
"""simple docstring"""







import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, 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():
		import jax.numpy as jnp

		from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		)







class 							__magic_name__     (		unittest.TestCase   ):





					def __init__(				self   :							Tuple							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[int]=13							,						snake_case_   :							List[str]=7							,						snake_case_   :							str=True							,						snake_case_   :							Optional[int]=True							,						snake_case_   :							Dict=True							,						snake_case_   :							int=True							,						snake_case_   :							Tuple=99							,						snake_case_   :							List[str]=32							,						snake_case_   :							Dict=5							,						snake_case_   :							List[str]=4							,						snake_case_   :							List[str]=37							,						snake_case_   :							List[str]="gelu"							,						snake_case_   :							int=0.1							,						snake_case_   :							Optional[int]=0.1							,						snake_case_   :							Optional[Any]=512							,						snake_case_   :							Union[str, Any]=16							,						snake_case_   :							List[Any]=2							,						snake_case_   :							str=0.02							,						snake_case_   :							str=4							,						):
												__snake_case     			=       parent
												__snake_case     			=       batch_size
												__snake_case     			=       seq_length
												__snake_case     			=       is_training
												__snake_case     			=       use_attention_mask
												__snake_case     			=       use_token_type_ids
												__snake_case     			=       use_labels
												__snake_case     			=       vocab_size
												__snake_case     			=       hidden_size
												__snake_case     			=       num_hidden_layers
												__snake_case     			=       num_attention_heads
												__snake_case     			=       intermediate_size
												__snake_case     			=       hidden_act
												__snake_case     			=       hidden_dropout_prob
												__snake_case     			=       attention_probs_dropout_prob
												__snake_case     			=       max_position_embeddings
												__snake_case     			=       type_vocab_size
												__snake_case     			=       type_sequence_label_size
												__snake_case     			=       initializer_range
												__snake_case     			=       num_choices





					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size    )

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

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

												__snake_case     			=       RobertaPreLayerNormConfig(
												    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=snake_case_							,						initializer_range=self.initializer_range							,						)

												return config, input_ids, token_type_ids, attention_mask





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       self.prepare_config_and_inputs()
												__snake_case			,   __snake_case			,   __snake_case			,   __snake_case     			=       config_and_inputs
												__snake_case     			=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
												return config, inputs_dict





					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       self.prepare_config_and_inputs()
												__snake_case			,   __snake_case			,   __snake_case			,   __snake_case     			=       config_and_inputs

												__snake_case     			=       True
												__snake_case     			=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
												__snake_case     			=       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
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 							__magic_name__     (		lowercase__      ,		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					str     							=   True

					_SCREAMING_SNAKE_CASE       :					List[str]     							=   (
					    (
					        FlaxRobertaPreLayerNormModel,
					        FlaxRobertaPreLayerNormForCausalLM,
					        FlaxRobertaPreLayerNormForMaskedLM,
					        FlaxRobertaPreLayerNormForSequenceClassification,
					        FlaxRobertaPreLayerNormForTokenClassification,
					        FlaxRobertaPreLayerNormForMultipleChoice,
					        FlaxRobertaPreLayerNormForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       FlaxRobertaPreLayerNormModelTester(self    )





					@slow
					def 							lowerCAmelCase      (				self   :							Dict    ):
												for model_class_name in self.all_model_classes:
																			__snake_case     			=       model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"							,						from_pt=snake_case_    )
																			__snake_case     			=       model(np.ones((1, 1)    )    )
																			self.assertIsNotNone(snake_case_    )







@require_flax
class 							__magic_name__     (		unittest.TestCase   ):





					@slow
					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"							,						from_pt=snake_case_    )

												__snake_case     			=       np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							,						dtype=jnp.intaa    )
												__snake_case     			=       model(snake_case_    )[0]
												__snake_case     			=       [1, 11, 50265]
												self.assertEqual(list(output.shape    )							,						snake_case_    )
												# compare the actual values for a slice.
												__snake_case     			=       np.array(
												    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]							,						dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]							,						snake_case_							,						atol=1e-4    )    )





					@slow
					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case     			=       FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"							,						from_pt=snake_case_    )

												__snake_case     			=       np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							,						dtype=jnp.intaa    )
												__snake_case     			=       model(snake_case_    )[0]
												# compare the actual values for a slice.
												__snake_case     			=       np.array(
												    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]							,						dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]							,						snake_case_							,						atol=1e-4    )    )


"""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))
