import jax
import jax.numpy as jnp
import flax.linen as nn

class PatchEmbed(nn.Module):
    height: int
    width: int
    patch_size: int
    in_channels: int
    embed_dim: int
    pos_embed_max_size: int

    @nn.compact
    def __call__(self, x):
        # Implementation details omitted for brevity
        return x

class CombinedTimestepTextProjEmbeddings(nn.Module):
    embedding_dim: int
    pooled_projection_dim: int

    @nn.compact
    def __call__(self, timestep, pooled_projections):
        # Implementation details omitted for brevity
        return jnp.array([])

class JointTransformerBlock(nn.Module):
    dim: int
    num_attention_heads: int
    attention_head_dim: int
    context_pre_only: bool

    @nn.compact
    def __call__(self, hidden_states, encoder_hidden_states, temb):
        # Implementation details omitted for brevity
        return hidden_states, encoder_hidden_states

class AdaLayerNormContinuous(nn.Module):
    dim: int
    eps: float = 1e-6

    @nn.compact
    def __call__(self, x, emb):
        # Implementation details omitted for brevity
        return x

class SD3Transformer2DModel(nn.Module):
    config: dict

    @nn.compact
    def __call__(self, hidden_states, encoder_hidden_states=None, pooled_projections=None, 
                 timestep=None, block_controlnet_hidden_states=None, train=False):
        sample_size = self.config['sample_size']
        patch_size = self.config['patch_size']
        in_channels = self.config['in_channels']
        num_layers = self.config['num_layers']
        attention_head_dim = self.config['attention_head_dim']
        num_attention_heads = self.config['num_attention_heads']
        joint_attention_dim = self.config['joint_attention_dim']
        caption_projection_dim = self.config['caption_projection_dim']
        pooled_projection_dim = self.config['pooled_projection_dim']
        out_channels = self.config['out_channels']
        pos_embed_max_size = self.config['pos_embed_max_size']

        inner_dim = num_attention_heads * attention_head_dim

        hidden_states = PatchEmbed(
            height=sample_size, width=sample_size, patch_size=patch_size,
            in_channels=in_channels, embed_dim=inner_dim, pos_embed_max_size=pos_embed_max_size
        )(hidden_states)

        temb = CombinedTimestepTextProjEmbeddings(
            embedding_dim=inner_dim, pooled_projection_dim=pooled_projection_dim
        )(timestep, pooled_projections)

        encoder_hidden_states = nn.Dense(caption_projection_dim)(encoder_hidden_states)

        for i in range(num_layers):
            block = JointTransformerBlock(
                dim=inner_dim, num_attention_heads=num_attention_heads,
                attention_head_dim=inner_dim, context_pre_only=(i == num_layers - 1)
            )
            encoder_hidden_states, hidden_states = block(
                hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb
            )

            if block_controlnet_hidden_states is not None and not block.context_pre_only:
                interval_control = num_layers // len(block_controlnet_hidden_states)
                hidden_states = hidden_states + block_controlnet_hidden_states[i // interval_control]

        hidden_states = AdaLayerNormContinuous(dim=inner_dim)(hidden_states, temb)
        hidden_states = nn.Dense(patch_size * patch_size * out_channels)(hidden_states)

        # Reshape and permute operations
        b, h, w, c = hidden_states.shape
        hidden_states = hidden_states.reshape(b, h, w, patch_size, patch_size, out_channels)
        hidden_states = jnp.transpose(hidden_states, (0, 5, 1, 3, 2, 4))
        output = hidden_states.reshape(b, out_channels, h * patch_size, w * patch_size)

        return output

# Test module
def test_sd3transformer2dmodel():
    config = {
        'sample_size': 128,
        'patch_size': 2,
        'in_channels': 16,
        'num_layers': 18,
        'attention_head_dim': 64,
        'num_attention_heads': 18,
        'joint_attention_dim': 4096,
        'caption_projection_dim': 1152,
        'pooled_projection_dim': 2048,
        'out_channels': 16,
        'pos_embed_max_size': 96,
    }

    model = SD3Transformer2DModel(config)
    
    # Initialize the model
    key = jax.random.PRNGKey(0)
    batch_size = 1
    hidden_states = jax.random.normal(key, (batch_size, config['in_channels'], config['sample_size'], config['sample_size']))
    encoder_hidden_states = jax.random.normal(key, (batch_size, 77, config['joint_attention_dim']))
    pooled_projections = jax.random.normal(key, (batch_size, config['pooled_projection_dim']))
    timestep = jnp.array([1000])
    
    params = model.init(key, hidden_states, encoder_hidden_states, pooled_projections, timestep)
    
    # Run the model
    output = model.apply(params, hidden_states, encoder_hidden_states, pooled_projections, timestep)
    
    print(f"Output shape: {output.shape}")
    expected_shape = (batch_size, config['out_channels'], config['sample_size'], config['sample_size'])
    assert output.shape == expected_shape, f"Expected shape {expected_shape}, but got {output.shape}"
    
    print("Test passed successfully!")

# Run the test
test_sd3transformer2dmodel()