text: # coding=utf-8
  # 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
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ BERT model configuration """
  
  
  import logging
  
  from .configuration_utils import PretrainedConfig
  
  
  logger = logging.getLogger(__name__)
  
  BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
  	"bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json",
  	"bert-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-config.json",
  	"bert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json",
  	"bert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-config.json",
  	"bert-base-multilingual-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-config.json",
  	"bert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-config.json",
  	"bert-base-chinese": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json",
  	"bert-base-german-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-config.json",
  	"bert-large-uncased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-config.json",
  	"bert-large-cased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-config.json",
  	"bert-large-uncased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-config.json",
  	"bert-large-cased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-config.json",
  	"bert-base-cased-finetuned-mrpc": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-config.json",
  	"bert-base-german-dbmdz-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-config.json",
  	"bert-base-german-dbmdz-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-config.json",
  	"bert-base-japanese": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-config.json",
  	"bert-base-japanese-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-whole-word-masking-config.json",
  	"bert-base-japanese-char": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-config.json",
  	"bert-base-japanese-char-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking-config.json",
  	"bert-base-finnish-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/config.json",
  	"bert-base-finnish-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/config.json",
  	"bert-base-dutch-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/config.json",
  }
  
  
  class BertConfig(PretrainedConfig):
  	r"""
  		This is the configuration class to store the configuration of a :class:`~transformers.BertModel`.
  		It is used to instantiate an 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  :class:`~transformers.PretrainedConfig` and can be used
  		to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
  		for more information.
  
  
  		Args:
  			vocab_size (:obj:`int`, optional, defaults to 30522):
  				Vocabulary size of the BERT model. Defines the different tokens that
  				can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.
  			hidden_size (:obj:`int`, optional, defaults to 768):
  				Dimensionality of the encoder layers and the pooler layer.
  			num_hidden_layers (:obj:`int`, optional, defaults to 12):
  				Number of hidden layers in the Transformer encoder.
  			num_attention_heads (:obj:`int`, optional, defaults to 12):
  				Number of attention heads for each attention layer in the Transformer encoder.
  			intermediate_size (:obj:`int`, optional, defaults to 3072):
  				Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
  			hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
  				The non-linear activation function (function or string) in the encoder and pooler.
  				If string, "gelu", "relu", "swish" and "gelu_new" are supported.
  			hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
  				The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
  			attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
  				The dropout ratio for the attention probabilities.
  			max_position_embeddings (:obj:`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 (:obj:`int`, optional, defaults to 2):
  				The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.
  			initializer_range (:obj:`float`, optional, defaults to 0.02):
  				The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
  			layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
  				The epsilon used by the layer normalization layers.
  
  		Example::
  
  			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
  
  		Attributes:
  			pretrained_config_archive_map (Dict[str, str]):
  				A dictionary containing all the available pre-trained checkpoints.
  	"""
  	pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  	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,
  		**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