"""simple docstring"""






from collections.abc import Sequence


def     UpperCAmelCase__ (       lowerCAmelCase__  :Sequence[int] | None = None	)				->					int:
      '''simple docstring'''


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

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

      return ans


if __name__ == "__main__":
       import doctest

       doctest.testmod()

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




"""simple docstring"""






import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.roberta.modeling_flax_roberta import (
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaModel,
       )





class 				_A		( unittest.TestCase			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase=13	, __lowerCAmelCase=7	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=99	, __lowerCAmelCase=32	, __lowerCAmelCase=5	, __lowerCAmelCase=4	, __lowerCAmelCase=37	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.1	, __lowerCAmelCase=512	, __lowerCAmelCase=16	, __lowerCAmelCase=2	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=4	, ):







            """simple docstring"""



            lowercase														=			parent
            lowercase														=			batch_size
            lowercase														=			seq_length
            lowercase														=			is_training
            lowercase														=			use_attention_mask
            lowercase														=			use_token_type_ids
            lowercase														=			use_labels
            lowercase														=			vocab_size
            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			max_position_embeddings
            lowercase														=			type_vocab_size
            lowercase														=			type_sequence_label_size
            lowercase														=			initializer_range
            lowercase														=			num_choices

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size		)

            lowercase														=			None
            if self.use_attention_mask:
                  lowercase														=			random_attention_mask([self.batch_size, self.seq_length]		)

            lowercase														=			None
            if self.use_token_type_ids:
                  lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.type_vocab_size		)

            lowercase														=			RobertaConfig(
                vocab_size=self.vocab_size	, hidden_size=self.hidden_size	, num_hidden_layers=self.num_hidden_layers	, num_attention_heads=self.num_attention_heads	, intermediate_size=self.intermediate_size	, hidden_act=self.hidden_act	, hidden_dropout_prob=self.hidden_dropout_prob	, attention_probs_dropout_prob=self.attention_probs_dropout_prob	, max_position_embeddings=self.max_position_embeddings	, type_vocab_size=self.type_vocab_size	, is_decoder=__lowerCAmelCase	, initializer_range=self.initializer_range	, )

            return config, input_ids, token_type_ids, attention_mask

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            lowercase							,							lowercase							,							lowercase							,							lowercase														=			config_and_inputs
            lowercase														=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
            return config, inputs_dict







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            lowercase							,							lowercase							,							lowercase							,							lowercase														=			config_and_inputs

            lowercase														=			True
            lowercase														=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
            lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, vocab_size=2		)

            return (
                config,
                input_ids,
                token_type_ids,
                encoder_hidden_states,
                encoder_attention_mask,
            )






@require_flax
class 				_A		( lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     Optional[int]													=			True

      snake_case__    :     List[str]													=			(
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			FlaxRobertaModelTester(self		)







      @slow
      def        A__ (		self		):







            """simple docstring"""



            for model_class_name in self.all_model_classes:
                  lowercase														=			model_class_name.from_pretrained("""roberta-base"""	, from_pt=__lowerCAmelCase		)
                  lowercase														=			model(np.ones((1, 1)		)		)
                  self.assertIsNotNone(__lowerCAmelCase		)



