





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


UpperCAmelCase_											=  logging.get_logger(__name__)

UpperCAmelCase_											=  {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}




class  __UpperCamelCase						(       A__						):
     __A  :     List[Any]            =      """vilt"""





     def __init__(    self							,       _UpperCamelCase=30522							,       _UpperCamelCase=2							,       _UpperCamelCase=2							,       _UpperCamelCase=40							,       _UpperCamelCase=768							,       _UpperCamelCase=12							,       _UpperCamelCase=12							,       _UpperCamelCase=3072							,       _UpperCamelCase="gelu"							,       _UpperCamelCase=0.0							,       _UpperCamelCase=0.0							,       _UpperCamelCase=0.02							,       _UpperCamelCase=1e-12							,       _UpperCamelCase=384							,       _UpperCamelCase=32							,       _UpperCamelCase=3							,       _UpperCamelCase=True							,       _UpperCamelCase=-1							,       _UpperCamelCase=False							,       _UpperCamelCase=-1							,       **_UpperCamelCase							,       ):
        super().__init__(tie_word_embeddings=_UpperCamelCase							,       **_UpperCamelCase				)

        _UpperCAmelCase											=      vocab_size
        _UpperCAmelCase											=      type_vocab_size
        _UpperCAmelCase											=      modality_type_vocab_size
        _UpperCAmelCase											=      max_position_embeddings

        _UpperCAmelCase											=      hidden_size
        _UpperCAmelCase											=      num_hidden_layers
        _UpperCAmelCase											=      num_attention_heads
        _UpperCAmelCase											=      intermediate_size
        _UpperCAmelCase											=      hidden_act
        _UpperCAmelCase											=      hidden_dropout_prob
        _UpperCAmelCase											=      attention_probs_dropout_prob
        _UpperCAmelCase											=      initializer_range
        _UpperCAmelCase											=      layer_norm_eps

        _UpperCAmelCase											=      image_size
        _UpperCAmelCase											=      patch_size
        _UpperCAmelCase											=      num_channels
        _UpperCAmelCase											=      qkv_bias
        _UpperCAmelCase											=      max_image_length
        _UpperCAmelCase											=      num_images






def     A__					(						SCREAMING_SNAKE_CASE_      :			list	)     ->     bool:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	):
      raise ValueError('''Input series is not valid, valid series - [1, 2/3, 2]'''	)
   if len(SCREAMING_SNAKE_CASE_	) == 0:
      raise ValueError('''Input list must be a non empty list'''	)
   if len(SCREAMING_SNAKE_CASE_	) == 1 and series[0] != 0:
      return True
   _UpperCAmelCase											=      []
   _UpperCAmelCase											=      len(SCREAMING_SNAKE_CASE_	)
   for i in range(0							,  SCREAMING_SNAKE_CASE_	):
      if series[i] == 0:
         raise ValueError('''Input series cannot have 0 as an element'''	)
      rec_series.append(1 / series[i]	)
   _UpperCAmelCase											=      rec_series[1] - rec_series[0]
   for index in range(2							,  SCREAMING_SNAKE_CASE_	):
      if rec_series[index] - rec_series[index - 1] != common_diff:
         return False
   return True
def     A__					(						SCREAMING_SNAKE_CASE_      :			list	)     ->     float:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	):
      raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''	)
   if len(SCREAMING_SNAKE_CASE_	) == 0:
      raise ValueError('''Input list must be a non empty list'''	)
   _UpperCAmelCase											=      0
   for val in series:
      answer += 1 / val
   return len(SCREAMING_SNAKE_CASE_	) / answer


if __name__ == "__main__":
   import doctest

   doctest.testmod()