modeling_friday.py

from __future__ import annotations

# Model Constants
IMAGE_TOKEN = "<image>"
IMG_START_TOKEN   = "<img_start>"
IMG_END_TOKEN     = "<img_end>"
IGNORE_INDEX      = -100
PAD_FOR_EOS       = -300





import torch
import torch.nn.functional as F

from PIL import Image


import torch

def mask_token_segment(
               start_id: int,
               end_id: int,
               input_ids: torch.Tensor,
               fill_value: int = -100):
    """
    Replace *every* token from each `start_id` **through** its matching `end_id`
    (boundaries included) with `fill_value`.  Any spans that start with some
    other token are left untouched.

    Works on CUDA, TorchScript, batched via vmap, etc.—no Python loops.
    """
    if input_ids.dim() != 1:
        raise ValueError("`input_ids` must be 1-D")

    device = input_ids.device
    n       = input_ids.size(0)

    # where the *target* start-tokens and end-tokens sit
    start_pos = (input_ids == start_id).nonzero(as_tuple=True)[0]      # ascending
    end_pos   = (input_ids == end_id).nonzero(as_tuple=True)[0]        # ascending

    if start_pos.numel() == 0:
        return input_ids.clone()

    # ── pair every start with the first end that comes *after* it ────────────────
    # searchsorted gives the insertion index into the (sorted) end positions
    idx_in_end = torch.searchsorted(end_pos, start_pos, right=False)

    have_match = idx_in_end < end_pos.size(0)                # safety: drop unmatched
    start_pos  = start_pos[have_match]
    end_pos    = end_pos[idx_in_end[have_match]]

    # (rare) guard against pathological orderings
    keep = end_pos > start_pos
    start_pos, end_pos = start_pos[keep], end_pos[keep]

    if start_pos.numel() == 0:
        return input_ids

    # ── differential “scan-line” trick to build the span mask in O(N) ───────────
    # +1 at each start index, -1 at the element *after* each end
    delta = torch.zeros(n + 1, dtype=torch.int8, device=device)
    delta[start_pos]        += 1
    delta[end_pos + 1]      -= 1          # +1 is safe because delta is length n+1

    inside = torch.cumsum(delta[:-1], dim=0) > 0   # boolean mask, incl. boundaries

    # ── apply ────────────────────────────────────────────────────────────────────
    out = input_ids.clone()
    out[inside] = fill_value
    return out



def maybe_zero_3(param, ignore_status=False, name=None):
    from deepspeed import zero
    from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
    if hasattr(param, "ds_id"):
        if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
            if not ignore_status:
                print(name, 'no ignore status')
        with zero.GatheredParameters([param]):
            param = param.data.detach().cpu().clone()
    else:
        param = param.detach().cpu().clone()
    return param


# Borrowed from peft.util.get_peft_model_state_dict
def get_peft_state_maybe_zero_3(named_params, bias):
    if bias == "none":
        to_return = {k: t for k, t in named_params if "lora_" in k}
    elif bias == "all":
        to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
    elif bias == "lora_only":
        to_return = {}
        maybe_lora_bias = {}
        lora_bias_names = set()
        for k, t in named_params:
            if "lora_" in k:
                to_return[k] = t
                bias_name = k.split("lora_")[0] + "bias"
                lora_bias_names.add(bias_name)
            elif "bias" in k:
                maybe_lora_bias[k] = t
        for k, t in maybe_lora_bias:
            if bias_name in lora_bias_names:
                to_return[bias_name] = t
    else:
        raise NotImplementedError
    to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()}
    return to_return


def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True):
    to_return = {k: t for k, t in named_params if "lora_" not in k}
    if require_grad_only:
        to_return = {k: t for k, t in to_return.items() if t.requires_grad}
    to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
    return to_return


def find_all_linear_names(modules):
    lora_module_names = set()
    for name, module in modules():
        if isinstance(module, torch.nn.Linear):
            names = name.split('.')
            lora_module_names.add(names[0] if len(names) == 1 else names[-1])

    if 'lm_head' in lora_module_names:  # needed for 16-bit
        lora_module_names.remove('lm_head')
    return list(lora_module_names)


def expand2square(pil_img, background_color):
    width, height = pil_img.size
    if width == height:
        return pil_img
    elif width > height:
        result = Image.new(pil_img.mode, (width, width), background_color)
        result.paste(pil_img, (0, (width - height) // 2))
        return result
    else:
        result = Image.new(pil_img.mode, (height, height), background_color)
        result.paste(pil_img, ((height - width) // 2, 0))
        return result

def pad_and_stack(img_list, pad_value=0.0):
    """
    img_list : list[Tensor]  each (C, H, W) already *normalised*
    pad_value: float or tuple/list of 3 floats (one per channel)
               Use 0.0 if your processor has already centred to mean 0.
    Returns
    -------
    batch : Tensor  (B, C, H_max, W_max)
    """

    # 1. target square size ---------------------------------------------------
    h_max = max(t.shape[1] for t in img_list)
    w_max = max(t.shape[2] for t in img_list)
    H, W  = max(h_max, w_max), max(h_max, w_max)

    # 2. create padded copies -------------------------------------------------
    padded = []
    for img in img_list:
        c, h, w = img.shape
        canvas   = img.new_full((c, H, W), pad_value)     # filled with mean/zeros
        canvas[:, :h, :w] = img                    # top-left corner
        padded.append(canvas)

    return torch.stack(padded, 0)                  # (B,C,H,W)





#  ------------------------------------------------------------------------------------------
#  Copyright (c) 2024 Baifeng Shi.
#  All rights reserved.
#
#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
#  ------------------------------------------------------------------------------------------

import torch

def split_chessboard(x, num_split):
    """
        x: b * c * h * w
        Deividing x into num_split**2 sub-squares, and concatenate all the sub-squares on the batch dimension
    """
    B, C, H, W = x.shape
    assert H % num_split == 0 and W % num_split == 0
    h, w = H // num_split, W // num_split
    x_split = torch.cat([x[:, :, i*h:(i+1)*h, j*w:(j+1)*w] for i in range(num_split) for j in range(num_split)], dim=0)
    return x_split

def merge_chessboard(x, num_split):
    """
        x: b * c * h * w
        Assuming x contains num_split**2 sub-squares concatenated along batch dimension, merge the sub-squares back to the original whole square.
        (inverse of split_chessboard)
    """
    B, C, H, W = x.shape
    assert B % (num_split**2) == 0
    b = B // (num_split**2)
    x_merge = torch.cat([torch.cat([x[(i*num_split + j)*b:(i*num_split + j + 1)*b] for j in range(num_split)], dim=-1)
                         for i in range(num_split)], dim=-2)
    return x_merge

def batched_forward(model, x, batch_size=-1):
    if batch_size == -1:
        return model(x)
    else:
        x_batched = x.split(batch_size)
        outs = [model(x) for x in x_batched]
        return torch.cat(outs, dim=0)







#  ------------------------------------------------------------------------------------------
#  Copyright (c) 2024 Baifeng Shi.
#  All rights reserved.
#
#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
#  ------------------------------------------------------------------------------------------

import math
import torch
import torch.nn.functional as F
from einops import rearrange

def multiscale_forward(model, input, scales=None, img_sizes=None, max_split_size=None, resize_output_to_idx=0, num_prefix_token=0,
            output_shape='bnc', split_forward=False):

    # print(f"Input shape: {input.shape}")

    assert input.dim() == 4, "Input image must be in the shape of BxCxHxW."
    assert input.shape[2] == input.shape[3], "Currently only square images are supported."
    assert output_shape in ['bnc', 'bchw'], "Output shape should be either BxNxC (e.g., ViT) or BxCxHxW (e.g., ConvNet)."
    assert output_shape == 'bnc' or num_prefix_token == 0, "For ConvNet there shouldn't be any prefix token."

    b, c, input_size, _ = input.shape

    # image size for each scale
    assert scales is not None or img_sizes is not None, "Please assign either scales or img_sizes."
    img_sizes = img_sizes or [int(input_size * scale) for scale in scales]

    # prepare multiscale inputs
    max_split_size = max_split_size or input_size   # The maximum size of each split of image. Set as the input size by default
    num_splits = [math.ceil(size / max_split_size) for size in img_sizes]   # number of splits each scale
    input_multiscale = []
    for size, num_split in zip(img_sizes, num_splits):
        x = F.interpolate(input.to(torch.float32), size=size, mode='bicubic').to(input.dtype)
        x = split_chessboard(x, num_split=num_split)
        input_multiscale.append(x)

    # run feedforward on each scale
    outs_multiscale = [batched_forward(model, x, b) if split_forward else model(x) for x in input_multiscale]
    if num_prefix_token > 0:
        outs_prefix_multiscale = [out[:, :num_prefix_token] for out in outs_multiscale]
        outs_multiscale = [out[:, num_prefix_token:] for out in outs_multiscale]
    if output_shape == 'bnc':
        outs_multiscale = [rearrange(out, 'b (h w) c -> b c h w', h=int(out.shape[1] ** 0.5), w=int(out.shape[1] ** 0.5))
                           for out in outs_multiscale]

    # merge outputs of different splits for each scale separately
    outs_multiscale = [merge_chessboard(out, num_split=num_split) for num_split, out in zip(num_splits, outs_multiscale)]

    # interpolate outputs from different scales and concat together
    output_size = outs_multiscale[resize_output_to_idx].shape[-2]
    out = torch.cat([F.interpolate(outs_multiscale[i].to(torch.float32), size=output_size,
                                   mode='area').to(outs_multiscale[i].dtype)
                     for i in range(len(outs_multiscale))], dim=1)
    if output_shape == 'bnc':
        out = rearrange(out, 'b c h w -> b (h w) c')
    if num_prefix_token > 0:
        # take the mean of prefix tokens from different splits for each scale
        outs_prefix_multiscale = [torch.stack(out.split(b, dim=0), dim=0).mean(dim=0) for out in outs_prefix_multiscale]
        out_prefix_multiscale = torch.cat(outs_prefix_multiscale, dim=-1)
        out = torch.cat([out_prefix_multiscale, out], dim=1)

    return out





import torch
import torch.nn as nn

class MLPAdapter(nn.Module):

    def __init__(self, input_dim, hidden_dim, output_dim, num_layers=2, activation='gelu', checkpoint_path=None, device=None, **kwargs):
        """
        Initialize the MLPAdapter with the given dimensions and activation function.

        Args:
            input_dim (int): Input dimension.
            hidden_dim (int): Hidden dimension.
            output_dim (int): Output dimension.
            layers (int): Number of layers in the MLP.
            activation (str): Activation function to use ('gelu' or 'relu').
        """
        super().__init__()
        self.num_layers = num_layers
        self.activation = activation
        self.output_dim = output_dim

        # Define the first layer
        layers_list = [nn.Linear(input_dim, hidden_dim, device=device)]
        if activation == 'gelu':
            layers_list.append(nn.GELU())
        elif activation == 'relu':
            layers_list.append(nn.ReLU())
        else:
            raise ValueError("Unsupported activation function. Use 'gelu' or 'relu'.")
        
        # Define the subsequent layers
        for _ in range(1, num_layers):
            layers_list.append(nn.Linear(hidden_dim, hidden_dim, device=device))
            if activation == 'gelu':
                layers_list.append(nn.GELU())
            elif activation == 'relu':
                layers_list.append(nn.ReLU())
        
        # Define the final output layer
        layers_list.append(nn.Linear(hidden_dim, output_dim, device=device))
        self.mlp = nn.Sequential(*layers_list)

        # Load checkpoint if provided
        if checkpoint_path:
            self.load_state_dict(torch.load(checkpoint_path, map_location=device), strict=False)
            print(f"Loaded MLPAdapter from {checkpoint_path}")
        
        if device:
            self.to(device)

    def forward(self, x):
        """
        Forward pass through the MLPAdapter.

        Args:
            x (torch.Tensor): Input tensor.

        Returns:
            torch.Tensor: Output tensor after passing through the MLP.
        """
        return self.mlp(x)
    



import torch
import torch.nn as nn
import torch.nn.functional as F

import PIL.Image
from typing import List

from transformers import AutoModel, AutoImageProcessor


class FastVitVisionTower(nn.Module):
    def __init__(self, pretrained_model_name_or_path, model_params={}, pad_to_square=True, **kwargs):
        super().__init__()

        self.is_loaded = False
        self.pretrained_model_name_or_path = pretrained_model_name_or_path
        self.model_params = model_params
        self.pad_to_square = pad_to_square
        self.load_model()

    @property
    def output_dim(self):
        return self.vision_tower.config.embed_dim if self.vision_tower else None
    
    def load_model(self):
        if self.is_loaded:
            return
        self.image_processor = AutoImageProcessor.from_pretrained(self.pretrained_model_name_or_path)
        self.image_processor.crop_size = self.image_processor.size
        self.vision_tower = AutoModel.from_pretrained(
            self.pretrained_model_name_or_path,
            **self.model_params,
        )
        self.vision_tower.requires_grad_(False)

        self.is_loaded = True
    
    def preprocess_images(self, imgs: List[PIL.Image.Image], pad_and_stack_tensors=True) -> torch.Tensor:
        img_mean = tuple(int(x * 255) for x in self.image_processor.image_mean)
        if self.pad_to_square:
            imgs = [expand2square(img, img_mean) for img in imgs]
        
        imgs = [self.image_processor(img, do_resize=True, do_center_crop=False, return_tensors="pt")['pixel_values'][0] for img in imgs]
        

        if pad_and_stack_tensors:
            imgs = pad_and_stack(imgs, pad_value=0.0)
            imgs = imgs.to(dtype=torch.float32, device=self.device)
        
        return imgs

    def forward(self, images):
        if type(images) is list:
            image_features = []
            for image in images:
                image_feature = self.vision_tower(
                    image.to(device=self.device, dtype=self.dtype).unsqueeze(0)
                )
                image_features.append(image_feature)
        else:
            image_features = self.vision_tower(
                images.to(device=self.device, dtype=self.dtype),
            )

        return image_features

    @property
    def dummy_feature(self):
        return torch.zeros(1, self.embed_dim, device=self.device, dtype=self.dtype)

    @property
    def dtype(self):
        return self.vision_tower.dtype

    @property
    def device(self):
        return self.vision_tower.device

    @property
    def config(self):
        if self.is_loaded:
            return self.vision_tower.config
        else:
            return self.cfg_only

    @property
    def hidden_size(self):
        return self.config.embed_dim

    @property
    def num_patches(self):
        return (self.config.image_size // self.config.patch_size) ** 2


class FastVitVisionTowerS2(FastVitVisionTower):
    def __init__(self, pretrained_model_name_or_path, s2_scales, model_params={}, **kwargs):
        self.s2_scales = list(map(int, s2_scales.split(',')))
        self.s2_scales.sort()
        self.s2_split_size = self.s2_scales[0]
        self.s2_image_size = self.s2_scales[-1]

        super().__init__(pretrained_model_name_or_path, model_params)

        self.multiscale_forward = multiscale_forward
    
    @property
    def output_dim(self):
        return (2*self.vision_tower.config.embed_dim) if self.vision_tower else None

    def load_model(self):
        if self.is_loaded:
            return
        
        super().load_model()
        self.image_processor.size = self.image_processor.crop_size = {
            "height": self.s2_image_size,
            "width":  self.s2_image_size
        }

    def forward_feature(self, images):
        image_size = self.vision_tower.config.image_size
        if images.shape[2] != image_size or images.shape[3] != image_size:
            images = F.interpolate(
                images,
                size=(image_size, image_size),
                mode="bilinear",
                align_corners=False,
                antialias=True
            )    

        return self.vision_tower(
            images.to(device=self.device, dtype=self.dtype),
        )

    def forward(self, images):
        if type(images) is list:
            image_features = []
            for image in images:
                image_feature = self.multiscale_forward(
                    self.forward_feature, 
                    image.unsqueeze(0),
                    img_sizes=self.s2_scales, 
                    max_split_size=self.s2_split_size
                )
                image_features.append(image_feature)
        else:
            image_features = self.multiscale_forward(
                self.forward_feature, 
                images, 
                img_sizes=self.s2_scales,
                max_split_size=self.s2_split_size
            )

        return image_features

    @property
    def hidden_size(self):
        return self.config.embed_dim * len(self.s2_scales)





import torch
import torch.nn as nn

import PIL.Image
from typing import List

from transformers import SiglipVisionModel, SiglipImageProcessor, SiglipVisionConfig


class SiglipVisionTower(nn.Module):
    def __init__(self, pretrained_model_name_or_path, model_params={}, pad_to_square=True, **kwargs):
        super().__init__()

        self.is_loaded = False
        self.pretrained_model_name_or_path = pretrained_model_name_or_path
        self.model_params = model_params
        self.pad_to_square = pad_to_square
        self.select_layer = -2
        self.load_model()

    @property
    def output_dim(self):
        return self.vision_tower.config.hidden_size if self.vision_tower else None
    
    def load_model(self):
        if self.is_loaded:
            return
        self.image_processor = SiglipImageProcessor.from_pretrained(self.pretrained_model_name_or_path)
        self.image_processor.crop_size = self.image_processor.size
        self.vision_tower = SiglipVisionModel.from_pretrained(
            self.pretrained_model_name_or_path,
            **self.model_params,
        )
        self.vision_tower.requires_grad_(False)

        self.is_loaded = True
    
    def preprocess_images(self, imgs: List[PIL.Image.Image], pad_and_stack_tensors=True) -> torch.Tensor:
        img_mean = tuple(int(x * 255) for x in self.image_processor.image_mean)
        if self.pad_to_square:
            imgs = [expand2square(img, img_mean) for img in imgs]
        imgs = [self.image_processor(img, return_tensors="pt")['pixel_values'][0] for img in imgs]

        if pad_and_stack_tensors:
            imgs = pad_and_stack(imgs, pad_value=0.0)
            imgs = imgs.to(dtype=torch.float32, device=self.device)
        
        return imgs

    def feature_select(self, image_forward_outs):
        image_features = image_forward_outs.hidden_states[self.select_layer]

        return image_features

    def forward(self, images):
        if type(images) is list:
            image_features = []
            for image in images:
                image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0),
                                                      output_hidden_states=True)
                image_feature = self.feature_select(image_forward_out).to(image.dtype)
                image_features.append(image_feature)
        else:
            image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype),
                                                   output_hidden_states=True)
            image_features = self.feature_select(image_forward_outs).to(images.dtype)

        return image_features

    @property
    def dummy_feature(self):
        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)

    @property
    def dtype(self):
        return self.vision_tower.dtype

    @property
    def device(self):
        return self.vision_tower.device

    @property
    def config(self):
        if self.is_loaded:
            return self.vision_tower.config
        else:
            return self.cfg_only

    @property
    def hidden_size(self):
        return self.config.hidden_size

    @property
    def num_patches(self):
        return (self.config.image_size // self.config.patch_size) ** 2


class SiglipVisionTowerS2(SiglipVisionTower):
    def __init__(self, pretrained_model_name_or_path, s2_scales, model_params={}, **kwargs):
        self.s2_scales = list(map(int, s2_scales.split(',')))
        self.s2_scales.sort()
        self.s2_split_size = self.s2_scales[0]
        self.s2_image_size = self.s2_scales[-1]

        super().__init__(pretrained_model_name_or_path, model_params)

        self.multiscale_forward = multiscale_forward

        self.image_processor.size['height'] = self.image_processor.size['width'] = self.s2_image_size
        self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
    
    @property
    def output_dim(self):
        return (2*self.vision_tower.config.hidden_size) if self.vision_tower else None

    def load_model(self):
        if self.is_loaded:
            return
        
        super().load_model()
        self.image_processor.size['height'] = self.image_processor.size['width'] = self.s2_image_size
        self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size

    def forward_feature(self, images):
        image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype),
                                               output_hidden_states=True)
        image_features = self.feature_select(image_forward_outs).to(images.dtype)
        return image_features

    def forward(self, images):
        if type(images) is list:
            image_features = []
            for image in images:
                image_feature = self.multiscale_forward(
                    self.forward_feature, 
                    image.unsqueeze(0),
                    img_sizes=self.s2_scales, 
                    max_split_size=self.s2_split_size
                )
                image_features.append(image_feature)
        else:
            image_features = self.multiscale_forward(
                self.forward_feature, 
                images, 
                img_sizes=self.s2_scales,
                max_split_size=self.s2_split_size
            )

        return image_features

    @property
    def hidden_size(self):
        return self.config.hidden_size * len(self.s2_scales)







import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms

from typing import List, Tuple, Optional, Union

import PIL

from transformers import AutoTokenizer, AutoConfig
from transformers.modeling_outputs import CausalLMOutputWithPast

from .configuration_phi3 import Phi3Config
from .modeling_phi3 import Phi3Model, Phi3ForCausalLM


DEFAULT_CFG_SPECIAL_TOKENS = {
    "image_token_id": 200029,
    "image_start_token_id": 200030,
    "image_end_token_id": 200031,
}
DEFAULT_CFG_VISION_TOWER = {
    "pretrained_model_name_or_path": "kevin510/fast-vit-hd",
    "type": "fastvit",
    "s2_scales": "512,1024",
    "use_s2": True,
    "pad_to_square": True,
    "freeze": False,
    "model_params": { "trust_remote_code": True }
}
DEFAULT_CFG_VISION_ADAPTER = {
    "input_dim": 6144,
    "hidden_dim": 3072,
    "output_dim": 3072,
    "layers": 2,
    "activation": "gelu",
    "freeze": False,
}


class FridayConfig(Phi3Config):
    model_type = "friday"

    def __init__(self, 
            base_model_name_or_path: str | None = "microsoft/Phi-4-mini-reasoning",
            delay_load=False, 
            tokenizer_model_max_length=None,
            **kwargs
        ):
        base_kwargs = {}
        if base_model_name_or_path is not None:
            base_cfg = Phi3Config.from_pretrained(
                base_model_name_or_path,
                trust_remote_code=True,   # Phi‑4 uses custom code in the repo
            )
            base_kwargs = base_cfg.to_dict()

        merged = {**base_kwargs, **kwargs}
        self.delay_load = delay_load
        self.tokenizer_model_max_length = tokenizer_model_max_length

        self._cfg_vision_tower = DEFAULT_CFG_VISION_TOWER.copy()
        if "cfg_vision_tower" in kwargs:
            self._cfg_vision_tower.update(kwargs["cfg_vision_tower"])

        self._cfg_vision_adapter = DEFAULT_CFG_VISION_ADAPTER.copy()
        if "cfg_vision_adapter" in kwargs:
            self._cfg_vision_adapter.update(kwargs["cfg_vision_adapter"])

        self._cfg_special_tokens = DEFAULT_CFG_SPECIAL_TOKENS.copy()
        if "cfg_special_tokens" in kwargs:
            self._cfg_special_tokens.update(kwargs["cfg_special_tokens"])

        super().__init__(**merged)
        
    
    @property
    def cfg_vision_tower(self):
        return self._cfg_vision_tower

    @cfg_vision_tower.setter
    def cfg_vision_tower(self, value):
        if not value:
            raise ValueError("Name cannot be empty")
        self._cfg_vision_tower.update(value)
    

    @property
    def cfg_vision_adapter(self):
        return self._cfg_vision_adapter
    
    @cfg_vision_adapter.setter
    def cfg_vision_adapter(self, value):
        if not value:
            raise ValueError("Name cannot be empty")
        self._cfg_vision_adapter.update(value)
    
    @property
    def cfg_special_tokens(self):
        return self._cfg_special_tokens
    
    @cfg_special_tokens.setter
    def cfg_special_tokens(self, value):
        if not value:
            raise ValueError("Name cannot be empty")
        self._cfg_special_tokens.update(value)


class FridayModel(Phi3Model):
    config_class = FridayConfig
    
    def __init__(self, config: FridayConfig):
        super().__init__(config)

        self.cfg_vision_adapter = config.cfg_vision_adapter
        self.cfg_vision_tower = config.cfg_vision_tower

        self.vision_tower = None
        self.mm_projector    = None
        if not config.delay_load:
            self.initialize_vision_modules()
    
    def get_vision_tower(self):
        return self.vision_tower
    
    def initialize_vision_modules(self):
        if self.vision_tower is not None:
            return

        if self.cfg_vision_tower.get("type", "siglip").lower() == "siglip":
            if self.cfg_vision_tower.get("use_s2", True):
                self.vision_tower = SiglipVisionTowerS2(**self.cfg_vision_tower)
            else:
                self.vision_tower = SiglipVisionTower(**self.cfg_vision_tower)
        elif self.cfg_vision_tower.get("type", "siglip").lower() == "fastvit":
            if self.cfg_vision_tower.get("use_s2", True):
                self.vision_tower = FastVitVisionTowerS2(**self.cfg_vision_tower)
            else:
                self.vision_tower = FastVitVisionTower(**self.cfg_vision_tower)
        else:
            raise ValueError(f"Unsupported vision tower type: {self.cfg_vision_tower.get('type', 'siglip')}. Supported types are 'siglip' and 'fastvit'.")
        
        self.vision_tower.load_model()
        self.mm_projector = MLPAdapter(**self.cfg_vision_adapter)

        if self.cfg_vision_tower.get("freeze", False):
            self.set_vision_tower_requires_grad(False)
        
        if self.cfg_vision_adapter.get("freeze", False):
            self.set_vision_adapter_requires_grad(False)
    
    def compute_image_features(self, imgs: torch.Tensor) -> torch.Tensor:
        features = self.vision_tower(imgs)
        if isinstance(features, list):
            features = torch.stack(features, dim=1)
        return self.mm_projector(features)
    
    def set_vision_tower_requires_grad(self, requires_grad: bool):
        if self.vision_tower is not None:
            for param in self.vision_tower.parameters():
                param.requires_grad = requires_grad
        else:
            raise ValueError("Vision tower is not initialized. Please call initialize_vision_modules() first.")
    
    def set_vision_adapter_requires_grad(self, requires_grad: bool):
        if self.mm_projector is not None:
            for param in self.mm_projector.parameters():
                param.requires_grad = requires_grad
        else:
            raise ValueError("Vision adapter is not initialized. Please call initialize_vision_modules() first.")
    
    def set_vision_tower_dtype(self, dtype: torch.dtype):
        if self.vision_tower is not None:
            for p in self.vision_tower.parameters():
                p.data = p.data.to(dtype)
        else:
            raise ValueError("Vision tower is not initialized. Please call initialize_vision_modules() first.")
    
    def set_vision_adapter_dtype(self, dtype: torch.dtype):
        if self.mm_projector is not None:
            for p in self.mm_projector.parameters():
                p.data = p.data.to(dtype)
        else:
            raise ValueError("Vision adapter is not initialized. Please call initialize_vision_modules() first.")
    
    def is_vision_tower_frozen(self):
        if self.vision_tower is not None:
            return all(not p.requires_grad for p in self.vision_tower.parameters())
        else:
            raise ValueError("Vision tower is not initialized. Please call initialize_vision_modules() first.")
    
    def is_vision_adapter_frozen(self):
        if self.mm_projector is not None:
            return all(not p.requires_grad for p in self.mm_projector.parameters())
        else:
            raise ValueError("Vision adapter is not initialized. Please call initialize_vision_modules() first.")


class FridayForCausalLM(Phi3ForCausalLM):
    config_class = FridayConfig

    def __init__(self, config: FridayConfig):
        super().__init__(config)

        self.config = config
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.image_token_id = config.cfg_special_tokens["image_token_id"]
        self.image_start_id       = config.cfg_special_tokens["image_start_token_id"]
        self.image_end_id         = config.cfg_special_tokens["image_end_token_id"]

        self.model = FridayModel(config)
        self.post_init()
    
    def get_model(self) -> FridayModel:
        return self.model
    
    def get_vision_tower(self) -> SiglipVisionTower:
        return self.model.get_vision_tower()
    
    def get_vision_adapter(self) -> MLPAdapter:
        return self.model.mm_projector

    def get_llm_parameters(self, exclude_lora: bool = False):
        return [
            p for n, p in self.named_parameters() 
            if "vision_tower" not in n and "mm_projector" not in n and (not exclude_lora or ("lora_" not in n))
        ]

    def get_llm_named_modules(self):
        return {n: m for n, m in self.named_modules() if "vision_tower" not in n and "mm_projector" not in n}
    
    def set_llm_requires_grad(self, requires_grad: bool, exclude_lora: bool = True):
        for n, p in self.named_parameters():
            if exclude_lora and ("lora_A" in n or "lora_B" in n):
                continue
            if "vision_tower" in n or "mm_projector" in n:
                continue
            p.requires_grad = requires_grad
    
    def set_vision_tower_requires_grad(self, requires_grad: bool):
        self.model.set_vision_tower_requires_grad(requires_grad)

    def set_vision_adapter_requires_grad(self, requires_grad: bool):
        self.model.set_vision_adapter_requires_grad(requires_grad)
    
    def set_llm_dtype(self, dtype: torch.dtype):
        for p in self.get_llm_parameters():
            p.data = p.data.to(dtype)

    def set_vision_tower_dtype(self, dtype: torch.dtype):
        self.model.set_vision_tower_dtype(dtype)
    
    def set_vision_adapter_dtype(self, dtype: torch.dtype):
        self.model.set_vision_adapter_dtype(dtype)
    
    def is_llm_frozen(self):
        return all(not p.requires_grad for p in self.get_llm_parameters())
    
    def is_vision_tower_frozen(self):
        return self.model.is_vision_tower_frozen()
    
    def is_vision_adapter_frozen(self):
        return self.model.is_vision_adapter_frozen()
    
    
    
    def initialize_vision_modules(self):
        self.model.initialize_vision_modules()
    
    def get_multimodal_input_embeddings(self, input_ids, image_features, return_labels=True) -> torch.Tensor:
        emb_start_image_id = self.model.embed_tokens(torch.tensor([self.image_start_id], device=self.device))
        emb_end_image_id   = self.model.embed_tokens(torch.tensor([self.image_end_id], device=self.device))
        id_ignore = torch.tensor([IGNORE_INDEX], device=self.device)

        # repetition‑penalty safety ????
        # input_ids[input_ids == self.image_token_id] = 0

        
        # Iterate over each batch item
        embeds_list, labels_list = [], []
        for batch_id, item_ids in enumerate(input_ids):
            
            image_token_positions = (item_ids == self.image_token_id).nonzero(as_tuple=True)[0]
            if len(image_token_positions) != image_features[batch_id].shape[0]:
                raise ValueError(
                    f"Mismatch between number of image tokens ({len(image_token_positions)}) and number of image features ({image_features[batch_id].shape[0]})"
                )


            cursor = 0
            emb_parts, lbl_parts = [], []
            for indx_image, image_token_pos in enumerate(image_token_positions):
                if image_token_pos > cursor:
                    span = item_ids[cursor:image_token_pos]
                    emb_parts.append(self.model.embed_tokens(span))
                    lbl_parts.append(span)

                # <image_start>
                emb_parts.append(emb_start_image_id)
                lbl_parts.append(id_ignore)

                # vision embeddings
                image_tokens = image_features[batch_id][indx_image]
                if image_tokens.shape[0] == 1 and image_tokens.ndim == 3:
                    image_tokens = image_tokens.squeeze(0)
                emb_parts.append(image_tokens)
                lbl_parts.append(id_ignore.repeat(image_tokens.shape[0]))

                # <image_end>
                emb_parts.append(emb_end_image_id)
                lbl_parts.append(id_ignore)

                cursor = image_token_pos + 1
            
            # tail text
            if cursor < item_ids.shape[0]:
                tail = item_ids[cursor:]
                emb_parts.append(self.model.embed_tokens(tail))
                lbl_parts.append(tail)
            
            embeds_list.append(torch.cat(emb_parts, dim=0))
            labels_list.append(torch.cat(lbl_parts, dim=0))
    
        return (embeds_list, labels_list) if return_labels else embeds_list

    def prepare_inputs_for_multimodal(
        self,
        input_ids: torch.LongTensor,
        images: List[List[PIL.Image.Image]], # B x N
        position_ids: Optional[torch.LongTensor],
        attention_mask: Optional[torch.Tensor],
        past_key_values: Optional[List[torch.FloatTensor]],
        labels: Optional[torch.LongTensor],
    ) -> Tuple[Optional[torch.Tensor], Optional[torch.LongTensor], Optional[torch.Tensor], Optional[List[torch.FloatTensor]], torch.Tensor, Optional[torch.Tensor]]:
        
        # ─────────────────── early return (no image / streaming step) ───────────────────
        # if we have already processed images and are in a streaming step we can skip the multimodal processing
        # but we need to ensure the attention mask and position ids are correct

        if past_key_values is not None and attention_mask is not None and input_ids.shape[1] == 1:
            tgt = past_key_values[-1][-1].shape[-2] + 1
            attention_mask = torch.cat(
                [attention_mask,
                torch.ones((attention_mask.size(0),
                            tgt - attention_mask.size(1)),
                            dtype=attention_mask.dtype,
                            device=attention_mask.device)],
                dim=1,
            )
            position_ids = (attention_mask.sum(dim=1, keepdim=True) - 1).long()

            return input_ids, position_ids, attention_mask, past_key_values, None, labels

        # ─────────────────────────── images: (B, N) ───────────────────────────
        if isinstance(images, list) and isinstance(images[0], list):
            # images is a list of lists, each containing multiple images, B x N
            # e.g. [[img1, img2], [img3, img4]]
            assert len(images) == input_ids.shape[0], f"Batch size mismatch: {len(images)} vs {input_ids.shape[0]}"
            image_features = []
            for sublst_images in images:
                if len(sublst_images) == 0:
                    image_features.append(torch.zeros((0, self.get_model().mm_projector.output_dim), device=self.device))
                else:
                    if isinstance(sublst_images[0], PIL.Image.Image):
                        image_features.append(
                            self.model.compute_image_features(
                                self.model.vision_tower.preprocess_images(sublst_images, pad_and_stack_tensors=True)
                            )
                        )
                    elif isinstance(sublst_images[0], torch.Tensor):
                        # This should be a list of tensors of pre-processed images, [(N X 3 X W x H), ...]
                        image_features.append(
                            self.model.compute_image_features(sublst_images)
                        )
        elif isinstance(images, list) and isinstance(images[0], PIL.Image.Image):
            # images is a list of images for a single batch item, 1 x N
            # e.g. [img1, img2, img3]
            assert input_ids.shape[0] == 1, f"Batch size mismatch: {len(images)} vs {input_ids.shape[0]}"
            image_features = [
                self.model.compute_image_features(
                    self.model.vision_tower.preprocess_images(images, pad_and_stack_tensors=True)
                )
            ]
        elif isinstance(images, list) and isinstance(images[0], torch.Tensor):
            # This should be a list of tensors of pre-processed images, [(N X 3 X W x H), ...]
            # The list length should match the batch size
            assert input_ids.shape[0] == len(images), f"Batch size mismatch: {len(images)} vs {input_ids.shape[0]}"
            image_features = [
                self.model.compute_image_features(imgs) for imgs in images
            ]
        elif isinstance(images, PIL.Image.Image):
            # images is a single image, 1 x 1
            # e.g. img1
            assert input_ids.shape[0] == 1, f"Batch size mismatch: {len(images)} vs {input_ids.shape[0]}"
            image_features = [
                self.model.compute_image_features(
                    self.model.vision_tower.preprocess_images([images])
                )
            ]
        else:
            raise ValueError(f"Unsupported images format: {type(images)}. Expected list of PIL images, a single PIL image or a Tensor of pre-processed images")
        
        # ─────────────────────────── image_features: (B x N x D) ───────────────────────────
        if isinstance(image_features, list):
            assert input_ids.shape[0] == len(image_features), f"Incorrectly formatted image_features: list length should match batch size"
            assert isinstance(image_features[0], torch.Tensor), f"Incorrectly formatted image_features: list items should be tensors"
        elif isinstance(image_features, torch.Tensor):
            assert input_ids.shape[0] == image_features.shape[0], f"Incorrectly formatted image_features: tensor should match batch size"
        

        # ───────────────────────────── pad handling prelims ──────────────────────────────
        if attention_mask is None:
            attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
        else:
            attention_mask = attention_mask.bool()
        if position_ids is None:
            position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)

        input_ids_nopad = [ids[mask] for ids, mask in zip(input_ids, attention_mask)]
        embeds_list, labels_list = self.get_multimodal_input_embeddings(
            input_ids_nopad,
            image_features,
            return_labels=True
        )

        # ───────────────────── truncate then pad back to rectangle ──────────────────────
        new_input_embeds = torch.nn.utils.rnn.pad_sequence(
            embeds_list,
            batch_first=True,
            padding_value=0.0
        ).to(dtype=self.dtype)

        new_labels = torch.nn.utils.rnn.pad_sequence(
            labels_list,
            batch_first=True,
            padding_value=IGNORE_INDEX
        ).long()

        if self.config.tokenizer_model_max_length is not None:
            new_input_embeds = new_input_embeds[:, :self.config.tokenizer_model_max_length]
            new_labels       = new_labels[:, :self.config.tokenizer_model_max_length]

        
        

        # ────────────────────────────── attention mask and position ids ────────────────
        
        attention_mask = (
            torch.arange(new_input_embeds.size(1), device=input_ids.device)
                  .unsqueeze(0)
            < torch.tensor([e.size(0) for e in embeds_list],
                           device=input_ids.device).unsqueeze(1)
        )

        raw_pos = attention_mask.cumsum(dim=1) - 1
        position_ids = raw_pos.masked_fill(~attention_mask, 0).long()

        if not self.training:
            new_labels = None

        return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels

    
    
    # ------------------------------------------------------------------
    def forward(
            self,
            input_ids: torch.LongTensor = None,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            labels: Optional[torch.LongTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            cache_position: Optional[torch.LongTensor] = None,
            logits_to_keep: Union[int, torch.Tensor] = 0,
            images: Optional[PIL.Image.Image] = None,
            **kwargs: Unpack[KwargsForCausalLM],
    ) -> Union[Tuple, CausalLMOutputWithPast]:

        is_multi_modal = images is not None and not (
            (
                isinstance(images, list) and (len(images) == 0 or all(i == [] for i in images))
            )
        )


        if inputs_embeds is None and is_multi_modal:
            (
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                inputs_embeds,
                labels
            ) = self.prepare_inputs_for_multimodal(
                input_ids=input_ids,
                images=images,
                position_ids=position_ids,
                attention_mask=attention_mask,
                past_key_values=past_key_values,
                labels=labels,
            )

            if cache_position is not None and inputs_embeds is not None and cache_position.shape[0] != inputs_embeds.shape[1]:
                cache_position = torch.arange(inputs_embeds.shape[1], device=self.device)
        
        
        return Phi3ForCausalLM.forward(
            self,
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            labels=labels,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
            logits_to_keep=logits_to_keep,
            **kwargs
        )
    
    def print_device_configuration(self):
        print("*************Device Configuration*********")
        if len(self.get_llm_parameters()) > 0:
            llm_device = set({str(p.device) for p in self.get_llm_parameters()})
            llm_dtype = set({p.dtype for p in self.get_llm_parameters()})
            print(f"LLM Parameters:\t\t\tdevice: {llm_device}\tdtype: {llm_dtype}\tfrozen: {self.is_llm_frozen()}")
        else:
            print("LLM parameters have not been initialized")
        
        if self.get_model().vision_tower is not None:
            vt_device = set({str(p.device) for p in self.get_model().vision_tower.parameters()})
            vt_dtype = set({p.dtype for p in self.get_model().vision_tower.parameters()})
            print(f"Vision Tower Parameters:\tdevice: {vt_device}\tdtype: {vt_dtype}\tfrozen: {self.is_vision_tower_frozen()}")
        else:
            print("Vision tower parameters have not been initialized")

        if self.get_model().mm_projector is not None:
            mm_device = set({str(p.device) for p in self.get_model().mm_projector.parameters()})
            mm_dtype = set({p.dtype for p in self.get_model().mm_projector.parameters()})
            print(f"MM Projector Parameters:\tdevice: {mm_device}\tdtype: {mm_dtype}\tfrozen: {self.is_vision_adapter_frozen()}")
        else:
            print("MM Projector parameters have not been initialized")
        print("******************************************")



def build_tokenizer(base_model_id: str) -> Tuple[AutoTokenizer, dict]:
    tok = AutoTokenizer.from_pretrained(base_model_id, padding_side="right")
    specials = {t: tok.convert_tokens_to_ids(t) for t in [IMAGE_TOKEN, IMG_START_TOKEN, IMG_END_TOKEN] if t in tok.vocab}
    if len(specials) < 3:
        n = tok.add_tokens([IMAGE_TOKEN, IMG_START_TOKEN, IMG_END_TOKEN], special_tokens=True)
        tok.pad_token = tok.eos_token
        specials = {
            "image": tok.convert_tokens_to_ids(IMAGE_TOKEN),
            "start": tok.convert_tokens_to_ids(IMG_START_TOKEN),
            "end": tok.convert_tokens_to_ids(IMG_END_TOKEN),
        }
    return tok, specials