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# # Copyright 2023 Haotian Liu
# #
# # Licensed under the Apache License, Version 2.0 (the "License");
# # you may not use this file except in compliance with the License.
# # You may obtain a copy of the License at
# #
# # http://www.apache.org/licenses/LICENSE-2.0
# #
# # Unless required by applicable law or agreed to in writing, software
# # distributed under the License is distributed on an "AS IS" BASIS,
# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# # See the License for the specific language governing permissions and
# # limitations under the License.
# from typing import List, Optional, Tuple, Union
# import torch
# import torch.nn as nn
# from torch.nn import CrossEntropyLoss
# from transformers import AutoConfig, AutoModelForCausalLM, \
# MistralConfig, MistralModel, MistralForCausalLM
# from transformers.modeling_outputs import CausalLMOutputWithPast
# from transformers.generation.utils import GenerateOutput
# from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
# class LlavaMistralConfig(MistralConfig):
# model_type = "llava_mistral"
# class LlavaMistralModel(LlavaMetaModel, MistralModel):
# config_class = LlavaMistralConfig
# def __init__(self, config: MistralConfig):
# super(LlavaMistralModel, self).__init__(config)
# class LlavaMistralForCausalLM(MistralForCausalLM, LlavaMetaForCausalLM):
# config_class = LlavaMistralConfig
# def __init__(self, config):
# super(MistralForCausalLM, self).__init__(config)
# self.model = LlavaMistralModel(config)
# self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# # Initialize weights and apply final processing
# self.post_init()
# def get_model(self):
# return self.model
# def forward(
# self,
# input_ids: 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,
# images: Optional[torch.FloatTensor] = None,
# image_sizes: Optional[List[List[int]]] = None,
# return_dict: Optional[bool] = None,
# ) -> Union[Tuple, CausalLMOutputWithPast]:
# if inputs_embeds is None:
# (
# input_ids,
# position_ids,
# attention_mask,
# past_key_values,
# inputs_embeds,
# labels
# ) = self.prepare_inputs_labels_for_multimodal(
# input_ids,
# position_ids,
# attention_mask,
# past_key_values,
# labels,
# images,
# image_sizes
# )
# return super().forward(
# 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
# )
# @torch.no_grad()
# def generate(
# self,
# inputs: Optional[torch.Tensor] = None,
# images: Optional[torch.Tensor] = None,
# image_sizes: Optional[torch.Tensor] = None,
# **kwargs,
# ) -> Union[GenerateOutput, torch.LongTensor]:
# position_ids = kwargs.pop("position_ids", None)
# attention_mask = kwargs.pop("attention_mask", None)
# if "inputs_embeds" in kwargs:
# raise NotImplementedError("`inputs_embeds` is not supported")
# if images is not None:
# (
# inputs,
# position_ids,
# attention_mask,
# _,
# inputs_embeds,
# _
# ) = self.prepare_inputs_labels_for_multimodal(
# inputs,
# position_ids,
# attention_mask,
# None,
# None,
# images,
# image_sizes=image_sizes
# )
# else:
# inputs_embeds = self.get_model().embed_tokens(inputs)
# return super().generate(
# position_ids=position_ids,
# attention_mask=attention_mask,
# inputs_embeds=inputs_embeds,
# **kwargs
# )
# def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
# inputs_embeds=None, **kwargs):
# images = kwargs.pop("images", None)
# image_sizes = kwargs.pop("image_sizes", None)
# inputs = super().prepare_inputs_for_generation(
# input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
# )
# if images is not None:
# inputs['images'] = images
# if image_sizes is not None:
# inputs['image_sizes'] = image_sizes
# return inputs
# AutoConfig.register("llava_mistral", LlavaMistralConfig)
# AutoModelForCausalLM.register(LlavaMistralConfig, LlavaMistralForCausalLM)
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