# Copyright © 2024 Apple Inc.

import math
import inspect
from dataclasses import dataclass, field
from typing import Optional, Dict, Union

import mlx.core as mx
import mlx.nn as nn
from mlx_lm.models.base import BaseModelArgs, KVCache
from .inference.shard import Shard
from .base import IdentityBlock
import numpy as np


@dataclass
class VisionConfig:
    model_type: str
    num_hidden_layers: int = 24
    hidden_size: int = 1024
    intermediate_size: int = 4096
    num_attention_heads: int = 16
    image_size: int = 336
    patch_size: int = 14
    projection_dim: int = 768
    vocab_size: int = 32000
    num_channels: int = 3
    layer_norm_eps: float = 1e-5

    @classmethod
    def from_dict(cls, params):
        return cls(
            **{
                k: v
                for k, v in params.items()
                if k in inspect.signature(cls).parameters
            }
        )


class VisionAttention(nn.Module):
    def __init__(
        self,
        dims: int,
        num_heads: int,
        query_input_dims: Optional[int] = None,
        key_input_dims: Optional[int] = None,
        value_input_dims: Optional[int] = None,
        value_dims: Optional[int] = None,
        value_output_dims: Optional[int] = None,
        bias: bool = False,
    ):
        super().__init__()

        if (dims % num_heads) != 0:
            raise ValueError(
                f"The input feature dimensions should be divisible by the number of heads ({dims} % {num_heads}) != 0"
            )

        query_input_dims = query_input_dims or dims
        key_input_dims = key_input_dims or dims
        value_input_dims = value_input_dims or key_input_dims
        value_dims = value_dims or dims
        value_output_dims = value_output_dims or dims

        self.num_heads = num_heads
        self.q_proj = nn.Linear(query_input_dims, dims, bias=bias)
        self.k_proj = nn.Linear(key_input_dims, dims, bias=bias)
        self.v_proj = nn.Linear(value_input_dims, value_dims, bias=bias)
        self.out_proj = nn.Linear(value_dims, value_output_dims, bias=bias)

    def __call__(self, queries, keys, values, mask=None):
        queries = self.q_proj(queries)
        keys = self.k_proj(keys)
        values = self.v_proj(values)

        num_heads = self.num_heads
        B, L, D = queries.shape
        _, S, _ = keys.shape
        queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
        keys = keys.reshape(B, S, num_heads, -1).transpose(0, 2, 3, 1)
        values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)

        scale = math.sqrt(1 / queries.shape[-1])
        scores = (queries * scale) @ keys
        if mask is not None:
            scores = scores + mask.astype(scores.dtype)
        scores = mx.softmax(scores, axis=-1)
        values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)

        return self.out_proj(values_hat)


class VisionMLP(nn.Module):
    def __init__(self, config: VisionConfig):
        super().__init__()
        self.activation_fn = nn.GELU(approx="fast")
        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

    def __call__(self, x: mx.array) -> mx.array:
        x = self.activation_fn(self.fc1(x))
        x = self.fc2(x)
        return x


class VisionEncoderLayer(nn.Module):
    def __init__(self, config: VisionConfig):
        super().__init__()
        self.embed_dim = config.hidden_size
        self.self_attn = VisionAttention(
            config.hidden_size, config.num_attention_heads, bias=True
        )
        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
        self.mlp = VisionMLP(config)
        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)

    def __call__(self, x: mx.array, mask: Optional[mx.array] = None) -> mx.array:
        y = self.layer_norm1(x)
        y = self.self_attn(y, y, y, mask)
        x = x + y
        y = self.layer_norm2(x)
        y = self.mlp(y)
        return x + y


class VisionEncoder(nn.Module):
    def __init__(self, config: VisionConfig):
        super().__init__()
        self.layers = [
            VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)
        ]


class VisionEmbeddings(nn.Module):
    def __init__(self, config: VisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

        self.class_embedding = mx.zeros((config.hidden_size,))

        self.patch_embedding = nn.Conv2d(
            in_channels=config.num_channels,
            out_channels=self.embed_dim,
            kernel_size=self.patch_size,
            stride=self.patch_size,
            bias=False,
        )

        self.num_patches = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches + 1
        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)

    def __call__(self, x: mx.array) -> mx.array:
        batch_size = x.shape[0]
        patch_embeddings = self.patch_embedding(x)
        patch_embeddings = mx.flatten(patch_embeddings, start_axis=1, end_axis=2)
        embed_dim = patch_embeddings.shape[-1]
        cls_embeddings = mx.broadcast_to(
            self.class_embedding, (batch_size, 1, embed_dim)
        )
        embeddings = mx.concatenate((cls_embeddings, patch_embeddings), axis=1)
        embeddings += self.position_embedding.weight
        return embeddings


class ClipVisionModel(nn.Module):
    def __init__(self, config: VisionConfig):
        super().__init__()
        self.embeddings = VisionEmbeddings(config)
        self.pre_layrnorm = nn.LayerNorm(config.hidden_size)
        self.encoder = VisionEncoder(config)
        self.post_layernorm = nn.LayerNorm(config.hidden_size)

    def __call__(
        self,
        x: mx.array,
        output_hidden_states: Optional[bool] = None,
    ) -> mx.array:
        x = self.embeddings(x)
        x = self.pre_layrnorm(x)

        encoder_states = (x,) if output_hidden_states else None

        for l in self.encoder.layers:
            x = l(x, mask=None)
            if output_hidden_states:
                encoder_states = encoder_states + (x,)

        pooler_output = self.post_layernorm(x[:, 0, :])
        return pooler_output, x, encoder_states


class VisionModel(nn.Module):
    def __init__(self, config: VisionConfig):
        super().__init__()

        self.model_type = config.model_type
        if self.model_type != "clip_vision_model":
            raise ValueError(f"Unsupported model type: {self.model_type}")

        self.vision_model = ClipVisionModel(config)

    def __call__(
        self, x: mx.array, output_hidden_states: Optional[bool] = None
    ) -> mx.array:
        return self.vision_model(x, output_hidden_states)

    def sanitize(self, weights):
        sanitized_weights = {}
        for k, v in weights.items():
            if "position_ids" in k:
                # Remove unused position_ids
                continue
            elif "patch_embedding.weight" in k:
                # PyTorch conv2d weight tensors have shape:
                #   [out_channels, in_channels, kH, KW]
                # MLX conv2d expects the weight be of shape:
                #   [out_channels, kH, KW, in_channels]
                sanitized_weights[k] = v.transpose(0, 2, 3, 1)
            else:
                sanitized_weights[k] = v

        return sanitized_weights


@dataclass
class TextConfig:
    model_type: str
    hidden_size: int = 4096
    num_hidden_layers: int = 32
    intermediate_size: int = 11008
    num_attention_heads: int = 32
    head_dim: int = None
    rms_norm_eps: float = 1e-6
    vocab_size: int = 32000
    num_key_value_heads: int = None
    rope_theta: float = 10000
    rope_traditional: bool = False
    rope_scaling: Optional[Dict[str, Union[float, str]]] = None

    @classmethod
    def from_dict(cls, params):
        return cls(
            **{
                k: v
                for k, v in params.items()
                if k in inspect.signature(cls).parameters
            }
        )

    def __post_init__(self):
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads

        if self.head_dim is None:
            self.head_dim = self.hidden_size // self.num_attention_heads

        if self.model_type is None:
            self.model_type = "llama"

        if self.rope_scaling:
            required_keys = {"factor", "type"}
            if not all(key in self.rope_scaling for key in required_keys):
                raise ValueError(f"rope_scaling must contain keys {required_keys}")

            if self.rope_scaling["type"] != "linear":
                raise ValueError("rope_scaling 'type' currently only supports 'linear'")


class TextAttention(nn.Module):
    def __init__(self, config: TextConfig):
        super().__init__()

        dim = config.hidden_size
        self.n_heads = n_heads = config.num_attention_heads
        self.n_kv_heads = n_kv_heads = config.num_key_value_heads

        self.repeats = n_heads // n_kv_heads

        head_dim = config.hidden_size // n_heads
        self.scale = head_dim**-0.5

        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)

        rope_scale = (
            1 / config.rope_scaling["factor"]
            if config.rope_scaling is not None
            and config.rope_scaling["type"] == "linear"
            else 1
        )
        self.rope = nn.RoPE(
            head_dim,
            traditional=config.rope_traditional,
            base=config.rope_theta,
            scale=rope_scale,
        )

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[KVCache] = None,
    ) -> mx.array:
        B, L, D = x.shape

        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)

        # Prepare the queries, keys and values for the attention computation
        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)

        if cache is not None:
            queries = self.rope(queries, offset=cache.offset)
            keys = self.rope(keys, offset=cache.offset)
            keys, values = cache.update_and_fetch(keys, values)
        else:
            queries = self.rope(queries)
            keys = self.rope(keys)

        output = mx.fast.scaled_dot_product_attention(
            queries, keys, values, scale=self.scale, mask=mask
        )
        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
        return self.o_proj(output)


class TextMLP(nn.Module):
    def __init__(self, dim, hidden_dim):
        super().__init__()
        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)

    def __call__(self, x) -> mx.array:
        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))


class TransformerBlock(nn.Module):
    def __init__(self, config: TextConfig):
        super().__init__()
        self.num_attention_heads = config.num_attention_heads
        self.hidden_size = config.hidden_size
        self.self_attn = TextAttention(config)
        self.mlp = TextMLP(config.hidden_size, config.intermediate_size)
        self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = nn.RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
        self.config = config

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[KVCache] = None,
    ) -> mx.array:
        r = self.self_attn(self.input_layernorm(x), mask, cache)
        h = x + r
        r = self.mlp(self.post_attention_layernorm(h))
        out = h + r
        return out


class Llama(nn.Module):
    def __init__(self, config: TextConfig, shard: Shard):
        super().__init__()
        self.config = config
        self.shard = shard
        self.vocab_size = config.vocab_size
        self.model_type = config.model_type
        self.num_hidden_layers = config.num_hidden_layers
        self.num_key_value_heads = config.num_key_value_heads
        self.head_dim = config.head_dim
        assert self.vocab_size > 0
        if self.shard.is_first_layer():
            self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = []
        for i in range(self.num_hidden_layers):
            if self.shard.start_layer <= i <= self.shard.end_layer:
                self.layers.append(TransformerBlock(config=config))
            else:
                self.layers.append(IdentityBlock())
        if self.shard.is_last_layer():
            self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
        inputs_embeds=None,
    ):
        # for passing merged input embeddings
        if inputs_embeds is None:
            if self.shard.is_first_layer():
                h = self.embed_tokens(inputs)
            else:
                h = inputs
        else:
            h = inputs_embeds

        mask = None
        if h.shape[1] > 1:
            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
            mask = mask.astype(h.dtype)

        if cache is None:
            cache = [None] * len(self.layers)

        for layer, c in zip(self.layers, cache):
            h = layer(h, mask, c)

        if self.shard.is_last_layer():
            h = self.norm(h)
        return h


class LanguageModel(nn.Module):
    def __init__(self, config: TextConfig, shard: Shard):
        super().__init__()
        self.model_type = config.model_type
        if self.model_type != "llama":
            raise ValueError(
                f"Model type {self.model_type} not supported. Currently only 'llama' is supported"
            )
        self.shard = shard
        self.model = Llama(config, shard)
        if self.shard.is_last_layer():
            self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
        inputs_embeds=None,
    ):
        out = self.model(inputs, cache, inputs_embeds)
        if self.shard.is_last_layer():
            out = self.lm_head(out)
        return out

    def sanitize(self, weights):
        shard_state_dict = {}
        for key, value in weights.items():
            if "self_attn.rotary_emb.inv_freq" in key:
                continue

            if key.startswith("language_model.model.layers."):
                layer_num = int(key.split(".")[3])
                if (
                    layer_num < self.shard.start_layer
                    or layer_num > self.shard.end_layer
                ):
                    continue
            if not self.shard.is_first_layer() and key.startswith(
                "language_model.model.embed_tokens"
            ):
                continue
            elif not self.shard.is_last_layer() and (
                key.startswith("language_model.model.norm")
                or key.startswith("language_model.lm_head")
            ):
                continue

            shard_state_dict[key] = value

        return shard_state_dict


@dataclass
class LlaVAConfig(BaseModelArgs):
    text_config: TextConfig
    vision_config: VisionConfig = None
    model_type: str = "llava"
    ignore_index: int = -100
    image_token_index: int = 32000
    vision_feature_select_strategy: str = "default"
    vision_feature_layer: int = -2
    vocab_size: int = 32000

    @classmethod
    def from_dict(cls, params):
        updated_params = {}
        class_params = inspect.signature(cls).parameters
        for k, v in params.items():
            if k in class_params:
                if k in ["text_config", "vision_config"]:
                    v = class_params[k].annotation.from_dict(v)
                updated_params.update({k: v})

        return cls(**updated_params)


@dataclass
class ModelArgs(LlaVAConfig):
    shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0))

    def __post_init__(self):
        if isinstance(self.shard, dict):
            self.shard = Shard(**self.shard)

        if not isinstance(self.shard, Shard):
            raise TypeError(
                f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead"
            )

        if not self.shard.is_first_layer():
            self.vision_config = None


class LlavaMultiModalProjector(nn.Module):
    def __init__(self, config: LlaVAConfig):
        super().__init__()
        self.linear_1 = nn.Linear(
            config.vision_config.hidden_size, config.text_config.hidden_size, bias=True
        )
        self.gelu = nn.GELU()
        self.linear_2 = nn.Linear(
            config.text_config.hidden_size, config.text_config.hidden_size, bias=True
        )

    def __call__(self, x: mx.array) -> mx.array:
        x = self.linear_1(x)
        x = self.gelu(x)
        x = self.linear_2(x)
        return x


class Model(nn.Module):
    def __init__(self, config: ModelArgs):
        super().__init__()
        self.config = config
        self.model_type = config.model_type
        if config.vision_config:
            self.vision_tower = VisionModel(config.vision_config)
            self.multi_modal_projector = LlavaMultiModalProjector(config)
            self.vision_feature_layer = config.vision_feature_layer
            self.vision_feature_select_strategy = config.vision_feature_select_strategy
        self.language_model = LanguageModel(config.text_config, config.shard)

    def get_input_embeddings(
        self,
        input_ids: Optional[mx.array] = None,
        pixel_values: Optional[mx.array] = None,
    ):
        if pixel_values is None:
            return self.language_model(input_ids)

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

        # Get the ouptut hidden states from the vision model
        *_, hidden_states = self.vision_tower(
            pixel_values.transpose(0, 2, 3, 1), 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(
                f"Unexpected feature selection strategy: {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 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
        num_images, num_image_patches, embed_dim = image_features.shape

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

        if len(image_positions) != num_images:
            raise ValueError(
                f"The number of image tokens ({len(image_positions)}) does not "
                f" match the number of image inputs ({num_images})."
            )

        text_segments = []
        start_idx = 0

        for position in image_positions:
            text_segments.append(inputs_embeds[:, start_idx:position])
            start_idx = position + 1

        image_embeddings = mx.split(image_features, image_features.shape[0])
        final_embeddings = [v for p in zip(text_segments, image_embeddings) for v in p]
        final_embeddings += [inputs_embeds[:, start_idx:]]

        # Create a final embedding of shape
        # (1, num_image_patches*num_images + sequence_len, embed_dim)
        return mx.concatenate(final_embeddings, axis=1)

    def __call__(self, input_ids: mx.array, pixel_values: mx.array = None, cache=None):
        input_embddings = None
        if pixel_values is not None:
            input_embddings = self.get_input_embeddings(input_ids, pixel_values)
        logits = self.language_model(
            input_ids, cache=cache, inputs_embeds=input_embddings
        )
        return logits

    def sanitize(self, weights):
        if self.config.vision_config:
            weights = self.vision_tower.sanitize(weights)
        else:
            weights = {
                k: v
                for k, v in weights.items()
                if not k.startswith(
                    (
                        "vision_tower",
                        "multi_modal_projector",
                        "vision_feature_layer",
                        "vision_feature_select_strategy",
                    )
                )
            }
        weights = self.language_model.sanitize(weights)
        return weights

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

    @property
    def head_dim(self):
        return (
            self.language_model.model.head_dim
            or self.language_model.model.hidden_size
            // self.language_model.model.num_attention_heads
        )

    @property
    def n_kv_heads(self):
        return self.language_model.model.num_key_value_heads
