"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a      :		Optional[int]           =   logging.get_logger(__name__)

_a      :		List[Any]           =   {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			List[str]							=		"ibert"






     def __init__(		self				,     a__=30522				,     a__=768				,     a__=12				,     a__=12				,     a__=3072				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=512				,     a__=2				,     a__=0.0_2				,     a__=1e-12				,     a__=1				,     a__=0				,     a__=2				,     a__="absolute"				,     a__=False				,     a__="none"				,     **a__				,     ):
       super().__init__(pad_token_id=a__				,     bos_token_id=a__				,     eos_token_id=a__				,     **a__  )

       _lowerCAmelCase						:       List[str]  			=     vocab_size
       _lowerCAmelCase						:       Tuple  			=     hidden_size
       _lowerCAmelCase						:       int  			=     num_hidden_layers
       _lowerCAmelCase						:       Union[str, Any]  			=     num_attention_heads
       _lowerCAmelCase						:       Optional[Any]  			=     hidden_act
       _lowerCAmelCase						:       List[Any]  			=     intermediate_size
       _lowerCAmelCase						:       List[str]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Dict  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       List[str]  			=     max_position_embeddings
       _lowerCAmelCase						:       Dict  			=     type_vocab_size
       _lowerCAmelCase						:       Union[str, Any]  			=     initializer_range
       _lowerCAmelCase						:       Union[str, Any]  			=     layer_norm_eps
       _lowerCAmelCase						:       Optional[int]  			=     position_embedding_type
       _lowerCAmelCase						:       Any  			=     quant_mode
       _lowerCAmelCase						:       Union[str, Any]  			=     force_dequant







class 						__A							( SCREAMING_SNAKE_CASE_		):






     @property
     def     __A  (		self  ):
       if self.task == "multiple-choice":
         _lowerCAmelCase						:       Any  			=     {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         _lowerCAmelCase						:       Union[str, Any]  			=     {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]  )





"""simple docstring"""




from __future__ import annotations

_a      :		List[str]           =   10





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       list[int]	)       ->      list[int]:
  _lowerCAmelCase						:       Optional[int]  			=     1
  _lowerCAmelCase						:       Union[str, Any]  			=     max(_lowerCamelCase	)
  while placement <= max_digit:
    # declare and initialize empty buckets
    _lowerCAmelCase						:       list[list]  			=     [[] for _ in range(_lowerCamelCase	)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      _lowerCAmelCase						:       Tuple  			=     int((i / placement) % RADIX	)
      buckets[tmp].append(_lowerCamelCase	)
    # put each buckets' contents into list_of_ints
    _lowerCAmelCase						:       List[str]  			=     0
    for b in range(_lowerCamelCase	):
      for i in buckets[b]:
        _lowerCAmelCase						:       Any  			=     i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
     import doctest

     doctest.testmod()




