


'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TF_MODEL_FOR_PRETRAINING_MAPPING,
         TFMobileBertForMaskedLM,
         TFMobileBertForMultipleChoice,
         TFMobileBertForNextSentencePrediction,
         TFMobileBertForPreTraining,
         TFMobileBertForQuestionAnswering,
         TFMobileBertForSequenceClassification,
         TFMobileBertForTokenClassification,
         TFMobileBertModel,
     )





@require_tf
class   snake_case					(					__lowercase    ,		__lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						(
      (
          TFMobileBertModel,
          TFMobileBertForMaskedLM,
          TFMobileBertForNextSentencePrediction,
          TFMobileBertForPreTraining,
          TFMobileBertForQuestionAnswering,
          TFMobileBertForSequenceClassification,
          TFMobileBertForTokenClassification,
          TFMobileBertForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  _lowerCamelCase						=						(
      {
          "feature-extraction": TFMobileBertModel,
          "fill-mask": TFMobileBertForMaskedLM,
          "question-answering": TFMobileBertForQuestionAnswering,
          "text-classification": TFMobileBertForSequenceClassification,
          "token-classification": TFMobileBertForTokenClassification,
          "zero-shot": TFMobileBertForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=False  ):







   """simple docstring"""
   lowerCamelCase_  				=						super()._prepare_for_class(UpperCamelCase    ,						UpperCamelCase    ,						return_labels=UpperCamelCase  )

   if return_labels:
    if model_class in get_values(UpperCamelCase  ):
     lowerCamelCase_  				=						tf.zeros(self.model_tester.batch_size    ,						dtype=tf.intaa  )

   return inputs_dict





  class   snake_case					(					__lowercase    ):





    """simple docstring"""




    def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=13    ,						UpperCamelCase=7    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase=99    ,						UpperCamelCase=32    ,						UpperCamelCase=32    ,						UpperCamelCase=2    ,						UpperCamelCase=4    ,						UpperCamelCase=37    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=512    ,						UpperCamelCase=16    ,						UpperCamelCase=2    ,						UpperCamelCase=0.02    ,						UpperCamelCase=3    ,						UpperCamelCase=4    ,						UpperCamelCase=None    ,						):







     """simple docstring"""
     lowerCamelCase_  				=						parent
     lowerCamelCase_  				=						batch_size
     lowerCamelCase_  				=						seq_length
     lowerCamelCase_  				=						is_training
     lowerCamelCase_  				=						use_input_mask
     lowerCamelCase_  				=						use_token_type_ids
     lowerCamelCase_  				=						use_labels
     lowerCamelCase_  				=						vocab_size
     lowerCamelCase_  				=						hidden_size
     lowerCamelCase_  				=						num_hidden_layers
     lowerCamelCase_  				=						num_attention_heads
     lowerCamelCase_  				=						intermediate_size
     lowerCamelCase_  				=						hidden_act
     lowerCamelCase_  				=						hidden_dropout_prob
     lowerCamelCase_  				=						attention_probs_dropout_prob
     lowerCamelCase_  				=						max_position_embeddings
     lowerCamelCase_  				=						type_vocab_size
     lowerCamelCase_  				=						type_sequence_label_size
     lowerCamelCase_  				=						initializer_range
     lowerCamelCase_  				=						num_labels
     lowerCamelCase_  				=						num_choices
     lowerCamelCase_  				=						scope
     lowerCamelCase_  				=						embedding_size






    def 						snake_case	(					self  ):







     """simple docstring"""
     lowerCamelCase_  				=						ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size  )

     lowerCamelCase_  				=						None
     if self.use_input_mask:
      lowerCamelCase_  				=						random_attention_mask([self.batch_size, self.seq_length]  )

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

     lowerCamelCase_  				=						None
     lowerCamelCase_  				=						None
     lowerCamelCase_  				=						None
     if self.use_labels:
      lowerCamelCase_  				=						ids_tensor([self.batch_size]    ,						self.type_sequence_label_size  )
      lowerCamelCase_  				=						ids_tensor([self.batch_size, self.seq_length]    ,						self.num_labels  )
      lowerCamelCase_  				=						ids_tensor([self.batch_size]    ,						self.num_choices  )

     lowerCamelCase_  				=						MobileBertConfig(
         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    ,						embedding_size=self.embedding_size    ,						)

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






    def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







     """simple docstring"""
     lowerCamelCase_  				=						TFMobileBertModel(config=UpperCamelCase  )
     lowerCamelCase_  				=						{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     lowerCamelCase_  				=						model(UpperCamelCase  )

     lowerCamelCase_  				=						[input_ids, input_mask]
     lowerCamelCase_  				=						model(UpperCamelCase  )

     lowerCamelCase_  				=						model(UpperCamelCase  )

     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 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







     """simple docstring"""
     lowerCamelCase_  				=						TFMobileBertForMaskedLM(config=UpperCamelCase  )
     lowerCamelCase_  				=						{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     lowerCamelCase_  				=						model(UpperCamelCase  )
     self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)  )






    def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







     """simple docstring"""
     lowerCamelCase_  				=						TFMobileBertForNextSentencePrediction(config=UpperCamelCase  )
     lowerCamelCase_  				=						{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     lowerCamelCase_  				=						model(UpperCamelCase  )
     self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, 2)  )






    def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







     """simple docstring"""
     lowerCamelCase_  				=						TFMobileBertForPreTraining(config=UpperCamelCase  )
     lowerCamelCase_  				=						{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     lowerCamelCase_  				=						model(UpperCamelCase  )
     self.parent.assertEqual(
         result.prediction_logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)  )
     self.parent.assertEqual(result.seq_relationship_logits.shape    ,						(self.batch_size, 2)  )






    def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







     """simple docstring"""
     lowerCamelCase_  				=						self.num_labels
     lowerCamelCase_  				=						TFMobileBertForSequenceClassification(config=UpperCamelCase  )
     lowerCamelCase_  				=						{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     lowerCamelCase_  				=						model(UpperCamelCase  )
     self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)  )






    def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







     """simple docstring"""
     lowerCamelCase_  				=						self.num_choices
     lowerCamelCase_  				=						TFMobileBertForMultipleChoice(config=UpperCamelCase  )
     lowerCamelCase_  				=						tf.tile(tf.expand_dims(UpperCamelCase    ,						1  )    ,						(1, self.num_choices, 1)  )
     lowerCamelCase_  				=						tf.tile(tf.expand_dims(UpperCamelCase    ,						1  )    ,						(1, self.num_choices, 1)  )
     lowerCamelCase_  				=						tf.tile(tf.expand_dims(UpperCamelCase    ,						1  )    ,						(1, self.num_choices, 1)  )
     lowerCamelCase_  				=						{
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
         "token_type_ids": multiple_choice_token_type_ids,
     }
     lowerCamelCase_  				=						model(UpperCamelCase  )
     self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_choices)  )






    def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







     """simple docstring"""
     lowerCamelCase_  				=						self.num_labels
     lowerCamelCase_  				=						TFMobileBertForTokenClassification(config=UpperCamelCase  )
     lowerCamelCase_  				=						{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     lowerCamelCase_  				=						model(UpperCamelCase  )
     self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.num_labels)  )






    def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







     """simple docstring"""
     lowerCamelCase_  				=						TFMobileBertForQuestionAnswering(config=UpperCamelCase  )
     lowerCamelCase_  				=						{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     lowerCamelCase_  				=						model(UpperCamelCase  )
     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 						snake_case	(					self  ):







     """simple docstring"""
     lowerCamelCase_  				=						self.prepare_config_and_inputs()
     (
         lowerCamelCase_		
     )  				=						config_and_inputs
     lowerCamelCase_  				=						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						TFMobileBertModelTest.TFMobileBertModelTester(self  )
   lowerCamelCase_  				=						ConfigTester(self    ,						config_class=UpperCamelCase    ,						hidden_size=37  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   self.config_tester.run_common_tests()






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mobilebert_model(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mobilebert_for_masked_lm(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCamelCase  )







  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   # for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   for model_name in ["google/mobilebert-uncased"]:
    lowerCamelCase_  				=						TFMobileBertModel.from_pretrained(UpperCamelCase  )
    self.assertIsNotNone(UpperCamelCase  )





@require_tf
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased"  )
   lowerCamelCase_  				=						tf.constant([[0, 1, 2, 3, 4, 5]]  )
   lowerCamelCase_  				=						model(UpperCamelCase  )[0]

   lowerCamelCase_  				=						[1, 6, 3_0522]
   self.assertEqual(output.shape    ,						UpperCamelCase  )

   lowerCamelCase_  				=						tf.constant(
       [
           [
               [-4.5_919_547, -9.248_295, -9.645_256],
               [-6.7_306_175, -6.440_284, -6.6_052_837],
               [-7.2_743_506, -6.7_847_915, -6.024_673],
           ]
       ]  )
   tf.debugging.assert_near(output[:, :3, :3]    ,						UpperCamelCase    ,						atol=1e-4  )







'''simple docstring'''

from collections.abc import Sequence





def __snake_case      (   UpperCAmelCase_		:		Sequence[int] | None = None	):
 if nums is None or not nums:
  raise ValueError("Input sequence should not be empty"	)

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

 return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

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



