






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}







class 							__magic_name__     (		_UpperCamelCase   ):
					_SCREAMING_SNAKE_CASE       :					int     							=   'xlm'
					_SCREAMING_SNAKE_CASE       :					int     							=   {
					    'hidden_size': 'emb_dim',
					    'num_attention_heads': 'n_heads',
					    'num_hidden_layers': 'n_layers',
					    'n_words': 'vocab_size',  # For backward compatibility
					}





					def __init__(				self   :							str							,						snake_case_   :							Optional[int]=30145							,						snake_case_   :							Union[str, Any]=2048							,						snake_case_   :							List[Any]=12							,						snake_case_   :							Optional[Any]=16							,						snake_case_   :							Optional[int]=0.1							,						snake_case_   :							str=0.1							,						snake_case_   :							List[Any]=True							,						snake_case_   :							Optional[int]=False							,						snake_case_   :							Any=False							,						snake_case_   :							List[str]=False							,						snake_case_   :							List[Any]=1							,						snake_case_   :							List[Any]=True							,						snake_case_   :							Any=512							,						snake_case_   :							Tuple=2048**-0.5							,						snake_case_   :							Dict=1e-12							,						snake_case_   :							Any=0.02							,						snake_case_   :							List[str]=0							,						snake_case_   :							Tuple=1							,						snake_case_   :							Optional[int]=2							,						snake_case_   :							Union[str, Any]=3							,						snake_case_   :							Optional[Any]=5							,						snake_case_   :							List[str]=True							,						snake_case_   :							str="first"							,						snake_case_   :							Optional[int]=True							,						snake_case_   :							Any=None							,						snake_case_   :							Tuple=True							,						snake_case_   :							Tuple=0.1							,						snake_case_   :							List[str]=5							,						snake_case_   :							List[str]=5							,						snake_case_   :							Dict=0							,						snake_case_   :							Tuple=0							,						snake_case_   :							int=2							,						snake_case_   :							Dict=0							,						**snake_case_   :							Dict							,						):
												__snake_case     			=       vocab_size
												__snake_case     			=       emb_dim
												__snake_case     			=       n_layers
												__snake_case     			=       n_heads
												__snake_case     			=       dropout
												__snake_case     			=       attention_dropout
												__snake_case     			=       gelu_activation
												__snake_case     			=       sinusoidal_embeddings
												__snake_case     			=       causal
												__snake_case     			=       asm
												__snake_case     			=       n_langs
												__snake_case     			=       use_lang_emb
												__snake_case     			=       layer_norm_eps
												__snake_case     			=       bos_index
												__snake_case     			=       eos_index
												__snake_case     			=       pad_index
												__snake_case     			=       unk_index
												__snake_case     			=       mask_index
												__snake_case     			=       is_encoder
												__snake_case     			=       max_position_embeddings
												__snake_case     			=       embed_init_std
												__snake_case     			=       init_std
												__snake_case     			=       summary_type
												__snake_case     			=       summary_use_proj
												__snake_case     			=       summary_activation
												__snake_case     			=       summary_proj_to_labels
												__snake_case     			=       summary_first_dropout
												__snake_case     			=       start_n_top
												__snake_case     			=       end_n_top
												__snake_case     			=       mask_token_id
												__snake_case     			=       lang_id

												if "n_words" in kwargs:
																			__snake_case     			=       kwargs["n_words"]

												super().__init__(pad_token_id=snake_case_							,						bos_token_id=snake_case_							,						**snake_case_    )







class 							__magic_name__     (		_UpperCamelCase   ):





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


"""simple docstring"""







from collections.abc import Sequence
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = None )  ->     int:







							"""simple docstring"""


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

							__snake_case     			=       nums[0]
							for i in range(1							,    len(SCREAMING_SNAKE_CASE ) ):
														__snake_case     			=       nums[i]
														__snake_case     			=       max(SCREAMING_SNAKE_CASE							,    ans + num							,    SCREAMING_SNAKE_CASE )

							return ans


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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