
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					(		_SCREAMING_SNAKE_CASE   ):



		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				,      )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     parent
				UpperCamelCase		=     batch_size
				UpperCamelCase		=     seq_length
				UpperCamelCase		=     is_training
				UpperCamelCase		=     use_input_mask
				UpperCamelCase		=     use_token_type_ids
				UpperCamelCase		=     use_labels
				UpperCamelCase		=     vocab_size
				UpperCamelCase		=     hidden_size
				UpperCamelCase		=     num_hidden_layers
				UpperCamelCase		=     num_attention_heads
				UpperCamelCase		=     intermediate_size
				UpperCamelCase		=     hidden_act
				UpperCamelCase		=     hidden_dropout_prob
				UpperCamelCase		=     attention_probs_dropout_prob
				UpperCamelCase		=     max_position_embeddings
				UpperCamelCase		=     type_vocab_size
				UpperCamelCase		=     type_sequence_label_size
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     num_labels
				UpperCamelCase		=     num_choices
				UpperCamelCase		=     relative_attention
				UpperCamelCase		=     position_biased_input
				UpperCamelCase		=     pos_att_type
				UpperCamelCase		=     scope



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     ids_tensor([self.batch_size, self.seq_length]				,      self.vocab_size       )

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

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

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

				UpperCamelCase		=     self.get_config()

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



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				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 __UpperCamelCase ( self				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				self.parent.assertListEqual(list(result.loss.size()       )				,      []       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     DebertaVaModel(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_				,      attention_mask=A_				,      token_type_ids=A_       )[0]
				UpperCamelCase		=     model(A_				,      token_type_ids=A_       )[0]
				UpperCamelCase		=     model(A_       )[0]

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



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     DebertaVaForMaskedLM(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     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 __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.num_labels
				UpperCamelCase		=     DebertaVaForSequenceClassification(A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     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 __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     self.num_labels
				UpperCamelCase		=     DebertaVaForTokenClassification(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     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 __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     DebertaVaForQuestionAnswering(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     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 __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     DebertaVaForMultipleChoice(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     input_ids.unsqueeze(1       ).expand(-1				,      self.num_choices				,      -1       ).contiguous()
				UpperCamelCase		=     token_type_ids.unsqueeze(1       ).expand(-1				,      self.num_choices				,      -1       ).contiguous()
				UpperCamelCase		=     input_mask.unsqueeze(1       ).expand(-1				,      self.num_choices				,      -1       ).contiguous()
				UpperCamelCase		=     model(
				    A_				,      attention_mask=A_				,      token_type_ids=A_				,      labels=A_				,      )
				self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_choices)       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.prepare_config_and_inputs()
				(
				    (
				    UpperCamelCase
				)	,  (
				    UpperCamelCase
				)	,  (
				    UpperCamelCase
				)	,  (
				    UpperCamelCase
				)	,  (
				    UpperCamelCase
				)	,  (
				    UpperCamelCase
				)	,  (
				    UpperCamelCase
				)	,  
				)		=     config_and_inputs
				UpperCamelCase		=     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
				return config, inputs_dict






@require_torch
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       str	     =				(
		    (
		        DebertaVaModel,
		        DebertaVaForMaskedLM,
		        DebertaVaForSequenceClassification,
		        DebertaVaForTokenClassification,
		        DebertaVaForQuestionAnswering,
		        DebertaVaForMultipleChoice,
		    )
		    if is_torch_available()
		    else ()
		)
		__lowercase       :       Any	     =				(
		    {
		        "feature-extraction": DebertaVaModel,
		        "fill-mask": DebertaVaForMaskedLM,
		        "question-answering": DebertaVaForQuestionAnswering,
		        "text-classification": DebertaVaForSequenceClassification,
		        "token-classification": DebertaVaForTokenClassification,
		        "zero-shot": DebertaVaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)

		__lowercase       :       Optional[int]	     =				True
		__lowercase       :       str	     =				False
		__lowercase       :       str	     =				False
		__lowercase       :       Union[str, Any]	     =				False
		__lowercase       :       Any	     =				False



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     DebertaVaModelTester(self       )
				UpperCamelCase		=     ConfigTester(self				,      config_class=A_				,      hidden_size=37       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_model(*A_       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_sequence_classification(*A_       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_masked_lm(*A_       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_question_answering(*A_       )



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_token_classification(*A_       )



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_multiple_choice(*A_       )



		@slow
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase		=     DebertaVaModel.from_pretrained(A_       )
						self.assertIsNotNone(A_       )






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



		@unittest.skip(reason='Model not available yet'       )
		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'       )

				UpperCamelCase		=     torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]       )
				UpperCamelCase		=     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
				with torch.no_grad():
						UpperCamelCase		=     model(A_				,      attention_mask=A_       )[0]
				# compare the actual values for a slice.
				UpperCamelCase		=     torch.tensor(
				    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]       )
				self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,      A_				,      atol=1e-4       )				,      F'''{output[:, 1:4, 1:4]}'''       )


def       A       (					lowercase )   ->  str:
		'''simple docstring'''

		return " ".join(
		    ''.join(word[::-1] ) if len(lowercase ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(reverse_long_words("Hey wollef sroirraw"))
