# 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
#
# 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.
""" 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-config.json",
    "albert-large-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-config.json",
    "albert-xlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-config.json",
    "albert-xxlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-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):
    """Configuration for `AlbertModel`.

    The default settings match the configuration of model `albert_xxlarge`.
    """

    pretrained_config_archive_map = ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    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,
        **kwargs
    ):
        """Constructs AlbertConfig.

        Args:
            vocab_size: Vocabulary size of `inputs_ids` in `AlbertModel`.
            embedding_size: size of voc embeddings.
            hidden_size: Size of the encoder layers and the pooler layer.
            num_hidden_layers: Number of hidden layers in the Transformer encoder.
            num_hidden_groups: Number of group for the hidden layers, parameters in
                the same group are shared.
            num_attention_heads: Number of attention heads for each attention layer in
                the Transformer encoder.
            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
                layer in the Transformer encoder.
            inner_group_num: int, number of inner repetition of attention and ffn.
            down_scale_factor: float, the scale to apply
            hidden_act: The non-linear activation function (function or string) in the
                encoder and pooler.
            hidden_dropout_prob: The dropout probability for all fully connected
                layers in the embeddings, encoder, and pooler.
            attention_probs_dropout_prob: The dropout ratio for the attention
                probabilities.
            max_position_embeddings: 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).
            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
                `AlbertModel`.
            initializer_range: The stdev of the truncated_normal_initializer for
                initializing all weight matrices.
        """
        super(AlbertConfig, self).__init__(**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
