# Copyright 2021 The TensorFlow Authors. 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.
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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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"""The main BERT model and related functions."""

import copy
import json

import six
import tensorflow as tf


class BertConfig(object):
    """Configuration for `BertModel`."""

    def __init__(
        self,
        vocab_size,
        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=16,
        initializer_range=0.02,
        embedding_size=None,
        backward_compatible=True,
    ):
        """Constructs BertConfig.

        Args:
          vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
          hidden_size: Size of the encoder layers and the pooler layer.
          num_hidden_layers: Number of hidden layers in the Transformer encoder.
          num_attention_heads: Number of attention heads for each attention layer in
            the Transformer encoder.
          intermediate_size: The size of the "intermediate" (i.e., feed-forward)
            layer in the Transformer encoder.
          hidden_act: The non-linear activation function (function or string) in the
            encoder and pooler.
          hidden_dropout_prob: The dropout probability for all fully connected
            layers in the embeddings, encoder, and pooler.
          attention_probs_dropout_prob: The dropout ratio for the attention
            probabilities.
          max_position_embeddings: 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: The vocabulary size of the `token_type_ids` passed into
            `BertModel`.
          initializer_range: The stdev of the truncated_normal_initializer for
            initializing all weight matrices.
          embedding_size: (Optional) width of the factorized word embeddings.
          backward_compatible: Boolean, whether the variables shape are compatible
            with checkpoints converted from TF 1.x BERT.
        """
        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.embedding_size = embedding_size
        self.backward_compatible = backward_compatible

    @classmethod
    def from_dict(cls, json_object):
        """Constructs a `BertConfig` from a Python dictionary of parameters."""
        config = BertConfig(vocab_size=None)
        for (key, value) in six.iteritems(json_object):
            config.__dict__[key] = value
        return config

    @classmethod
    def from_json_file(cls, json_file):
        """Constructs a `BertConfig` from a json file of parameters."""
        # with tf.io.gfile.GFile(json_file, "r") as reader:
        #     text = reader.read()
        with open(json_file, encoding="UTF-8") as f:
            return cls.from_dict(json.loads(f.read()))

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"