






from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase	:		Any										=   logging.get_logger(__name__)

__lowerCamelCase	:		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  _lowercase   (   _A						):
      _a							:						Dict										=							'xlm-roberta-xl'
      def __init__(     self				,			a=2_5_0_8_8_0				,			a=2_5_6_0				,			a=3_6				,			a=3_2				,			a=1_0_2_4_0				,			a="gelu"				,			a=0.1				,			a=0.1				,			a=5_1_4				,			a=1				,			a=0.02				,			a=1e-05				,			a=1				,			a=0				,			a=2				,			a="absolute"				,			a=True				,			a=None				,			**a				,			):
       super().__init__(pad_token_id=a				,			bos_token_id=a				,			eos_token_id=a				,			**a   )
       snake_case__  :    List[str]							       =vocab_size
       snake_case__  :    List[Any]							       =hidden_size
       snake_case__  :    int							       =num_hidden_layers
       snake_case__  :    Dict							       =num_attention_heads
       snake_case__  :    List[str]							       =hidden_act
       snake_case__  :    Dict							       =intermediate_size
       snake_case__  :    Tuple							       =hidden_dropout_prob
       snake_case__  :    str							       =attention_probs_dropout_prob
       snake_case__  :    Optional[int]							       =max_position_embeddings
       snake_case__  :    Any							       =type_vocab_size
       snake_case__  :    Dict							       =initializer_range
       snake_case__  :    List[str]							       =layer_norm_eps
       snake_case__  :    Any							       =position_embedding_type
       snake_case__  :    str							       =use_cache
       snake_case__  :    Optional[Any]							       =classifier_dropout







class  _lowercase   (   _A						):
      @property
      def 						lowercase__					(     self   ):
       if self.task == "multiple-choice":
        snake_case__  :    Dict							       ={0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        snake_case__  :    str							       ={0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]   )













def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) <= 1:
  return [tuple(_a	)]

 snake_case__  :    Optional[int]							       =[]

 def generate(_a  :					int		,  _a  :					list	):
  if k == 1:
   res.append(tuple(arr[:]	)	)
   return

  generate(k - 1		,  _a	)

  for i in range(k - 1	):
   if k % 2 == 0:  # k is even
    snake_case__       ,   snake_case__  :    Dict							       =arr[k - 1], arr[i]
   else:  # k is odd
    snake_case__       ,   snake_case__  :    int							       =arr[k - 1], arr[0]
   generate(k - 1		,  _a	)

 generate(len(_a	)		,  _a	)
 return res


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





