import random
import numpy as np
import torch
from transformers import StoppingCriteria, StoppingCriteriaList

def auto_configure_device_map(num_gpus):
    # visual_encoder 算4层
    # internlm_model.model.embed_tokens 占用1层
    # norm 和 lm_head 占用1层
    # transformer.layers 占用 32 层
    # 总共34层分配到num_gpus张卡上
    num_trans_layers = 32
    per_gpu_layers = 38 / num_gpus

    device_map = {
        'vit': 0,
        'vision_proj': 0,
        'model.tok_embeddings': 0,
        'plora_glb_GN': 0,
        'plora_sub_GN': 0,
        'model.norm': num_gpus - 1,
        'output': num_gpus - 1,
    }

    used = 3
    gpu_target = 0
    for i in range(num_trans_layers):
        if used >= per_gpu_layers:
            gpu_target += 1
            used = 0
        assert gpu_target < num_gpus
        device_map[f'model.layers.{i}'] = gpu_target
        used += 1

    return device_map

class StoppingCriteriaSub(StoppingCriteria):

    def __init__(self, stops=[], encounters=1):
        super().__init__()
        self.stops = stops

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
        for stop in self.stops:
            if torch.all((stop == input_ids[:, -len(stop):])).item():
                return True
        return False


def get_stopping_criteria(stop_words_ids):
    stop_words_ids = [torch.tensor([i]).cuda() for i in stop_words_ids]
    stopping_criteria = StoppingCriteriaList(
        [StoppingCriteriaSub(stops=stop_words_ids)])
    return stopping_criteria


meta_instruction = """You are an AI assistant whose name is InternLM (书生·浦语).
- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.
"""


def set_random_seed(seed, set_cudnn=False):
    """Set the random seed for reproducibility.

    Parameters:
    seed (int): The seed to use for generating random numbers.
    """
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)  # For multi-GPU.
    np.random.seed(seed)
    random.seed(seed)
    if set_cudnn and torch.backends.cudnn.is_available():
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False