





'''simple docstring'''

class lowerCamelCase						:







  def __init__(		self     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     arr.split(''','''		)







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





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


if __name__ == "__main__":
   UpperCAmelCase					:		List[str]		=							input('please input some numbers:')
   UpperCAmelCase					:		str		=							SubArray(whole_array)
   UpperCAmelCase					:		List[Any]		=							array.solve_sub_array()
   print(('the results is:', re))









'''simple docstring'''

import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class lowerCamelCase						(unittest.TestCase					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Tuple					     =     FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
        _snake_case		:	Any					     =     AutoTokenizer.from_pretrained('''google/mt5-small'''		)

        _snake_case		:	List[str]					     =     tokenizer('''Hello there'''     ,     return_tensors='''np'''		).input_ids
        _snake_case		:	Dict					     =     tokenizer('''Hi I am'''     ,     return_tensors='''np'''		).input_ids

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     model.config.pad_token_id     ,     model.config.decoder_start_token_id		)

        _snake_case		:	Any					     =     model(lowercase__     ,     decoder_input_ids=lowercase__		).logits
        _snake_case		:	Tuple					     =     optax.softmax_cross_entropy(lowercase__     ,     onehot(lowercase__     ,     logits.shape[-1]		)		).mean()

        _snake_case		:	Tuple					     =     -(labels.shape[-1] * loss.item())

        _snake_case		:	Union[str, Any]					     =     -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)


