




'''simple docstring'''


from collections.abc import Sequence
def 						snake_case_       (UpperCamelCase			:    Sequence[int] | None = None ):





							'''simple docstring'''


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

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

							return ans


if __name__ == "__main__":
				import doctest

				doctest.testmod()

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







'''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      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
														_a      	=							AutoTokenizer.from_pretrained('''google/mt5-small'''   )

														_a      	=							tokenizer('''Hello there'''				,     return_tensors='''np'''   ).input_ids
														_a      	=							tokenizer('''Hi I am'''				,     return_tensors='''np'''   ).input_ids

														_a      	=							shift_tokens_right(lowerCAmelCase_				,     model.config.pad_token_id				,     model.config.decoder_start_token_id   )

														_a      	=							model(lowerCAmelCase_				,     decoder_input_ids=lowerCAmelCase_   ).logits
														_a      	=							optax.softmax_cross_entropy(lowerCAmelCase_				,     onehot(lowerCAmelCase_				,     logits.shape[-1]   )   ).mean()

														_a      	=							-(labels.shape[-1] * loss.item())

														_a      	=							-8_4.9_1_2_7
														self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )

