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import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import hf_hub_download, snapshot_download
from accelerate.utils import load_and_quantize_model
import gc
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)

LONGLLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "syzymon/long_llama_3b": "https://huggingface.co/syzymon/long_llama_3b/resolve/main/config.json",
}

class LongLlamaConfig(PretrainedConfig):

    r"""
    This is the configuration class to store the configuration of a [`LongLlamaModel`]. It is used to instantiate an LongLLaMA
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the LongLLaMA-7B.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 32000):
            Vocabulary size of the LongLLaMA model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`LongLlamaModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        mem_layers (`List[int]`, defaults to `[]`):
            Layers with memory
        mem_positionals (`bool`, *optional*, defaults to `True`):
            Whether to use positional embeddings in memory layers
        mem_dtype (`str`, *optional*, defaults to `"bfloat16"`):
            Type for keys and values stored in memory
        mem_attention_grouping (`Tuple[int, int]`, *optional*, defaults to `None`):
            One can trade speed for memory by performing attention
            in memory layers sequentially.
            When equal to `(4, 2048)` the memory layers will process at most 4 heads and 2048 queries from each head at once.
            That is at most 4*2048 queries at once.
        torch_attention (`bool`, *optional*, defaults to `False`):
            Whether to use torch scaled_dot_product_attention
        gradient_checkpoint_every_ith (`int`, *optional*, defaults to `1`):
            When gradient checkpointing is enabled checkpoint every ith layer

        Example:

    ```python
    >>> from transformers import LongLlamaModel, LongLlamaConfig

    >>> # Initializing a LongLLaMA longllama-7b style configuration
    >>> configuration = LongLlamaConfig()

    >>> # Initializing a model from the longllama-7b style configuration
    >>> model = LongLlamaModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "longllama"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=32000,
        hidden_size=4096,
        intermediate_size=11008,
        num_hidden_layers=32,
        num_attention_heads=32,
        hidden_act="silu",
        max_position_embeddings=2048,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        rope_scaling=None,
        last_context_length=1024,
        mem_layers=[],
        mem_positionals=True,
        mem_dtype="bfloat16",
        mem_attention_grouping=None,
        torch_attention=False,
        gradient_checkpoint_every_ith=1,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.last_context_length = last_context_length
        self.mem_layers = mem_layers
        self.mem_positionals = mem_positionals
        self.mem_dtype = mem_dtype
        self.mem_attention_grouping = mem_attention_grouping
        self.torch_attention = torch_attention
        self.gradient_checkpoint_every_ith = gradient_checkpoint_every_ith

        self._rope_scaling_validation()
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )

    def _rope_scaling_validation(self):
        """
        Validate the `rope_scaling` configuration.
        """
        if self.rope_scaling is not None:
            raise ValueError("LongLLaMA does not use rope_scaling")


def load_and_quantize_llama_model(model_path: str, tokenizer_path: str, device: torch.device, quantized: bool = True, num_bit: int = 8):
    """
    Wczytuje i kwantyzuje model LongLLaMA.
    
    Returns:
        model (LongLlamaForCausalLM): Wczytany i skwantyzowany model.
        tokenizer (AutoTokenizer): Tokenizator dla modelu.
    """
    try:
        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)

        if not quantized:
            model = AutoModelForCausalLM.from_pretrained(
                model_path,
                torch_dtype=torch.bfloat16,
                device_map=device,
                trust_remote_code=True,
                mem_attention_grouping=(1, 1024),
            )
            model.eval()
        else:
            model = _quantized_model_loading(num_bit, model_path, device)
        
        return model, tokenizer
    except Exception as e:
        raise RuntimeError(f"Błąd podczas wczytywania modelu: {str(e)}")

def _quantized_model_loading(num_bit, model_path, device):
    """
    Procedura ładująca skwantyzowany model LongLLaMA.
    """
    print(f"!!!!!WARNING!!!!! Model będzie kwantyzowany do {num_bit} bitów!\n"
          "Może to wpłynąć na wydajność modelu!")

    cfg = LongLlamaConfig.from_pretrained(model_path)
    cfg.mem_attention_grouping = (1, 1024)
    with init_empty_weights():
        empty_model = LongLlamaForCausalLM(cfg)

    gc.collect()
    if num_bit == 8:
        weights_loc = hf_hub_download(repo_id=model_path, filename="quantized/pytorch_model_8bit.bin")
        bnb_quantization_config = BnbQuantizationConfig(load_in_8bit=True, llm_int8_threshold=6)
    elif num_bit == 4:
        weights_loc = snapshot_download(model_path)
        bnb_quantization_config = BnbQuantizationConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)
    else:
        raise ValueError(f"Kwantyzacja {num_bit} nie jest obsługiwana.")

    gc.collect()
    model = transformers_load_and_quantize_model(empty_model, weights_location=weights_loc, bnb_quantization_config=bnb_quantization_config, device_map="auto")
    model.eval()
    return model