# Copyright 2025 Bytedance Ltd. and/or its affiliates
# Copyright (c) 2025, NVIDIA CORPORATION. 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 verl.utils.megatron_utils import unwrap_model

from .util import postprocess_packed_seqs, preprocess_packed_seqs, recover_left_padding, remove_left_padding


def gptmodel_forward_dense(model, input_ids, attention_mask, position_ids, sequence_parallel, value_model=False, pack_seqs=True):
    pre_process = unwrap_model(model).pre_process
    post_process = unwrap_model(model).post_process
    if pack_seqs:
        batch_size, seq_len = attention_mask.shape[:2]
        input_ids_rmpad, packed_seq_params = preprocess_packed_seqs(input_ids, attention_mask, pre_process=pre_process)
        input_ids_rmpad = input_ids_rmpad.contiguous()
        output_orig = model(
            input_ids=input_ids_rmpad,
            attention_mask=None,
            position_ids=position_ids,
            packed_seq_params=packed_seq_params,
        )

        output = postprocess_packed_seqs(output_orig, packed_seq_params, attention_mask, batch_size, seq_len, post_process=post_process)
    else:
        batch_size, sequence_length = attention_mask.shape
        new_input_ids, new_attention_mask, new_position_ids = remove_left_padding(input_ids, attention_mask, position_ids, sequence_parallel, pre_process=pre_process)
        output = model(input_ids=new_input_ids, attention_mask=new_attention_mask, position_ids=new_position_ids)
        output = recover_left_padding(output, new_attention_mask, attention_mask, sequence_length, post_process=post_process)
    if value_model and post_process:
        output = output[..., 0]
    return output


def gptmodel_forward_qwen2_moe(model, input_ids, attention_mask, position_ids, sequence_parallel, value_model=False, pack_seqs=True):
    return gptmodel_forward_dense(model, input_ids, attention_mask, position_ids, sequence_parallel, value_model, pack_seqs)


def gptmodel_forward_llama4(model, input_ids, attention_mask, position_ids, sequence_parallel, value_model=False, pack_seqs=True):
    return gptmodel_forward_dense(model, input_ids, attention_mask, position_ids, sequence_parallel, value_model, pack_seqs)


def gptmodel_forward_dpskv3(model, input_ids, attention_mask, position_ids, sequence_parallel, value_model=False, pack_seqs=True):
    return gptmodel_forward_dense(model, input_ids, attention_mask, position_ids, sequence_parallel, value_model, pack_seqs)


def gptmodel_forward_qwen2_5_vl(model, input_ids, attention_mask, position_ids, sequence_parallel, value_model=False, pack_seqs=True):
    raise NotImplementedError("VLM is not supported yet")
