from functools import partial
from math import sqrt
from typing import Dict, Optional, Union

import mlx.core as mx
import mlx.nn as nn

from .config import MLPConfig, VisionConfig


def check_array_shape(arr):
    shape = arr.shape

    # Check if the shape has 4 dimensions
    if len(shape) != 4:
        return False

    out_channels, kH, KW, _ = shape

    # Check if out_channels is the largest, and kH and KW are the same
    if (out_channels >= kH) and (out_channels >= KW) and (kH == KW):
        return True
    else:
        return False


class AttentionPoolLatent(nn.Module):
    """Attention pooling w/ latent query"""

    def __init__(
        self,
        in_features: int,
        out_features: int = None,
        embed_dim: int = None,
        num_heads: int = 8,
        mlp_ratio: float = 4.0,
        qkv_bias: bool = True,
        qk_norm: bool = False,
        latent_len: int = 1,
        latent_dim: int = None,
        pos_embed: str = "",
        pool_type: str = "token",
        norm_layer: Optional[nn.Module] = None,
        drop: float = 0.0,
    ):
        super().__init__()

        embed_dim = embed_dim or in_features
        out_features = out_features or in_features
        assert embed_dim % num_heads == 0
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads
        self.scale = self.head_dim**-0.5
        self.pool = pool_type

        self.latent_dim = latent_dim or embed_dim
        self.latent_len = latent_len
        self.latent = mx.zeros((self.latent_len, embed_dim))[None, :]

        self.q = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
        self.kv = nn.Linear(embed_dim, embed_dim * 2, bias=qkv_bias)
        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.proj = nn.Linear(embed_dim, embed_dim)
        self.proj_drop = nn.Dropout(drop)

        if pos_embed == "abs":
            spatial_len = self.feat_size
            self.pos_embed = mx.zeros((spatial_len, in_features))
        else:
            self.pos_embed = None

        self.norm = nn.LayerNorm(out_features)
        config = MLPConfig(
            width=embed_dim, intermediate_size=int(embed_dim * mlp_ratio)
        )
        self.mlp = MLP(config)

    def __call__(self, x: mx.array):
        B, N, C = x.shape

        if self.pos_embed is not None:
            x = x + self.pos_embed.unsqueeze(0).to(x.dtype)

        q_latent = mx.array(self.latent)

        q = (
            self.q(q_latent)
            .reshape(B, self.latent_len, self.num_heads, self.head_dim)
            .transpose(0, 2, 1, 3)
        )

        kv = (
            self.kv(x)
            .reshape(B, N, 2, self.num_heads, self.head_dim)
            .transpose(2, 0, 3, 1, 4)
        )
        k, v = mx.split(kv, 2, axis=0)

        q, k = self.q_norm(q), self.k_norm(k)

        x = mx.fast.scaled_dot_product_attention(
            q, k[0], v[0], scale=(1.0 / sqrt(q.shape[-1])), mask=None
        )

        x = x.transpose(0, 2, 1, 3).reshape(B, self.latent_len, C)
        x = self.proj(x)
        x = self.proj_drop(x)

        x = x + self.mlp(self.norm(x))

        # optional pool if latent seq_len > 1 and pooled output is desired
        if self.pool == "token":
            x = x[:, 0]
        elif self.pool == "avg":
            x = x.mean(1)
        return x


class Attention(nn.Module):
    def __init__(
        self,
        dims: int,
        num_heads: int,
        qkv_bias: bool = True,
    ):
        super().__init__()

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

        self.num_heads = num_heads = num_heads
        head_dim = dims // num_heads
        self.scale = head_dim**-0.5

        self.qkv = nn.Linear(dims, dims * 3, bias=qkv_bias)
        self.proj = nn.Linear(dims, dims, bias=True)

    def __call__(self, x, mask=None):
        qkv = self.qkv(x)
        queries, keys, values = mx.split(qkv, 3, axis=-1)

        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, 1, 3)
        values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)

        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.proj(output)


class MLP(nn.Module):
    def __init__(self, config: Union[VisionConfig, Dict], bias: bool = True):
        super().__init__()
        self.activation_fn = nn.GELU(approx="precise")
        self.fc1 = nn.Linear(config.width, config.intermediate_size, bias=bias)
        self.fc2 = nn.Linear(config.intermediate_size, config.width, bias=bias)

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


class EncoderLayer(nn.Module):
    def __init__(self, config: VisionConfig):
        super().__init__()
        self.embed_dim = config.width
        self.attn = Attention(config.width, config.num_attention_heads, qkv_bias=True)
        self.norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
        self.mlp = MLP(config)
        self.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.norm1(x)
        y = self.attn(y, mask)
        x = x + y
        y = self.norm2(x)
        y = self.mlp(y)
        return x + y


class VisionEmbeddings(nn.Module):
    def __init__(self, config: VisionConfig, norm_layer: bool = False):
        super().__init__()
        self.config = config
        self.embed_dim = config.width
        self.image_size = config.image_size
        self.patch_size = config.patch_size

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

        self.num_patches = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches

        self.norm = (
            nn.LayerNorm(config.width, eps=config.layer_norm_eps)
            if norm_layer
            else nn.Identity()
        )

    def __call__(self, x: mx.array) -> mx.array:
        patch_embeddings = self.proj(x)
        patch_embeddings = mx.flatten(patch_embeddings, start_axis=1, end_axis=2)
        return self.norm(patch_embeddings)


class SigLipVisionModel(nn.Module):
    def __init__(
        self,
        config: VisionConfig,
        ignore_head: bool,
        pre_norm: bool = False,
        no_embed_class: bool = True,
    ):
        super().__init__()
        self.num_prefix_tokens = 1
        self.no_embed_class = False
        self.dynamic_img_size = False
        self.ignore_head = ignore_head
        self.cls_token = None
        self.reg_token = None
        self.patch_embed = VisionEmbeddings(config)
        self.norm_pre = nn.LayerNorm(config.width) if pre_norm else nn.Identity()
        self.blocks = [EncoderLayer(config) for _ in range(config.layers)]
        self.norm = nn.LayerNorm(config.width)
        num_patches = self.patch_embed.num_patches
        embed_len = (
            num_patches if no_embed_class else num_patches + self.num_prefix_tokens
        )
        self.pos_embed = mx.random.normal((embed_len, config.width))[None, :]

        norm_layer = partial(nn.LayerNorm, eps=1e-5)
        self.attn_pool = AttentionPoolLatent(
            config.width,
            num_heads=config.num_attention_heads,
            norm_layer=norm_layer,
            mlp_ratio=config.mlp_ratio,
        )

    def __call__(
        self,
        x: mx.array,
        output_hidden_states: Optional[bool] = None,
    ) -> mx.array:
        x = self.patch_embed(x)
        x += self.pos_embed
        x = self.norm_pre(x)

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

        pooler_output = self.norm(x)

        if not self.ignore_head:
            pooler_output = self.attn_pool(pooler_output)
        return pooler_output, x, encoder_states


class VisionModel(nn.Module):
    def __init__(self, config: VisionConfig, ignore_head: bool = True):
        super().__init__()

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

        self.vision_tower = SigLipVisionModel(config, ignore_head)

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

    def sanitize(self, weights):
        sanitized_weights = {}
        weight_keys = {
            "neck.0.weight",
            "neck.2.weight",
            "neck_hd.0.weight",
            "neck_hd.2.weight",
            "downsamples.0.weight",
            "downsamples.1.weight",
            "patch_embed.proj.weight",
        }
        for k, v in weights.items():
            if "position_ids" in k:
                # Remove unused position_ids
                continue

            elif ".".join(k.split(".")[-3:]) in weight_keys:
                # 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]
                if check_array_shape(v):
                    sanitized_weights[k] = v
                else:
                    sanitized_weights[k] = v.transpose(0, 2, 3, 1)

            else:
                sanitized_weights[k] = v

        return sanitized_weights