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	# coding=utf-8
	# Copyright 2024 JetMoe AI 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.
	"""JetMoe model configuration"""

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


	logger = logging.get_logger(__name__)


	class JetMoeConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`JetMoeModel`]. It is used to instantiate a
	JetMoe model according to the specified arguments, defining the model architecture. Instantiating a configuration
	with the defaults will yield a configuration of the JetMoe-4B.

	[jetmoe/jetmoe-8b](https://huggingface.co/jetmoe/jetmoe-8b)

	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 32000):
	Vocabulary size of the JetMoe model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`JetMoeModel`]
	hidden_size (`int`, optional, defaults to 2048):
	Dimension of the hidden representations.
	num_hidden_layers (`int`, optional, defaults to 12):
	Number of hidden layers in the Transformer encoder.
	num_key_value_heads (`int`, optional, defaults to 16):
	Number of attention heads for each key and value in the Transformer encoder.
	kv_channels (`int`, optional, defaults to 128):
	Defines the number of channels for the key and value tensors.
	intermediate_size (`int`, optional, defaults to 5632):
	Dimension of the MLP representations.
	max_position_embeddings (`int`, optional, defaults to 4096):
	The maximum sequence length that this model might ever be used with. JetMoe's attention allows sequence of
	up to 4096 tokens.
	activation_function (`string`, optional, defaults to `"silu"`):
	Defines the activation function for MLP experts.
	num_local_experts (`int`, optional, defaults to 8):
	Defines the number of experts in the MoE and MoA.
	num_experts_per_tok (`int, optional, defaults to 2):
	The number of experts to route per-token and for MoE and MoA.
	output_router_logits (`bool`, optional, defaults to `False`):
	Whether or not the router logits should be returned by the model. Enabeling this will also
	allow the model to output the auxiliary loss.
	aux_loss_coef (`float`, optional, defaults to 0.01):
	The coefficient for the auxiliary loss.
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models). Only
	relevant if `config.is_decoder=True`.
	bos_token_id (`int`, optional, defaults to 1):
	The id of the "beginning-of-sequence" token.
	eos_token_id (`int`, optional, defaults to 2):
	The id of the "end-of-sequence" token.
	tie_word_embeddings (`bool`, optional, defaults to `True`):
	Whether the model's input and output word embeddings should be tied.
	rope_theta (`float`, optional, defaults to 10000.0):
	The base period of the RoPE embeddings.
	rms_norm_eps (`float`, optional, defaults to 1e-06):
	The epsilon used by the rms normalization layers.
	initializer_range (`float`, optional, defaults to 0.01):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	attention_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for the attention probabilities.

	```python
	>>> from transformers import JetMoeModel, JetMoeConfig

	>>> # Initializing a JetMoe 4B style configuration
	>>> configuration = JetMoeConfig()

	>>> # Initializing a model from the JetMoe 4B style configuration
	>>> model = JetMoeModel(configuration)

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

	model_type = "jetmoe"
	keys_to_ignore_at_inference = ["past_key_values"]

	def __init__(
	self,
	vocab_size=32000,
	hidden_size=2048,
	num_hidden_layers=12,
	num_key_value_heads=16,
	kv_channels=128,
	intermediate_size=5632,
	max_position_embeddings=4096,
	activation_function="silu",
	num_local_experts=8,
	num_experts_per_tok=2,
	output_router_logits=False,
	aux_loss_coef=0.01,
	use_cache=True,
	bos_token_id=1,
	eos_token_id=2,
	tie_word_embeddings=True,
	rope_theta=10000.0,
	rms_norm_eps=1e-6,
	initializer_range=0.01,
	attention_dropout=0.0,
	**kwargs,
	):
	if num_experts_per_tok > num_local_experts:
	raise ValueError("`num_experts_per_tok` must be less than or equal to `num_local_experts`")
	self.vocab_size = vocab_size
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_key_value_heads * num_experts_per_tok
	self.num_key_value_heads = num_key_value_heads
	self.kv_channels = kv_channels
	self.intermediate_size = intermediate_size
	self.max_position_embeddings = max_position_embeddings
	self.activation_function = activation_function
	self.num_local_experts = num_local_experts
	self.num_experts_per_tok = num_experts_per_tok
	self.output_router_logits = output_router_logits
	self.aux_loss_coef = aux_loss_coef
	self.use_cache = use_cache
	self.initializer_range = initializer_range
	self.attention_dropout = attention_dropout

	self.bos_token_id = bos_token_id
	self.eos_token_id = eos_token_id

	self.rope_theta = rope_theta
	self.rms_norm_eps = rms_norm_eps

	super().__init__(
	bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
	)