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	# 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.
	""" ALBERT model configuration"""
	from collections import OrderedDict
	from typing import Mapping

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


	ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
	"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
	"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
	"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
	"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
	"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
	"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
	"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
	}


	class AlbertConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`AlbertModel`] or a [`TFAlbertModel`]. It is used
	to instantiate an ALBERT 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 ALBERT
	[albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) 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 30000):
	Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`AlbertModel`] or [`TFAlbertModel`].
	embedding_size (`int`, optional, defaults to 128):
	Dimensionality of vocabulary embeddings.
	hidden_size (`int`, optional, defaults to 4096):
	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_hidden_groups (`int`, optional, defaults to 1):
	Number of groups for the hidden layers, parameters in the same group are shared.
	num_attention_heads (`int`, optional, defaults to 64):
	Number of attention heads for each attention layer in the Transformer encoder.
	intermediate_size (`int`, optional, defaults to 16384):
	The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
	inner_group_num (`int`, optional, defaults to 1):
	The number of inner repetition of attention and ffn.
	hidden_act (`str` or `Callable`, optional, defaults to `"gelu_new"`):
	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):
	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
	attention_probs_dropout_prob (`float`, optional, defaults to 0):
	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
	(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 [`AlbertModel`] or [`TFAlbertModel`].
	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.
	classifier_dropout_prob (`float`, optional, defaults to 0.1):
	The dropout ratio for attached classifiers.
	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).
	pad_token_id (`int`, optional, defaults to 0):
	Padding token id.
	bos_token_id (`int`, optional, defaults to 2):
	Beginning of stream token id.
	eos_token_id (`int`, optional, defaults to 3):
	End of stream token id.

	Examples:

	```python
	>>> from transformers import AlbertConfig, AlbertModel

	>>> # Initializing an ALBERT-xxlarge style configuration
	>>> albert_xxlarge_configuration = AlbertConfig()

	>>> # Initializing an ALBERT-base style configuration
	>>> albert_base_configuration = AlbertConfig(
	... hidden_size=768,
	... num_attention_heads=12,
	... intermediate_size=3072,
	... )

	>>> # Initializing a model (with random weights) from the ALBERT-base style configuration
	>>> model = AlbertModel(albert_xxlarge_configuration)

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

	model_type = "albert"

	def __init__(
	self,
	vocab_size=30000,
	embedding_size=128,
	hidden_size=4096,
	num_hidden_layers=12,
	num_hidden_groups=1,
	num_attention_heads=64,
	intermediate_size=16384,
	inner_group_num=1,
	hidden_act="gelu_new",
	hidden_dropout_prob=0,
	attention_probs_dropout_prob=0,
	max_position_embeddings=512,
	type_vocab_size=2,
	initializer_range=0.02,
	layer_norm_eps=1e-12,
	classifier_dropout_prob=0.1,
	position_embedding_type="absolute",
	pad_token_id=0,
	bos_token_id=2,
	eos_token_id=3,
	**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.embedding_size = embedding_size
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_hidden_groups = num_hidden_groups
	self.num_attention_heads = num_attention_heads
	self.inner_group_num = inner_group_num
	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.classifier_dropout_prob = classifier_dropout_prob
	self.position_embedding_type = position_embedding_type


	# Copied from transformers.models.bert.configuration_bert.BertOnnxConfig with Roberta->Albert
	class AlbertOnnxConfig(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),
	("token_type_ids", dynamic_axis),
	]
	)