




"""simple docstring"""






import unittest

from transformers import DebertaVaConfig, 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 (
			    DebertaVaForMaskedLM,
			    DebertaVaForMultipleChoice,
			    DebertaVaForQuestionAnswering,
			    DebertaVaForSequenceClassification,
			    DebertaVaForTokenClassification,
			    DebertaVaModel,
			)
			from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



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

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

							_lowerCamelCase	:     Tuple       =       None
							if self.use_token_type_ids:
											_lowerCamelCase	:     Optional[int]       =       ids_tensor([self.batch_size, self.seq_length]    , self.type_vocab_size    )

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

							_lowerCamelCase	:     Tuple       =       self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
			def    A_       (    self    ):
							return DebertaVaConfig(
							    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    A_       (    self    , lowercase    ):
							self.parent.assertListEqual(list(result.loss.size()    )    , []    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     str       =       DebertaVaModel(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(lowercase    , attention_mask=lowercase    , token_type_ids=lowercase    )[0]
							_lowerCamelCase	:     Optional[int]       =       model(lowercase    , token_type_ids=lowercase    )[0]
							_lowerCamelCase	:     Dict       =       model(lowercase    )[0]

							self.parent.assertListEqual(list(sequence_output.size()    )    , [self.batch_size, self.seq_length, self.hidden_size]    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Union[str, Any]       =       DebertaVaForMaskedLM(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Union[str, Any]       =       model(lowercase    , attention_mask=lowercase    , token_type_ids=lowercase    , labels=lowercase    )

							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.vocab_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Union[str, Any]       =       self.num_labels
							_lowerCamelCase	:     List[Any]       =       DebertaVaForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(lowercase    , attention_mask=lowercase    , token_type_ids=lowercase    , labels=lowercase    )
							self.parent.assertListEqual(list(result.logits.size()    )    , [self.batch_size, self.num_labels]    )
							self.check_loss_output(lowercase    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Dict       =       self.num_labels
							_lowerCamelCase	:     str       =       DebertaVaForTokenClassification(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[str]       =       model(lowercase    , attention_mask=lowercase    , token_type_ids=lowercase    , labels=lowercase    )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.num_labels)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Any       =       DebertaVaForQuestionAnswering(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(
							    lowercase    , attention_mask=lowercase    , token_type_ids=lowercase    , start_positions=lowercase    , end_positions=lowercase    , )
							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    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Dict       =       DebertaVaForMultipleChoice(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     int       =       input_ids.unsqueeze(1    ).expand(-1    , self.num_choices    , -1    ).contiguous()
							_lowerCamelCase	:     int       =       token_type_ids.unsqueeze(1    ).expand(-1    , self.num_choices    , -1    ).contiguous()
							_lowerCamelCase	:     Union[str, Any]       =       input_mask.unsqueeze(1    ).expand(-1    , self.num_choices    , -1    ).contiguous()
							_lowerCamelCase	:     Any       =       model(
							    lowercase    , attention_mask=lowercase    , token_type_ids=lowercase    , labels=lowercase    , )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_choices)    )






			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       self.prepare_config_and_inputs()
							(
							    (
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					
							)	:     Any       =       config_and_inputs
							_lowerCamelCase	:     Optional[Any]       =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_torch
class lowerCAmelCase__      (	lowercase,      lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     (
			    (
			        DebertaVaModel,
			        DebertaVaForMaskedLM,
			        DebertaVaForSequenceClassification,
			        DebertaVaForTokenClassification,
			        DebertaVaForQuestionAnswering,
			        DebertaVaForMultipleChoice,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__ =     (
			    {
			        """feature-extraction""": DebertaVaModel,
			        """fill-mask""": DebertaVaForMaskedLM,
			        """question-answering""": DebertaVaForQuestionAnswering,
			        """text-classification""": DebertaVaForSequenceClassification,
			        """token-classification""": DebertaVaForTokenClassification,
			        """zero-shot""": DebertaVaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__ =     True
			lowerCamelCase__ =     False
			lowerCamelCase__ =     False
			lowerCamelCase__ =     False
			lowerCamelCase__ =     False
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       DebertaVaModelTester(self    )
							_lowerCamelCase	:     Any       =       ConfigTester(self    , config_class=lowercase    , hidden_size=37    )
			def    A_       (    self    ):
							self.config_tester.run_common_tests()
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     int       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_multiple_choice(*lowercase    )






			@slow
			def    A_       (    self    ):
							for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_lowerCamelCase	:     Optional[Any]       =       DebertaVaModel.from_pretrained(lowercase    )
											self.assertIsNotNone(lowercase    )






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






			'''simple docstring'''



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






			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'    )

							_lowerCamelCase	:     int       =       torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]    )
							_lowerCamelCase	:     Optional[Any]       =       torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
							with torch.no_grad():
											_lowerCamelCase	:     Any       =       model(lowercase    , attention_mask=lowercase    )[0]
							# compare the actual values for a slice.
							_lowerCamelCase	:     List[str]       =       torch.tensor(
							    [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]    )
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    , lowercase    , atol=1E-4    )    , F'''{output[:, 1:4, 1:4]}'''    )





"""simple docstring"""






from __future__ import annotations







def 	_snake_case     (							lowercase__						):  # This function is recursive
				_lowerCamelCase	:     Optional[Any]       =       len(lowercase__						)
				# If the array contains only one element, we return it (it's the stop condition of
				# recursion)
				if array_length <= 1:
								return array
								# Else
				_lowerCamelCase	:     Optional[Any]       =       array[0]
				_lowerCamelCase	:     Optional[int]       =       False
				_lowerCamelCase	:     List[str]       =       1
				_lowerCamelCase	:     list[int]       =       []
				while not is_found and i < array_length:
								if array[i] < pivot:
												_lowerCamelCase	:     Union[str, Any]       =       True
												_lowerCamelCase	:     str       =       [element for element in array[i:] if element >= array[i]]
												_lowerCamelCase	:     str       =       longest_subsequence(lowercase__						)
												if len(lowercase__						) > len(lowercase__						):
																_lowerCamelCase	:     Any       =       temp_array
								else:
												i += 1

				_lowerCamelCase	:     Optional[Any]       =       [element for element in array[1:] if element >= pivot]
				_lowerCamelCase	:     Dict       =       [pivot, *longest_subsequence(lowercase__						)]
				if len(lowercase__						) > len(lowercase__						):
								return temp_array
				else:
								return longest_subseq


if __name__ == "__main__":
			import doctest

			doctest.testmod()