"""simple docstring"""






import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
       import torch

       from transformers import (
           DebertaForMaskedLM,
           DebertaForQuestionAnswering,
           DebertaForSequenceClassification,
           DebertaForTokenClassification,
           DebertaModel,
       )
       from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=13    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase="gelu"    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=512    ,			_UpperCAmelCase=16    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="None"    ,			_UpperCAmelCase=3    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=None    ,			):
       lowercase__:   int					=      parent
       lowercase__:   Optional[int]					=      batch_size
       lowercase__:   Optional[int]					=      seq_length
       lowercase__:   List[str]					=      is_training
       lowercase__:   Any					=      use_input_mask
       lowercase__:   Dict					=      use_token_type_ids
       lowercase__:   Optional[Any]					=      use_labels
       lowercase__:   int					=      vocab_size
       lowercase__:   str					=      hidden_size
       lowercase__:   Tuple					=      num_hidden_layers
       lowercase__:   List[str]					=      num_attention_heads
       lowercase__:   List[str]					=      intermediate_size
       lowercase__:   Dict					=      hidden_act
       lowercase__:   Optional[Any]					=      hidden_dropout_prob
       lowercase__:   int					=      attention_probs_dropout_prob
       lowercase__:   Optional[Any]					=      max_position_embeddings
       lowercase__:   Optional[int]					=      type_vocab_size
       lowercase__:   Tuple					=      type_sequence_label_size
       lowercase__:   Union[str, Any]					=      initializer_range
       lowercase__:   Any					=      num_labels
       lowercase__:   Any					=      num_choices
       lowercase__:   str					=      relative_attention
       lowercase__:   Tuple					=      position_biased_input
       lowercase__:   Optional[Any]					=      pos_att_type
       lowercase__:   Tuple					=      scope






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size							)

       lowercase__:   List[str]					=      None
       if self.use_input_mask:
          lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length]    ,			vocab_size=2							)

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

       lowercase__:   Dict					=      None
       lowercase__:   Dict					=      None
       lowercase__:   int					=      None
       if self.use_labels:
          lowercase__:   Tuple					=      ids_tensor([self.batch_size]    ,			self.type_sequence_label_size							)
          lowercase__:   List[Any]					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.num_labels							)
          lowercase__:   Any					=      ids_tensor([self.batch_size]    ,			self.num_choices							)

       lowercase__:   int					=      self.get_config()

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






    def _snake_case			(   self							):
       return DebertaConfig(
           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    ,			relative_attention=self.relative_attention    ,			position_biased_input=self.position_biased_input    ,			pos_att_type=self.pos_att_type    ,			)






    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      self.get_config()
       lowercase__:   Union[str, Any]					=      300
       return config






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       self.parent.assertListEqual(list(result.loss.size()							)    ,			[]							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Optional[int]					=      DebertaModel(config=_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.eval()
       lowercase__:   Union[str, Any]					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			token_type_ids=_UpperCAmelCase							)[0]
       lowercase__:   Union[str, Any]					=      model(_UpperCAmelCase    ,			token_type_ids=_UpperCAmelCase							)[0]
       lowercase__:   Tuple					=      model(_UpperCAmelCase							)[0]

       self.parent.assertListEqual(list(sequence_output.size()							)    ,			[self.batch_size, self.seq_length, self.hidden_size]							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      DebertaForMaskedLM(config=_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.eval()
       lowercase__:   Tuple					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			token_type_ids=_UpperCAmelCase    ,			labels=_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							):
       lowercase__:   str					=      self.num_labels
       lowercase__:   int					=      DebertaForSequenceClassification(_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.eval()
       lowercase__:   Tuple					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			token_type_ids=_UpperCAmelCase    ,			labels=_UpperCAmelCase							)
       self.parent.assertListEqual(list(result.logits.size()							)    ,			[self.batch_size, self.num_labels]							)
       self.check_loss_output(_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Optional[int]					=      self.num_labels
       lowercase__:   Optional[int]					=      DebertaForTokenClassification(config=_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.eval()
       lowercase__:   List[str]					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			token_type_ids=_UpperCAmelCase    ,			labels=_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							):
       lowercase__:   List[Any]					=      DebertaForQuestionAnswering(config=_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.eval()
       lowercase__:   Optional[Any]					=      model(
           _UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			token_type_ids=_UpperCAmelCase    ,			start_positions=_UpperCAmelCase    ,			end_positions=_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							):
       lowercase__:   str					=      self.prepare_config_and_inputs()
       (
           (
           lowercase__
       ), (
           lowercase__
       ), (
           lowercase__
       ), (
           lowercase__
       ), (
           lowercase__
       ), (
           lowercase__
       ), (
           lowercase__
       ), 
       ):   Tuple					=      config_and_inputs
       lowercase__:   Union[str, Any]					=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict



@require_torch
class     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:int						    = (
        (
            DebertaModel,
            DebertaForMaskedLM,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    _UpperCAmelCase	:Dict						    = (
        {
            "feature-extraction": DebertaModel,
            "fill-mask": DebertaForMaskedLM,
            "question-answering": DebertaForQuestionAnswering,
            "text-classification": DebertaForSequenceClassification,
            "token-classification": DebertaForTokenClassification,
            "zero-shot": DebertaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    _UpperCAmelCase	:List[str]						    = True
    _UpperCAmelCase	:str						    = False
    _UpperCAmelCase	:List[str]						    = False
    _UpperCAmelCase	:List[str]						    = False
    _UpperCAmelCase	:Tuple						    = False






    def _snake_case			(   self							):
       lowercase__:   int					=      DebertaModelTester(self							)
       lowercase__:   Tuple					=      ConfigTester(self    ,			config_class=_UpperCAmelCase    ,			hidden_size=37							)






    def _snake_case			(   self							):
       self.config_tester.run_common_tests()






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__:   str					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__:   int					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*_UpperCAmelCase							)






    @slow
    def _snake_case			(   self							):
       for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase__:   Tuple					=      DebertaModel.from_pretrained(_UpperCAmelCase							)
          self.assertIsNotNone(_UpperCAmelCase							)



@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    @unittest.skip(reason='''Model not available yet'''							)
    def _snake_case			(   self							):
       pass






    @slow
    def _snake_case			(   self							):
       lowercase__:   List[str]					=      DebertaModel.from_pretrained('''microsoft/deberta-base'''							)

       lowercase__:   Union[str, Any]					=      torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							)
       lowercase__:   Dict					=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
       with torch.no_grad():
          lowercase__:   Union[str, Any]					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase							)[0]
       # compare the actual values for a slice.
       lowercase__:   Optional[Any]					=      torch.tensor(
           [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]							)
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,			_UpperCAmelCase    ,			atol=1e-4							)    ,			F"""{output[:, 1:4, 1:4]}"""							)

"""simple docstring"""






from collections.abc import Sequence



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase = False )  ->  float:
   if not arr:
      return 0

   lowercase__:   Any					=      0 if allow_empty_subarrays else float('''-inf''' )
   lowercase__:   Union[str, Any]					=      0.0
   for num in arr:
      lowercase__:   List[str]					=      max(0 if allow_empty_subarrays else num       ,    curr_sum + num )
      lowercase__:   int					=      max(__UpperCAmelCase       ,    __UpperCAmelCase )

   return max_sum


if __name__ == "__main__":
       from doctest import testmod

       testmod()

       __A			   = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
       print(f'''{max_subarray_sum(nums) = }''')
