text: # coding=utf-8
  # Copyright 2021 The EleutherAI and HuggingFace Teams. 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.
  """ GPT-J model configuration """
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP = {
      "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
      # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
  }
  
  
  class GPTJConfig(PretrainedConfig):
      r"""
      This is the configuration class to store the configuration of a :class:`~transformers.GPTJModel`. It is used to
      instantiate a GPT-J 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 GPT-J `gpt-j-6B
      <https://huggingface.co/EleutherAI/gpt-j-6B>`__ 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 50400):
              Vocabulary size of the GPT-J model. Defines the number of different tokens that can be represented by the
              :obj:`inputs_ids` passed when calling :class:`~transformers.GPTJModel`.
          n_positions (:obj:`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).
          n_embd (:obj:`int`, `optional`, defaults to 4096):
              Dimensionality of the embeddings and hidden states.
          n_layer (:obj:`int`, `optional`, defaults to 28):
              Number of hidden layers in the Transformer encoder.
          n_head (:obj:`int`, `optional`, defaults to 16):
              Number of attention heads for each attention layer in the Transformer encoder.
          rotary_dim (:obj:`int`, `optional`, defaults to 64):
              Number of dimensions in the embedding that Rotary Position Embedding is applied to.
          n_inner (:obj:`int`, `optional`, defaults to None):
              Dimensionality of the inner feed-forward layers. :obj:`None` will set it to 4 times n_embd
          activation_function (:obj:`str`, `optional`, defaults to :obj:`"gelu_new"`):
              Activation function, to be selected in the list :obj:`["relu", "silu", "gelu", "tanh", "gelu_new"]`.
          resid_pdrop (:obj:`float`, `optional`, defaults to 0.1):
              The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
          embd_pdrop (:obj:`int`, `optional`, defaults to 0.1):
              The dropout ratio for the embeddings.
          attn_pdrop (:obj:`float`, `optional`, defaults to 0.1):
              The dropout ratio for the attention.
          layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5):
              The epsilon to use in the layer normalization layers.
          initializer_range (:obj:`float`, `optional`, defaults to 0.02):
              The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
          scale_attn_weights (:obj:`bool`, `optional`, defaults to :obj:`True`):
              Scale attention weights by dividing by sqrt(hidden_size).
          use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
              Whether or not the model should return the last key/values attentions (not used by all models).
  
      Example::
  
          >>> from transformers import GPTJModel, GPTJConfig
  
          >>> # Initializing a GPT-J 6B configuration
          >>> configuration = GPTJConfig()
  
          >>> # Initializing a model from the configuration
          >>> model = GPTJModel(configuration)
  
          >>> # Accessing the model configuration
          >>> configuration = model.config
      """
      model_type = "gptj"
      attribute_map = {
          "max_position_embeddings": "n_positions",
          "hidden_size": "n_embd",
          "num_attention_heads": "n_head",
          "num_hidden_layers": "n_layer",
      }
  
      def __init__(
          self,
          vocab_size=50400,
          n_positions=2048,
          n_embd=4096,
          n_layer=28,
          n_head=16,
          rotary_dim=64,
          n_inner=None,
          activation_function="gelu_new",
          resid_pdrop=0.0,
          embd_pdrop=0.0,
          attn_pdrop=0.0,
          layer_norm_epsilon=1e-5,
          initializer_range=0.02,
          scale_attn_weights=True,
          use_cache=True,
          bos_token_id=50256,
          eos_token_id=50256,
          **kwargs
      ):
          self.vocab_size = vocab_size
          self.n_positions = n_positions
          self.n_embd = n_embd
          self.n_layer = n_layer
          self.n_head = n_head
          self.n_inner = n_inner
          self.rotary_dim = rotary_dim
          self.activation_function = activation_function
          self.resid_pdrop = resid_pdrop
          self.embd_pdrop = embd_pdrop
          self.attn_pdrop = attn_pdrop
          self.layer_norm_epsilon = layer_norm_epsilon
          self.initializer_range = initializer_range
          self.scale_attn_weights = scale_attn_weights
          self.use_cache = use_cache
  
          self.bos_token_id = bos_token_id
          self.eos_token_id = eos_token_id
  
          super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
  