# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     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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""" RoFormer model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json",
    "junnyu/roformer_chinese_char_base": "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json",
    "junnyu/roformer_small_discriminator": "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json",
    "junnyu/roformer_small_generator": "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json",
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class RoFormerConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a :class:`~transformers.RoFormerModel`. It is used to
    instantiate an RoFormer 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 RoFormer
    `junnyu/roformer_chinese_base <https://huggingface.co/junnyu/roformer_chinese_base>`__ 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 50000):
            Vocabulary size of the RoFormer model. Defines the number of different tokens that can be represented by
            the :obj:`inputs_ids` passed when calling :class:`~transformers.RoFormerModel` or
            :class:`~transformers.TFRoFormerModel`.
        embedding_size (:obj:`int`, `optional`, defaults to None):
            Dimensionality of the encoder layers and the pooler layer. Defaults to the :obj:`hidden_size` if not
            provided.
        hidden_size (:obj:`int`, `optional`, defaults to 768):
            Dimension 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):
            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (:obj:`str` or :obj:`function`, `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:`"selu"` 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 1536):
            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 1536).
        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
            The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.RoFormerModel`
            or :class:`~transformers.TFRoFormerModel`.
        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.
        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if ``config.is_decoder=True``.
        rotary_value (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not apply rotary position embeddings on value layer.
        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
            If :obj:`True`, use gradient checkpointing to save memory at the expense of slower backward pass.

    Example::

        >>> from transformers import RoFormerModel, RoFormerConfig

        >>> # Initializing a RoFormer junnyu/roformer_chinese_base style configuration
        >>> configuration = RoFormerConfig()

        >>> # Initializing a model from the junnyu/roformer_chinese_base style configuration
        >>> model = RoFormerModel(configuration)

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

    def __init__(
        self,
        vocab_size=50000,
        embedding_size=None,
        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=1536,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        gradient_checkpointing=False,
        rotary_value=False,
        use_cache=True,
        **kwargs
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.embedding_size = hidden_size if embedding_size is None else embedding_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.gradient_checkpointing = gradient_checkpointing
        self.rotary_value = rotary_value
        self.use_cache = use_cache
