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# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
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""" OpenAI GPT-2 configuration """
from collections import OrderedDict
from typing import Any, Mapping, Optional

from transformers import PreTrainedTokenizer, TensorType, is_torch_available

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


logger = logging.get_logger(__name__)

GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
    "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
    "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
    "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
    "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
}


class GPT2Config(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a [`GPT2Model`] or a
    [`TFGPT2Model`]. It is used to instantiate a GPT-2 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-2 [small](https://huggingface.co/gpt2) 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 50257):
            Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GPT2Model`] or
            [`TFGPT2Model`].
        n_positions (`int`, *optional*, defaults to 1024):
            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.
        n_inner (`int`, *optional*, defaults to None):
            Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
        activation_function (`str`, *optional*, defaults to `"gelu"`):
            Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
        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.
        summary_type (`string`, *optional*, defaults to `"cls_index"`):
            Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`]
            and [`TFGPT2DoubleHeadsModel`].

            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 [`GPT2DoubleHeadsModel`]
            and [`TFGPT2DoubleHeadsModel`].

            Whether or not to add a projection after the vector extraction.
        summary_activation (`str`, *optional*):
            Argument used when doing sequence summary. Used in for the multiple choice head in
            [`GPT2DoubleHeadsModel`].

            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 [`GPT2DoubleHeadsModel`]
            and [`TFGPT2DoubleHeadsModel`].

            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 [`GPT2DoubleHeadsModel`]
            and [`TFGPT2DoubleHeadsModel`].

            The dropout ratio to be used after the projection and activation.
        scale_attn_weights (`bool`, *optional*, defaults to `True`):
            Scale attention weights by dividing by sqrt(hidden_size)..
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
            Whether to additionally scale attention weights by `1 / layer_idx + 1`.
        reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
            Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
            dot-product/softmax to float() when training with mixed precision.

    Example:

    ```python
    >>> from transformers import GPT2Model, GPT2Config

    >>> # Initializing a GPT2 configuration
    >>> configuration = GPT2Config()

    >>> # Initializing a model from the configuration
    >>> model = GPT2Model(configuration)

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

    model_type = "gpt2"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "hidden_size": "n_embd",
        "max_position_embeddings": "n_positions",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }

    def __init__(
        self,
        vocab_size=50257,
        n_positions=1024,
        n_embd=768,
        n_layer=12,
        n_head=12,
        n_inner=None,
        activation_function="gelu_new",
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-5,
        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,
        scale_attn_weights=True,
        use_cache=True,
        bos_token_id=50256,
        eos_token_id=50256,
        scale_attn_by_inverse_layer_idx=False,
        reorder_and_upcast_attn=False,
        **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.n_inner = n_inner
        self.activation_function = activation_function
        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
        self.scale_attn_weights = scale_attn_weights
        self.use_cache = use_cache
        self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
        self.reorder_and_upcast_attn = reorder_and_upcast_attn

        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id

        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)


class GPT2OnnxConfig(OnnxConfigWithPast):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        common_inputs = OrderedDict({"input_ids": {0: "batch"}})
        if self.use_past:
            for i in range(self._config.n_layer * 2):
                common_inputs[f"past_key_values.{i}"] = {0: "batch", 2: "sequence"}

            common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
        else:
            common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}

        return common_inputs

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        common_outputs = OrderedDict({"last_hidden_state": {0: "batch", 1: "sequence"}})
        if self.use_past:
            for i in range(self._config.n_layer * 2):
                common_outputs[f"present.{i}"] = {0: "batch", 2: "sequence"}

            return common_outputs

        return common_outputs

    def generate_dummy_inputs(
        self,
        tokenizer: PreTrainedTokenizer,
        batch_size: int = -1,
        seq_length: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
    ) -> Mapping[str, Any]:
        common_inputs = super().generate_dummy_inputs(tokenizer, batch_size, seq_length, is_pair, framework)

        # We need to order the input in the way they appears in the forward()
        ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})

        # Need to add the past_keys
        if self.use_past:
            if not is_torch_available():
                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
            else:
                import torch

                batch = common_inputs["input_ids"].shape[0]
                ordered_inputs["past_key_values"] = [
                    (
                        torch.zeros((batch, self._config.n_head, 1, self._config.hidden_size // self._config.n_head)),
                        torch.zeros((batch, self._config.n_head, 1, self._config.hidden_size // self._config.n_head)),
                    )
                    for _ in range(self._config.n_layer)
                ]

        ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
        return ordered_inputs
