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# Copyright 2024 Answer.AI, LightOn, and contributors, 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,
# 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 typing import Literal

from ...configuration_utils import PreTrainedConfig, layer_type_validation
from ...modeling_rope_utils import RopeParameters


class ModernBertConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ModernBertModel`]. It is used to instantiate an ModernBert
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the ModernBERT-base.
    e.g. [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)

    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 50368):
            Vocabulary size of the ModernBert model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`ModernBertModel`]
        hidden_size (`int`, *optional*, defaults to 768):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 1152):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 22):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer decoder.
        hidden_activation (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the decoder. Will default to `"gelu"`
            if not specified.
        max_position_embeddings (`int`, *optional*, defaults to 8192):
            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.
        initializer_cutoff_factor (`float`, *optional*, defaults to 2.0):
            The cutoff factor for the truncated_normal_initializer for initializing all weight matrices.
        norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.
        norm_bias (`bool`, *optional*, defaults to `False`):
            Whether to use bias in the normalization layers.
        pad_token_id (`int`, *optional*, defaults to 50283):
            Padding token id.
        eos_token_id (`int`, *optional*, defaults to 50282):
            End of stream token id.
        bos_token_id (`int`, *optional*, defaults to 50281):
            Beginning of stream token id.
        cls_token_id (`int`, *optional*, defaults to 50281):
            Classification token id.
        sep_token_id (`int`, *optional*, defaults to 50282):
            Separation token id.
        attention_bias (`bool`, *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.
        layer_types (`list`, *optional*):
            Attention pattern for each layer.
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary 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`.
        local_attention (`int`, *optional*, defaults to 128):
            The window size for local attention.
        embedding_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the embeddings.
        mlp_bias (`bool`, *optional*, defaults to `False`):
            Whether to use bias in the MLP layers.
        mlp_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the MLP layers.
        decoder_bias (`bool`, *optional*, defaults to `True`):
            Whether to use bias in the decoder layers.
        classifier_pooling (`str`, *optional*, defaults to `"cls"`):
            The pooling method for the classifier. Should be either `"cls"` or `"mean"`. In local attention layers, the
            CLS token doesn't attend to all tokens on long sequences.
        classifier_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the classifier.
        classifier_bias (`bool`, *optional*, defaults to `False`):
            Whether to use bias in the classifier.
        classifier_activation (`str`, *optional*, defaults to `"gelu"`):
            The activation function for the classifier.
        deterministic_flash_attn (`bool`, *optional*, defaults to `False`):
            Whether to use deterministic flash attention. If `False`, inference will be faster but not deterministic.
        sparse_prediction (`bool`, *optional*, defaults to `False`):
            Whether to use sparse prediction for the masked language model instead of returning the full dense logits.
        sparse_pred_ignore_index (`int`, *optional*, defaults to -100):
            The index to ignore for the sparse prediction.
        reference_compile (`bool`, *optional*):
            Whether to compile the layers of the model which were compiled during pretraining. If `None`, then parts of
            the model will be compiled if 1) `triton` is installed, 2) the model is not on MPS, 3) the model is not
            shared between devices, and 4) the model is not resized after initialization. If `True`, then the model may
            be faster in some scenarios.
        repad_logits_with_grad (`bool`, *optional*, defaults to `False`):
            When True, ModernBertForMaskedLM keeps track of the logits' gradient when repadding for output. This only
            applies when using Flash Attention 2 with passed labels. Otherwise output logits always have a gradient.

    Examples:

    ```python
    >>> from transformers import ModernBertModel, ModernBertConfig

    >>> # Initializing a ModernBert style configuration
    >>> configuration = ModernBertConfig()

    >>> # Initializing a model from the modernbert-base style configuration
    >>> model = ModernBertModel(configuration)

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

    model_type = "modernbert"
    keys_to_ignore_at_inference = ["past_key_values"]
    default_theta = {"global": 160_000.0, "local": 10_000.0}

    def __init__(
        self,
        vocab_size: int | None = 50368,
        hidden_size: int | None = 768,
        intermediate_size: int | None = 1152,
        num_hidden_layers: int | None = 22,
        num_attention_heads: int | None = 12,
        hidden_activation: str | None = "gelu",
        max_position_embeddings: int | None = 8192,
        initializer_range: float | None = 0.02,
        initializer_cutoff_factor: float | None = 2.0,
        norm_eps: int | None = 1e-5,
        norm_bias: bool | None = False,
        pad_token_id: int | None = 50283,
        eos_token_id: int | None = 50282,
        bos_token_id: int | None = 50281,
        cls_token_id: int | None = 50281,
        sep_token_id: int | None = 50282,
        attention_bias: bool | None = False,
        attention_dropout: float | None = 0.0,
        layer_types: list[str] | None = None,
        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
        local_attention: int | None = 128,
        embedding_dropout: float | None = 0.0,
        mlp_bias: bool | None = False,
        mlp_dropout: float | None = 0.0,
        decoder_bias: bool | None = True,
        classifier_pooling: Literal["cls", "mean"] = "cls",
        classifier_dropout: float | None = 0.0,
        classifier_bias: bool | None = False,
        classifier_activation: str | None = "gelu",
        deterministic_flash_attn: bool | None = False,
        sparse_prediction: bool | None = False,
        sparse_pred_ignore_index: int | None = -100,
        reference_compile: bool | None = None,
        repad_logits_with_grad: bool | None = 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.initializer_range = initializer_range
        self.initializer_cutoff_factor = initializer_cutoff_factor
        self.norm_eps = norm_eps
        self.norm_bias = norm_bias
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.hidden_activation = hidden_activation
        self.local_attention = local_attention
        self.embedding_dropout = embedding_dropout
        self.mlp_bias = mlp_bias
        self.mlp_dropout = mlp_dropout
        self.decoder_bias = decoder_bias
        self.classifier_pooling = classifier_pooling
        self.classifier_dropout = classifier_dropout
        self.classifier_bias = classifier_bias
        self.classifier_activation = classifier_activation
        self.deterministic_flash_attn = deterministic_flash_attn
        self.sparse_prediction = sparse_prediction
        self.sparse_pred_ignore_index = sparse_pred_ignore_index
        self.reference_compile = reference_compile
        self.repad_logits_with_grad = repad_logits_with_grad

        if self.classifier_pooling not in ["cls", "mean"]:
            raise ValueError(
                f'Invalid value for `classifier_pooling`, should be either "cls" or "mean", but is {self.classifier_pooling}.'
            )

        self.layer_types = layer_types

        # BC -> the pattern used to be a simple int, and it's still present in configs on the Hub
        self.global_attn_every_n_layers = kwargs.get("global_attn_every_n_layers", 3)

        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention" if bool(i % self.global_attn_every_n_layers) else "full_attention"
                for i in range(self.num_hidden_layers)
            ]
        layer_type_validation(self.layer_types, self.num_hidden_layers)

        self.rope_parameters = rope_parameters
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            cls_token_id=cls_token_id,
            sep_token_id=sep_token_id,
            **kwargs,
        )

    def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation=None, **kwargs):
        rope_scaling = kwargs.pop("rope_scaling", None)

        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`. If we find `rope_parameters`
        # as arg in the inputs, we can safely assume that it is in the new format. New naming used -> new format
        default_rope_params = {
            "sliding_attention": {"rope_type": "default"},
            "full_attention": {"rope_type": "default"},
        }
        self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else default_rope_params
        if rope_scaling is not None:
            self.rope_parameters["full_attention"].update(rope_scaling)
            self.rope_parameters["sliding_attention"].update(rope_scaling)
        self.rope_parameters["full_attention"].setdefault(
            "rope_theta", kwargs.pop("global_rope_theta", self.default_theta["global"])
        )
        self.rope_parameters["sliding_attention"].setdefault(
            "rope_theta", kwargs.pop("local_rope_theta", self.default_theta["local"])
        )

        # Standardize and validate the correctness of rotary position embeddings parameters
        self.standardize_rope_params()
        self.validate_rope(ignore_keys=ignore_keys_at_rope_validation)
        return kwargs

    def to_dict(self):
        output = super().to_dict()
        output.pop("reference_compile", None)
        return output


__all__ = ["ModernBertConfig"]
