# coding=utf-8
# Copyright 2018 Salesforce and 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.
""" Salesforce CTRL configuration """

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


logger = logging.get_logger(__name__)

CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}


class CTRLConfig(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a :class:`~transformers.CTRLModel` or a
    :class:`~transformers.TFCTRLModel`. It is used to instantiate a CTRL 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 `ctrl <https://huggingface.co/ctrl>`__ architecture from SalesForce.

    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 246534):
            Vocabulary size of the CTRL model. Defines the number of different tokens that can be represented by the
            :obj:`inputs_ids` passed when calling :class:`~transformers.CTRLModel` or
            :class:`~transformers.TFCTRLModel`.
        n_positions (:obj:`int`, `optional`, defaults to 256):
            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).
        n_ctx (:obj:`int`, `optional`, defaults to 256):
            Dimensionality of the causal mask (usually same as n_positions).
        n_embd (:obj:`int`, `optional`, defaults to 1280):
            Dimensionality of the embeddings and hidden states.
        dff (:obj:`int`, `optional`, defaults to 8192):
            Dimensionality of the inner dimension of the feed forward networks (FFN).
        n_layer (:obj:`int`, `optional`, defaults to 48):
            Number of hidden layers in the Transformer encoder.
        n_head (:obj:`int`, `optional`, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        resid_pdrop (:obj:`float`, `optional`, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        embd_pdrop (:obj:`int`, `optional`, defaults to 0.1):
            The dropout ratio for the embeddings.
        attn_pdrop (:obj:`float`, `optional`, defaults to 0.1):
            The dropout ratio for the attention.
        layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-6):
            The epsilon to use in the layer normalization layers
        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

    Examples::

        >>> from transformers import CTRLModel, CTRLConfig

        >>> # Initializing a CTRL configuration
        >>> configuration = CTRLConfig()

        >>> # Initializing a model from the configuration
        >>> model = CTRLModel(configuration)

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

    model_type = "ctrl"

    def __init__(
        self,
        vocab_size=246534,
        n_positions=256,
        n_ctx=256,
        n_embd=1280,
        dff=8192,
        n_layer=48,
        n_head=16,
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-6,
        initializer_range=0.02,
        summary_type="cls_index",
        summary_use_proj=True,
        summary_activation=None,
        summary_proj_to_labels=True,
        summary_first_dropout=0.1,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.n_ctx = n_ctx
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.dff = dff
        self.resid_pdrop = resid_pdrop
        self.embd_pdrop = embd_pdrop
        self.attn_pdrop = attn_pdrop
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range

        self.summary_type = summary_type
        self.summary_use_proj = summary_use_proj
        self.summary_activation = summary_activation
        self.summary_first_dropout = summary_first_dropout
        self.summary_proj_to_labels = summary_proj_to_labels

    @property
    def max_position_embeddings(self):
        return self.n_positions

    @property
    def hidden_size(self):
        return self.n_embd

    @property
    def num_attention_heads(self):
        return self.n_head

    @property
    def num_hidden_layers(self):
        return self.n_layer
