text: # coding=utf-8
  # The MIT License (MIT)
  
  # Copyright (c) Microsoft Corporation
  
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  """ MiniLM model configuration """
  
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import json
  import logging
  import sys
  from io import open
  
  from transformers.configuration_utils import PretrainedConfig
  
  logger = logging.getLogger(__name__)
  
  MINILM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
      'minilm-l12-h384-uncased': "https://conversationhub.blob.core.windows.net/beit-share-public/ckpt/minilm-l12-h384-uncased-config.json",
  }
  
  
  class MinilmConfig(PretrainedConfig):
      r"""
          :class:`~transformers.MinilmConfig` is the configuration class to store the configuration of a
          `MinilmModel`.
          Arguments:
              vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `MiniLMModel`.
              hidden_size: Size of the encoder layers and the pooler layer.
              num_hidden_layers: Number of hidden layers in the Transformer encoder.
              num_attention_heads: Number of attention heads for each attention layer in
                  the Transformer encoder.
              intermediate_size: The size of the "intermediate" (i.e., feed-forward)
                  layer in the Transformer encoder.
              hidden_act: The non-linear activation function (function or string) in the
                  encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported.
              hidden_dropout_prob: The dropout probabilitiy for all fully connected
                  layers in the embeddings, encoder, and pooler.
              attention_probs_dropout_prob: The dropout ratio for the attention
                  probabilities.
              max_position_embeddings: 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: The vocabulary size of the `token_type_ids` passed into
                  `MiniLMModel`.
              initializer_range: The sttdev of the truncated_normal_initializer for
                  initializing all weight matrices.
              layer_norm_eps: The epsilon used by LayerNorm.
      """
      pretrained_config_archive_map = MINILM_PRETRAINED_CONFIG_ARCHIVE_MAP
  
      def __init__(self,
                   vocab_size=28996,
                   hidden_size=768,
                   num_hidden_layers=12,
                   num_attention_heads=12,
                   intermediate_size=3072,
                   hidden_act="gelu",
                   hidden_dropout_prob=0.1,
                   attention_probs_dropout_prob=0.1,
                   max_position_embeddings=512,
                   type_vocab_size=6,
                   initializer_range=0.02,
                   layer_norm_eps=1e-12,
                   source_type_id=0, 
                   target_type_id=1,
                   **kwargs):
          super(MinilmConfig, self).__init__(**kwargs)
          if isinstance(vocab_size, str) or (sys.version_info[0] == 2
                                             and isinstance(vocab_size, unicode)):
              with open(vocab_size, "r", encoding='utf-8') as reader:
                  json_config = json.loads(reader.read())
              for key, value in json_config.items():
                  self.__dict__[key] = value
          elif isinstance(vocab_size, int):
              self.vocab_size = vocab_size
              self.hidden_size = hidden_size
              self.num_hidden_layers = num_hidden_layers
              self.num_attention_heads = num_attention_heads
              self.hidden_act = hidden_act
              self.intermediate_size = intermediate_size
              self.hidden_dropout_prob = hidden_dropout_prob
              self.attention_probs_dropout_prob = attention_probs_dropout_prob
              self.max_position_embeddings = max_position_embeddings
              self.type_vocab_size = type_vocab_size
              self.initializer_range = initializer_range
              self.layer_norm_eps = layer_norm_eps
              self.source_type_id = source_type_id
              self.target_type_id = target_type_id
          else:
              raise ValueError("First argument must be either a vocabulary size (int)"
                               " or the path to a pretrained model config file (str)")
  