# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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
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# distributed under the License is distributed on an "AS IS" BASIS,
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""" ALBERT model configuration """

from .configuration_utils import PretrainedConfig


ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "albert-base-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v1-config.json",
    "albert-large-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v1-config.json",
    "albert-xlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v1-config.json",
    "albert-xxlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v1-config.json",
    "albert-base-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-config.json",
    "albert-large-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-config.json",
    "albert-xlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-config.json",
    "albert-xxlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-config.json",
}


class AlbertConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a :class:`~transformers.AlbertModel` or a
    :class:`~transformers.TFAlbertModel`. It is used to instantiate an ALBERT 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 ALBERT `xxlarge <https://huggingface.co/albert-xxlarge-v2>`__ 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 30000):
            Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the
            :obj:`inputs_ids` passed when calling :class:`~transformers.AlbertModel` or
            :class:`~transformers.TFAlbertModel`.
        embedding_size (:obj:`int`, `optional`, defaults to 128):
            Dimensionality of vocabulary embeddings.
        hidden_size (:obj:`int`, `optional`, defaults to 4096):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_hidden_groups (:obj:`int`, `optional`, defaults to 1):
            Number of groups for the hidden layers, parameters in the same group are shared.
        num_attention_heads (:obj:`int`, `optional`, defaults to 64):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (:obj:`int`, `optional`, defaults to 16384):
            The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        inner_group_num (:obj:`int`, `optional`, defaults to 1):
            The number of inner repetition of attention and ffn.
        hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu_new"`):
            The non-linear activation function (function or string) in the encoder and pooler.
            If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0):
            The dropout ratio for the attention probabilities.
        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 (e.g., 512 or 1024 or 2048).
        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
            The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.AlbertModel` or
            :class:`~transformers.TFAlbertModel`.
        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        classifier_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
            The dropout ratio for attached classifiers.

    Examples::

        >>> from transformers import AlbertConfig, AlbertModel
        >>> # Initializing an ALBERT-xxlarge style configuration
        >>> albert_xxlarge_configuration = AlbertConfig()

        >>> # Initializing an ALBERT-base style configuration
        >>> albert_base_configuration = AlbertConfig(
        ...      hidden_size=768,
        ...      num_attention_heads=12,
        ...      intermediate_size=3072,
        ...  )

        >>> # Initializing a model from the ALBERT-base style configuration
        >>> model = AlbertModel(albert_xxlarge_configuration)

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

    model_type = "albert"

    def __init__(
        self,
        vocab_size=30000,
        embedding_size=128,
        hidden_size=4096,
        num_hidden_layers=12,
        num_hidden_groups=1,
        num_attention_heads=64,
        intermediate_size=16384,
        inner_group_num=1,
        hidden_act="gelu_new",
        hidden_dropout_prob=0,
        attention_probs_dropout_prob=0,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        classifier_dropout_prob=0.1,
        pad_token_id=0,
        bos_token_id=2,
        eos_token_id=3,
        **kwargs
    ):
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.embedding_size = embedding_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_hidden_groups = num_hidden_groups
        self.num_attention_heads = num_attention_heads
        self.inner_group_num = inner_group_num
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.classifier_dropout_prob = classifier_dropout_prob
