from typing import Optional, List

import torch

from transformers import LlavaForConditionalGeneration

class LlemrForConditionalGeneration(LlavaForConditionalGeneration):

    def forward(

        self,

        input_ids: torch.LongTensor = None,

        pixel_values: torch.FloatTensor = None,

        pixel_values_is_padding: torch.BoolTensor = 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,

        vision_feature_layer: Optional[int] = None,

        vision_feature_select_strategy: Optional[str] = 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,

    ):

        if pixel_values is not None and pixel_values_is_padding is not None:

            pixel_values = pixel_values[~pixel_values_is_padding].unsqueeze(1)

        return super().forward(

            input_ids=input_ids,

            pixel_values=pixel_values,

            attention_mask=attention_mask,

            position_ids=position_ids,

            past_key_values=past_key_values,

            inputs_embeds=inputs_embeds,

            vision_feature_layer=vision_feature_layer,

            vision_feature_select_strategy=vision_feature_select_strategy,

            labels=labels,

            use_cache=use_cache,

            output_attentions=output_attentions,

            output_hidden_states=output_hidden_states,

            return_dict=return_dict,

        )

    def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels):

        num_image_patches = image_features.shape[1]

        assert num_image_patches == 1, "Only one image patch is supported."

        left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id))

        assert left_padding, "Input ids should be left-padded."

        (

            final_embedding,

            final_attention_mask,

            final_labels,

            position_ids

        ) = super()._merge_input_ids_with_image_features(

            image_features=image_features,

            inputs_embeds=inputs_embeds,

            input_ids=input_ids,

            attention_mask=attention_mask,

            labels=labels,

        )

        return final_embedding, final_attention_mask, final_labels, position_ids

    def prepare_inputs_for_generation(

        self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, attention_mask=None,

        pixel_values_is_padding=None, **kwargs

    ):

        model_inputs = super().prepare_inputs_for_generation(

            input_ids=input_ids,

            past_key_values=past_key_values,

            inputs_embeds=inputs_embeds,

            pixel_values=pixel_values,

            attention_mask=attention_mask,

            **kwargs,

        )

        model_inputs["pixel_values_is_padding"] = pixel_values_is_padding

        return model_inputs