_embeddings[627:1204] = image_embeddings[:, 5]
        expected_embeddings[1204:1303] = language_embeddings[4, 51:150]
        expected_embeddings[1303:1880] = image_embeddings[:, 6]
        expected_embeddings[1880:] = language_embeddings[4, 151:]

        expected_labels = torch.empty(max_seq_len, dtype=torch.int).cuda()
        expected_labels[:49] = torch.arange(1, 50)
        expected_labels[49:1203] = -100  # image
        expected_labels[1203:1302] = torch.arange(51, 150)
        expected_labels[1302:1879] = -100  # image
        expected_labels[1879:] = torch.arange(151, 1025)

        expected_loss_mask = torch.empty(max_seq_len, dtype=torch.float).cuda()
        expected_loss_mask[:49] = 1
        expected_loss_mask[49:1204] = 0
        expected_loss_mask[1204:1302] = 1
        expected_loss_mask[1302:1880] = 0
        expected_loss_mask[1880:] = 1

        assert torch.allclose(embeddings[:, 4], expected_embeddings)
        assert torch.allclose(labels[4], expected_labels)
        assert torch.allclose(loss_mask[4], expected_loss_mask)

    @pytest.mark.internal
    def test_forward(self):
        self.model.cuda()

        # 3 images with 1 tile and 2 images with 2 tiles.
        img = torch.randn((7, 3, 336, 336)).cuda()

        image_token_index = self.model.image_token_index
        input_ids = torch.randint(0, 2048, (5, 1024)).cuda()
        input_ids[0, 0] = image_token_index  # image before text
        input_ids[1, 100] = image_token_index  # image in between
        input_ids[2, -1] = image_token_index  # image at the end
        # input_ids[3] - no image
        input_ids[4, 50] = image_token_index
        input_ids[4, 150] = image_token_index

        position_ids = torch.arange(0, 1024, dtype=torch.int).expand(5, 1024).cuda()

        loss_mask = torch.ones((5, 1024)).cuda()

        attention_mask = None  # Causal.

        labels = torch.randint(0, 2048, (5, 1024)).cuda()
        labels[1, 99] = image_token_index
        labels[2, -2] = image_token_index

        num_image_tiles = torch.tensor([1, 2, 1, 2, 1], dtype=torch.int).cuda()

        # Try with labels.
        loss, new_loss_mask = self.model.forward(
            img,
            input_ids,
            position_ids,
            attention_mask,
            labels,
            loss_mask,
            num_image_tiles=num_image_tiles,
        )

        # The maximum sequence length is given by the sample with 2 images in 3 tiles, minus two image token indices, plus other text tokens.
        img_seq_len = 577
        max_seq_len = img_seq_len * 3 - 2 + 1024
        assert loss.shape == new_loss_mask.shape == torch.Size((5, max_seq_len))

        # Try with labels and PackedSeqParams. Only micro batch size 1 is supported in this mode.
        packed_seq_params = PackedSeqParams(
            qkv_format="thd",
            cu_seqlens_q=torch.tensor(
                [0, 512, 1024, 1600], dtype=torch.int32
            ).cuda(),  # Just example values.
            cu_seqlens_kv=torch.tensor([0, 512, 1024, 1600], dtype=torch.int32).cuda(),
            max_seqlen_q=torch.tensor(1600, dtype=torch.int32).cuda(),
            max_seqlen_kv=torch.tensor(1600, dtype=torch.int32).cuda(),
        )

        # NOTE: Packing is only supported with BF16. Use BF16 here and switch back to default.
        self.model.to(torch.bfloat16)
        loss, new_loss_mask = self.model.forward(
            img[:1].to(torch.bfloat16),
            input_ids[:1],
            position_ids[:1],
            attention_mask,
            labels[:1],
            loss_mask[:1],
            num_image_tiles=num_image_tiles[:1],
            packed_seq_params=packed_seq_params,
        )
        self.model.to(torch.float32)

        # 1600 = 577 (img_seq_len) + 1024 (text tokens in the first sample) - 1 (image token).
        assert loss.shape == new_loss_mask.shape == torch.Size((1, 1600))

        # Try text-only input.
        loss, new_loss_mask = self.model.forward(
            torch.tensor([], dtype=torch.float).cuda(),
            torch.randint(0, 2048, (5, 1024)).cuda(),
            position_ids,
            attention_mask,
            torch.randint(0, 2048, (5, 1024)).cuda(),
            loss_mask,
            num_image_tiles=torch.tensor([], dtype=torch.int).cuda(),
        )

        assert loss.shape == new_loss_mask.shape == torch.Size((5, 1024))

        # Try without labels and without inference params.
        logits, _ = self.model.forward(
            img,
            input_ids,
            position_ids,
            attention_mask,
            labels=None,
            loss_mask=None,
            num_image_tiles=num_image_tiles,
        )
        assert logits.shape == torch.Size((5, max_seq_len, 8192))

        # Try without labels and with inference params.
        inference_context = StaticInferenceContext(5, max_seq_len)
        logits, _ = self.model.forward(
            img,
            input_ids,
            position_ids,
            attention_mask,
            labels=None,
            loss_mask=None,
            num_image_tiles=num_image_tiles,
            inference_context=inference_context,
        )
        assert logits.shape == torch.Size((5, max_seq_len, 8192))

        # Check KV cache got populated correctly.
        kv_dict = inference_context.key_value_memory_dict

        assert kv_dict["image_tokens_count"] == 577 * 7
        for layer_no in range(1, 4):  # 3 layers in the model.
            layer_kv = kv_dict[layer_no]
            # Expected shape is [sequence_len, batch_size, num_heads, hidden_size_per_head]
            assert (
                layer_kv[0].shape
                == layer_kv[1].shape
                == torch.Size((max_seq_len, 5, self.language_num_attention_heads, 16))
            )

    @pytest.mark.internal
    def test_forward_fsdp(self):
        """Test FSDP workaround for text-only data.

        FSDP can hang with text-only data. As a workaround, we run the vision model with a dummy image,
        but then effectively discard the image embeddings.
        """
        self.model.cuda()

        # Dummy image for the FSDP workaround but not image tiles.
        img = torch.zeros((1, 3, 336, 336)).cuda()
        num_image_tiles = torch.tensor([], dtype=torch.int).cuda()

        # No image tag in the input ids (text-only sample).
        image_token_index = self.model.image_token_index
        input_ids = torch.arange(1024, device="cuda").unsqueeze(0)
        assert (
            torch.sum(input_ids == image_token_index) == 0
        ), "expected no image tag in the input ids"

        position_ids = torch.arange(1024, device="cuda").unsqueeze(0)

        loss_mask = torch.ones((1, 1024), device="cuda")

        attention_mask = None  # Causal.

        labels = torch.arange(1, 1025, device="cuda").unsqueeze(0)

        # Mock the FSDP attribute.
        self.model.vision_model._is_fsdp_managed_module = True
        loss, new_loss_mask = self.model.forward(
            img,
            input_ids,
            position_ids,
            attention_mask,
            labels,
            loss_mask,
            num_image_tiles=num_image_tiles,
        )
        self.model.vision_model._is_fsdp_managed_module = False

        assert loss.shape == new_loss_mask.shape == torch.Size((1, 1024))

    @pytest.mark.internal
    def test_save_load(self, tmp_path):
        path = tmp_path / "model.pt"
        torch.save(self.model.state_dict(), path)

        self.model.load_state_dict(torch.load(path))

    @pytest.mark.internal
    def test_freeze(self):
        self.model.freeze(
            freeze_language_model=True, freeze_vision_model=True, freeze_vision_projection=False
        )

        for module in [self.model.language_model, self.model.vision_model]:
            for param in module.parameters():
                assert not param.requires_grad

        for param in self.model.vision_projection.parameters():
            assert param.requires_grad


@pytest.fixture(scope='class', params=["siglip", "radio-g"])
def setup_and_teardown_llava_model(request):
    Utils.initialize_model_parallel(1, 1)
    model_parallel_cuda_manual_seed(123)

    language_config = TransformerConfig(
        num_layers=3, hidden_size=128, num_attention_heads=8, use_cpu_initialization=False
    )
    vision_config = TransformerConfig(
        num_layers=2, hidden_size=64, num_attention_heads=4, use_cpu_initialization=False
    )
    vision_projection_config = TransformerConfig(
        num_layers=2,
        hidden_size=128,
        ffn_hidden_size=72,
        num_attention_heads=1,
        use_cpu_initialization=False,
    )

    language_layer_spec = get_gpt_layer_with_transformer_engine_spec()
    vision_layer_spec = deepcopy(language_layer_spec)
    vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules)

    language_config.language_model_type = "dummy"
    vision_model_type = request.param
    vision_config.vision_model_type = vision_model_type
    model = LLaVAModel(
        language_transformer_config=language_config,
        language_transformer_layer_spec=language_layer_spec,
        language_vocab_size=2048,
        language_max_sequence_length=4096,
        vision_transformer_config=vision_config,
        vision_transformer_layer_spec=vision_layer_spec,
        drop_vision_class_token=False,
        vision_projection_config=vision_projection_config,
        vision_projection_layer_spec=vision_projection_spec,
        img_h=336,
        img_w=336,
        patch_dim=14,
    )

    yield model, vision_model_type

    Utils.destroy_model_parallel()


class TestLLaVAModelVisionEncoders:
    num_weights_by_encoder = {"siglip": 1832456, "radio-g": 2844552}

    @pytest.mark.internal
    def test_constructor(self, setup_and_teardown_llava_model):
        model, vision_model_type = setup_and_teardown_llava_model
        assert isinstance(model, LLaVAModel)

        num_weights = sum([p.numel() for p in model.parameters()])
        assert num_weights == self.num_weights_by_encoder[vision_model_type]

    @pytest.mark.internal
    def test_set_input_tensor(self, setup_and_teardown_llava_model):
        model, _ = setup_and_teardown_llava_model
        expected_shape = (1, 2, 3, 4)
        input_tensor = torch.zeros(expected_shape)
        model.set_input_tensor(input_tensor)
        assert model.vision_model.decoder.input_tensor.shape == expected_shape


def create_test_args(cp_size, sequence_parallel):
    # Set dummy values for the args.
    args = SimpleNamespace()
    args.context_parallel_size = cp_size
    args.sequence_parallel = sequence_parallel

    return args


class TestLLaVAModelTokenParallel:

    def _init_llava_model(self, cp_size, tp_size, sequence_parallel, cp_group=None):
        language_hidden_size = 64
        language_num_attention_heads = 16

        language_config = TransformerConfig(
            num_layers=3,
            hidden_size=language_hidden_size,
            num_attention_heads=language_num_attention_heads,
            use_cpu_initialization=False,
            tensor_model_parallel_size=tp_size,
            sequence_parallel=sequence_parallel,
            context_parallel_size=cp_size,
        )
        # SP and CP are not yet supported for the Vision Backbone
        vision_config = TransformerConfig(
            num_layers=2,
            hidden_size=16,
            num_attention_heads=8,
            use_cpu_initialization=False,
            tensor_model_parallel_size=tp_size,
            sequence_parallel=False,
            context_parallel_size=1,
        )
        vision_projection_config = TransformerConfig(
            num_layers=2,
            hidden_size=language_hidden_size,
            ffn_hidden_size=128,
            num_attention_heads=8,
            use_cpu_initialization=False,
            tensor_model_parallel_size=tp_size,
            sequence_parallel=False,
            context_parallel_size=1,
        )

        language_layer_spec = get_gpt_layer_with_transformer_engine_spec()
        # SP/CP either requires user to ensure token lengths do not require padding OR change mask type to padding
        if (
            language_layer_spec.submodules.self_attention.params.get('attn_mask_type', '')
            == AttnMaskType.causal
        ):
            language_layer_spec.submodules.self_attention.params['attn_mask_type'] = (
                AttnMaskType.padding_causal
            )
        elif (
            language_layer_spec.submodules.self_attention.params.get('attn_mask_type', '')
            == AttnMaskType.no_mask
        ):
            language_layer_spec.submodules.self_attention.params['attn_mask_type'] = (
                AttnMaskType.padding
            )

        vision_layer_spec = deepcopy(language_layer_spec)
        vision_projection_spec = deepcopy(language_layer_spec.submodules.mlp.submodules)

        language_config.language_model_type = "dummy"
        vision_config.vision_model_type = "clip"
        model = LLaVAModel(
            language_transformer_config=language_config,
            language_transformer_layer_spec=language_layer_spec,
            language_vocab_size=8192,
            language_max_sequence_length=4096,
            vision_transformer_config=vision_config,
            vision_transformer_layer_spec=vision_layer_spec,
            drop_vision_class_token=False,
            vision_projection_config=vision_projection_config,
            vision_projection_layer_spec=vision_projection_spec,
            img_h=336,
            img_w=336,
            patch_dim=14,
            cp_group=cp_group,
        )

        return model

    def _prepare_inputs(self, cp_size, tp_size, sequence_parallel, padding):
        self.batch_size = 2
        if padding:
            self.combined_valid_seqlen = 2049
            self.combined_padded_seqlen = 2064
        else:
            self.combined_valid_seqlen = 2048
            self.combined_padded_seqlen = 2048

        if cp_size > 1:
            combined_embeddings = torch.ones(
                [self.batch_size, self.combined_padded_seqlen, 4096],
                device='cuda',
                dtype=torch.bfloat16,
            )  # [B, S, H]
        else:
            combined_embeddings = torch.ones(
                [self.combined_padded_seqlen, self.batch_size, 4096],
                device='cuda',
                dtype=torch.bfloat16,
            )  # [S, B, H]
        new_labels = torch.ones(
            [self.batch_size, self.combined_padded_seqlen], device='cuda', dtype=torch.bfloat16
        )  # [B, S]
        new_loss_mask = torch.ones(
            [self.batch_size, self.combined_padded_seqlen], device='cuda', dtype=torch.bfloat16
        )  # [B, S]

        cu_seqlens = torch.arange(
            0,
            (self.batch_size + 1) * (self.combined_valid_seqlen),
            step=(self.combined_valid_seqlen),
            dtype=torch.int32,
            device=combined_embeddings.device,
        )
        cu_seqlens_padded = torch.arange(
            0,
            (self.batch_size + 1) * (self.combined_padded_seqlen),
            step=(self.combined_padded_seqlen),
            dtype=torch.int32,
            device=combined_embeddings.device,
        )

        qkv_format = 'sbhd'  # Default format when not using padding
        if cp_size > 1 and padding:
            # Reshape from [B,S] to [1,T]
            combined_embeddings = (
                combined_embeddings.contiguous()
                .view(combined_embeddings.shape[0] * combined_embeddings.shape[1], -1)
                .unsqueeze(0)
            )
            new_labels = new_labels.view(new_labels.shape[0] * new_labels.shape[1]).unsqueeze(0)
            new_loss_mask = new_loss_mask.view(
                new_loss_mask.shape[0] * new_loss_mask.shape[1]
            ).unsqueeze(0)
            qkv_format = 'thd'

        packed_seq_params = PackedSeqParams(
            cu_seqlens_q=cu_seqlens,
            cu_seqlens_kv=cu_seqlens,
            cu_seqlens_q_padded=cu_seqlens_padded,
            cu_seqlens_kv_padded=cu_seqlens_padded,
            max_seqlen_q=self.combined_padded_seqlen,
            max_seqlen_kv=self.combined_padded_seqlen,
            qkv_format=qkv_format,
        )

        return combined_embeddings, new_labels, new_loss_mask, packed_seq_params

    @pytest.mark.internal
    def setup_method(self, method):
        Utils.destroy_model_parallel()

    @pytest.mark.internal
    def teardown_method(self, method):
        Utils.destroy_model_parallel()

    @pytest.mark.internal
    @pytest.mark.parametrize(
        "cp_size,tp_size,sequence_parallel,padding",
        [(1, 8, True, True), (2, 4, False, True), (2, 4, True, False), (2, 4, True, True)],
    )
    def test_process_embedding_token_parallel(self, cp_size, tp_size, sequence_parallel, padding):
        """Test _process_embedding_token_parallel.

        Note: This test requires TE version >= 1.10.0 to run properly.
        """
        Utils.initialize_model_parallel(
            tensor_model_parallel_size=tp_size, context_parallel_size=cp_size
        )
        model_parallel_cuda_manual_seed(123)

        # TE version must be at least 1.10.0 if using context parallelism. Exit otherwise.
        ctx = (
            nullcontext()
            if (is_te_min_version("1.10.0") or cp_size <= 1)
            else pytest.raises(AssertionError)
        )
        model = None
        with ctx:
            model = self._init_llava_model(
                cp_size, tp_size, sequence_parallel, cp_group=ps.get_context_parallel_group()
            )

        if model is None:
            return

        model.cuda()

        args = create_test_args(cp_size, sequence_parallel)
        set_args(args)

        combined_embeddings, new_labels, new_loss_mask, packed_seq_params = self._prepare_inputs(
            cp_size, tp_size, sequence_parallel, padding
        )

        combined_embeddings, new_labels, new_loss_mask, packed_seq_params = (
            model._process_embedding_token_parallel(
                combined_embeddings, new_labels, new_loss_mask, packed_seq_params
            )
        )

        # Check if output shape is as expected
        if cp_size > 1 and sequence_parallel:
            if padding:
                # THD format
                assert combined_embeddings.shape[0] == self.batch_size * (
                    self.combined_padded_seqlen / (tp_size * cp_size)
                )
                assert combined_embeddings.shape[1] == 1
            else:
                # SBHD format
                assert combined_embeddings.shape[0] == (
                    self.combined_padded_seqlen / (tp_size * cp_size)
                )
                assert combined_embeddings.shape[1] == self.batch_size
        elif cp_size > 1:
            if padding:
                # THD format
                assert combined_embeddings.shape[0] == self.batch_size * (
                    self.combined_padded_seqlen / cp_size
                )
                assert combined_embeddings.shape[1] == 1
            else:
                # SBHD format
                assert combined_embeddings.shape[0] == (self.combined_padded_seqlen / cp_size)
                assert combined_embeddings.shape[1] == self.batch_size
        else:
            # SBHD format
            assert combined_embeddings.shape[0] == self.combined_padded_seqlen / tp_size
            assert combined_embeddings.shape[1] == self.batch_size


def count_parameters(model):
    return sum(p.numel() for p in model.parameters())


@pytest.mark.internal
@pytest.mark.parametrize(
    "cp_size, tp_size, has_sp, seq_len, expected_padding",
    [(1, 1, False, 99, 0), (2, 2, True, 99, 5), (2, 2, False, 99, 1)],
)
def test_get_padding(cp_size, tp_size, has_sp, seq_len, expected_padding):
    """Test calculating padding for context parallel."""
    padding = context_parallel.get_padding(seq_len, cp_size, tp_size, has_sp)

    assert padding == expected_padding


@pytest.mark.internal
@pytest.mark.parametrize(
    "tokens, img_seq_len, padding_needed, cp_size, expected_seq_len",
    [(torch.ones((1, 100)), 100, 0, 2, 200), (torch.ones((1, 100)), 128, 1, 2, 227)],
)
def test_get_packed_seq_params(tokens, img_seq_len, padding_needed, cp_size, expected_seq_len):
    """Test creating PackedSeqParams for context parallel."""
    packed_seq_params = context_parallel.get_packed_seq_params(
        tokens, img_seq_len, padding_needed, cp_size
    )

    assert torch.equal(
        packed_seq_params.cu_seqlens_q, torch.tensor([0, expected_seq_len], dtype=torch.int32)
    )

    if padding_needed > 0:
        padded_seq_len = tokens.shape[1] + img_seq_len
        assert torch.equal(
            packed_seq_params.cu_seqlens_q_padded,
            torch.tensor([0, padded_seq_len], dtype=torch.int32),
        )
        assert packed_seq_params.max_seqlen_q == padded_seq_len
