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"""X-MOD configuration"""

from collections import OrderedDict
from typing import Mapping

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


logger = logging.get_logger(__name__)


class XmodConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`XmodModel`]. It is used to instantiate an X-MOD
    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
    [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) 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 X-MOD model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`XmodModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`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.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`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).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`XmodModel`].
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
        is_decoder (`bool`, *optional*, defaults to `False`):
            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
        use_cache (`bool`, *optional*, defaults to `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`.
        classifier_dropout (`float`, *optional*):
            The dropout ratio for the classification head.
        pre_norm (`bool`, *optional*, defaults to `False`):
            Whether to apply layer normalization before each block.
        adapter_reduction_factor (`int` or `float`, *optional*, defaults to 2):
            The factor by which the dimensionality of the adapter is reduced relative to `hidden_size`.
        adapter_layer_norm (`bool`, *optional*, defaults to `False`):
            Whether to apply a new layer normalization before the adapter modules (shared across all adapters).
        adapter_reuse_layer_norm (`bool`, *optional*, defaults to `True`):
            Whether to reuse the second layer normalization and apply it before the adapter modules as well.
        ln_before_adapter (`bool`, *optional*, defaults to `True`):
            Whether to apply the layer normalization before the residual connection around the adapter module.
        languages (`Iterable[str]`, *optional*, defaults to `["en_XX"]`):
            An iterable of language codes for which adapter modules should be initialized.
        default_language (`str`, *optional*):
            Language code of a default language. It will be assumed that the input is in this language if no language
            codes are explicitly passed to the forward method.

    Examples:

    ```python
    >>> from transformers import XmodConfig, XmodModel

    >>> # Initializing an X-MOD facebook/xmod-base style configuration
    >>> configuration = XmodConfig()

    >>> # Initializing a model (with random weights) from the facebook/xmod-base style configuration
    >>> model = XmodModel(configuration)

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

    model_type = "xmod"

    def __init__(
        self,
        vocab_size=30522,
        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=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        position_embedding_type="absolute",
        use_cache=True,
        classifier_dropout=None,
        pre_norm=False,
        adapter_reduction_factor=2,
        adapter_layer_norm=False,
        adapter_reuse_layer_norm=True,
        ln_before_adapter=True,
        languages=("en_XX",),
        default_language=None,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

        self.vocab_size = vocab_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.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.classifier_dropout = classifier_dropout
        self.pre_norm = pre_norm
        self.adapter_reduction_factor = adapter_reduction_factor
        self.adapter_layer_norm = adapter_layer_norm
        self.adapter_reuse_layer_norm = adapter_reuse_layer_norm
        self.ln_before_adapter = ln_before_adapter
        self.languages = list(languages)
        self.default_language = default_language


# Copied from transformers.models.roberta.configuration_roberta.RobertaOnnxConfig with Roberta->Xmod
class XmodOnnxConfig(OnnxConfig):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
        else:
            dynamic_axis = {0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
            ]
        )
