# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" DistilBERT model configuration """


import logging

from .configuration_utils import PretrainedConfig


logger = logging.getLogger(__name__)

DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "distilbert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json",
    "distilbert-base-uncased-distilled-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json",
    "distilbert-base-german-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-config.json",
    "distilbert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-config.json",
}


class DistilBertConfig(PretrainedConfig):
    r"""
        This is the configuration class to store the configuration of a :class:`~transformers.DistilBertModel`.
        It is used to instantiate a DistilBERT 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 DistilBERT `distilbert-base-uncased <https://huggingface.co/distilbert-base-uncased>`__ architecture.

        Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
        to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
        for more information.


        Args:
            vocab_size (:obj:`int`, optional, defaults to 30522):
                Vocabulary size of the DistilBERT model. Defines the different tokens that
                can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.
            max_position_embeddings (:obj:`int`, optional, defaults to 512):
                The maximum sequence length that this model might ever be used with.
                Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
            sinusoidal_pos_embds (:obj:`boolean`, optional, defaults to :obj:`False`):
                Whether to use sinusoidal positional embeddings.
            n_layers (:obj:`int`, optional, defaults to 6):
                Number of hidden layers in the Transformer encoder.
            n_heads (:obj:`int`, optional, defaults to 12):
                Number of attention heads for each attention layer in the Transformer encoder.
            dim (:obj:`int`, optional, defaults to 768):
                Dimensionality of the encoder layers and the pooler layer.
            intermediate_size (:obj:`int`, optional, defaults to 3072):
                The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
            dropout (:obj:`float`, optional, defaults to 0.1):
                The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
            attention_dropout (:obj:`float`, optional, defaults to 0.1):
                The dropout ratio for the attention probabilities.
            activation (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
                The non-linear activation function (function or string) in the encoder and pooler.
                If string, "gelu", "relu", "swish" and "gelu_new" are supported.
            initializer_range (:obj:`float`, optional, defaults to 0.02):
                The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
            qa_dropout (:obj:`float`, optional, defaults to 0.1):
                The dropout probabilities used in the question answering model
                :class:`~tranformers.DistilBertForQuestionAnswering`.
            seq_classif_dropout (:obj:`float`, optional, defaults to 0.2):
                The dropout probabilities used in the sequence classification model
                :class:`~tranformers.DistilBertForSequenceClassification`.

        Example::

            from transformers import DistilBertModel, DistilBertConfig

            # Initializing a DistilBERT configuration
            configuration = DistilBertConfig()

            # Initializing a model from the configuration
            model = DistilBertModel(configuration)

            # Accessing the model configuration
            configuration = model.config

        Attributes:
            pretrained_config_archive_map (Dict[str, str]):
                A dictionary containing all the available pre-trained checkpoints.
    """
    pretrained_config_archive_map = DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    model_type = "distilbert"

    def __init__(
        self,
        vocab_size=30522,
        max_position_embeddings=512,
        sinusoidal_pos_embds=False,
        n_layers=6,
        n_heads=12,
        dim=768,
        hidden_dim=4 * 768,
        dropout=0.1,
        attention_dropout=0.1,
        activation="gelu",
        initializer_range=0.02,
        qa_dropout=0.1,
        seq_classif_dropout=0.2,
        **kwargs
    ):
        super(DistilBertConfig, self).__init__(**kwargs)
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.sinusoidal_pos_embds = sinusoidal_pos_embds
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.dim = dim
        self.hidden_dim = hidden_dim
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation = activation
        self.initializer_range = initializer_range
        self.qa_dropout = qa_dropout
        self.seq_classif_dropout = seq_classif_dropout

    @property
    def hidden_size(self):
        return self.dim

    @property
    def num_attention_heads(self):
        return self.n_heads

    @property
    def num_hidden_layers(self):
        return self.n_layers
