text: # coding=utf-8
  # Copyright 2020 The Microsoft Authors and The HuggingFace Inc. team.
  #
  # 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.
  """ ProphetNet model configuration """
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
      "microsoft/prophetnet-large-uncased": "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json",
  }
  
  
  class ProphetNetConfig(PretrainedConfig):
      r"""
      This is the configuration class to store the configuration of a [`ProphetNetModel`]. It is used
      to instantiate a ProphetNet model according to the specified arguments, defining the model architecture.
  
      Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model
      outputs. Read the documentation from [`PretrainedConfig`] for more information.
  
      Args:
          activation_dropout (`float`, *optional*, defaults to 0.1):
              The dropout ratio for activations inside the fully connected layer.
          activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
              The non-linear activation function (function or string) in the encoder and pooler. If string,
              `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
          vocab_size (`int`, *optional*, defaults to 30522):
              Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by
              the `inputs_ids` passed when calling [`ProphetNetModel`].
          hidden_size (`int`, *optional*, defaults to 1024):
              Dimensionality of the layers and the pooler layer.
          encoder_ffn_dim (`int`, *optional*, defaults to 4096):
              Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
          num_encoder_layers (`int`, *optional*, defaults to 12):
              Number of encoder layers.
          num_encoder_attention_heads (`int`, *optional*, defaults to 16):
              Number of attention heads for each attention layer in the Transformer encoder.
          decoder_ffn_dim (`int`, *optional*, defaults to 4096):
              Dimensionality of the `intermediate` (often named feed-forward) layer in decoder.
          num_decoder_layers (`int`, *optional*, defaults to 12):
              Number of decoder layers.
          num_decoder_attention_heads (`int`, *optional*, defaults to 16):
              Number of attention heads for each attention layer in the Transformer decoder.
          attention_dropout (`float`, *optional*, defaults to 0.1):
              The dropout ratio for the attention probabilities.
          dropout (`float`, *optional*, defaults to 0.1):
              The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
          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).
          init_std (`float`, *optional*, defaults to 0.02):
              The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
          add_cross_attention (`bool`, *optional*, defaults to `True`):
              Whether cross-attention layers should be added to the model.
          is_encoder_decoder (`bool`, *optional*, defaults to `True`):
              Whether this is an encoder/decoder model.
          pad_token_id (`int`, *optional*, defaults to 1)
              Padding token id.
          bos_token_id (`int`, *optional*, defaults to 0)
              Beginning of stream token id.
          eos_token_id (`int`, *optional*, defaults to 2)
              End of stream token id.
          ngram (`int`, *optional*, defaults to 2)
              Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
              token.
          num_buckets (`int`, *optional*, defaults to 32)
              The number of buckets to use for each attention layer. This is for relative position calculation. See the
              [T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
          relative_max_distance (`int`, *optional*, defaults to 128)
              Relative distances greater than this number will be put into the last same bucket. This is for relative
              position calculation. See the [T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
          disable_ngram_loss (`bool`, *optional*, defaults to `False`):
              Whether be trained predicting only the next first token.
          eps (`float`, *optional*, defaults to 0.0):
              Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label
              smoothing is performed.
          use_cache (`bool`, *optional*, defaults to `True`):
              Whether or not the model should return the last key/values attentions (not used by all models).
      """
      model_type = "prophetnet"
      keys_to_ignore_at_inference = ["past_key_values"]
      attribute_map = {
          "num_attention_heads": "num_encoder_attention_heads",
      }
  
      def __init__(
          self,
          activation_dropout=0.1,
          activation_function="gelu",
          vocab_size=30522,
          hidden_size=1024,
          encoder_ffn_dim=4096,
          num_encoder_layers=12,
          num_encoder_attention_heads=16,
          decoder_ffn_dim=4096,
          num_decoder_layers=12,
          num_decoder_attention_heads=16,
          attention_dropout=0.1,
          dropout=0.1,
          max_position_embeddings=512,
          init_std=0.02,
          is_encoder_decoder=True,
          add_cross_attention=True,
          decoder_start_token_id=0,
          ngram=2,
          num_buckets=32,
          relative_max_distance=128,
          disable_ngram_loss=False,
          eps=0.0,
          use_cache=True,
          pad_token_id=0,
          bos_token_id=1,
          eos_token_id=2,
          **kwargs
      ):
          self.vocab_size = vocab_size
          self.hidden_size = hidden_size
          self.encoder_ffn_dim = encoder_ffn_dim
          self.num_encoder_layers = num_encoder_layers
          self.num_encoder_attention_heads = num_encoder_attention_heads
          self.decoder_ffn_dim = decoder_ffn_dim
          self.num_decoder_layers = num_decoder_layers
          self.num_decoder_attention_heads = num_decoder_attention_heads
          self.max_position_embeddings = max_position_embeddings
          self.init_std = init_std  # Normal(0, this parameter)
          self.activation_function = activation_function
  
          # parameters for prophetnet
          self.ngram = ngram
          self.num_buckets = num_buckets
          self.relative_max_distance = relative_max_distance
          self.disable_ngram_loss = disable_ngram_loss
          self.eps = eps
  
          # 3 Types of Dropout
          self.attention_dropout = attention_dropout
          self.activation_dropout = activation_dropout
          self.dropout = dropout
  
          self.use_cache = use_cache
  
          super().__init__(
              pad_token_id=pad_token_id,
              bos_token_id=bos_token_id,
              eos_token_id=eos_token_id,
              is_encoder_decoder=is_encoder_decoder,
              add_cross_attention=add_cross_attention,
              decoder_start_token_id=decoder_start_token_id,
              **kwargs,
          )
  
      @property
      def num_hidden_layers(self) -> int:
          return self.num_encoder_layers + self.num_decoder_layers
  
      @num_hidden_layers.setter
      def num_hidden_layers(self, value):
          raise NotImplementedError(
              "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and `num_decoder_layers`."
          )
  