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# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" OpenAI GPT configuration """

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


logger = logging.get_logger(__name__)

OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}


class OpenAIGPTConfig(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a [`OpenAIGPTModel`] or a
    [`TFOpenAIGPTModel`]. It is used to instantiate a GPT 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 [GPT](https://huggingface.co/openai-gpt) architecture from OpenAI.

    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 40478):
            Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`OpenAIGPTModel`] or
            [`TFOpenAIGPTModel`].
        n_positions (`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).
        n_embd (`int`, *optional*, defaults to 768):
            Dimensionality of the embeddings and hidden states.
        n_layer (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        n_head (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        afn (`str` or `Callable`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string,
            `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
        resid_pdrop (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        embd_pdrop (`int`, *optional*, defaults to 0.1):
            The dropout ratio for the embeddings.
        attn_pdrop (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention.
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
            The epsilon to use in the layer normalization layers
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        predict_special_tokens (`bool`, *optional*, defaults to `True`):
            Whether or not special tokens should be predicted when the model has a language modeling head.
        summary_type (`str`, *optional*, defaults to `"cls_index"`):
            Argument used when doing sequence summary, used in the models
            [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`].

            Has to be one of the following options:

                - `"last"`: Take the last token hidden state (like XLNet).
                - `"first"`: Take the first token hidden state (like BERT).
                - `"mean"`: Take the mean of all tokens hidden states.
                - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
                - `"attn"`: Not implemented now, use multi-head attention.
        summary_use_proj (`bool`, *optional*, defaults to `True`):
            Argument used when doing sequence summary, used in the models
            [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`].

            Whether or not to add a projection after the vector extraction.
        summary_activation (`str`, *optional*):
            Argument used when doing sequence summary, used in the models
            [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`].

            Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
        summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
            Argument used when doing sequence summary, used in the models
            [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`].

            Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
        summary_first_dropout (`float`, *optional*, defaults to 0.1):
            Argument used when doing sequence summary, used in the models
            [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`].

            The dropout ratio to be used after the projection and activation.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).


    Examples:

    ```python
    >>> from transformers import OpenAIGPTConfig, OpenAIGPTModel

    >>> # Initializing a GPT configuration
    >>> configuration = OpenAIGPTConfig()

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

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

    model_type = "openai-gpt"
    attribute_map = {
        "max_position_embeddings": "n_positions",
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }

    def __init__(
        self,
        vocab_size=40478,
        n_positions=512,
        n_embd=768,
        n_layer=12,
        n_head=12,
        afn="gelu",
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,
        predict_special_tokens=True,
        summary_type="cls_index",
        summary_use_proj=True,
        summary_activation=None,
        summary_proj_to_labels=True,
        summary_first_dropout=0.1,
        **kwargs
    ):
        self.vocab_size = vocab_size
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.afn = afn
        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.predict_special_tokens = predict_special_tokens
        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
        super().__init__(**kwargs)
