




"""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 __lowercase(      lowercase__				):




     '''simple docstring'''


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

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

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

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

        __lowerCamelCase					:	Optional[Any]	       =				self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     def     snake_case_				(    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     snake_case_				(    self						,	__a     ):
        self.parent.assertListEqual(list(result.loss.size()     )						,	[]     )
     def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
        __lowerCamelCase					:	int	       =				DebertaVaModel(config=__a     )
        model.to(__a     )
        model.eval()
        __lowerCamelCase					:	str	       =				model(__a						,	attention_mask=__a						,	token_type_ids=__a     )[0]
        __lowerCamelCase					:	str	       =				model(__a						,	token_type_ids=__a     )[0]
        __lowerCamelCase					:	Optional[Any]	       =				model(__a     )[0]

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

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





     def     snake_case_				(    self     ):
        __lowerCamelCase					:	Any	       =				self.prepare_config_and_inputs()
        (
            __lowerCamelCase      
        )					:	List[str]	       =				config_and_inputs
        __lowerCamelCase					:	int	       =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict



@require_torch
class __lowercase(      lowercase__	,	lowercase__	,	unittest.TestCase				):




     '''simple docstring'''


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

     __a						:						str     		= True
     __a						:						Dict     		= False
     __a						:						Tuple     		= False
     __a						:						Optional[Any]     		= False
     __a						:						List[Any]     		= False
     def     snake_case_				(    self     ):
        __lowerCamelCase					:	List[str]	       =				DebertaVaModelTester(self     )
        __lowerCamelCase					:	Tuple	       =				ConfigTester(self						,	config_class=__a						,	hidden_size=37     )
     def     snake_case_				(    self     ):
        self.config_tester.run_common_tests()
     def     snake_case_				(    self     ):
        __lowerCamelCase					:	int	       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_model(*__a     )
     def     snake_case_				(    self     ):
        __lowerCamelCase					:	Tuple	       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_sequence_classification(*__a     )
     def     snake_case_				(    self     ):
        __lowerCamelCase					:	List[Any]	       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_masked_lm(*__a     )
     def     snake_case_				(    self     ):
        __lowerCamelCase					:	str	       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_question_answering(*__a     )
     def     snake_case_				(    self     ):
        __lowerCamelCase					:	List[Any]	       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_token_classification(*__a     )
     def     snake_case_				(    self     ):
        __lowerCamelCase					:	Any	       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_multiple_choice(*__a     )





     @slow
     def     snake_case_				(    self     ):
        for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase					:	Tuple	       =				DebertaVaModel.from_pretrained(__a     )
           self.assertIsNotNone(__a     )



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




     '''simple docstring'''


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





     @slow
     def     snake_case_				(    self     ):
        __lowerCamelCase					:	Any	       =				DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'     )

        __lowerCamelCase					:	Any	       =				torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]     )
        __lowerCamelCase					:	Any	       =				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
        with torch.no_grad():
           __lowerCamelCase					:	Union[str, Any]	       =				model(__a						,	attention_mask=__a     )[0]
        # compare the actual values for a slice.
        __lowerCamelCase					:	str	       =				torch.tensor(
            [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]     )
        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,	__a						,	atol=1E-4     )						,	f'''{output[:, 1:4, 1:4]}'''     )









"""simple docstring"""
import random





def 						UpperCAmelCase				(			A__:    Union[str, Any]							,				A__:    List[str]							,				A__:    Union[str, Any]     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				a[left_index]
			__lowerCamelCase					:	int	       =				left_index + 1
			for j in range(left_index + 1							,				A__     ):
						if a[j] < pivot:
									__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				a[i], a[j]
									i += 1
			__lowerCamelCase       ,      __lowerCamelCase					:	str	       =				a[i - 1], a[left_index]
			return i - 1





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    Tuple							,				A__:    Tuple     )  ->     Dict:
			if left < right:
						__lowerCamelCase					:	Optional[int]	       =				random.randint(A__							,				right - 1     )
						__lowerCamelCase       ,      __lowerCamelCase					:	int	       =				(
						    a[left],
						    a[pivot],
						)  # switches the pivot with the left most bound
						__lowerCamelCase					:	Union[str, Any]	       =				partition(A__							,				A__							,				A__     )
						quick_sort_random(
						    A__							,				A__							,				A__     )  # recursive quicksort to the left of the pivot point
						quick_sort_random(
						    A__							,				pivot_index + 1							,				A__     )  # recursive quicksort to the right of the pivot point





def 						UpperCAmelCase				(			)  ->     int:
			__lowerCamelCase					:	Dict	       =				input('Enter numbers separated by a comma:\n'     ).strip()
			__lowerCamelCase					:	int	       =				[int(A__     ) for item in user_input.split(','     )]

			quick_sort_random(A__							,				0							,				len(A__     )     )

			print(A__     )


if __name__ == "__main__":
					main()



