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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig, SubConfigSpec
from ...utils import auto_docstring
from ..auto import AutoConfig


@auto_docstring(checkpoint="mistralai/Voxtral-Mini-3B-2507")
@strict
class VoxtralEncoderConfig(PreTrainedConfig):
    r"""
    max_source_positions (`int`, *optional*, defaults to 1500):
        The maximum sequence length of log-mel filter-bank features that this model might ever be used with.

    ```python
    >>> from transformers import VoxtralEncoderConfig, VoxtralEncoder

    >>> # Initializing a VoxtralEncoderConfig
    >>> configuration = VoxtralEncoderConfig()

    >>> # Initializing a VoxtralEncoder (with random weights)
    >>> model = VoxtralEncoder(configuration)

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

    model_type = "voxtral_encoder"

    attribute_map = {
        "d_model": "hidden_size",
        "encoder_layers": "num_hidden_layers",
        "encoder_attention_heads": "num_attention_heads",
        "encoder_ffn_dim": "intermediate_size",
        "encoder_layerdrop": "layerdrop",
    }

    vocab_size: int = 51866
    hidden_size: int = 1280
    intermediate_size: int = 5120
    num_hidden_layers: int = 32
    num_attention_heads: int = 20
    scale_embedding: bool = False
    activation_function: str = "gelu"
    num_mel_bins: int = 128
    max_source_positions: int = 1500
    initializer_range: float = 0.02
    attention_dropout: float | int = 0.0

    # TODO: @eustlb, we do not use dropout and layerdrop, yet we need to hardcode them
    # to be able to use Whisper with modular (here actually from Qwen2-Audio and copied from).
    # After a future Whisper refactor, we should remove this.
    dropout: float | int = 0.0
    layerdrop: float | int = 0.0
    activation_dropout: float | int = 0.0


@auto_docstring(checkpoint="mistralai/Voxtral-Mini-3B-2507")
@strict
class VoxtralConfig(PreTrainedConfig):
    r"""
    Example:

    ```python
    >>> from transformers import VoxtralForConditionalGeneration, VoxtralConfig

    >>> # Initializing a Voxtral configuration
    >>> configuration = VoxtralConfig(audio_token_id=24, projector_hidden_act="gelu")

    >>> # Initializing a 3B model with random weights
    >>> model = VoxtralForConditionalGeneration(configuration)

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

    model_type = "voxtral"
    sub_configs_defaults = {
        "text_config": SubConfigSpec(
            config_class=AutoConfig,
            model_type="llama",
            init_kwargs={
                "vocab_size": 131072,
                "hidden_size": 3072,
                "intermediate_size": 8192,
                "num_hidden_layers": 30,
                "num_key_value_heads": 8,
                "max_position_embeddings": 131072,
                "rms_norm_eps": 1e-05,
                "use_cache": True,
                "rope_theta": 100000000.0,
                "head_dim": 128,
            },
        ),
        "audio_config": SubConfigSpec(config_class=AutoConfig, model_type="voxtral_encoder"),
    }

    audio_config: dict | PreTrainedConfig | None = None
    text_config: dict | PreTrainedConfig | None = None
    audio_token_id: int | None = None
    projector_hidden_act: str = "gelu"

    def __post_init__(self, **kwargs):
        # Keep for BC only - shouldn't appear in new releases!
        if isinstance(self.text_config, dict):
            self.text_config = {**self.sub_configs_defaults["text_config"].init_kwargs, **self.text_config}
        super().__post_init__(**kwargs)
        self.hidden_size = self.text_config.hidden_size


__all__ = ["VoxtralEncoderConfig", "VoxtralConfig"]