from typing import Optional

import mlx.core as mx
import mlx.nn as nn
import numpy as np

from ..base import InputEmbeddingsFeatures
from . import processing_aya_vision  # noqa: F401
from .config import ModelConfig
from .language import LanguageModel
from .vision import VisionModel


class AyaVisionMultiModalProjector(nn.Module):
    def __init__(self, config: ModelConfig):
        super().__init__()
        self.config = config
        self.downsample_factor = config.downsample_factor
        self.alignment_intermediate_size = getattr(
            config, "alignment_intermediate_size", config.text_config.hidden_size
        )
        if config.model_type == "aya_vision":
            self.layernorm = nn.LayerNorm(
                config.vision_config.hidden_size * (config.downsample_factor**2),
                eps=config.adapter_layer_norm_eps,
            )

        self.linear_1 = nn.Linear(
            config.vision_config.hidden_size * (config.downsample_factor**2),
            self.alignment_intermediate_size,
            bias=True,
        )

        self.act = nn.SiLU()  # SwiGLU uses SiLU activation

        # For SwiGLU, project down to half size since we split intermediate dim
        self.linear_2 = nn.Linear(
            self.alignment_intermediate_size // 2,
            config.text_config.hidden_size,
            bias=True,
        )

    def __call__(self, image_features):
        image_features = self.pixel_shuffle(image_features)
        if self.config.model_type == "aya_vision":
            image_features = self.layernorm(image_features)
        hidden_states = self.linear_1(image_features)

        # Split along last dimension and apply SwiGLU
        x, gate = mx.split(hidden_states, 2, axis=-1)
        hidden_states = self.act(gate) * x

        hidden_states = self.linear_2(hidden_states)
        return hidden_states

    def pixel_shuffle(self, image_features):  # B, S, D
        batch_size, seq_length, feature_dim = image_features.shape
        height = width = int(seq_length**0.5)
        image_features = image_features.reshape(
            image_features.shape[0], width, height, -1
        )
        channels = image_features.shape[-1]
        image_features = image_features.reshape(
            batch_size,
            width,
            int(height / self.downsample_factor),
            int(channels * self.downsample_factor),
        )
        image_features = image_features.transpose(0, 2, 1, 3)
        image_features = image_features.reshape(
            batch_size,
            int(height / self.downsample_factor),
            int(width / self.downsample_factor),
            -1,
        )
        image_features = image_features.transpose(0, 2, 1, 3)
        return image_features


class Model(nn.Module):
    def __init__(self, config: ModelConfig):
        super().__init__()
        self.config = config
        self.vision_tower = VisionModel(config.vision_config)
        self.language_model = LanguageModel(config.text_config)
        self.multi_modal_projector = AyaVisionMultiModalProjector(config)
        self.vision_feature_layer = config.vision_feature_layer
        self.vision_feature_select_strategy = config.vision_feature_select_strategy

    def get_input_embeddings(
        self,
        input_ids: Optional[mx.array] = None,
        pixel_values: Optional[mx.array] = None,
        **kwargs,
    ):
        if pixel_values is None:
            return InputEmbeddingsFeatures(
                inputs_embeds=self.language_model.model.embed_tokens(input_ids)
            )

        # Get the input embeddings from the language model
        inputs_embeds = self.language_model.model.embed_tokens(input_ids)

        spatial_shapes = kwargs.get("spatial_shapes", None)
        # Get the ouptut hidden states from the vision model
        *_, hidden_states = self.vision_tower(
            pixel_values.transpose(0, 2, 3, 1),
            spatial_shapes=spatial_shapes,
            output_hidden_states=True,
        )

        # Select the hidden states from the desired layer
        selected_image_feature = hidden_states[self.vision_feature_layer]

        if self.vision_feature_select_strategy == "default":
            selected_image_feature = selected_image_feature[:, 1:]
        elif self.vision_feature_select_strategy == "full":
            selected_image_feature = selected_image_feature
        else:
            raise ValueError(
                "Unexpected feature selection strategy: "
                f"{self.vision_feature_select_strategy}"
            )

        # Pass image features through the multi-modal projector
        image_features = self.multi_modal_projector(selected_image_feature)

        # Insert special image tokens in the input_ids
        final_inputs_embeds = self._merge_input_ids_with_image_features(
            image_features, inputs_embeds, input_ids
        )
        return InputEmbeddingsFeatures(inputs_embeds=final_inputs_embeds)

    def _merge_input_ids_with_image_features(
        self, image_features, inputs_embeds, input_ids
    ):
        image_token_index = self.config.image_token_index

        # Positions of <image> tokens in input_ids, assuming batch size is 1
        image_positions = np.where(input_ids[0] == image_token_index)[0].tolist()
        num_images, _, _, vision_hidden_size = image_features.shape

        reshaped_image_hidden_states = image_features.reshape(-1, vision_hidden_size)

        # cast to the dtype of the input_embeds to support quantized models
        reshaped_image_hidden_states = reshaped_image_hidden_states.astype(
            inputs_embeds.dtype
        )
        inputs_embeds[:, image_positions, :] = reshaped_image_hidden_states
        return inputs_embeds

    @property
    def layers(self):
        return self.language_model.model.layers

    def __call__(
        self,
        input_ids: mx.array,
        pixel_values: mx.array,
        mask: mx.array,
        cache=None,
        **kwargs,
    ):

        input_embeddings_features = self.get_input_embeddings(
            input_ids, pixel_values, **kwargs
        )
        logits = self.language_model(
            input_ids,
            cache=cache,
            inputs_embeds=input_embeddings_features.inputs_embeds,
        )
        return logits

    def sanitize(self, weights):
        def transform_key(key):
            if "model.vision_tower" in key:
                key = key.replace("model.vision_tower", "vision_tower")
            if "model.multi_modal_projector" in key:
                key = key.replace(
                    "model.multi_modal_projector", "multi_modal_projector"
                )
            if "model.language_model" in key:
                key = key.replace("model.language_model", "language_model.model")
            if "lm_head" in key and not key.startswith("language_model"):
                key = key.replace("lm_head", "language_model.lm_head")
            return key

        return {transform_key(k): v for k, v in weights.items()}
