
'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__snake_case:  List[Any]								=			logging.get_logger(__name__)

__snake_case:  Union[str, Any]								=			{
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"xlm-roberta-xl"


     def __init__(      self							,		lowerCAmelCase_=25_08_80							,		lowerCAmelCase_=25_60							,		lowerCAmelCase_=36							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_02_40							,		lowerCAmelCase_="gelu"							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=5_14							,		lowerCAmelCase_=1							,		lowerCAmelCase_=0.02							,		lowerCAmelCase_=1E-0_5							,		lowerCAmelCase_=1							,		lowerCAmelCase_=0							,		lowerCAmelCase_=2							,		lowerCAmelCase_="absolute"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(pad_token_id=lowerCAmelCase_							,		bos_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Tuple      =							vocab_size
       a_      :	str      =							hidden_size
       a_      :	Optional[Any]      =							num_hidden_layers
       a_      :	List[str]      =							num_attention_heads
       a_      :	str      =							hidden_act
       a_      :	Tuple      =							intermediate_size
       a_      :	List[Any]      =							hidden_dropout_prob
       a_      :	List[str]      =							attention_probs_dropout_prob
       a_      :	Optional[Any]      =							max_position_embeddings
       a_      :	List[str]      =							type_vocab_size
       a_      :	int      =							initializer_range
       a_      :	Optional[Any]      =							layer_norm_eps
       a_      :	Union[str, Any]      =							position_embedding_type
       a_      :	List[Any]      =							use_cache
       a_      :	int      =							classifier_dropout







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       if self.task == "multiple-choice":
         a_      :	Any      =							{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         a_      :	Tuple      =							{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ] )




'''simple docstring'''

def 	_snake_case						(      A_       :    list   ):
  """simple docstring"""







  if len(A_   ) <= 1:
    return lst

  a_      :	Any      =							1

  while i < len(A_   ):
    if lst[i - 1] <= lst[i]:
      i += 1
    else:
      a_      , a_      :	int      =							lst[i], lst[i - 1]
      i -= 1
      if i == 0:
        a_      :	List[str]      =							1

  return lst


if __name__ == "__main__":
    __snake_case:  List[Any]								=			input("Enter numbers separated by a comma:\n").strip()
    __snake_case:  Optional[int]								=			[int(item) for item in user_input.split(",")]
    print(gnome_sort(unsorted))


