




'''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 							_a				(			SCREAMING_SNAKE_CASE ):





       '''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,							):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						ids_tensor([self.batch_size, self.seq_length],							self.vocab_size  )

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

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

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

             SCREAMING_SNAKE_CASE   :      int    					=						self.get_config()

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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.get_config()
             SCREAMING_SNAKE_CASE   :      Tuple    					=						300
             return config







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						DebertaModel(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      Dict    					=						model(A,							attention_mask=A,							token_type_ids=A  )[0]
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						model(A,							token_type_ids=A  )[0]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						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  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						DebertaForMaskedLM(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      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        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.num_labels
             SCREAMING_SNAKE_CASE   :      Dict    					=						DebertaForSequenceClassification(A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      str    					=						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  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.num_labels
             SCREAMING_SNAKE_CASE   :      List[str]    					=						DebertaForTokenClassification(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      List[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.num_labels)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						DebertaForQuestionAnswering(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.prepare_config_and_inputs()
             (
                 (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    
             )   :      int    					=						config_and_inputs
             SCREAMING_SNAKE_CASE   :      int    					=						{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
             return config, inputs_dict






@require_torch
class 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				int        =						(
           (
               DebertaModel,
               DebertaForMaskedLM,
               DebertaForSequenceClassification,
               DebertaForTokenClassification,
               DebertaForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       A						:				List[Any]        =						(
           {
               '''feature-extraction''': DebertaModel,
               '''fill-mask''': DebertaForMaskedLM,
               '''question-answering''': DebertaForQuestionAnswering,
               '''text-classification''': DebertaForSequenceClassification,
               '''token-classification''': DebertaForTokenClassification,
               '''zero-shot''': DebertaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						DebertaModelTester(self  )
             SCREAMING_SNAKE_CASE   :      str    					=						ConfigTester(self,							config_class=A,							hidden_size=37  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self.config_tester.run_common_tests()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_deberta_model(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_deberta_for_sequence_classification(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_deberta_for_masked_lm(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_deberta_for_question_answering(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_deberta_for_token_classification(*A  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   SCREAMING_SNAKE_CASE   :      str    					=						DebertaModel.from_pretrained(A  )
                   self.assertIsNotNone(A  )






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





       '''simple docstring'''






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







             '''simple docstring'''







             pass







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						DebertaModel.from_pretrained('microsoft/deberta-base'  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						model(A,							attention_mask=A  )[0]
             # compare the actual values for a slice.
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						torch.tensor(
                 [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]  )
             self.assertTrue(torch.allclose(output[:, 1:4, 1:4],							A,							atol=1E-4  ),							F"{output[:, 1:4, 1:4]}"  )




"""simple docstring"""

from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

SCREAMING_SNAKE_CASE_            =    np.array(data['''data'''])
SCREAMING_SNAKE_CASE_            =    np.array(data['''target'''])
SCREAMING_SNAKE_CASE_            =    data['''target_names''']

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




    return np.linalg.norm(np.array(lowerCAmelCase			) - np.array(lowerCAmelCase			)			)




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       zip(lowerCAmelCase			,					lowerCAmelCase			)
    # List of distances of all points from the point to be classified
    UpperCAmelCase          =       []
    for data_point in data:
        UpperCAmelCase          =       euclidean_distance(data_point[0]			,					lowerCAmelCase			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    UpperCAmelCase          =       [i[1] for i in sorted(lowerCAmelCase			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    UpperCAmelCase          =       Counter(lowerCAmelCase			).most_common(1			)[0][0]
    return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



