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from transformers import (
    AutoConfig,
    AutoProcessor,
    ProcessorMixin,
    Qwen2TokenizerFast,
    BaseImageProcessor,
    Qwen2_5_VLForConditionalGeneration,
)
from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
    Qwen2_5_VLCausalLMOutputWithPast,
    Qwen2RMSNorm,
)
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.processing_utils import Unpack
from transformers.feature_extraction_sequence_utils import BatchFeature

from typing import List, Optional, TypedDict

# from tabpfn_extensions import TabPFNRegressor
# from tabpfn_extensions.embedding import TabPFNEmbedding
import numpy as np

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from pprint import pprint


class TabularProcessorKwargs(TypedDict):
    """
    Keyword arguments for tabular processing.
    """

    pass


class TabularPreprocessor(BaseImageProcessor):
    def __call__(self, X: list | np.ndarray | torch.Tensor) -> torch.Tensor:
        if not isinstance(X, list): 
            X = [X]
        
        res = []
        for X_sample in X:
            if isinstance(X_sample, torch.Tensor):
                X_sample = X_sample.cpu().numpy()
                
            res.append(X_sample)
        res = np.array(res)
        return BatchFeature(data={"tabular_values": torch.from_numpy(res).to(torch.float32)})

AutoProcessor.register("TabularPreprocessor", TabularPreprocessor)

class TabularProcessor(nn.Module):
    def __init__(self, **kwargs: Unpack[TabularProcessorKwargs]):
        super().__init__(**kwargs)
        self.tabpfn = TabPFNRegressor(
            n_estimators=1,
            model_path="./tabpfn-v2-regressor.ckpt", device="cuda:1"
        )

    def __call__(self, X: np.ndarray | torch.Tensor) -> torch.Tensor:
        # Will convert specified categorical indices to category dtype, as well
        # as handle `np.object` arrays or otherwise `object` dtype pandas columns.
        if len(X.shape) == 2:
            X = [X]
        res = []
        for X_sample in X:
            if isinstance(X_sample, torch.Tensor):
                X_sample = X_sample.cpu().numpy()
                
            X_sample = X_sample[0]
            self.tabpfn.fit(X_sample, np.random.random(X_sample.shape[0]))

            embs = self.tabpfn.get_embeddings(X_sample)
            embs_t = torch.from_numpy(embs).to(self.tabpfn.device)
            embs_t = embs_t.mean(dim=0)
            res.append(embs_t)

        res = torch.stack(res)
        res = res.view(-1, 192)
        return res

class TabularBlock(nn.Module):
    def __init__(self, input_dim: int, hidden_dim: int = 192):
        super().__init__()
        self.linear1 = nn.Linear(input_dim, hidden_dim)
        self.activation = nn.GELU()
        self.linear2 = nn.Linear(hidden_dim, input_dim)
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        residual = x
        x = self.linear1(x)
        x = self.activation(x)
        x = self.linear2(x)
        return x + residual

class TabularLearnableProcessor(nn.Module):
    def __init__(self, num_features: int = 1):
        super().__init__()
        # Each cell is processed individually as a scalar
        self.input_proj = nn.Linear(num_features, 192)
        self.nodes = nn.Sequential(
            nn.GELU(),
            TabularBlock(192, 64),
            nn.GELU(),
            TabularBlock(192, 64),
            nn.GELU(),
            TabularBlock(192, 64),
            nn.GELU(),
            TabularBlock(192, 64),
            nn.GELU(),
            TabularBlock(192, 64),
            nn.GELU(),
            TabularBlock(192, 64),
            nn.GELU(),
            TabularBlock(192, 64),
        )

    def forward(self, X: np.ndarray | torch.Tensor) -> torch.Tensor:
        if isinstance(X, np.ndarray):
            X = torch.from_numpy(X)
        
        param_dtype = self.input_proj.weight.dtype
        X = X.to(param_dtype)
        
        # Flatten the table - each cell becomes a separate token
        # X shape: (batch_size, rows, cols) -> (batch_size * rows * cols, 1)
        batch_size = X.shape[0]
        X_flat = X.reshape(-1, 1)  # Flatten to individual cells
        
        # RMS normalization per cell for stability
        # X_normalized = X_flat * torch.rsqrt(X_flat.pow(2) + 1e-5)
        
        projected = self.input_proj(X_flat)
        # res = self.nodes(projected)
        return projected

class Qwen_2_5_TabularProcessor(ProcessorMixin):
    r"""
    Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor.
    [`Qwen2_5_VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
    [`~Qwen2_5_VLProcessor.__call__`] and [`~Qwen2_5_VLProcessor.decode`] for more information.
    Args:
        image_processor ([`Qwen2VLImageProcessor`], *optional*):
            The image processor is a required input.
        tokenizer ([`Qwen2TokenizerFast`], *optional*):
            The tokenizer is a required input.
        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
            in a chat into a tokenizable string.
    """

    attributes = ["tokenizer"]
    valid_kwargs = ["chat_template"]

    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")

    def __init__(
        self,
        tabular_processor: TabularPreprocessor | None = None,
        tokenizer=None,
        chat_template=None,
        **kwargs,
    ):
        self.tabular_token = (
            "<|tabular_pad|>"
            if not hasattr(tokenizer, "tabular_token")
            else tokenizer.tabular_token
        )
        self.tabular_processor = tabular_processor
        super().__init__(tokenizer, chat_template=chat_template)

    def __call__(
        self,
        tabular_values: np.ndarray | torch.Tensor | None = None,
        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
        **kwargs: Unpack[TabularProcessorKwargs],
    ) -> BatchFeature:
        """
        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
        Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`.
        Args:
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. Both channels-first and channels-last formats are supported.
            text (`str`, `List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
                tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors of a particular framework. Acceptable values are:
                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.
                - `'jax'`: Return JAX `jnp.ndarray` objects.
        Returns:
            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
              `None`).
            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
            - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
        """
        # print("Tabular values: ", tabular_values)
        if tabular_values is not None:
            tabular_inputs = self.tabular_processor(tabular_values)
        else:
            print("Warning! No tabular values provided!")
            tabular_inputs = {}

        if not isinstance(text, list):
            text = [text]

        if tabular_values is not None:
            index = 0
            for i in range(len(text)):
                while self.tabular_token in text[i]:
                    # Each cell becomes a token: num_tokens = rows * cols
                    table_shape = tabular_inputs["tabular_values"][index].shape
                    rows, cols = table_shape[0], table_shape[1]
                    # Build pattern: for each row, add col tokens + row separator
                    row_pattern = "<|placeholder|>" * cols + "<|tabular_row|>"
                    replacement = row_pattern * rows
                    text[i] = text[i].replace(
                        self.tabular_token,
                        replacement,
                        1,
                    )
                    index += 1
                text[i] = text[i].replace("<|placeholder|>", self.tabular_token)

        text_inputs = self.tokenizer(text, **kwargs)
        return BatchFeature(data={**text_inputs, **tabular_inputs})

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    def post_process_image_text_to_text(
        self,
        generated_outputs,
        skip_special_tokens=True,
        clean_up_tokenization_spaces=False,
        **kwargs,
    ):
        """
        Post-process the output of the model to decode the text.
        Args:
            generated_outputs (`torch.Tensor` or `np.ndarray`):
                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
                or `(sequence_length,)`.
            skip_special_tokens (`bool`, *optional*, defaults to `True`):
                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
            Clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
                Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
            **kwargs:
                Additional arguments to be passed to the tokenizer's `batch_decode method`.
        Returns:
            `List[str]`: The decoded text.
        """
        return self.tokenizer.batch_decode(
            generated_outputs,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            **kwargs,
        )

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        tabular_processor_input_names = self.tabular_processor.model_input_names if hasattr(self.tabular_processor, 'model_input_names') else []
        names_from_processor = list(
            dict.fromkeys(tokenizer_input_names + tabular_processor_input_names)
        )
        return names_from_processor + ["tabular_values"]


class Qwen2_5_TabularModel(Qwen2_5_VLForConditionalGeneration):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.tabular_processor = TabularLearnableProcessor(num_features=1)
        
        self.tabular_projection = nn.Sequential(
            nn.Linear(192, self.config.hidden_size),
            nn.ReLU(),
            TabularBlock(self.config.hidden_size, self.config.hidden_size),
            nn.ReLU(),
            TabularBlock(self.config.hidden_size, self.config.hidden_size),
            nn.ReLU(),
            TabularBlock(self.config.hidden_size, self.config.hidden_size),
        )

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[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,
        pixel_values: Optional[torch.Tensor] = None,
        pixel_values_videos: Optional[torch.FloatTensor] = None,
        tabular_values: Optional[torch.Tensor] = None,
        image_grid_thw: Optional[torch.LongTensor] = None,
        video_grid_thw: Optional[torch.LongTensor] = None,
        rope_deltas: Optional[torch.LongTensor] = None,
        cache_position: Optional[torch.LongTensor] = None,
        second_per_grid_ts: Optional[torch.Tensor] = None,
    ):
        r"""
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
        Returns:
        Example:
        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
        >>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
        >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
        >>> messages = [
            {
                "role": "user",
                "content": [
                    {"type": "image"},
                    {"type": "text", "text": "What is shown in this image?"},
                ],
            },
        ]
        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
        ```"""

        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        if inputs_embeds is None:
            inputs_embeds = self.language_model.embed_tokens(input_ids)
            if pixel_values is not None:
                pixel_values = pixel_values.type(self.visual.dtype)
                image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
                n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
                n_image_features = image_embeds.shape[0]
                if n_image_tokens != n_image_features:
                    raise ValueError(
                        f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
                    )

                mask = input_ids == self.config.image_token_id
                mask_unsqueezed = mask.unsqueeze(-1)
                mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
                image_mask = mask_expanded.to(inputs_embeds.device)

                image_embeds = image_embeds.to(
                    inputs_embeds.device, inputs_embeds.dtype
                )
                inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)

            if pixel_values_videos is not None:
                pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
                video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
                n_video_tokens = (input_ids == self.config.video_token_id).sum().item()
                n_video_features = video_embeds.shape[0]
                if n_video_tokens != n_video_features:
                    raise ValueError(
                        f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}"
                    )

                mask = input_ids == self.config.video_token_id
                mask_unsqueezed = mask.unsqueeze(-1)
                mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
                video_mask = mask_expanded.to(inputs_embeds.device)

                video_embeds = video_embeds.to(
                    inputs_embeds.device, inputs_embeds.dtype
                )
                inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)

            if tabular_values is not None:
                proc_feats = self.tabular_processor(tabular_values.to(self.device, torch.float32))
                proc_feats = proc_feats.to(inputs_embeds.dtype).to(self.device)
                tabular_embeds = self.tabular_projection(proc_feats)
                
                tabular_token_id = getattr(self.config, "tabular_token_id", None)
                if tabular_token_id is None:
                    raise ValueError("Tabular token id (config.tabular_token_id) is not set.")
                mask = (input_ids == int(tabular_token_id))

                tabular_no_mask = mask.sum().item()
                if tabular_no_mask != tabular_embeds.shape[0]:
                    raise ValueError(
                        f"Tabular features and tabular tokens do not match: tokens: {tabular_no_mask}, features {tabular_embeds.shape[0]}"
                    )

                mask_unsqueezed = mask.unsqueeze(-1)
                mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
                tabular_mask = mask_expanded.to(inputs_embeds.device)
                tabular_embeds = tabular_embeds.to(
                    inputs_embeds.device, inputs_embeds.dtype
                )
                inputs_embeds = inputs_embeds.masked_scatter(
                    tabular_mask, tabular_embeds
                )
    
            if attention_mask is not None:
                attention_mask = attention_mask.to(inputs_embeds.device)

        # if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme
        if position_ids is None and (
            attention_mask is None or attention_mask.ndim == 2
        ):
            # calculate RoPE index once per generation in the pre-fill stage only
            if (
                (cache_position is not None and cache_position[0] == 0)
                or self.rope_deltas is None
                or (past_key_values is None or past_key_values.get_seq_length() == 0)
            ):
                position_ids, rope_deltas = self.model.get_rope_index(
                    input_ids,
                    image_grid_thw,
                    video_grid_thw,
                    second_per_grid_ts,
                    attention_mask,
                )
                self.rope_deltas = rope_deltas
            # then use the prev pre-calculated rope-deltas to get the correct position ids
            else:
                batch_size, seq_length, _ = inputs_embeds.shape
                delta = (
                    (cache_position[0] + self.rope_deltas).to(inputs_embeds.device)
                    if cache_position is not None
                    else 0
                )
                position_ids = torch.arange(seq_length, device=inputs_embeds.device)
                position_ids = position_ids.view(1, -1).expand(batch_size, -1)
                if cache_position is not None:  # otherwise `deltas` is an int `0`
                    delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)
                position_ids = position_ids.add(delta)
                position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)

        outputs = self.model(
            input_ids=None,
            position_ids=position_ids,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            # Upcast to float if we need to compute the loss to avoid potential precision issues
            logits = logits.float()
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)
            
        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return Qwen2_5_VLCausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            rope_deltas=self.rope_deltas,
        )
        
    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        inputs_embeds=None,
        cache_position=None,
        position_ids=None,
        use_cache=True,
        pixel_values=None,
        pixel_values_videos=None,
        image_grid_thw=None,
        video_grid_thw=None,
        second_per_grid_ts=None,
        **kwargs,
    ):
        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model

        model_inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            cache_position=cache_position,
            position_ids=position_ids,
            pixel_values=pixel_values,
            pixel_values_videos=pixel_values_videos,
            image_grid_thw=image_grid_thw,
            video_grid_thw=video_grid_thw,
            second_per_grid_ts=second_per_grid_ts,
            use_cache=use_cache,
            **kwargs,
        )

        # Qwen2-5-VL position_ids are prepareed with rope_deltas in forward
        model_inputs["position_ids"] = None

        if cache_position[0] != 0:
            model_inputs["pixel_values"] = None
            model_inputs["pixel_values_videos"] = None
            model_inputs["tabular_values"] = None

        return model_inputs

if __name__ == "__main__":
    template = """"{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% set tabular_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif content['type'] == 'tabular' or 'tabular' in content %}{% set tabular_count.value = tabular_count.value + 1 %}{% if add_vision_id %}Table {{ tabular_count.value }}: {% endif %}<|vision_start|><|tabular_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"""

    MODE = "reconstruction_variable"

    model_name_trained = f"./models/Tabular-LM-v0.1-{MODE}"
    # model_name_trained = "Qwen/Qwen2.5-VL-3B-Instruct"
    # model_name_trained = "./models/checkpoints/checkpoint-1000"

    tabular_processor = TabularPreprocessor()
    qwen_tabular_processor = Qwen_2_5_TabularProcessor(
        tabular_processor=tabular_processor,
        tokenizer=Qwen2TokenizerFast.from_pretrained(model_name_trained),
    )

    qwen_tabular_processor.tabular_token = "<|tabular_pad|>"
    qwen_tabular_processor.tokenizer.add_tokens([qwen_tabular_processor.tabular_token, "<|tabular_row|>"])
    qwen_tabular_processor.tokenizer.chat_template = template

    tabular_data = np.random.randn(4,6).round(2)
 
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "This is a table."},
                {"index": 0, "type": "tabular"},
                {"type": "text", "text": "Give me its content in csv format."},
                # {"type": "text", "text": "Give me a statistical summary."},
                # {"type": "text", "text": "Give me the correlation matrix in csv format"},
                # {"type": "text", "text": "Give me the content of the table"},
            ],
        }
    ]

    preprocessed = qwen_tabular_processor.tokenizer.apply_chat_template(
        messages, tokenize=False
    )

    processed = qwen_tabular_processor(
        [tabular_data], text=preprocessed, return_tensors="pt"
    )

    model = Qwen2_5_TabularModel.from_pretrained(model_name_trained).to("cuda:1")
    model.config.tabular_token_id = (
        qwen_tabular_processor.tokenizer.convert_tokens_to_ids("<|tabular_pad|>")
    )
    model.config.tabular_row_token_id = (
        qwen_tabular_processor.tokenizer.convert_tokens_to_ids("<|tabular_row|>")
    )
    
    processed = {key: value.to("cuda:1") for key, value in processed.items()}
    
    res = model.generate(**processed, max_new_tokens=512, do_sample=False)
    generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(processed["input_ids"], res, strict=True)]
    output_text = qwen_tabular_processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
    
    print("="*80)
    print("Original table:")
    print(tabular_data)
    print("\nModel output:")
    print(output_text[0])
    print("="*80)
     
    if MODE in ["reconstruction", "reconstruction_variable"]:
        # Try to evaluate reconstruction quality
        from utils import text_to_array
        generated_array = text_to_array(output_text[0])
        
        # Round original to match expected precision
        tabular_data_rounded = tabular_data.round(1)
        
        print("\nReconstruction evaluation:")
        print(f"Original shape: {tabular_data_rounded.shape}")
        print(f"Generated shape: {generated_array.shape}")
        
        if generated_array.shape == tabular_data_rounded.shape:
            mse = np.mean((generated_array - tabular_data_rounded) ** 2)
            mae = np.mean(np.abs(generated_array - tabular_data_rounded))
            print(f"MSE: {mse:.4f}")
            print(f"MAE: {mae:.4f}")
        else:
            print(f"Shape mismatch - cannot compute metrics")
    
    if MODE == "summary":
        summary_parts = []
        
        # Podstawowe statystyki
        summary_parts.append(f"Mean: {tabular_data.mean():.2f}")
        summary_parts.append(f"Median: {np.median(tabular_data):.2f}")
        summary_parts.append(f"Std: {tabular_data.std():.2f}")
        summary_parts.append(f"Min: {tabular_data.min():.2f}")
        summary_parts.append(f"Max: {tabular_data.max():.2f}")
        
        # Średnie po wierszach
        row_means = tabular_data.mean(axis=1)
        row_means_str = ", ".join([f"{m:.2f}" for m in row_means])
        summary_parts.append(f"Row means: [{row_means_str}]")
        
        # Średnie po kolumnach
        col_means = tabular_data.mean(axis=0)
        col_means_str = ", ".join([f"{m:.2f}" for m in col_means])
        summary_parts.append(f"Column means: [{col_means_str}]")
        
        # Macierz korelacji (jeśli mamy więcej niż 1 kolumnę)
        if tabular_data.shape[1] > 1:
            try:
                corrcoef = np.corrcoef(tabular_data.T)
                corr_str = "Correlation matrix:\n"
                for i in range(corrcoef.shape[0]):
                    corr_row = ", ".join([f"{corrcoef[i, j]:.2f}" for j in range(corrcoef.shape[1])])
                    corr_str += f"  [{corr_row}]\n"
                summary_parts.append(corr_str.strip())
            except:
                pass
        
        summary_text = "\n".join(summary_parts)
        print("True summary:")
        print(summary_text)