from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase					      =							logging.get_logger(__name__)

UpperCAmelCase					      =							{
    "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        snake_case__							(					UpperCamelCase__				):
   _SCREAMING_SNAKE_CASE						:			str         =   "xlm-roberta-xl"



   def __init__(					self : Dict      ,						A__ : Optional[Any]=25_08_80      ,						A__ : List[Any]=25_60      ,						A__ : Optional[Any]=36      ,						A__ : Any=32      ,						A__ : int=1_02_40      ,						A__ : List[Any]="gelu"      ,						A__ : Union[str, Any]=0.1      ,						A__ : Optional[Any]=0.1      ,						A__ : str=5_14      ,						A__ : Union[str, Any]=1      ,						A__ : Optional[int]=0.02      ,						A__ : str=1E-05      ,						A__ : str=1      ,						A__ : int=0      ,						A__ : Tuple=2      ,						A__ : Optional[int]="absolute"      ,						A__ : str=True      ,						A__ : Any=None      ,						**A__ : Dict      ,						)				->  int:




         '''simple docstring'''




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



class        snake_case__							(					UpperCamelCase__				):



   @property
   def        UpperCAmelCase__							(					self : Dict )				->  Any:




         '''simple docstring'''




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









from collections.abc import Sequence




def 		lowerCAmelCase_ (						lowerCamelCase = None ):
				if nums is None or not nums:
								raise ValueError("""Input sequence should not be empty""" )

				__magic_name__						:						str										=nums[0]
				for i in range(1						,  len(lowerCamelCase ) ):
								__magic_name__						:						Any										=nums[i]
								__magic_name__						:						Dict										=max(lowerCamelCase						,  ans + num						,  lowerCamelCase )

				return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							UpperCAmelCase_		:					List[str]          =  int(input("Enter number of elements : ").strip())
							UpperCAmelCase_		:					Tuple          =  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
							print(max_subsequence_sum(array))




