"""Llama Model."""

import warnings

from megatron import get_args
from .enums import PositionEmbeddingType
from . import GPTModel


class GemmaModel(GPTModel):
    def __init__(self,
                 num_tokentypes: int = 0,
                 parallel_output: bool = True,
                 pre_process: bool = True,
                 post_process: bool = True,
                 model_type=None,
                 version: int = 2):

        args = get_args()

        # mandatory arguments
        assert version in {1, 2}, f"Unknown llama version {version}"
        assert args.position_embedding_type == PositionEmbeddingType.rotary, \
            f"Llama uses rotary embedding, not {args.position_embedding_type}"
        assert not args.use_post_ln, "Llama does not use post_ln"
        assert args.glu_activation == "geglu", "Llama works with swiglu activation"
        assert not args.use_bias, "Llama does not use bias"
        assert not args.parallel_attn, "Llama does not use parallel_attn"
        assert args.use_rms_norm, "Llama uses rms_norm"
        assert args.tie_embed_logits , "Gemma ties embedding and lm_head weights"

        # recomended arguments
        if args.bias_gelu_fusion:
            warnings.warn("Llama is not intended to use bias_gelu_fusion")
        if args.bias_dropout_fusion:
            warnings.warn("Llama is not intended to use bias_dropout_fusion")
        if args.hidden_dropout > 0.0 and not args.lima_dropout:
            warnings.warn( "Llama is not intended to use dropout")
        if args.attention_dropout > 0.0:
            warnings.warn( "Llama is not intended to use dropout")
        super().__init__(num_tokentypes=num_tokentypes, parallel_output=parallel_output,
                         pre_process=pre_process, post_process=post_process,
                         model_type=model_type)