# coding=utf-8
# Copyright 2020, Hugging Face
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Funnel Transformer model configuration"""

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


logger = logging.get_logger(__name__)


class FunnelConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`FunnelModel`] or a [`TFBertModel`]. It is used to
    instantiate a Funnel Transformer 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 Funnel
    Transformer [funnel-transformer/small](https://huggingface.co/funnel-transformer/small) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 30522):
            Vocabulary size of the Funnel transformer. Defines the number of different tokens that can be represented
            by the `inputs_ids` passed when calling [`FunnelModel`] or [`TFFunnelModel`].
        block_sizes (`List[int]`, *optional*, defaults to `[4, 4, 4]`):
            The sizes of the blocks used in the model.
        block_repeats (`List[int]`, *optional*):
            If passed along, each layer of each block is repeated the number of times indicated.
        num_decoder_layers (`int`, *optional*, defaults to 2):
            The number of layers in the decoder (when not using the base model).
        d_model (`int`, *optional*, defaults to 768):
            Dimensionality of the model's hidden states.
        n_head (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        d_head (`int`, *optional*, defaults to 64):
            Dimensionality of the model's heads.
        d_inner (`int`, *optional*, defaults to 3072):
            Inner dimension in the feed-forward blocks.
        hidden_act (`str` or `callable`, *optional*, defaults to `"gelu_new"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        hidden_dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability used between the two layers of the feed-forward blocks.
        initializer_range (`float`, *optional*, defaults to 0.1):
            The upper bound of the *uniform initializer* for initializing all weight matrices in attention layers.
        initializer_std (`float`, *optional*):
            The standard deviation of the *normal initializer* for initializing the embedding matrix and the weight of
            linear layers. Will default to 1 for the embedding matrix and the value given by Xavier initialization for
            linear layers.
        layer_norm_eps (`float`, *optional*, defaults to 1e-09):
            The epsilon used by the layer normalization layers.
        pooling_type (`str`, *optional*, defaults to `"mean"`):
            Possible values are `"mean"` or `"max"`. The way pooling is performed at the beginning of each block.
        attention_type (`str`, *optional*, defaults to `"relative_shift"`):
            Possible values are `"relative_shift"` or `"factorized"`. The former is faster on CPU/GPU while the latter
            is faster on TPU.
        separate_cls (`bool`, *optional*, defaults to `True`):
            Whether or not to separate the cls token when applying pooling.
        truncate_seq (`bool`, *optional*, defaults to `True`):
            When using `separate_cls`, whether or not to truncate the last token when pooling, to avoid getting a
            sequence length that is not a multiple of 2.
        pool_q_only (`bool`, *optional*, defaults to `True`):
            Whether or not to apply the pooling only to the query or to query, key and values for the attention layers.
    """

    model_type = "funnel"
    attribute_map = {
        "hidden_size": "d_model",
        "num_attention_heads": "n_head",
    }

    def __init__(
        self,
        vocab_size=30522,
        block_sizes=[4, 4, 4],
        block_repeats=None,
        num_decoder_layers=2,
        d_model=768,
        n_head=12,
        d_head=64,
        d_inner=3072,
        hidden_act="gelu_new",
        hidden_dropout=0.1,
        attention_dropout=0.1,
        activation_dropout=0.0,
        initializer_range=0.1,
        initializer_std=None,
        layer_norm_eps=1e-9,
        pooling_type="mean",
        attention_type="relative_shift",
        separate_cls=True,
        truncate_seq=True,
        pool_q_only=True,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.block_sizes = block_sizes
        self.block_repeats = [1] * len(block_sizes) if block_repeats is None else block_repeats
        assert len(block_sizes) == len(
            self.block_repeats
        ), "`block_sizes` and `block_repeats` should have the same length."
        self.num_decoder_layers = num_decoder_layers
        self.d_model = d_model
        self.n_head = n_head
        self.d_head = d_head
        self.d_inner = d_inner
        self.hidden_act = hidden_act
        self.hidden_dropout = hidden_dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.initializer_range = initializer_range
        self.initializer_std = initializer_std
        self.layer_norm_eps = layer_norm_eps
        assert pooling_type in [
            "mean",
            "max",
        ], f"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."
        self.pooling_type = pooling_type
        assert attention_type in [
            "relative_shift",
            "factorized",
        ], f"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."
        self.attention_type = attention_type
        self.separate_cls = separate_cls
        self.truncate_seq = truncate_seq
        self.pool_q_only = pool_q_only

        super().__init__(**kwargs)

    @property
    def num_hidden_layers(self):
        return sum(self.block_sizes)

    @num_hidden_layers.setter
    def num_hidden_layers(self, value):
        raise NotImplementedError(
            "This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`."
        )

    @property
    def num_blocks(self):
        return len(self.block_sizes)

    @num_blocks.setter
    def num_blocks(self, value):
        raise NotImplementedError("This model does not support the setting of `num_blocks`. Please set `block_sizes`.")
