"""
# Copyright (c) 2025 PaddlePaddle Authors. 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 __future__ import annotations

import os
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, List, Optional

import paddle

from fastdeploy.model_executor.layers.attention.ops import (
    append_attention,
    append_attention_with_output,
    get_block_shape_and_split_kv_block,
    init_kv_signal_per_query,
    init_signal_layerwise,
    open_shm_and_get_meta_signal,
)

if TYPE_CHECKING:
    from fastdeploy.model_executor.forward_meta import ForwardMeta

from fastdeploy.config import FDConfig
from fastdeploy.model_executor.layers.attention.attention import Attention
from fastdeploy.model_executor.layers.attention.base_attention_backend import (
    AttentionBackend,
    AttentionMetadata,
)
from fastdeploy.model_executor.layers.attention.utils import init_rank_and_device_id


@dataclass
class AppendAttentionMetadata(AttentionMetadata):
    """
    AppendAttentionMetadata
    """

    encoder_batch_ids: paddle.Tensor = None
    encoder_tile_ids_per_batch: paddle.Tensor = None
    encoder_num_blocks: paddle.Tensor = None
    kv_batch_ids: paddle.Tensor = None
    kv_tile_ids_per_batch: paddle.Tensor = None
    kv_num_blocks: paddle.Tensor = None
    max_len_kv: paddle.Tensor = None

    _dtype: paddle.dtype = paddle.bfloat16
    encoder_max_partition_size: int = 32768
    max_partition_size: int = 32768
    block_tables: Optional[paddle.Tensor] = None
    rotary_embs: Optional[paddle.Tensor] = None
    attn_mask: Optional[paddle.Tensor] = None
    _fuse_kernel_compute_dtype: str = "bf16"

    # pd_disaggregation
    kv_signal_metadata: Optional[paddle.Tensor] = None
    kv_signal_data_list: List[Optional[paddle.Tensor]] = field(default_factory=list)


class AppendAttentionBackend(AttentionBackend):
    """
    AppendAttentionBackend backend implementation.
    """

    __infer_dynamic_dims_fields__ = ["attention_metadata"]
    attention_metadata: AppendAttentionMetadata

    def __init__(
        self,
        fd_config: FDConfig,
        kv_num_heads: int,
        num_heads: int,
        head_dim: int,
        encoder_block_shape_q: int = -1,
        decoder_block_shape_q: int = -1,
    ) -> None:
        """
        AppendAttentionBackend __init__
        """
        super().__init__()
        self.attention_metadata: AppendAttentionMetadata = None
        self.block_size: int = fd_config.cache_config.block_size
        self.max_seq_len: int = fd_config.parallel_config.max_model_len
        self.rope_theta: float = (
            10000.0 if fd_config.model_config.rope_theta is None else fd_config.model_config.rope_theta
        )
        self.rope_3d: bool = getattr(fd_config.model_config, "rope_3d", False) or getattr(
            fd_config.model_config, "use_3d_rope", False
        )
        self.causal: bool = getattr(fd_config.model_config, "causal", True)
        self.speculative_method: str = fd_config.speculative_config.method
        self.use_speculate: bool = self.speculative_method is not None
        self.speculate_max_draft_token_num: int = fd_config.speculative_config.num_speculative_tokens
        self.keep_pd_step_flag: bool = fd_config.speculative_config.model_type == "mtp"
        self.num_layers_draft_model: int = int(fd_config.speculative_config.method in ["mtp"])

        self.kv_num_heads: int = kv_num_heads
        self.num_heads: int = num_heads
        self.group_size: int = self.num_heads // self.kv_num_heads
        self.head_dim: int = fd_config.model_config.head_dim
        self.num_layers: int = fd_config.model_config.num_hidden_layers
        self.max_partition_size: int = int(os.getenv("FLAGS_max_partition_size", 1024))
        self.encoder_block_shape_q: int = encoder_block_shape_q
        self.decoder_block_shape_q: int = decoder_block_shape_q

        self.pd_disaggregation_mode: str = fd_config.parallel_config.pd_disaggregation_mode

        self.start_layer_index: int = fd_config.model_config.start_layer_index

        if fd_config.parallel_config.expert_parallel_rank is None:
            fd_config.parallel_config.expert_parallel_rank = 0

        self.rank, self.device_id = init_rank_and_device_id(fd_config)
        self.use_output = not fd_config.graph_opt_config.full_cuda_graph

    def init_attention_metadata(self, forward_meta: ForwardMeta):
        """Initialize attntion metadata hence all layers in the forward pass can reuse it."""
        metadata = AppendAttentionMetadata()
        metadata.max_partition_size = self.max_partition_size
        metadata.encoder_max_partition_size = self.max_seq_len
        metadata._dtype = paddle.get_default_dtype()
        if metadata._dtype == "bfloat16":
            metadata._fuse_kernel_compute_dtype = "bf16"
        elif metadata._dtype == "float16":
            metadata._fuse_kernel_compute_dtype = "fp16"
        elif metadata._dtype == "float32":
            metadata._fuse_kernel_compute_dtype = "fp32"
        metadata.block_tables = forward_meta.block_tables
        metadata.rotary_embs = forward_meta.rotary_embs
        metadata.attn_mask = forward_meta.attn_mask
        metadata.pre_caches_length = forward_meta.pre_caches_length
        (
            metadata.encoder_batch_ids,
            metadata.encoder_tile_ids_per_batch,
            metadata.encoder_num_blocks,
            metadata.kv_batch_ids,
            metadata.kv_tile_ids_per_batch,
            metadata.kv_num_blocks,
            metadata.max_len_kv,
        ) = get_block_shape_and_split_kv_block(
            forward_meta.seq_lens_encoder,
            forward_meta.seq_lens_decoder,
            forward_meta.seq_lens_this_time,
            forward_meta.decoder_batch_ids,
            forward_meta.decoder_tile_ids_per_batch,
            forward_meta.decoder_num_blocks_cpu,
            forward_meta.max_len_tensor_cpu,
            self.encoder_block_shape_q,
            self.decoder_block_shape_q,
            self.group_size,
            self.block_size,
            self.speculate_max_draft_token_num + 1,
        )

        # pd_disaggregation
        metadata.kv_signal_data_list = [None] * self.num_layers
        if self.pd_disaggregation_mode == "per_chunk":
            if not self.keep_pd_step_flag:
                init_kv_signal_per_query(
                    forward_meta.seq_lens_encoder,
                    forward_meta.seq_lens_this_time,
                    forward_meta.seq_lens_decoder,
                    self.rank,
                    self.num_layers + self.num_layers_draft_model,
                )
        elif self.pd_disaggregation_mode == "per_query":
            metadata.kv_signal_metadata = open_shm_and_get_meta_signal(
                self.rank, int(self.device_id), self.keep_pd_step_flag
            )

        self.attention_metadata: AttentionMetadata = metadata

    def get_attntion_meta(self) -> AttentionMetadata:
        """get_attntion_meta"""
        return self.attention_metadata

    def get_kv_cache_shape(
        self,
        max_num_blocks: int,
        kv_cache_quant_type: str = None,
    ):
        """
        Calculate kv cache shape
        """
        if kv_cache_quant_type is not None and kv_cache_quant_type == "int4_zp":
            return (
                max_num_blocks,
                self.kv_num_heads,
                self.block_size,
                self.head_dim // 2,
            )
        else:
            return (
                max_num_blocks,
                self.kv_num_heads,
                self.block_size,
                self.head_dim,
            )

    def forward_mixed(
        self,
        q: paddle.Tensor,
        k: paddle.Tensor,
        v: paddle.Tensor,
        qkv: paddle.Tensor,
        compressed_kv: paddle.Tensor,
        k_pe: paddle.Tensor,
        layer: Attention,
        forward_meta: ForwardMeta,
    ) -> paddle.Tensor:
        """
        forward_mixed
        """
        metadata = self.attention_metadata

        if self.pd_disaggregation_mode == "per_query":
            metadata.kv_signal_data_list[layer.layer_id] = init_signal_layerwise(
                metadata.kv_signal_metadata,
                layer.layer_id + self.start_layer_index,
            )

        if self.use_output:
            quant_max_bound = getattr(layer, "quant_max_bound", 0.0)
            cache_quant_type = getattr(layer, "cache_quant_type_str", "none")
            compute_type = metadata._fuse_kernel_compute_dtype
            out_scale = getattr(layer, "out_scale", -1.0)
            # 1. get output datatype
            qkv_dtype = qkv.dtype
            if qkv_dtype == paddle.float16:
                D_type = paddle.float16
            elif qkv_dtype == paddle.bfloat16:
                D_type = paddle.bfloat16
            elif qkv_dtype == paddle.int32:
                if compute_type == "bf16":
                    D_type = paddle.bfloat16
                elif compute_type == "fp16":
                    D_type = paddle.float16
                else:
                    raise NotImplementedError("Only supported attr of qkv_type in ['float16', 'bfloat16'].")
            else:
                raise NotImplementedError("Only supported attr of qkv_type in ['float16', 'bfloat16', 'int32'].")
            # 2.Extract related parameters
            token_nums = qkv.shape[0]
            head_dims = self.head_dim if cache_quant_type != "cache_int4_zp" else self.head_dim * 2
            q_num_heads = self.num_heads
            # 3. generate output tensor of different dtypes
            if out_scale > 0.0:
                if abs(quant_max_bound - 127) < 0.000001:
                    res = paddle.empty([token_nums, q_num_heads * head_dims], dtype="int8").to(qkv.place)
                elif abs(quant_max_bound - 448) < 0.000001:
                    res = paddle.empty([token_nums, q_num_heads * head_dims], dtype="float8_e4m3fn").to(qkv.place)
                else:
                    raise NotImplementedError("Only supported attr of quant_max_bound in ['127', '448'].")
            else:
                res = paddle.empty([token_nums, q_num_heads * head_dims], dtype=D_type).to(qkv.place)

            append_attention_with_output(
                qkv,
                forward_meta.caches[2 * layer.layer_id],
                forward_meta.caches[2 * layer.layer_id + 1],
                forward_meta.seq_lens_encoder,
                forward_meta.seq_lens_decoder,
                forward_meta.seq_lens_this_time,
                forward_meta.batch_id_per_token,
                forward_meta.cu_seqlens_q,
                metadata.block_tables,
                metadata.encoder_batch_ids,
                metadata.encoder_tile_ids_per_batch,
                metadata.encoder_num_blocks,
                metadata.kv_batch_ids,
                metadata.kv_tile_ids_per_batch,
                metadata.kv_num_blocks,
                forward_meta.decoder_batch_ids,
                forward_meta.decoder_tile_ids_per_batch,
                forward_meta.decoder_num_blocks_cpu,
                forward_meta.max_len_tensor_cpu,
                metadata.max_len_kv,
                res,
                metadata.rotary_embs,
                metadata.attn_mask,
                layer.qkv_bias,
                layer.qkv_scale,
                getattr(layer, "cache_k_scale", None),
                getattr(layer, "cache_v_scale", None),
                getattr(layer, "cache_k_out_scale", None),
                getattr(layer, "cache_v_out_scale", None),
                getattr(layer, "cache_k_zp", None),
                getattr(layer, "cache_v_zp", None),
                layer.linear_shift,
                layer.linear_smooth,
                forward_meta.attn_mask_offsets,
                metadata.kv_signal_data_list[layer.layer_id],
                getattr(layer, "q_norm_weight", None),
                getattr(layer, "k_norm_weight", None),
                getattr(layer, "rms_norm_eps", 1e-6),
                metadata._fuse_kernel_compute_dtype,
                getattr(layer, "cache_quant_type_str", "none"),
                layer.use_neox_rotary_style,
                self.rope_3d,
                self.max_seq_len,
                getattr(layer, "quant_max_bound", 0.0),
                getattr(layer, "quant_min_bound", 0.0),
                getattr(layer, "out_scale", -1.0),
                self.encoder_block_shape_q,
                self.decoder_block_shape_q,
                metadata.max_partition_size,
                metadata.encoder_max_partition_size,
                self.speculate_max_draft_token_num + 1,
                self.causal,
                self.speculative_method is not None,
            )
        else:
            res = append_attention(
                qkv,
                forward_meta.caches[2 * layer.layer_id],
                forward_meta.caches[2 * layer.layer_id + 1],
                forward_meta.seq_lens_encoder,
                forward_meta.seq_lens_decoder,
                forward_meta.seq_lens_this_time,
                forward_meta.batch_id_per_token,
                forward_meta.cu_seqlens_q,
                metadata.block_tables,
                metadata.encoder_batch_ids,
                metadata.encoder_tile_ids_per_batch,
                metadata.encoder_num_blocks,
                metadata.kv_batch_ids,
                metadata.kv_tile_ids_per_batch,
                metadata.kv_num_blocks,
                forward_meta.decoder_batch_ids,
                forward_meta.decoder_tile_ids_per_batch,
                forward_meta.decoder_num_blocks_cpu,
                forward_meta.max_len_tensor_cpu,
                metadata.max_len_kv,
                metadata.rotary_embs,
                metadata.attn_mask,
                layer.qkv_bias,
                layer.qkv_scale,
                getattr(layer, "cache_k_scale", None),
                getattr(layer, "cache_v_scale", None),
                getattr(layer, "cache_k_out_scale", None),
                getattr(layer, "cache_v_out_scale", None),
                getattr(layer, "cache_k_zp", None),
                getattr(layer, "cache_v_zp", None),
                layer.linear_shift,
                layer.linear_smooth,
                forward_meta.attn_mask_offsets,
                metadata.kv_signal_data_list[layer.layer_id],
                getattr(layer, "q_norm_weight", None),
                getattr(layer, "k_norm_weight", None),
                getattr(layer, "rms_norm_eps", 1e-6),
                metadata._fuse_kernel_compute_dtype,
                getattr(layer, "cache_quant_type_str", "none"),
                layer.use_neox_rotary_style,
                self.rope_3d,
                self.max_seq_len,
                getattr(layer, "quant_max_bound", 0.0),
                getattr(layer, "quant_min_bound", 0.0),
                getattr(layer, "out_scale", -1.0),
                self.encoder_block_shape_q,
                self.decoder_block_shape_q,
                metadata.max_partition_size,
                metadata.encoder_max_partition_size,
                self.speculate_max_draft_token_num + 1,
                self.causal,
                self.speculative_method is not None,
            )
        return res