
"""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				(		SCREAMING_SNAKE_CASE_				):




      def __init__(				self ,							lowercase_ ,							lowercase_=1_3 ,							lowercase_=7 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=9_9 ,							lowercase_=3_2 ,							lowercase_=5 ,							lowercase_=4 ,							lowercase_=3_7 ,							lowercase_="gelu" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=5_1_2 ,							lowercase_=1_6 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=False ,							lowercase_=True ,							lowercase_="None" ,							lowercase_=3 ,							lowercase_=4 ,							lowercase_=None ,							)    ->       Tuple:
          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      a_   (				self				)    ->       Optional[Any]:
          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      a_   (				self				)    ->       Optional[int]:
          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      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       self.get_config()
          UpperCAmelCase          =       3_0_0
          return config




      def      a_   (				self ,							lowercase_				)    ->       str:
          self.parent.assertListEqual(list(result.loss.size()				) ,							[]				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Any:
          UpperCAmelCase          =       DebertaModel(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_ ,							token_type_ids=lowercase_				)[0]
          UpperCAmelCase          =       model(lowercase_ ,							token_type_ids=lowercase_				)[0]
          UpperCAmelCase          =       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_				)    ->       Any:
          UpperCAmelCase          =       DebertaForMaskedLM(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       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_				)    ->       str:
          UpperCAmelCase          =       self.num_labels
          UpperCAmelCase          =       DebertaForSequenceClassification(lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       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_				)    ->       List[str]:
          UpperCAmelCase          =       self.num_labels
          UpperCAmelCase          =       DebertaForTokenClassification(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       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_				)    ->       List[str]:
          UpperCAmelCase          =       DebertaForQuestionAnswering(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       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				)    ->       Tuple:
          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   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				str				   =						(
          (
              DebertaModel,
              DebertaForMaskedLM,
              DebertaForSequenceClassification,
              DebertaForTokenClassification,
              DebertaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						(
          {
              "feature-extraction": DebertaModel,
              "fill-mask": DebertaForMaskedLM,
              "question-answering": DebertaForQuestionAnswering,
              "text-classification": DebertaForSequenceClassification,
              "token-classification": DebertaForTokenClassification,
              "zero-shot": DebertaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      __SCREAMING_SNAKE_CASE :				Any				   =						True
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						False
      __SCREAMING_SNAKE_CASE :				int				   =						False
      __SCREAMING_SNAKE_CASE :				Dict				   =						False
      __SCREAMING_SNAKE_CASE :				str				   =						False




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase          =       DebertaModelTester(self				)
          UpperCAmelCase          =       ConfigTester(self ,							config_class=lowercase_ ,							hidden_size=3_7				)




      def      a_   (				self				)    ->       List[Any]:
          self.config_tester.run_common_tests()




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*lowercase_				)




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*lowercase_				)




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase_				)




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*lowercase_				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*lowercase_				)




      @slow
      def      a_   (				self				)    ->       str:
          for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase          =       DebertaModel.from_pretrained(lowercase_				)
              self.assertIsNotNone(lowercase_				)

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




      @unittest.skip(reason='Model not available yet'				)
      def      a_   (				self				)    ->       Dict:
          pass




      @slow
      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       DebertaModel.from_pretrained('microsoft/deberta-base'				)

          UpperCAmelCase          =       torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				)
          UpperCAmelCase          =       torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
          with torch.no_grad():
              UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_				)[0]
          # compare the actual values for a slice.
          UpperCAmelCase          =       torch.tensor(
              [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]				)
          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

from collections.abc import Iterator
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       value
          UpperCAmelCase          =       None
          UpperCAmelCase          =       None

class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       tree




      def      a_   (				self ,							lowercase_				)    ->       int:
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left				) + self.depth_first_search(node.right				)
          )




      def __iter__(				self				)    ->       Iterator[int]:
          yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



