"""Continue-1-OSS Model Implementation"""

from transformers.models.llama.modeling_llama import \
    LlamaAttention as _BaseAttention
from transformers.models.llama.modeling_llama import \
    LlamaDecoderLayer as _BaseDecoderLayer
from transformers.models.llama.modeling_llama import \
    LlamaForCausalLM as _BaseModel
from transformers.models.llama.modeling_llama import LlamaMLP as _BaseMLP
from transformers.models.llama.modeling_llama import \
    LlamaModel as _BaseTransformer
from transformers.models.llama.modeling_llama import \
    LlamaRMSNorm as _BaseRMSNorm
from transformers.models.llama.modeling_llama import \
    LlamaRotaryEmbedding as _BaseRotaryEmbedding

from .configuration_continue_oss import Continue1Config


# Continue-1-OSS Core Components
class Continue1RMSNorm(_BaseRMSNorm):
    """Continue-1-OSS Root Mean Square Layer Normalization"""
    pass


class Continue1RotaryEmbedding(_BaseRotaryEmbedding):
    """Continue-1-OSS Rotary Position Embeddings"""
    pass


class Continue1MLP(_BaseMLP):
    """Continue-1-OSS MLP (Feed-Forward Network)"""
    pass


class Continue1Attention(_BaseAttention):
    """Continue-1-OSS Multi-Head Attention"""
    pass


class Continue1DecoderLayer(_BaseDecoderLayer):
    """Continue-1-OSS Transformer Decoder Layer"""
    pass


class Continue1Model(_BaseTransformer):
    """
    Continue-1-OSS Transformer Model
    
    Core transformer model without the language modeling head.
    """
    config_class = Continue1Config
    
    def __init__(self, config: Continue1Config):
        super().__init__(config)


class Continue1ForCausalLM(_BaseModel):
    """
    Continue-1-OSS Model for Causal Language Modeling
    
    Designed by SVECTOR Corporation for high-quality text generation,
    instruction following, and long-context understanding.
    
    Example:
        ```python
        from transformers import AutoTokenizer, AutoModelForCausalLM
        
        model = AutoModelForCausalLM.from_pretrained(
            "SVECTOR-CORPORATION/Continue-1-OSS",
            trust_remote_code=True
        )
        tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Continue-1-OSS")
        
        messages = [{"role": "user", "content": "Hello There!"}]
        inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
        outputs = model.generate(inputs, max_new_tokens=100)
        ```
    """
    config_class = Continue1Config
    
    def __init__(self, config: Continue1Config):
        super().__init__(config)