


'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}





class 						__SCREAMING_SNAKE_CASE					(      __lowerCAmelCase ):
     UpperCAmelCase 					=				'''deberta-v2'''






     def __init__(			self     ,							__UpperCamelCase=128_100     ,							__UpperCamelCase=1_536     ,							__UpperCamelCase=24     ,							__UpperCamelCase=24     ,							__UpperCamelCase=6_144     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=512     ,							__UpperCamelCase=0     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-7     ,							__UpperCamelCase=False     ,							__UpperCamelCase=-1     ,							__UpperCamelCase=0     ,							__UpperCamelCase=True     ,							__UpperCamelCase=None     ,							__UpperCamelCase=0     ,							__UpperCamelCase="gelu"     ,							**__UpperCamelCase     ,							)       ->    Any:
           super().__init__(**_UpperCamelCase		)

           _a						=		hidden_size
           _a						=		num_hidden_layers
           _a						=		num_attention_heads
           _a						=		intermediate_size
           _a						=		hidden_act
           _a						=		hidden_dropout_prob
           _a						=		attention_probs_dropout_prob
           _a						=		max_position_embeddings
           _a						=		type_vocab_size
           _a						=		initializer_range
           _a						=		relative_attention
           _a						=		max_relative_positions
           _a						=		pad_token_id
           _a						=		position_biased_input

           # Backwards compatibility
           if type(_UpperCamelCase		) == str:
                 _a						=		[x.strip() for x in pos_att_type.lower().split("|"		)]

           _a						=		pos_att_type
           _a						=		vocab_size
           _a						=		layer_norm_eps

           _a						=		kwargs.get("pooler_hidden_size"     ,							_UpperCamelCase		)
           _a						=		pooler_dropout
           _a						=		pooler_hidden_act







class 						__SCREAMING_SNAKE_CASE					(      __lowerCAmelCase ):






     @property
     def  a_					(			self		)       ->    Mapping[str, Mapping[int, str]]:


           if self.task == "multiple-choice":
                 _a						=		{0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
                 _a						=		{0: """batch""", 1: """sequence"""}
           if self._config.type_vocab_size > 0:
                 return OrderedDict(
                     [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]		)
           else:
                 return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]		)






     @property
     def  a_					(			self		)       ->    int:
           return 12






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = -1     ,							__UpperCamelCase = -1     ,							__UpperCamelCase = -1     ,							__UpperCamelCase = False     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 3     ,							__UpperCamelCase = 40     ,							__UpperCamelCase = 40     ,							__UpperCamelCase = None     ,							)       ->    Mapping[str, Any]:
           _a						=		super().generate_dummy_inputs(preprocessor=_UpperCamelCase     ,							framework=_UpperCamelCase		)
           if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                 del dummy_inputs["token_type_ids"]
           return dummy_inputs





'''simple docstring'''

class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase		)       ->    Optional[Any]:
          # we need a list not a string, so do something to change the type
          _a						=		arr.split(","		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		[int(self.array[0]		)] * len(self.array		)
          _a						=		[int(self.array[0]		)] * len(self.array		)
          for i in range(1     ,							len(self.array		)		):
               _a						=		max(
                   int(self.array[i]		) + sum_value[i - 1]     ,							int(self.array[i]		)		)
               _a						=		max(sum_value[i]     ,							rear[i - 1]		)
          return rear[len(self.array		) - 1]


if __name__ == "__main__":
   lowercase__				     =		input("please input some numbers:")
   lowercase__				     =		SubArray(whole_array)
   lowercase__				     =		array.solve_sub_array()
   print(("the results is:", re))

