# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Utility functions for attention-related v1 tests."""

from dataclasses import dataclass
from typing import Union

import pytest
import torch

from vllm.config import (CacheConfig, CompilationConfig, DeviceConfig,
                         LoadConfig, ModelConfig, ModelDType, ParallelConfig,
                         SchedulerConfig, VllmConfig)
from vllm.platforms import _Backend, current_platform
from vllm.utils import resolve_obj_by_qualname
from vllm.v1.attention.backends.utils import CommonAttentionMetadata
from vllm.v1.kv_cache_interface import FullAttentionSpec


@dataclass
class BatchSpec:
    """Specification for a batch configuration (workload shape only)."""
    seq_lens: list[int]
    query_lens: list[int]

    name: str = "unnamed"

    @property
    def batch_size(self):
        return len(self.seq_lens)

    def __post_init__(self):
        assert len(self.seq_lens) == len(self.query_lens)

    def compute_num_tokens(self):
        return sum(self.query_lens)


def create_common_attn_metadata(
        batch_spec: BatchSpec,
        block_size: int,
        device: torch.device,
        max_block_idx: int = 1000,
        arange_block_indices: bool = False) -> CommonAttentionMetadata:
    """Create CommonAttentionMetadata from a BatchSpec and ModelParams."""
    # Create query start locations
    query_start_loc = torch.zeros(batch_spec.batch_size + 1,
                                  dtype=torch.int32,
                                  device=device)
    query_start_loc[1:] = torch.tensor(batch_spec.query_lens,
                                       dtype=torch.int32,
                                       device=device).cumsum(0)
    query_start_loc_cpu = query_start_loc.cpu()
    num_tokens = batch_spec.compute_num_tokens()

    # Create sequence lengths
    seq_lens = torch.tensor(batch_spec.seq_lens,
                            dtype=torch.int32,
                            device=device)
    seq_lens_cpu = seq_lens.cpu()
    max_seq_len = int(seq_lens_cpu.max())

    # Create computed tokens (context length for each sequence)
    context_lens = [
        batch_spec.seq_lens[i] - batch_spec.query_lens[i]
        for i in range(batch_spec.batch_size)
    ]
    num_computed_tokens_cpu = torch.tensor(context_lens, dtype=torch.int32)

    # Create block table and slot mapping
    max_blocks = (max(batch_spec.seq_lens) + block_size - 1) // block_size
    if arange_block_indices:
        num_blocks = batch_spec.batch_size * max_blocks
        block_table_tensor = torch.arange(num_blocks,
                                          dtype=torch.int32,
                                          device=device).view(
                                              batch_spec.batch_size,
                                              max_blocks)
        slot_mapping = torch.arange(num_tokens,
                                    dtype=torch.int64,
                                    device=device).view(num_tokens)
    else:
        block_table_tensor = torch.randint(0,
                                           max_block_idx,
                                           (batch_spec.batch_size, max_blocks),
                                           dtype=torch.int32,
                                           device=device)
        slot_mapping = torch.randint(0,
                                     max_block_idx, (num_tokens, ),
                                     dtype=torch.int64,
                                     device=device)

    # Calculate max query length
    max_query_len = max(batch_spec.query_lens)

    return CommonAttentionMetadata(
        query_start_loc=query_start_loc,
        query_start_loc_cpu=query_start_loc_cpu,
        seq_lens=seq_lens,
        seq_lens_cpu=seq_lens_cpu,
        num_computed_tokens_cpu=num_computed_tokens_cpu,
        num_reqs=batch_spec.batch_size,
        num_actual_tokens=num_tokens,
        max_query_len=max_query_len,
        max_seq_len=max_seq_len,
        block_table_tensor=block_table_tensor,
        slot_mapping=slot_mapping,
        causal=True,
    )


def get_attention_backend(backend_name: _Backend):
    """Set up attention backend classes for testing.

    Args:
        backend_name: Name of the backend ("flash_attn", "flashinfer", etc.)
        vllm_config: VllmConfig instance

    Returns:
        Tuple of (backend_builder_class, backend_impl_class)
    """
    backend_map = {
        _Backend.FLASH_ATTN_VLLM_V1:
        ("vllm.v1.attention.backends.flash_attn.FlashAttentionBackend"
         if current_platform.is_cuda() else
         "vllm.v1.attention.backends.rocm_aiter_fa.AiterFlashAttentionBackend"
         ),
        _Backend.FLASHINFER_VLLM_V1:
        "vllm.v1.attention.backends.flashinfer.FlashInferBackend",
        _Backend.FLEX_ATTENTION:
        "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend",
        _Backend.TRITON_ATTN_VLLM_V1:
        "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend",
        _Backend.TREE_ATTN:
        "vllm.v1.attention.backends.tree_attn.TreeAttentionBackend",
        _Backend.XFORMERS_VLLM_V1:
        "vllm.v1.attention.backends.xformers.XFormersAttentionBackend",
        _Backend.CUTLASS_MLA:
        "vllm.v1.attention.backends.mla.cutlass_mla.CutlassMLABackend",
        _Backend.FLASHMLA_VLLM_V1:
        "vllm.v1.attention.backends.mla.flashmla.FlashMLABackend",
        _Backend.TRITON_MLA_VLLM_V1:
        "vllm.v1.attention.backends.mla.triton_mla.TritonMLABackend",
    }

    if backend_name not in backend_map:
        raise ValueError(f"Unknown backend: {backend_name}")

    backend_class_name = backend_map[backend_name]

    try:
        backend_class = resolve_obj_by_qualname(backend_class_name)
        return backend_class.get_builder_cls(), backend_class.get_impl_cls()
    except ImportError as e:
        pytest.skip(f"{backend_name} not available: {e}")


def create_standard_kv_cache_spec(
        vllm_config: VllmConfig) -> FullAttentionSpec:
    """Create a FullAttentionSpec from ModelParams only."""
    return FullAttentionSpec(
        block_size=vllm_config.cache_config.block_size,
        num_kv_heads=vllm_config.model_config.get_num_kv_heads(
            vllm_config.parallel_config),
        head_size=vllm_config.model_config.get_head_size(),
        dtype=vllm_config.model_config.dtype,
        use_mla=vllm_config.model_config.use_mla,
        sliding_window=vllm_config.model_config.get_sliding_window(),
    )


def create_vllm_config(model_name: str = "meta-llama/Meta-Llama-3-8B",
                       tensor_parallel_size: int = 1,
                       max_model_len: int = 1024,
                       dtype: Union[ModelDType, torch.dtype] = "auto",
                       num_gpu_blocks: int = 1000,
                       block_size: int = 16,
                       max_num_seqs: int = 256,
                       max_num_batched_tokens: int = 8192,
                       enable_chunked_prefill: bool = True,
                       add_mock_model_methods: bool = True) -> VllmConfig:
    """Create a VllmConfig for testing with reasonable defaults."""

    model_config = ModelConfig(
        model=model_name,
        tokenizer=model_name,
        trust_remote_code=False,
        dtype=dtype,
        seed=0,
        max_model_len=max_model_len,
    )

    cache_config = CacheConfig(
        block_size=block_size,
        cache_dtype="auto",
        swap_space=0,
    )
    # Set cache blocks for testing
    #   (these may be set during initialization normally)
    cache_config.num_gpu_blocks = num_gpu_blocks
    cache_config.num_cpu_blocks = 0

    parallel_config = ParallelConfig(
        tensor_parallel_size=tensor_parallel_size, )

    scheduler_config = SchedulerConfig(
        max_num_seqs=max_num_seqs,
        max_num_batched_tokens=max_num_batched_tokens,
        enable_chunked_prefill=enable_chunked_prefill,
    )

    device_config = DeviceConfig()
    load_config = LoadConfig()
    compilation_config = CompilationConfig()

    if add_mock_model_methods:
        # Add mock methods to satisfy backends that need them
        # This is a workaround because tests don't build full, real models,
        # but some backends expect to query the model for layer-specific
        # parameters
        import types
        model_config.get_num_layers = types.MethodType(lambda self: 1,
                                                       model_config)
        model_config.get_sliding_window_for_layer = types.MethodType(
            lambda self, i: None, model_config)
        model_config.get_logits_soft_cap_for_layer = types.MethodType(
            lambda self, i: 0.0, model_config)
        model_config.get_sm_scale_for_layer = types.MethodType(
            lambda self, i: 1.0 / model_config.get_head_size()**0.5,
            model_config)

    return VllmConfig(
        model_config=model_config,
        cache_config=cache_config,
        parallel_config=parallel_config,
        scheduler_config=scheduler_config,
        device_config=device_config,
        load_config=load_config,
        compilation_config=compilation_config,
    )


def create_dummy_kv_cache(block_size: int,
                          num_kv_heads: int,
                          head_size: int,
                          dtype: torch.dtype,
                          device: torch.device,
                          num_blocks: int = 100) -> torch.Tensor:
    """Create a dummy KV cache tensor for testing."""
    kv_cache = torch.randn(
        num_blocks,
        2,  # K and V
        block_size,
        num_kv_heads,
        head_size,
        dtype=dtype,
        device=device)
    return kv_cache
