#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
# Adapted from vllm/tests/entrypoints/llm/test_guided_generate.py
# Copyright 2023 The vLLM team.
#
# 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.
#

import torch
from vllm.config import ModelConfig, VllmConfig
from vllm.v1.core.kv_cache_utils import get_kv_cache_configs
from vllm.v1.kv_cache_interface import FullAttentionSpec


def new_kv_cache_spec(
    block_size=16,
    num_kv_heads=2,
    head_size=64,
    dtype=torch.float32,
    page_size_padded=None,
    sliding_window=None,
    attention_chunk_size=None,
):
    return FullAttentionSpec(
        block_size=block_size,
        num_kv_heads=num_kv_heads,
        head_size=head_size,
        dtype=dtype,
        page_size_padded=page_size_padded,
        sliding_window=sliding_window,
        attention_chunk_size=attention_chunk_size,
    )

def test_auto_fit_max_model_len():
    """Test that max_model_len=-1 auto-fits to available NPU memory."""
    # Create config with original_max_model_len=-1 to trigger auto-fit
    model_config = ModelConfig(max_model_len=1024)
    # Simulate the user passing -1 by setting original_max_model_len
    model_config.original_max_model_len = -1
    vllm_config = VllmConfig(model_config=model_config)

    # block_size * 2 * head_size * num_kv_heads * dtype_size
    mem_per_block_per_layer = 16 * 2 * 64 * 4 * 2  # 16KB per block per layer
    kv_cache_specs = {
        "layer_1": new_kv_cache_spec(),
        "layer_2": new_kv_cache_spec(),
    }

    # With enough memory, max_model_len stays at the derived max
    large_available_memory = mem_per_block_per_layer * 2 * 1024  # plenty of memory
    _kv_cache_configs = get_kv_cache_configs(
        vllm_config, [kv_cache_specs], [large_available_memory]
    )
    assert vllm_config.model_config.max_model_len == 1024

    # Reset for next test
    model_config = ModelConfig(max_model_len=1024)
    model_config.original_max_model_len = -1
    vllm_config = VllmConfig(model_config=model_config)

    # With limited memory, max_model_len should be reduced
    # Need memory for at least max_model_len tokens
    # 32 blocks worth of memory for 2 layers = can fit 32*16=512 tokens
    limited_memory = mem_per_block_per_layer * 2 * 32
    _kv_cache_configs = get_kv_cache_configs(
        vllm_config, [kv_cache_specs], [limited_memory]
    )
    # Should be reduced to fit in memory
    assert vllm_config.model_config.max_model_len < 1024
    assert vllm_config.model_config.max_model_len > 0


def test_auto_fit_max_model_len_not_triggered():
    """Test that auto-fit is not triggered when original_max_model_len is not -1."""
    model_config = ModelConfig(max_model_len=16)
    # original_max_model_len should be None by default, not -1
    vllm_config = VllmConfig(model_config=model_config)

    mem_per_block_per_layer = 16 * 2 * 64 * 4 * 2
    kv_cache_specs = {
        "layer_1": new_kv_cache_spec(),
        "layer_2": new_kv_cache_spec(),
    }

    # This should work normally without auto-fit
    _kv_cache_configs = get_kv_cache_configs(
        vllm_config, [kv_cache_specs], [mem_per_block_per_layer * 2 * 32]
    )
    assert vllm_config.model_config.max_model_len == 16