# Copyright (c) 2023-2024 DeepSeek.
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
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# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

from threading import Thread
from typing import List

import torch
import transformers
from joblib.externals.cloudpickle import instance
from transformers import (
    AutoModelForCausalLM,
    StoppingCriteria,
    StoppingCriteriaList,
    TextIteratorStreamer,
)

from deepseek_vl.models import DeepseekVLV2Processor, DeepseekVLV2ForCausalLM
from deepseek_vl.models.conversation import Conversation


def load_model(model_path, dtype=torch.bfloat16):
    vl_chat_processor = DeepseekVLV2Processor.from_pretrained(model_path)
    tokenizer = vl_chat_processor.tokenizer

    vl_gpt: DeepseekVLV2ForCausalLM = AutoModelForCausalLM.from_pretrained(
        model_path, trust_remote_code=True, torch_dtype=dtype
    )
    vl_gpt = vl_gpt.cuda().eval()
    return tokenizer, vl_gpt, vl_chat_processor


def convert_conversation_to_prompts(conversation: Conversation):
    conv_prompts = []
    pil_images = []
    messages = conversation.messages
    for i in range(0, len(messages), 2):

        if isinstance(messages[i][1], tuple):
            text, images = messages[i][1]
        else:
            text, images = messages[i][1], []
        pil_images.extend(images)

        prompt = {
            "role": messages[i][0],
            "content": text,
        }
        response = {"role": messages[i + 1][0], "content": messages[i + 1][1]}
        conv_prompts.extend([prompt, response])

    return conv_prompts, pil_images


class StoppingCriteriaSub(StoppingCriteria):
    def __init__(self, stops=[], encounters=1):
        super().__init__()
        self.stops = [stop.to("cuda") for stop in stops]

    def __call__(
        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
    ):
        for stop in self.stops:
            if input_ids.shape[-1] < len(stop):
                continue
            if torch.all((stop == input_ids[0][-len(stop) :])).item():
                return True

        return False


@torch.inference_mode()
def deepseek_generate(
    conv_prompts: list,
    pil_images: list,
    vl_gpt: torch.nn.Module,
    vl_chat_processor: DeepseekVLV2Processor,
    tokenizer: transformers.PreTrainedTokenizer,
    stop_words: list,
    max_length: int = 256,
    temperature: float = 1.0,
    top_p: float = 1.0,
    repetition_penalty=1.1,
):

    prepare_inputs = vl_chat_processor.__call__(
        conversations=conv_prompts,
        images=pil_images,
        inference_mode=True,
        force_batchify=True,
        system_prompt=""
    ).to(vl_gpt.device)

    return generate(
        vl_gpt,
        tokenizer,
        prepare_inputs,
        max_length,
        temperature,
        repetition_penalty,
        top_p,
        stop_words,
    )


@torch.inference_mode()
def generate(
    vl_gpt,
    tokenizer,
    prepare_inputs,
    max_gen_len: int = 256,
    temperature: float = 0,
    repetition_penalty=1.1,
    top_p: float = 0.95,
    stop_words: List[str] = [],
):
    """Stream the text output from the multimodality model with prompt and image inputs."""
    inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)

    streamer = TextIteratorStreamer(tokenizer)

    stop_words_ids = [
        torch.tensor(tokenizer.encode(stop_word)) for stop_word in stop_words
    ]
    stopping_criteria = StoppingCriteriaList(
        [StoppingCriteriaSub(stops=stop_words_ids)]
    )

    generation_config = dict(
        inputs_embeds=inputs_embeds,
        attention_mask=prepare_inputs.attention_mask,
        pad_token_id=tokenizer.eos_token_id,
        bos_token_id=tokenizer.bos_token_id,
        eos_token_id=tokenizer.eos_token_id,
        max_new_tokens=max_gen_len,
        do_sample=True,
        use_cache=True,
        streamer=streamer,
        stopping_criteria=stopping_criteria,
    )

    if temperature > 0:
        generation_config.update(
            {
                "do_sample": True,
                "top_p": top_p,
                "temperature": temperature,
                "repetition_penalty": repetition_penalty,
            }
        )
    else:
        generation_config["do_sample"] = False

    thread = Thread(target=vl_gpt.generate, kwargs=generation_config)
    thread.start()

    yield from streamer