



import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    AlbertForMaskedLM,
							    AlbertForMultipleChoice,
							    AlbertForPreTraining,
							    AlbertForQuestionAnswering,
							    AlbertForSequenceClassification,
							    AlbertForTokenClassification,
							    AlbertModel,
							)
							from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			List[Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Dict=13	,	__lowerCamelCase      :			Any=7	,	__lowerCamelCase      :			Optional[int]=True	,	__lowerCamelCase      :			Union[str, Any]=True	,	__lowerCamelCase      :			Optional[int]=True	,	__lowerCamelCase      :			Dict=True	,	__lowerCamelCase      :			Union[str, Any]=99	,	__lowerCamelCase      :			Union[str, Any]=16	,	__lowerCamelCase      :			Optional[Any]=36	,	__lowerCamelCase      :			Optional[int]=6	,	__lowerCamelCase      :			Any=6	,	__lowerCamelCase      :			Any=6	,	__lowerCamelCase      :			List[Any]=37	,	__lowerCamelCase      :			int="gelu"	,	__lowerCamelCase      :			Dict=0.1	,	__lowerCamelCase      :			Optional[Any]=0.1	,	__lowerCamelCase      :			Optional[Any]=5_12	,	__lowerCamelCase      :			Tuple=16	,	__lowerCamelCase      :			Tuple=2	,	__lowerCamelCase      :			str=0.02	,	__lowerCamelCase      :			Dict=3	,	__lowerCamelCase      :			Tuple=4	,	__lowerCamelCase      :			List[str]=None	,	)      ->				Tuple:
									A							:					Tuple              =						parent
									A							:					Any              =						batch_size
									A							:					List[str]              =						seq_length
									A							:					Dict              =						is_training
									A							:					Tuple              =						use_input_mask
									A							:					int              =						use_token_type_ids
									A							:					List[Any]              =						use_labels
									A							:					Dict              =						vocab_size
									A							:					int              =						embedding_size
									A							:					str              =						hidden_size
									A							:					List[str]              =						num_hidden_layers
									A							:					Tuple              =						num_hidden_groups
									A							:					List[str]              =						num_attention_heads
									A							:					str              =						intermediate_size
									A							:					str              =						hidden_act
									A							:					Optional[Any]              =						hidden_dropout_prob
									A							:					str              =						attention_probs_dropout_prob
									A							:					Tuple              =						max_position_embeddings
									A							:					Dict              =						type_vocab_size
									A							:					Tuple              =						type_sequence_label_size
									A							:					int              =						initializer_range
									A							:					List[str]              =						num_labels
									A							:					Optional[Any]              =						num_choices
									A							:					List[str]              =						scope







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									A							:					List[Any]              =						ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size )

									A							:					Dict              =						None
									if self.use_input_mask:
												A							:					Optional[int]              =						random_attention_mask([self.batch_size, self.seq_length] )

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

									A							:					str              =						None
									A							:					Any              =						None
									A							:					str              =						None
									if self.use_labels:
												A							:					Optional[Any]              =						ids_tensor([self.batch_size]	,	self.type_sequence_label_size )
												A							:					Optional[Any]              =						ids_tensor([self.batch_size, self.seq_length]	,	self.num_labels )
												A							:					Tuple              =						ids_tensor([self.batch_size]	,	self.num_choices )

									A							:					List[Any]              =						self.get_config()

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				int:
									return AlbertConfig(
									    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	,	initializer_range=self.initializer_range	,	num_hidden_groups=self.num_hidden_groups	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[Any] )      ->				str:
									A							:					Dict              =						AlbertModel(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					List[Any]              =						model(__lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase )
									A							:					int              =						model(__lowerCamelCase	,	token_type_ids=__lowerCamelCase )
									A							:					Dict              =						model(__lowerCamelCase )
									self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size) )
									self.parent.assertEqual(result.pooler_output.shape	,	(self.batch_size, self.hidden_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			str )      ->				Union[str, Any]:
									A							:					Any              =						AlbertForPreTraining(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					str              =						model(
									    __lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase	,	labels=__lowerCamelCase	,	sentence_order_label=__lowerCamelCase	,	)
									self.parent.assertEqual(result.prediction_logits.shape	,	(self.batch_size, self.seq_length, self.vocab_size) )
									self.parent.assertEqual(result.sop_logits.shape	,	(self.batch_size, config.num_labels) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			List[Any] )      ->				Any:
									A							:					Optional[Any]              =						AlbertForMaskedLM(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					List[str]              =						model(__lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase	,	labels=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.vocab_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Tuple )      ->				List[Any]:
									A							:					Dict              =						AlbertForQuestionAnswering(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Union[str, Any]              =						model(
									    __lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase	,	start_positions=__lowerCamelCase	,	end_positions=__lowerCamelCase	,	)
									self.parent.assertEqual(result.start_logits.shape	,	(self.batch_size, self.seq_length) )
									self.parent.assertEqual(result.end_logits.shape	,	(self.batch_size, self.seq_length) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			int )      ->				Any:
									A							:					List[Any]              =						self.num_labels
									A							:					str              =						AlbertForSequenceClassification(__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Any              =						model(__lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase	,	labels=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_labels) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			List[Any] )      ->				Tuple:
									A							:					Dict              =						self.num_labels
									A							:					int              =						AlbertForTokenClassification(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Any              =						model(__lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase	,	labels=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.num_labels) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Optional[Any]:
									A							:					int              =						self.num_choices
									A							:					Optional[Any]              =						AlbertForMultipleChoice(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Union[str, Any]              =						input_ids.unsqueeze(1 ).expand(-1	,	self.num_choices	,	-1 ).contiguous()
									A							:					Optional[int]              =						token_type_ids.unsqueeze(1 ).expand(-1	,	self.num_choices	,	-1 ).contiguous()
									A							:					List[str]              =						input_mask.unsqueeze(1 ).expand(-1	,	self.num_choices	,	-1 ).contiguous()
									A							:					Optional[Any]              =						model(
									    __lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase	,	labels=__lowerCamelCase	,	)
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_choices) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Optional[int]:
									A							:					List[Any]              =						self.prepare_config_and_inputs()
									(
									    (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , 
									)							:					Optional[Any]              =						config_and_inputs
									A							:					Dict              =						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
									return config, inputs_dict







@require_torch
class 	lowerCamelCase_    ( _A						,_A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  (
						    (
						        AlbertModel,
						        AlbertForPreTraining,
						        AlbertForMaskedLM,
						        AlbertForMultipleChoice,
						        AlbertForSequenceClassification,
						        AlbertForTokenClassification,
						        AlbertForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						a__   =  (
						    {
						        "feature-extraction": AlbertModel,
						        "fill-mask": AlbertForMaskedLM,
						        "question-answering": AlbertForQuestionAnswering,
						        "text-classification": AlbertForSequenceClassification,
						        "token-classification": AlbertForTokenClassification,
						        "zero-shot": AlbertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						a__   =  True







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Any=False )      ->				List[Any]:
									A							:					str              =						super()._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )

									if return_labels:
												if model_class in get_values(__lowerCamelCase ):
															A							:					str              =						torch.zeros(
															    (self.model_tester.batch_size, self.model_tester.seq_length)	,	dtype=torch.long	,	device=__lowerCamelCase )
															A							:					Tuple              =						torch.zeros(
															    self.model_tester.batch_size	,	dtype=torch.long	,	device=__lowerCamelCase )
									return inputs_dict







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Optional[int]:
									A							:					List[str]              =						AlbertModelTester(self )
									A							:					Optional[int]              =						ConfigTester(self	,	config_class=__lowerCamelCase	,	hidden_size=37 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				int:
									self.config_tester.run_common_tests()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									A							:					Optional[int]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Optional[Any]:
									A							:					Optional[int]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Optional[int]:
									A							:					Dict              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Dict:
									A							:					Tuple              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Optional[Any]:
									A							:					Tuple              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[int]:
									A							:					List[Any]              =						self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
												A							:					int              =						type
												self.model_tester.create_and_check_model(*__lowerCamelCase )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				List[str]:
									for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A							:					Any              =						AlbertModel.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )







@require_torch
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Tuple:
									A							:					Optional[Any]              =						AlbertModel.from_pretrained("albert-base-v2" )
									A							:					Union[str, Any]              =						torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
									A							:					Tuple              =						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
									with torch.no_grad():
												A							:					Optional[int]              =						model(__lowerCamelCase	,	attention_mask=__lowerCamelCase )[0]
									A							:					List[Any]              =						torch.Size((1, 11, 7_68) )
									self.assertEqual(output.shape	,	__lowerCamelCase )
									A							:					List[str]              =						torch.tensor(
									    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )

									self.assertTrue(torch.allclose(output[:, 1:4, 1:4]	,	__lowerCamelCase	,	atol=1e-4 ) )




from collections.abc import Sequence




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

			A							:					Dict              =						nums[0]
			for i in range(1 ,    len(_lowerCamelCase    )    ):
						A							:					Tuple              =						nums[i]
						A							:					List[Any]              =						max(_lowerCamelCase ,    ans + num ,    _lowerCamelCase    )

			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))