import math
from typing import Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.models.dinov3_vit.configuration_dinov3_vit import DINOv3ViTConfig


def get_patches_center_coordinates(num_patches_h: int, num_patches_w: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
    coords_h = torch.arange(0.5, num_patches_h, dtype=dtype, device=device)
    coords_w = torch.arange(0.5, num_patches_w, dtype=dtype, device=device)
    coords_h = coords_h / num_patches_h
    coords_w = coords_w / num_patches_w
    coords = torch.stack(torch.meshgrid(coords_h, coords_w, indexing="ij"), dim=-1)
    coords = coords.flatten(0, 1)
    coords = 2.0 * coords - 1.0
    return coords


def augment_patches_center_coordinates(coords: torch.Tensor, shift: Optional[float] = None,
                                     jitter: Optional[float] = None, rescale: Optional[float] = None) -> torch.Tensor:
    if shift is not None:
        shift_hw = torch.empty((1, 2), device=coords.device, dtype=coords.dtype)
        shift_hw = shift_hw.uniform_(-shift, shift)
        coords = coords + shift_hw

    if jitter is not None:
        jitter_range = np.log(jitter)
        jitter_hw = torch.empty((1, 2), device=coords.device, dtype=coords.dtype)
        jitter_hw = jitter_hw.uniform_(-jitter_range, jitter_range).exp()
        coords = coords * jitter_hw

    if rescale is not None:
        rescale_range = np.log(rescale)
        rescale_hw = torch.empty(1, device=coords.device, dtype=coords.dtype)
        rescale_hw = rescale_hw.uniform_(-rescale_range, rescale_range).exp()
        coords = coords * rescale_hw

    return coords


def rotate_half(x):
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    num_tokens = q.shape[-2]
    num_patches = sin.shape[-2]
    num_prefix_tokens = num_tokens - num_patches

    q_prefix_tokens, q_patches = q.split((num_prefix_tokens, num_patches), dim=-2)
    k_prefix_tokens, k_patches = k.split((num_prefix_tokens, num_patches), dim=-2)

    q_patches = (q_patches * cos) + (rotate_half(q_patches) * sin)
    k_patches = (k_patches * cos) + (rotate_half(k_patches) * sin)

    q = torch.cat((q_prefix_tokens, q_patches), dim=-2)
    k = torch.cat((k_prefix_tokens, k_patches), dim=-2)

    return q, k


def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
    if drop_prob == 0.0 or not training:
        return input
    keep_prob = 1 - drop_prob
    shape = (input.shape[0],) + (1,) * (input.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
    random_tensor.floor_()
    output = input.div(keep_prob) * random_tensor
    return output


class DINOv3ViTEmbeddings(nn.Module):
    def __init__(self, config: DINOv3ViTConfig):
        super().__init__()
        self.config = config
        self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
        self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.register_tokens = nn.Parameter(torch.empty(1, config.num_register_tokens, config.hidden_size))
        self.patch_embeddings = nn.Conv2d(
            config.num_channels, config.hidden_size, kernel_size=config.patch_size, stride=config.patch_size
        )

    def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None) -> torch.Tensor:
        batch_size = pixel_values.shape[0]
        target_dtype = self.patch_embeddings.weight.dtype

        patch_embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype))
        patch_embeddings = patch_embeddings.flatten(2).transpose(1, 2)

        if bool_masked_pos is not None:
            mask_token = self.mask_token.to(patch_embeddings.dtype)
            patch_embeddings = torch.where(bool_masked_pos.unsqueeze(-1), mask_token, patch_embeddings)

        cls_token = self.cls_token.expand(batch_size, -1, -1)
        register_tokens = self.register_tokens.expand(batch_size, -1, -1)
        embeddings = torch.cat([cls_token, register_tokens, patch_embeddings], dim=1)

        return embeddings


class DINOv3ViTRopePositionEmbedding(nn.Module):
    def __init__(self, config: DINOv3ViTConfig):
        super().__init__()
        self.config = config
        self.base = config.rope_theta
        self.head_dim = config.hidden_size // config.num_attention_heads
        self.num_patches_h = config.image_size // config.patch_size
        self.num_patches_w = config.image_size // config.patch_size

        inv_freq = 1 / self.base ** torch.arange(0, 1, 4 / self.head_dim, dtype=torch.float32)
        self.register_buffer("inv_freq", inv_freq, persistent=False)

    def forward(self, pixel_values: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        _, _, height, width = pixel_values.shape
        num_patches_h = height // self.config.patch_size
        num_patches_w = width // self.config.patch_size

        device = pixel_values.device
        device_type = device.type if isinstance(device.type, str) and device.type != "mps" else "cpu"

        with torch.autocast(device_type=device_type, enabled=False):
            patch_coords = get_patches_center_coordinates(
                num_patches_h, num_patches_w, dtype=torch.float32, device=device
            )
            if self.training:
                patch_coords = augment_patches_center_coordinates(
                    patch_coords,
                    shift=self.config.pos_embed_shift,
                    jitter=self.config.pos_embed_jitter,
                    rescale=self.config.pos_embed_rescale,
                )

            angles = 2 * math.pi * patch_coords[:, :, None] * self.inv_freq[None, None, :] # type: ignore
            angles = angles.flatten(1, 2)
            angles = angles.tile(2)

            cos = torch.cos(angles)
            sin = torch.sin(angles)

        dtype = pixel_values.dtype
        return cos.to(dtype=dtype), sin.to(dtype=dtype)


class DINOv3ViTAttention(nn.Module):
    def __init__(self, config: DINOv3ViTConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        self.scaling = self.head_dim**-0.5
        self.dropout = config.attention_dropout

        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.query_bias)
        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.key_bias)
        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.value_bias)
        self.o_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.proj_bias)

    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None,
                position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
        assert position_embeddings is not None

        batch_size, patches, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        attn_weights = torch.matmul(query_states, key_states.transpose(-1, -2)) * self.scaling
        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)

        if self.training:
            attn_weights = F.dropout(attn_weights, p=self.dropout, training=self.training)

        if attention_mask is not None:
            attn_weights = attn_weights * attention_mask

        attn_output = torch.matmul(attn_weights, value_states)
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(batch_size, patches, -1).contiguous()
        attn_output = self.o_proj(attn_output)

        return attn_output, attn_weights


class DINOv3ViTLayerScale(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.lambda1 = nn.Parameter(config.layerscale_value * torch.ones(config.hidden_size))

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        return hidden_state * self.lambda1


class DINOv3ViTDropPath(nn.Module):
    def __init__(self, drop_prob: float):
        super().__init__()
        self.drop_prob = drop_prob

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return drop_path(hidden_states, self.drop_prob, self.training)


class DINOv3ViTMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)

        if config.hidden_act == "gelu":
            self.act_fn = F.gelu
        elif config.hidden_act == "relu":
            self.act_fn = F.relu
        elif config.hidden_act == "silu":
            self.act_fn = F.silu
        else:
            self.act_fn = F.gelu

    def forward(self, x):
        return self.down_proj(self.act_fn(self.up_proj(x)))


class DINOv3ViTGatedMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)

        if config.hidden_act == "gelu":
            self.act_fn = F.gelu
        elif config.hidden_act == "relu":
            self.act_fn = F.relu
        elif config.hidden_act == "silu":
            self.act_fn = F.silu
        else:
            self.act_fn = F.gelu

    def forward(self, x):
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


class DINOv3ViTLayer(nn.Module):
    def __init__(self, config: DINOv3ViTConfig):
        super().__init__()
        self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attention = DINOv3ViTAttention(config)
        self.layer_scale1 = DINOv3ViTLayerScale(config)
        self.drop_path = DINOv3ViTDropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity()

        self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

        if config.use_gated_mlp:
            self.mlp = DINOv3ViTGatedMLP(config)
        else:
            self.mlp = DINOv3ViTMLP(config)
        self.layer_scale2 = DINOv3ViTLayerScale(config)

    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None,
                position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None) -> torch.Tensor:
        assert position_embeddings is not None

        residual = hidden_states
        hidden_states = self.norm1(hidden_states)
        hidden_states, _ = self.attention(
            hidden_states,
            attention_mask=attention_mask,
            position_embeddings=position_embeddings,
        )
        hidden_states = self.layer_scale1(hidden_states)
        hidden_states = self.drop_path(hidden_states) + residual

        residual = hidden_states
        hidden_states = self.norm2(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.layer_scale2(hidden_states)
        hidden_states = self.drop_path(hidden_states) + residual

        return hidden_states


class DINOv3ViTModel(nn.Module):
    def __init__(self, config: DINOv3ViTConfig):
        super().__init__()
        self.config = config
        self.embeddings = DINOv3ViTEmbeddings(config)
        self.rope_embeddings = DINOv3ViTRopePositionEmbedding(config)
        self.layers = nn.ModuleList([DINOv3ViTLayer(config) for _ in range(config.num_hidden_layers)])
        self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

        self._init_weights()

    def _init_weights(self):
        for module in self.modules():
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                module.weight.data = nn.init.trunc_normal_(
                    module.weight.data.to(torch.float32),
                    mean=0.0,
                    std=self.config.initializer_range,
                ).to(module.weight.dtype)
                if module.bias is not None:
                    module.bias.data.zero_()
            elif isinstance(module, nn.LayerNorm):
                module.bias.data.zero_()
                module.weight.data.fill_(1.0)
            elif isinstance(module, DINOv3ViTEmbeddings):
                module.cls_token.data = nn.init.trunc_normal_(
                    module.cls_token.data.to(torch.float32),
                    mean=0.0,
                    std=self.config.initializer_range,
                ).to(module.cls_token.dtype)
                if module.config.num_register_tokens > 0:
                    module.register_tokens.data = nn.init.trunc_normal_(
                        module.register_tokens.data.to(torch.float32),
                        mean=0.0,
                        std=self.config.initializer_range,
                    ).to(module.register_tokens.dtype)
                module.mask_token.data.zero_()
            elif isinstance(module, DINOv3ViTLayerScale):
                module.lambda1.data.fill_(self.config.layerscale_value)

    def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None,
                head_mask: Optional[torch.Tensor] = None):
        pixel_values = pixel_values.to(self.embeddings.patch_embeddings.weight.dtype)
        hidden_states = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
        position_embeddings = self.rope_embeddings(pixel_values)

        for i, layer_module in enumerate(self.layers):
            layer_head_mask = head_mask[i] if head_mask is not None else None
            hidden_states = layer_module(
                hidden_states,
                attention_mask=layer_head_mask,
                position_embeddings=position_embeddings,
            )

        sequence_output = self.norm(hidden_states)
        pooled_output = sequence_output[:, 0, :]

        return {
            "last_hidden_state": sequence_output,
            "pooler_output": pooled_output,
        }