# coding=utf-8
# Copyright 2025-present, the HuggingFace Inc. Team and AIRAS Inc. Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from transformers import PreTrainedModel, AutoConfig
import torch
import torch.nn as nn

class SapnousT1ForCausalLM(PreTrainedModel):
    config_class = AutoConfig

    def __init__(self, config):
        super().__init__(config)
        self.hidden_size = config.hidden_size
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = nn.ModuleList([
            nn.Linear(config.hidden_size, config.hidden_size) for _ in range(config.num_hidden_layers)
        ])
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

    def forward(self, input_ids):
        hidden_states = self.embed_tokens(input_ids)
        for layer in self.layers:
            hidden_states = layer(hidden_states)
        logits = self.lm_head(hidden_states)
        return logits

# Register model with transformers
from transformers import AutoModelForCausalLM
AutoModelForCausalLM.register(SapnousT1ForCausalLM, "sapnous_t1")