# Copyright 2026 Mistral AI and The 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,
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"""Mistral4 model configuration"""

from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...modeling_rope_utils import RopeParameters
from ...utils import auto_docstring


@auto_docstring(checkpoint="mistralai/Mistral-Small-4-119B-2603")
@strict
class Mistral4Config(PreTrainedConfig):
    r"""
    n_group (`int`, *optional*, defaults to 1):
        Number of groups for routed experts.
    first_k_dense_replace (`int`, *optional*, defaults to 0):
        Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
                                                        \--k dense layers--/
    rope_interleave (`bool`, *optional*, defaults to `True`):
        Whether to interleave the rotary position embeddings.

    Example:

    ```python
    >>> from transformers import Mistral4Model, Mistral4Config

    >>> # Initializing a Mistral4 style configuration
    >>> configuration = Mistral4Config()

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

    model_type = "mistral4"
    keys_to_ignore_at_inference = ["past_key_values"]
    base_model_tp_plan = {
        "layers.*.mlp.experts.gate_up_proj": "packed_colwise",
        "layers.*.mlp.experts.down_proj": "rowwise",
        "layers.*.mlp.experts": "moe_tp_experts",
        "layers.*.mlp.shared_experts.gate_proj": "colwise",
        "layers.*.mlp.shared_experts.up_proj": "colwise",
        "layers.*.mlp.shared_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",
    }

    vocab_size: int = 131072
    hidden_size: int = 4096
    intermediate_size: int = 12288
    moe_intermediate_size: int = 2048
    num_hidden_layers: int = 36
    num_attention_heads: int = 32
    num_key_value_heads: int | None = 32
    n_shared_experts: int = 1
    n_routed_experts: int = 128
    routed_scaling_factor: float = 1.0
    kv_lora_rank: int = 256
    q_lora_rank: int | None = 1024
    qk_rope_head_dim: int = 64
    v_head_dim: int | None = 128
    qk_nope_head_dim: int = 64
    n_group: int | None = 1
    topk_group: int | None = 1
    num_experts_per_tok: int | None = 4
    first_k_dense_replace: int | None = 0
    norm_topk_prob: bool | None = True
    hidden_act: str = "silu"
    max_position_embeddings: int = 1048576
    initializer_range: float = 0.02
    rms_norm_eps: float = 1e-6
    use_cache: bool = True
    pad_token_id: int | None = 11
    bos_token_id: int | None = 1
    eos_token_id: int | list[int] | None = 2
    pretraining_tp: int | None = 1
    tie_word_embeddings: bool = False
    rope_parameters: RopeParameters | dict | None = None
    rope_interleave: bool | None = True
    attention_bias: bool = False
    attention_dropout: float | int | None = 0.0

    def __post_init__(self, **kwargs):
        if self.rope_parameters is None:
            self.rope_parameters = {
                "type": "yarn",
                "rope_theta": 10000.0,
                "factor": 128.0,
                "original_max_position_embeddings": 8192,
                "max_position_embeddings": self.max_position_embeddings,
                "beta_fast": 32.0,
                "beta_slow": 1.0,
                "mscale_all_dim": 1.0,
                "mscale": 1.0,
                "llama_4_scaling_beta": 0.1,
                "partial_rotary_factor": self.qk_rope_head_dim / (self.qk_nope_head_dim + self.qk_rope_head_dim),
            }

        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads

        self.qk_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
        self.head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
        self.rope_parameters.setdefault("partial_rotary_factor", self.qk_rope_head_dim / self.head_dim)
        super().__post_init__(
            ignore_keys_at_rope_validation={"llama_4_scaling_beta", "max_position_embeddings"}, **kwargs
        )

    def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation: set | None = None, **kwargs):
        rope_scaling = kwargs.pop("rope_scaling", None)
        self.rope_parameters = rope_scaling or self.rope_parameters
        self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else {}

        # Standardize and validate the correctness of rotary position embeddings parameters
        self.rope_parameters.setdefault("rope_theta", kwargs.pop("rope_theta", self.default_theta))
        self.standardize_rope_params()
        if ignore_keys_at_rope_validation is not None:
            self.ignore_keys_at_rope_validation = self.ignore_keys_at_rope_validation | ignore_keys_at_rope_validation
        self.validate_rope()

        # Convert to float because RoPE fn expect a float. Models on the hub were saved as int
        for key in ["beta_fast", "beta_slow", "factor"]:
            if key in self.rope_parameters:
                self.rope_parameters[key] = float(self.rope_parameters[key])
        return kwargs


__all__ = ["Mistral4Config"]