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	# coding=utf-8
	# Copyright 2022 The Metaseq Authors and 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.
	"""OPT model configuration"""

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


	logger = logging.get_logger(__name__)


	class OPTConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate a OPT 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 OPT
	[facebook/opt-350m](https://huggingface.co/facebook/opt-350m) 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 50272):
	Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`OPTModel`]
	hidden_size (`int`, optional, defaults to 768):
	Dimensionality of the layers and the pooler layer.
	num_hidden_layers (`int`, optional, defaults to 12):
	Number of decoder layers.
	ffn_dim (`int`, optional, defaults to 3072):
	Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
	num_attention_heads (`int`, optional, defaults to 12):
	Number of attention heads for each attention layer in the Transformer decoder.
	activation_function (`str` or `function`, optional, defaults to `"relu"`):
	The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
	`"relu"`, `"silu"` and `"gelu_new"` are supported.
	max_position_embeddings (`int`, optional, defaults to 2048):
	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).
	do_layer_norm_before (`bool`, optional, defaults to `True`):
	Whether to perform layer normalization before the attention block.
	word_embed_proj_dim (`int`, optional):
	`word_embed_proj_dim` can be set to down-project word embeddings, e.g. `opt-350m`. Defaults to
	`hidden_size`.
	dropout (`float`, optional, defaults to 0.1):
	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
	attention_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for the attention probabilities.
	layerdrop (`float`, optional, defaults to 0.0):
	The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
	details.
	init_std (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models).
	enable_bias (`bool`, optional, defaults to `True`):
	Whether or not if the linear layers in the attention blocks should use the bias term.
	layer_norm_elementwise_affine (`bool`, optional, defaults to `True`):
	Whether or not if the layer norms should have learnable parameters.

	Example:

	```python
	>>> from transformers import OPTConfig, OPTModel

	>>> # Initializing a OPT facebook/opt-large style configuration
	>>> configuration = OPTConfig()

	>>> # Initializing a model (with random weights) from the facebook/opt-large style configuration
	>>> model = OPTModel(configuration)

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

	model_type = "opt"
	keys_to_ignore_at_inference = ["past_key_values"]

	def __init__(
	self,
	vocab_size=50272,
	hidden_size=768,
	num_hidden_layers=12,
	ffn_dim=3072,
	max_position_embeddings=2048,
	do_layer_norm_before=True,
	_remove_final_layer_norm=False,
	word_embed_proj_dim=None,
	dropout=0.1,
	attention_dropout=0.0,
	num_attention_heads=12,
	activation_function="relu",
	layerdrop=0.0,
	init_std=0.02,
	use_cache=True,
	pad_token_id=1,
	bos_token_id=2,
	eos_token_id=2,
	enable_bias=True,
	layer_norm_elementwise_affine=True,
	**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.num_attention_heads = num_attention_heads
	self.word_embed_proj_dim = word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
	self.ffn_dim = ffn_dim
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.dropout = dropout
	self.attention_dropout = attention_dropout
	self.activation_function = activation_function
	self.init_std = init_std
	self.layerdrop = layerdrop
	self.use_cache = use_cache
	self.do_layer_norm_before = do_layer_norm_before
	# We keep these variables at `True` for backward compatibility.
	self.enable_bias = enable_bias
	self.layer_norm_elementwise_affine = layer_norm_elementwise_affine

	# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
	# with checkpoints that have been fine-tuned before transformers v4.20.1
	# see https://github.com/facebookresearch/metaseq/pull/164
	self._remove_final_layer_norm = _remove_final_layer_norm