


'''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 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__=13   ,						snake_case__=7   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=99   ,						snake_case__=32   ,						snake_case__=5   ,						snake_case__=4   ,						snake_case__=37   ,						snake_case__="gelu"   ,						snake_case__=0.1   ,						snake_case__=0.1   ,						snake_case__=512   ,						snake_case__=16   ,						snake_case__=2   ,						snake_case__=0.02   ,						snake_case__=False   ,						snake_case__=True   ,						snake_case__="None"   ,						snake_case__=3   ,						snake_case__=4   ,						snake_case__=None   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							parent
            _lowerCAmelCase     :				List[Any]       =							batch_size
            _lowerCAmelCase     :				Dict       =							seq_length
            _lowerCAmelCase     :				List[str]       =							is_training
            _lowerCAmelCase     :				Any       =							use_input_mask
            _lowerCAmelCase     :				Dict       =							use_token_type_ids
            _lowerCAmelCase     :				Union[str, Any]       =							use_labels
            _lowerCAmelCase     :				Dict       =							vocab_size
            _lowerCAmelCase     :				Tuple       =							hidden_size
            _lowerCAmelCase     :				int       =							num_hidden_layers
            _lowerCAmelCase     :				Dict       =							num_attention_heads
            _lowerCAmelCase     :				Optional[int]       =							intermediate_size
            _lowerCAmelCase     :				str       =							hidden_act
            _lowerCAmelCase     :				Optional[Any]       =							hidden_dropout_prob
            _lowerCAmelCase     :				int       =							attention_probs_dropout_prob
            _lowerCAmelCase     :				Any       =							max_position_embeddings
            _lowerCAmelCase     :				Optional[int]       =							type_vocab_size
            _lowerCAmelCase     :				Optional[int]       =							type_sequence_label_size
            _lowerCAmelCase     :				List[Any]       =							initializer_range
            _lowerCAmelCase     :				str       =							num_labels
            _lowerCAmelCase     :				Dict       =							num_choices
            _lowerCAmelCase     :				int       =							relative_attention
            _lowerCAmelCase     :				int       =							position_biased_input
            _lowerCAmelCase     :				List[Any]       =							pos_att_type
            _lowerCAmelCase     :				Optional[Any]       =							scope







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size )

            _lowerCAmelCase     :				List[Any]       =							None
            if self.use_input_mask:
                   _lowerCAmelCase     :				Optional[Any]       =							ids_tensor([self.batch_size, self.seq_length]   ,						vocab_size=2 )

            _lowerCAmelCase     :				List[Any]       =							None
            if self.use_token_type_ids:
                   _lowerCAmelCase     :				Optional[int]       =							ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size )

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

            _lowerCAmelCase     :				int       =							self.get_config()

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







     def a						(			self ):

            '''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 a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            self.parent.assertListEqual(list(result.loss.size() )   ,						[] )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							DebertaVaModel(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				Union[str, Any]       =							model(snake_case__   ,						attention_mask=snake_case__   ,						token_type_ids=snake_case__ )[0]
            _lowerCAmelCase     :				Dict       =							model(snake_case__   ,						token_type_ids=snake_case__ )[0]
            _lowerCAmelCase     :				List[Any]       =							model(snake_case__ )[0]

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







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							DebertaVaForMaskedLM(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				Dict       =							model(snake_case__   ,						attention_mask=snake_case__   ,						token_type_ids=snake_case__   ,						labels=snake_case__ )

            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.num_labels
            _lowerCAmelCase     :				int       =							DebertaVaForSequenceClassification(snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				List[str]       =							model(snake_case__   ,						attention_mask=snake_case__   ,						token_type_ids=snake_case__   ,						labels=snake_case__ )
            self.parent.assertListEqual(list(result.logits.size() )   ,						[self.batch_size, self.num_labels] )
            self.check_loss_output(snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							self.num_labels
            _lowerCAmelCase     :				Union[str, Any]       =							DebertaVaForTokenClassification(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				Optional[int]       =							model(snake_case__   ,						attention_mask=snake_case__   ,						token_type_ids=snake_case__   ,						labels=snake_case__ )
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.num_labels) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							DebertaVaForQuestionAnswering(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				Dict       =							model(
                snake_case__   ,						attention_mask=snake_case__   ,						token_type_ids=snake_case__   ,						start_positions=snake_case__   ,						end_positions=snake_case__   ,						)
            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   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							DebertaVaForMultipleChoice(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()
            _lowerCAmelCase     :				Dict       =							input_ids.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
            _lowerCAmelCase     :				Union[str, Any]       =							token_type_ids.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
            _lowerCAmelCase     :				Dict       =							input_mask.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
            _lowerCAmelCase     :				Tuple       =							model(
                snake_case__   ,						attention_mask=snake_case__   ,						token_type_ids=snake_case__   ,						labels=snake_case__   ,						)
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_choices) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							self.prepare_config_and_inputs()
            (
                (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  
            )     :				Union[str, Any]       =							config_and_inputs
            _lowerCAmelCase     :				Optional[int]       =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
            return config, inputs_dict
@require_torch
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






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

     __magic_name__           =     True
     __magic_name__           =     False
     __magic_name__           =     False
     __magic_name__           =     False
     __magic_name__           =     False







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							DebertaVaModelTester(self )
            _lowerCAmelCase     :				Optional[Any]       =							ConfigTester(self   ,						config_class=snake_case__   ,						hidden_size=37 )







     def a						(			self ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_model(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_question_answering(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_token_classification(*snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_multiple_choice(*snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _lowerCAmelCase     :				Any       =							DebertaVaModel.from_pretrained(snake_case__ )
                   self.assertIsNotNone(snake_case__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






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

            '''simple docstring'''

            pass







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )

            _lowerCAmelCase     :				Union[str, Any]       =							torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 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(snake_case__   ,						attention_mask=snake_case__ )[0]
            # compare the actual values for a slice.
            _lowerCAmelCase     :				Optional[int]       =							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]   ,						snake_case__   ,						atol=1E-4 )   ,						F'{output[:, 1:4, 1:4]}' )





'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							value
            _lowerCAmelCase     :				Node | None       =							None
            _lowerCAmelCase     :				Node | None       =							None
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							tree







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left ) + self.depth_first_search(node.right )
            )







     def __iter__(			self ):

            '''simple docstring'''

            yield self.depth_first_search(self.tree )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

