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	# coding=utf-8
	# Copyright 2022 The HuggingFace Inc. team. 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.
	"""YOSO model configuration"""

	from ...configuration_utils import PretrainedConfig
	from ...utils import logging


	logger = logging.get_logger(__name__)


	class YosoConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`YosoModel`]. It is used to instantiate an YOSO
	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 YOSO
	[uw-madison/yoso-4096](https://huggingface.co/uw-madison/yoso-4096) 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 50265):
	Vocabulary size of the YOSO model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`YosoModel`].
	hidden_size (`int`, optional, defaults to 768):
	Dimension 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):
	Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
	hidden_act (`str` or `function`, optional, defaults to `"gelu"`):
	The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
	`"relu"`, `"selu"` 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 [`YosoModel`].
	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"`.
	use_expectation (`bool`, optional, defaults to `True`):
	Whether or not to use YOSO Expectation. Overrides any effect of num_hash.
	hash_code_len (`int`, optional, defaults to 9):
	The length of hashes generated by the hash functions.
	num_hash (`int`, optional, defaults to 64):
	Number of hash functions used in [`YosoSelfAttention`].
	conv_window (`int`, optional):
	Kernel size of depth-wise convolution.
	use_fast_hash (`bool`, optional, defaults to `False`):
	Whether or not to use custom cuda kernels which perform fast random projection via hadamard transform.
	lsh_backward (`bool`, optional, defaults to `True`):
	Whether or not to perform backpropagation using Locality Sensitive Hashing.

	Example:

	```python
	>>> from transformers import YosoConfig, YosoModel

	>>> # Initializing a YOSO uw-madison/yoso-4096 style configuration
	>>> configuration = YosoConfig()

	>>> # Initializing a model (with random weights) from the uw-madison/yoso-4096 style configuration
	>>> model = YosoModel(configuration)

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

	model_type = "yoso"

	def __init__(
	self,
	vocab_size=50265,
	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=4096,
	type_vocab_size=1,
	initializer_range=0.02,
	layer_norm_eps=1e-12,
	position_embedding_type="absolute",
	use_expectation=True,
	hash_code_len=9,
	num_hash=64,
	conv_window=None,
	use_fast_hash=True,
	lsh_backward=True,
	pad_token_id=1,
	bos_token_id=0,
	eos_token_id=2,
	**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.max_position_embeddings = max_position_embeddings
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.intermediate_size = intermediate_size
	self.hidden_act = hidden_act
	self.hidden_dropout_prob = hidden_dropout_prob
	self.attention_probs_dropout_prob = attention_probs_dropout_prob
	self.initializer_range = initializer_range
	self.type_vocab_size = type_vocab_size
	self.layer_norm_eps = layer_norm_eps
	self.position_embedding_type = position_embedding_type
	self.use_expectation = use_expectation
	self.hash_code_len = hash_code_len
	self.num_hash = num_hash
	self.conv_window = conv_window
	self.use_fast_hash = use_fast_hash
	self.lsh_backward = lsh_backward