# coding=utf-8
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"""PyTorch GLM-4-MOE model."""

from typing import Optional

import torch
from torch import nn

from ...configuration_utils import PreTrainedConfig
from ...modeling_rope_utils import RopeParameters, rope_config_validation, standardize_rope_params
from ...utils import logging
from ..cohere.modeling_cohere import CohereAttention
from ..deepseek_v3.modeling_deepseek_v3 import (
    DeepseekV3DecoderLayer,
    DeepseekV3ForCausalLM,
    DeepseekV3MLP,
    DeepseekV3Model,
    DeepseekV3PreTrainedModel,
    DeepseekV3RMSNorm,
    DeepseekV3TopkRouter,
)
from ..glm.modeling_glm import GlmRotaryEmbedding
from ..gpt_neox.modeling_gpt_neox import apply_rotary_pos_emb  # noqa


logger = logging.get_logger(__name__)


class Glm4MoeConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Glm4MoeModel`]. It is used to instantiate a
    Glm4Moe model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of [THUDM/GLM-4-100B-A10B](https://huggingface.co/THUDM/GLM-4-100B-A10B).

    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 151552):
            Vocabulary size of the Glm4Moe model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Glm4MoeModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 10944):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 46):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 96):
            Number of attention heads for each attention layer in the Transformer encoder.
        partial_rotary_factor (`float`, *optional*, defaults to 0.5):
            The factor of the partial rotary position.
        num_key_value_heads (`int`, *optional*, defaults to 8):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.

        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 131072):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionaty should contain
            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
            with longer `max_position_embeddings`.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        moe_intermediate_size (`int`, *optional*, defaults to 1408):
            Intermediate size of the routed expert.
        num_experts_per_tok (`int`, *optional*, defaults to 8):
            number of experts per token.
        n_shared_experts (`int`, *optional*, defaults to 1):
            Number of shared experts.
        n_routed_experts (`int`, *optional*, defaults to 128):
            Number of routed experts.
        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
            Scaling factor or routed experts.
        n_group (`int`, *optional*, defaults to 1):
            Number of groups for routed experts.
        topk_group (`int`, *optional*, defaults to 1):
            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
        first_k_dense_replace (`int`, *optional*, defaults to 1):
            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
                                                            \--k dense layers--/
        norm_topk_prob (`bool`, *optional*, defaults to `True`):
            Whether to normalize the topk probabilities.
        use_qk_norm (`bool`, *optional*, defaults to `False`):
            Whether to use query-key normalization in the attention
    ```python
    >>> from transformers import Glm4MoeModel, Glm4MoeConfig

    >>> # Initializing a Glm4Moe style configuration
    >>> configuration = Glm4MoeConfig()

    >>> # Initializing a model from the GLM-4-MOE-100B-A10B style configuration
    >>> model = Glm4MoeModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "glm4_moe"
    keys_to_ignore_at_inference = ["past_key_values"]

    # Default tensor parallel plan for base model `Glm4Moe`
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.experts.*.gate_proj": "colwise",
        "layers.*.mlp.experts.*.up_proj": "colwise",
        "layers.*.mlp.experts.*.down_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }
    attribute_map = {
        "num_local_experts": "n_routed_experts",
    }

    def __init__(
        self,
        vocab_size: Optional[int] = 151552,
        hidden_size: Optional[int] = 4096,
        intermediate_size: Optional[int] = 10944,
        num_hidden_layers: Optional[int] = 46,
        num_attention_heads: Optional[int] = 96,
        partial_rotary_factor: Optional[float] = 0.5,
        num_key_value_heads: Optional[int] = 8,
        hidden_act: Optional[str] = "silu",
        max_position_embeddings: Optional[int] = 131072,
        initializer_range: Optional[float] = 0.02,
        rms_norm_eps: Optional[int] = 1e-5,
        use_cache: Optional[bool] = True,
        tie_word_embeddings: Optional[bool] = False,
        rope_parameters: Optional[RopeParameters | dict[RopeParameters]] = None,
        attention_bias: Optional[bool] = False,
        attention_dropout: Optional[float] = 0.0,
        moe_intermediate_size: Optional[int] = 1408,
        num_experts_per_tok: Optional[int] = 8,
        n_shared_experts: Optional[int] = 1,
        n_routed_experts: Optional[int] = 128,
        routed_scaling_factor: Optional[float] = 1.0,
        n_group: Optional[int] = 1,
        topk_group: Optional[int] = 1,
        first_k_dense_replace: Optional[int] = 1,
        norm_topk_prob: Optional[bool] = True,
        use_qk_norm: Optional[bool] = False,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.partial_rotary_factor = partial_rotary_factor

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
        rope_scaling = kwargs.pop("rope_scaling", None)
        self.rope_parameters = rope_scaling or rope_parameters

        # Validate the correctness of rotary position embeddings parameters
        rope_theta = kwargs.get("rope_theta", 10000.0)
        standardize_rope_params(self, rope_theta=rope_theta)
        rope_config_validation(self)

        # MoE arguments
        self.moe_intermediate_size = moe_intermediate_size
        self.num_experts_per_tok = num_experts_per_tok
        self.n_group = n_group
        self.topk_group = topk_group
        self.n_shared_experts = n_shared_experts
        self.n_routed_experts = n_routed_experts
        self.routed_scaling_factor = routed_scaling_factor
        self.first_k_dense_replace = first_k_dense_replace
        self.norm_topk_prob = norm_topk_prob
        self.use_qk_norm = use_qk_norm

        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


class Glm4MoeRotaryEmbedding(GlmRotaryEmbedding):
    pass


class Glm4MoeAttention(CohereAttention):
    def __init__(self, config: Glm4MoeConfig, layer_idx: Optional[int] = None):
        nn.Module.__init__(self)
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.rope_parameters = config.rope_parameters
        self.attention_dropout = config.attention_dropout
        self.is_causal = True

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
        self.use_qk_norm = config.use_qk_norm
        if self.use_qk_norm:
            self.q_norm = Glm4MoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
            self.k_norm = Glm4MoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)


class Glm4MoeMLP(DeepseekV3MLP):
    pass


class Glm4MoeTopkRouter(DeepseekV3TopkRouter):
    def __init__(self, config: Glm4MoeConfig):
        nn.Module.__init__(self)
        self.config = config
        self.top_k = config.num_experts_per_tok
        self.n_routed_experts = config.n_routed_experts
        self.routed_scaling_factor = config.routed_scaling_factor
        self.n_group = config.n_group
        self.topk_group = config.topk_group
        self.norm_topk_prob = config.norm_topk_prob

        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size)))
        self.register_buffer("e_score_correction_bias", torch.zeros((self.n_routed_experts), dtype=torch.float32))


class Glm4MoeRMSNorm(DeepseekV3RMSNorm):
    pass


class Glm4MoeDecoderLayer(DeepseekV3DecoderLayer):
    pass


class Glm4MoePreTrainedModel(DeepseekV3PreTrainedModel):
    _can_compile_fullgraph = False


class Glm4MoeModel(DeepseekV3Model):
    _keys_to_ignore_on_load_unexpected = [r"model\.layers\.92.*", r"model\.layers\.46.*"]


class Glm4MoeForCausalLM(DeepseekV3ForCausalLM):
    pass


__all__ = [
    "Glm4MoeConfig",
    "Glm4MoePreTrainedModel",
    "Glm4MoeModel",
    "Glm4MoeForCausalLM",
]
