# Copyright (c) Alibaba, Inc. and its affiliates.
# Copyright 2018 The Google AI Language Team Authors and The 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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""" BERT model configuration """
from collections import OrderedDict
from typing import Mapping

from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxConfig

from modelscope.utils.logger import get_logger

logger = get_logger()


class BertConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a
    [`BertModel`] or a [`TFBertModel`]. It is used to instantiate a BERT 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 BERT
    [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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 BERT model. Defines the number of different
            tokens that can be represented by the `inputs_ids` passed when
            calling [`BertModel`] or [`TFBertModel`].
        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
            [`BertModel`] or [`TFBertModel`].
        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).
        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.

    Examples:

    >>> from transformers import BertModel, BertConfig

    >>> # Initializing a BERT bert-base-uncased style configuration
    >>> configuration = BertConfig()

    >>> # Initializing a model from the bert-base-uncased style configuration
    >>> model = BertModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    """
    model_type = 'bert'

    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=0,
                 position_embedding_type='absolute',
                 use_cache=True,
                 classifier_dropout=None,
                 **kwargs):
        super().__init__(pad_token_id=pad_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


class BertOnnxConfig(OnnxConfig):

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict([
            ('input_ids', {
                0: 'batch',
                1: 'sequence'
            }),
            ('attention_mask', {
                0: 'batch',
                1: 'sequence'
            }),
            ('token_type_ids', {
                0: 'batch',
                1: 'sequence'
            }),
        ])
