text: # coding=utf-8
  # Copyright 2022 The REALM 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.
  """ REALM model configuration."""
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  REALM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
      "qqaatw/realm-cc-news-pretrained-embedder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-embedder/resolve/main/config.json",
      "qqaatw/realm-cc-news-pretrained-encoder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-encoder/resolve/main/config.json",
      "qqaatw/realm-cc-news-pretrained-scorer": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-scorer/resolve/main/config.json",
      "qqaatw/realm-cc-news-pretrained-openqa": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-openqa/aresolve/main/config.json",
      "qqaatw/realm-orqa-nq-openqa": "https://huggingface.co/qqaatw/realm-orqa-nq-openqa/resolve/main/config.json",
      "qqaatw/realm-orqa-nq-reader": "https://huggingface.co/qqaatw/realm-orqa-nq-reader/resolve/main/config.json",
      "qqaatw/realm-orqa-wq-openqa": "https://huggingface.co/qqaatw/realm-orqa-wq-openqa/resolve/main/config.json",
      "qqaatw/realm-orqa-wq-reader": "https://huggingface.co/qqaatw/realm-orqa-wq-reader/resolve/main/config.json",
      # See all REALM models at https://huggingface.co/models?filter=realm
  }
  
  
  class RealmConfig(PretrainedConfig):
      r"""
      This is the configuration class to store the configuration of
  
      1. [`RealmEmbedder`]
      2. [`RealmScorer`]
      3. [`RealmKnowledgeAugEncoder`]
      4. [`RealmRetriever`]
      5. [`RealmReader`]
      6. [`RealmForOpenQA`]
  
      It is used to instantiate an REALM 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 REALM
      [realm-cc-news-pretrained](https://huggingface.co/qqaatw/realm-cc-news-pretrained-embedder) 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 30522):
              Vocabulary size of the REALM model. Defines the number of different tokens that can be represented by the
              `inputs_ids` passed when calling [`RealmEmbedder`], [`RealmScorer`], [`RealmKnowledgeAugEncoder`], or
              [`RealmReader`].
          hidden_size (`int`, *optional*, defaults to 768):
              Dimension of the encoder layers and the pooler layer.
          retriever_proj_size (`int`, *optional*, defaults to 128):
              Dimension of the retriever(embedder) projection.
          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.
          num_candidates (`int`, *optional*, defaults to 8):
              Number of candidates inputted to the RealmScorer or RealmKnowledgeAugEncoder.
          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_new"`):
              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 probabilitiy 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 [`RealmEmbedder`], [`RealmScorer`],
              [`RealmKnowledgeAugEncoder`], or [`RealmReader`].
          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.
          span_hidden_size (`int`, *optional*, defaults to 256):
              Dimension of the reader's spans.
          max_span_width (`int`, *optional*, defaults to 10):
              Max span width of the reader.
          reader_layer_norm_eps (`float`, *optional*, defaults to 1e-3):
              The epsilon used by the reader's layer normalization layers.
          reader_beam_size (`int`, *optional*, defaults to 5):
              Beam size of the reader.
          reader_seq_len (`int`, *optional*, defaults to 288+32):
              Maximum sequence length of the reader.
          num_block_records (`int`, *optional*, defaults to 13353718):
              Number of block records.
          searcher_beam_size (`int`, *optional*, defaults to 5000):
              Beam size of the searcher. Note that when eval mode is enabled, *searcher_beam_size* will be the same as
              *reader_beam_size*.
          searcher_seq_len (`int`, *optional*, defaults to 64):
              Maximum sequence length of the searcher.
  
      Example:
  
      ```python
      >>> from transformers import RealmEmbedder, RealmConfig
  
      >>> # Initializing a REALM realm-cc-news-pretrained-* style configuration
      >>> configuration = RealmConfig()
  
      >>> # Initializing a model from the qqaatw/realm-cc-news-pretrained-embedder style configuration
      >>> model = RealmEmbedder(configuration)
  
      >>> # Accessing the model configuration
      >>> configuration = model.config
      ```"""
      model_type = "realm"
  
      def __init__(
          self,
          vocab_size=30522,
          hidden_size=768,
          retriever_proj_size=128,
          num_hidden_layers=12,
          num_attention_heads=12,
          num_candidates=8,
          intermediate_size=3072,
          hidden_act="gelu_new",
          hidden_dropout_prob=0.1,
          attention_probs_dropout_prob=0.1,
          max_position_embeddings=512,
          type_vocab_size=2,
          initializer_range=0.02,
          layer_norm_eps=1e-12,
          span_hidden_size=256,
          max_span_width=10,
          reader_layer_norm_eps=1e-3,
          reader_beam_size=5,
          reader_seq_len=320,  # 288 + 32
          num_block_records=13353718,
          searcher_beam_size=5000,
          searcher_seq_len=64,
          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)
  
          # Common config
          self.vocab_size = vocab_size
          self.max_position_embeddings = max_position_embeddings
          self.hidden_size = hidden_size
          self.retriever_proj_size = retriever_proj_size
          self.num_hidden_layers = num_hidden_layers
          self.num_attention_heads = num_attention_heads
          self.num_candidates = num_candidates
          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
  
          # Reader config
          self.span_hidden_size = span_hidden_size
          self.max_span_width = max_span_width
          self.reader_layer_norm_eps = reader_layer_norm_eps
          self.reader_beam_size = reader_beam_size
          self.reader_seq_len = reader_seq_len
  
          # Retrieval config
          self.num_block_records = num_block_records
          self.searcher_beam_size = searcher_beam_size
          self.searcher_seq_len = searcher_seq_len
  