# Copyright (c) 2025 Baidu, Inc. and HuggingFace 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
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"""PyTorch Ernie 4.5 MoE model."""

import torch
import torch.nn.functional as F
from torch import nn

from ... import initialization as init
from ...cache_utils import Cache, DynamicCache
from ...masking_utils import create_causal_mask
from ...modeling_outputs import MoeModelOutputWithPast
from ...modeling_utils import PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
from ...utils.generic import maybe_autocast, merge_with_config_defaults
from ...utils.output_capturing import OutputRecorder, capture_outputs
from ..ernie4_5.modeling_ernie4_5 import Ernie4_5RotaryEmbedding, apply_rotary_pos_emb, rotate_half  # noqa: F401
from ..llama.modeling_llama import LlamaAttention, LlamaRMSNorm
from ..mixtral.modeling_mixtral import (
    MixtralExperts,
    MixtralForCausalLM,
    MixtralPreTrainedModel,
)
from ..qwen3_moe.modeling_qwen3_moe import Qwen3MoeDecoderLayer, Qwen3MoeMLP
from .configuration_ernie4_5_moe import Ernie4_5_MoeConfig


logger = logging.get_logger(__name__)


class Ernie4_5_MoeRMSNorm(LlamaRMSNorm):
    pass


class Ernie4_5_MoeMLP(Qwen3MoeMLP):
    def __init__(self, config, intermediate_size=None):
        super().__init__(config, intermediate_size)

        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.use_bias)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.use_bias)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias)


class Ernie4_5_MoeRotaryEmbedding(Ernie4_5RotaryEmbedding):
    def __init__(self, config: Ernie4_5_MoeConfig, device=None):
        super().__init__(config, device)


class Ernie4_5_MoeAttention(LlamaAttention):
    def __init__(self, config: Ernie4_5_MoeConfig, layer_idx: int):
        super().__init__(config, layer_idx)

        self.attention_dropout = 0.0

        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.use_bias)
        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.use_bias)
        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.use_bias)
        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.use_bias)


class Ernie4_5_MoeStatics(nn.Module):
    """
    Stores MoE (Mixture of Experts) statistics
        - Bias for the gating
        - Additionally, usage per expert in the original codebase
    """

    def __init__(self, config):
        super().__init__()

        num_experts_groups = 1
        num_experts = config.moe_num_experts

        self.e_score_correction_bias = nn.Parameter(
            torch.zeros(num_experts_groups, num_experts, dtype=torch.float32),
            requires_grad=False,
        )

    def forward(self, hidden_states):
        # NOTE: This is a workaround to enable TP with a module that only has parameters
        #
        # Otherwise, it stays as `DTensor` when called in the "super" forward
        #   1. All other tensors are local (`torch.Tensor`)
        #   2. Isolate does not work on `nn.Module` which only has parameters
        return hidden_states + self.e_score_correction_bias.squeeze()


class Ernie4_5_MoeExperts(MixtralExperts):
    def __init__(self, config):
        super().__init__()
        self.num_experts = config.moe_num_experts
        self.intermediate_dim = config.moe_intermediate_size


class Ernie4_5_MoeTopKRouter(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.weight = nn.Parameter(torch.zeros(config.moe_num_experts, config.hidden_size, dtype=torch.float32))
        self.moe_statics = Ernie4_5_MoeStatics(config)
        self.top_k = config.moe_k
        self.norm_min = config.moe_norm_min

    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        device_type = (
            hidden_states.device.type
            if isinstance(hidden_states.device.type, str) and hidden_states.device.type != "mps"
            else "cpu"
        )

        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
            router_logits = F.linear(hidden_states.float(), self.weight.float())
            routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
            _, selected_experts = torch.topk(self.moe_statics(routing_weights), self.top_k, dim=-1)
            routing_weights = torch.gather(routing_weights, dim=-1, index=selected_experts)
            routing_weights = routing_weights / torch.clamp(
                routing_weights.sum(dim=-1, keepdim=True), min=self.norm_min
            )
        routing_weights = routing_weights.to(hidden_states.dtype)
        return router_logits, selected_experts, routing_weights


class Ernie4_5_MoeSparseMoeBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_dim = config.hidden_size
        self.num_experts = config.moe_num_experts
        self.top_k = config.moe_k
        self.gate = Ernie4_5_MoeTopKRouter(config)
        self.experts = Ernie4_5_MoeExperts(config)

        self.shared_experts = None
        if config.moe_num_shared_experts > 0:
            self.shared_experts = Ernie4_5_MoeMLP(config, config.moe_intermediate_size * config.moe_num_shared_experts)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        batch_size, sequence_length, _ = hidden_states.shape
        hidden_states = hidden_states.view(-1, self.hidden_dim)

        if self.shared_experts is not None:
            shared_output = self.shared_experts(hidden_states)

        _, top_k_index, top_k_weights = self.gate(hidden_states)
        final_hidden_states = self.experts(hidden_states, top_k_index, top_k_weights)

        if self.shared_experts is not None:
            final_hidden_states = final_hidden_states + shared_output

        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, self.hidden_dim)
        return final_hidden_states.to(hidden_states.dtype)


class Ernie4_5_MoeDecoderLayer(Qwen3MoeDecoderLayer):
    def __init__(self, config, layer_idx):
        nn.Module.__init__(self)
        self.hidden_size = config.hidden_size

        self.self_attn = Ernie4_5_MoeAttention(config, layer_idx)

        if (
            ((layer_idx + 1) % config.moe_layer_interval == 0)
            and layer_idx >= config.moe_layer_start_index
            and layer_idx <= config.moe_layer_end_index
        ):
            self.mlp = Ernie4_5_MoeSparseMoeBlock(config)
        else:
            self.mlp = Ernie4_5_MoeMLP(config)

        self.input_layernorm = Ernie4_5_MoeRMSNorm(config.hidden_size, config.rms_norm_eps)
        self.post_attention_layernorm = Ernie4_5_MoeRMSNorm(config.hidden_size, config.rms_norm_eps)


@auto_docstring
class Ernie4_5_MoePreTrainedModel(MixtralPreTrainedModel):
    config: Ernie4_5_MoeConfig
    _no_split_modules = ["Ernie4_5_MoeDecoderLayer"]
    # Not supporting multi-token prediction (MTP) atm
    _keys_to_ignore_on_load_unexpected = ["mtp"]
    _can_record_outputs = {
        "router_logits": OutputRecorder(Ernie4_5_MoeTopKRouter, index=0),
        "hidden_states": Ernie4_5_MoeDecoderLayer,
        "attentions": Ernie4_5_MoeAttention,
    }
    _keep_in_fp32_modules_strict = ["gate.weight", "moe_statics"]

    @torch.no_grad()
    def _init_weights(self, module):
        PreTrainedModel._init_weights(self, module)
        if isinstance(module, Ernie4_5_MoeStatics):
            init.zeros_(module.e_score_correction_bias)
        elif isinstance(module, Ernie4_5_MoeExperts):
            init.normal_(module.gate_up_proj, mean=0.0, std=self.config.initializer_range)
            init.normal_(module.down_proj, mean=0.0, std=self.config.initializer_range)


@auto_docstring
class Ernie4_5_MoeModel(Ernie4_5_MoePreTrainedModel):
    def __init__(self, config: Ernie4_5_MoeConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [Ernie4_5_MoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = Ernie4_5_MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = Ernie4_5_MoeRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    @merge_with_config_defaults
    @capture_outputs
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> MoeModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )
        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = create_causal_mask(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            past_key_values=past_key_values,
            position_ids=position_ids,
        )

        hidden_states = inputs_embeds

        # create position embeddings to be shared across the decoder layers
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            hidden_states = decoder_layer(
                hidden_states,
                position_embeddings=position_embeddings,
                attention_mask=causal_mask,
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)

        return MoeModelOutputWithPast(  # only diff with Mistral is the output type, we need MoE
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


@auto_docstring
class Ernie4_5_MoeForCausalLM(MixtralForCausalLM):
    def __init__(self, config):
        PreTrainedModel.__init__(self, config)
        self.model = Ernie4_5_MoeModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=config.use_bias)

        self.router_aux_loss_coef = config.router_aux_loss_coef
        self.num_experts = config.moe_num_experts
        self.num_experts_per_tok = config.moe_k

        # Initialize weights and apply final processing
        self.post_init()

    @can_return_tuple
    @auto_docstring
    def forward(self, **super_kwargs):
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
        """
        super().forward(**super_kwargs)


__all__ = [
    "Ernie4_5_MoeForCausalLM",
    "Ernie4_5_MoeModel",
    "Ernie4_5_MoePreTrainedModel",
]