# coding=utf-8
# Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team.
#
# 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
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""" SqueezeBERT model configuration """

from .configuration_utils import PretrainedConfig
from .utils import logging


logger = logging.get_logger(__name__)

SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "squeezebert/squeezebert-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/squeezebert/squeezebert-uncased/config.json",
    "squeezebert/squeezebert-mnli": "https://s3.amazonaws.com/models.huggingface.co/bert/squeezebert/squeezebert-mnli/config.json",
    "squeezebert/squeezebert-mnli-headless": "https://s3.amazonaws.com/models.huggingface.co/bert/squeezebert/squeezebert-mnli-headless/config.json",
}


class SqueezeBertConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a :class:`~transformers.SqueezeBertModel`.
    It is used to instantiate a SqueezeBERT model according to the specified arguments, defining the model
    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 SqueezeBERT model. Defines the number of different tokens that can be
            represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.SqueezeBertModel`.
        hidden_size (:obj:`int`, `optional`, defaults to 768):
            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_attention_heads (:obj:`int`, `optional`, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (:obj:`int`, `optional`, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
            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 0.1):
            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
            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 just in case (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.BertModel` or
            :class:`~transformers.TFBertModel`.
        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):

        pad_token_id (:obj:`int`, `optional`, defaults to 0):
            The ID of the token in the word embedding to use as padding.
        embedding_size (:obj:`int`, `optional`, defaults to 768):
            The dimension of the word embedding vectors.

        q_groups (:obj:`int`, `optional`, defaults to 4):
            The number of groups in Q layer.
        k_groups (:obj:`int`, `optional`, defaults to 4):
            The number of groups in K layer.
        v_groups (:obj:`int`, `optional`, defaults to 4):
            The number of groups in V layer.
        post_attention_groups (:obj:`int`, `optional`, defaults to 1):
            The number of groups in the first feed forward network layer.
        intermediate_groups (:obj:`int`, `optional`, defaults to 4):
            The number of groups in the second feed forward network layer.
        output_groups (:obj:`int`, `optional`, defaults to 4):
            The number of groups in the third feed forward network layer.

    Example:

        >>> from transformers import SqueezeBertModel, SqueezeBertConfig

        >>> # Initializing a SqueezeBERT configuration
        >>> configuration = SqueezeBertConfig()

        >>> # Initializing a model from the configuration above
        >>> model = SqueezeBertModel(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 = SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    model_type = "squeezebert"

    def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        embedding_size=768,
        q_groups=4,
        k_groups=4,
        v_groups=4,
        post_attention_groups=1,
        intermediate_groups=4,
        output_groups=4,
        **kwargs
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        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.embedding_size = embedding_size
        self.q_groups = q_groups
        self.k_groups = k_groups
        self.v_groups = v_groups
        self.post_attention_groups = post_attention_groups
        self.intermediate_groups = intermediate_groups
        self.output_groups = output_groups
