






'''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     (	snake_case_		):




   """simple docstring"""

   def __init__(						self					:      List[str] , __UpperCAmelCase					:      List[str] , __UpperCAmelCase					:      int=13 , __UpperCAmelCase					:      Tuple=7 , __UpperCAmelCase					:      List[str]=True , __UpperCAmelCase					:      List[Any]=True , __UpperCAmelCase					:      Optional[Any]=True , __UpperCAmelCase					:      Any=True , __UpperCAmelCase					:      Optional[Any]=99 , __UpperCAmelCase					:      Tuple=32 , __UpperCAmelCase					:      str=5 , __UpperCAmelCase					:      Optional[Any]=4 , __UpperCAmelCase					:      Any=37 , __UpperCAmelCase					:      str="gelu" , __UpperCAmelCase					:      str=0.1 , __UpperCAmelCase					:      Dict=0.1 , __UpperCAmelCase					:      Union[str, Any]=512 , __UpperCAmelCase					:      Union[str, Any]=16 , __UpperCAmelCase					:      str=2 , __UpperCAmelCase					:      Optional[int]=0.02 , __UpperCAmelCase					:      int=False , __UpperCAmelCase					:      Optional[Any]=True , __UpperCAmelCase					:      Any="None" , __UpperCAmelCase					:      List[str]=3 , __UpperCAmelCase					:      List[Any]=4 , __UpperCAmelCase					:      int=None , ):





         '''simple docstring'''





         _A     	=      parent
         _A     	=      batch_size
         _A     	=      seq_length
         _A     	=      is_training
         _A     	=      use_input_mask
         _A     	=      use_token_type_ids
         _A     	=      use_labels
         _A     	=      vocab_size
         _A     	=      hidden_size
         _A     	=      num_hidden_layers
         _A     	=      num_attention_heads
         _A     	=      intermediate_size
         _A     	=      hidden_act
         _A     	=      hidden_dropout_prob
         _A     	=      attention_probs_dropout_prob
         _A     	=      max_position_embeddings
         _A     	=      type_vocab_size
         _A     	=      type_sequence_label_size
         _A     	=      initializer_range
         _A     	=      num_labels
         _A     	=      num_choices
         _A     	=      relative_attention
         _A     	=      position_biased_input
         _A     	=      pos_att_type
         _A     	=      scope

   def 		lowerCAmelCase					(						self					:      Union[str, Any]						):





         '''simple docstring'''





         _A     	=      ids_tensor([self.batch_size, self.seq_length] , self.vocab_size						)

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

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

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

         _A     	=      self.get_config()

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

   def 		lowerCAmelCase					(						self					:      List[Any]						):





         '''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 		lowerCAmelCase					(						self					:      Optional[Any]						):





         '''simple docstring'''





         _A     	=      self.get_config()
         _A     	=      300
         return config

   def 		lowerCAmelCase					(						self					:      List[str] , __UpperCAmelCase					:      Optional[Any]						):





         '''simple docstring'''





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

   def 		lowerCAmelCase					(						self					:      List[str] , __UpperCAmelCase					:      Any , __UpperCAmelCase					:      Tuple , __UpperCAmelCase					:      Dict , __UpperCAmelCase					:      int , __UpperCAmelCase					:      Optional[Any] , __UpperCAmelCase					:      Any , __UpperCAmelCase					:      List[str]						):





         '''simple docstring'''





         _A     	=      DebertaModel(config=a_						)
         model.to(a_						)
         model.eval()
         _A     	=      model(a_ , attention_mask=a_ , token_type_ids=a_						)[0]
         _A     	=      model(a_ , token_type_ids=a_						)[0]
         _A     	=      model(a_						)[0]

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

   def 		lowerCAmelCase					(						self					:      Union[str, Any] , __UpperCAmelCase					:      List[str] , __UpperCAmelCase					:      Optional[Any] , __UpperCAmelCase					:      List[str] , __UpperCAmelCase					:      int , __UpperCAmelCase					:      Union[str, Any] , __UpperCAmelCase					:      List[Any] , __UpperCAmelCase					:      Optional[int]						):





         '''simple docstring'''





         _A     	=      DebertaForMaskedLM(config=a_						)
         model.to(a_						)
         model.eval()
         _A     	=      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 		lowerCAmelCase					(						self					:      Optional[Any] , __UpperCAmelCase					:      int , __UpperCAmelCase					:      Dict , __UpperCAmelCase					:      int , __UpperCAmelCase					:      Optional[Any] , __UpperCAmelCase					:      Union[str, Any] , __UpperCAmelCase					:      Union[str, Any] , __UpperCAmelCase					:      Tuple						):





         '''simple docstring'''





         _A     	=      self.num_labels
         _A     	=      DebertaForSequenceClassification(a_						)
         model.to(a_						)
         model.eval()
         _A     	=      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 		lowerCAmelCase					(						self					:      Dict , __UpperCAmelCase					:      int , __UpperCAmelCase					:      Optional[Any] , __UpperCAmelCase					:      Optional[Any] , __UpperCAmelCase					:      Dict , __UpperCAmelCase					:      List[Any] , __UpperCAmelCase					:      Optional[Any] , __UpperCAmelCase					:      Optional[Any]						):





         '''simple docstring'''





         _A     	=      self.num_labels
         _A     	=      DebertaForTokenClassification(config=a_						)
         model.to(a_						)
         model.eval()
         _A     	=      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 		lowerCAmelCase					(						self					:      str , __UpperCAmelCase					:      List[Any] , __UpperCAmelCase					:      Tuple , __UpperCAmelCase					:      Dict , __UpperCAmelCase					:      str , __UpperCAmelCase					:      Tuple , __UpperCAmelCase					:      Tuple , __UpperCAmelCase					:      str						):





         '''simple docstring'''





         _A     	=      DebertaForQuestionAnswering(config=a_						)
         model.to(a_						)
         model.eval()
         _A     	=      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 		lowerCAmelCase					(						self					:      int						):





         '''simple docstring'''





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





@require_torch
class   _UpperCAmelCase     (	snake_case_				,       snake_case_				,       unittest.TestCase		):




   """simple docstring"""

   snake_case       							=		(
       (
           DebertaModel,
           DebertaForMaskedLM,
           DebertaForSequenceClassification,
           DebertaForTokenClassification,
           DebertaForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   snake_case       							=		(
       {
           """feature-extraction""": DebertaModel,
           """fill-mask""": DebertaForMaskedLM,
           """question-answering""": DebertaForQuestionAnswering,
           """text-classification""": DebertaForSequenceClassification,
           """token-classification""": DebertaForTokenClassification,
           """zero-shot""": DebertaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )

   snake_case       							=		True
   snake_case       							=		False
   snake_case       							=		False
   snake_case       							=		False
   snake_case       							=		False

   def 		lowerCAmelCase					(						self					:      Optional[int]						):





         '''simple docstring'''





         _A     	=      DebertaModelTester(self						)
         _A     	=      ConfigTester(self , config_class=a_ , hidden_size=37						)

   def 		lowerCAmelCase					(						self					:      int						):





         '''simple docstring'''





         self.config_tester.run_common_tests()

   def 		lowerCAmelCase					(						self					:      Dict						):





         '''simple docstring'''





         _A     	=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*a_						)

   def 		lowerCAmelCase					(						self					:      List[Any]						):





         '''simple docstring'''





         _A     	=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*a_						)

   def 		lowerCAmelCase					(						self					:      Tuple						):





         '''simple docstring'''





         _A     	=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*a_						)

   def 		lowerCAmelCase					(						self					:      List[Any]						):





         '''simple docstring'''





         _A     	=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*a_						)

   def 		lowerCAmelCase					(						self					:      str						):





         '''simple docstring'''





         _A     	=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*a_						)

   @slow
   def 		lowerCAmelCase					(						self					:      Any						):





         '''simple docstring'''





         for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _A     	=      DebertaModel.from_pretrained(a_						)
               self.assertIsNotNone(a_						)





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




   """simple docstring"""

   @unittest.skip(reason="Model not available yet"						)
   def 		lowerCAmelCase					(						self					:      Any						):





         '''simple docstring'''





         pass

   @slow
   def 		lowerCAmelCase					(						self					:      int						):





         '''simple docstring'''





         _A     	=      DebertaModel.from_pretrained("microsoft/deberta-base"						)

         _A     	=      torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]						)
         _A     	=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
         with torch.no_grad():
               _A     	=      model(a_ , attention_mask=a_						)[0]
         # compare the actual values for a slice.
         _A     	=      torch.tensor(
             [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]						)
         self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a_ , atol=1E-4						) , f'''{output[:, 1:4, 1:4]}'''						)










'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=0
			if start < end:
						snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=a[end]
						snake_case:    List[Any]      							=a[pivot]
						snake_case:    int      							=temp

						snake_case							,  snake_case:    Optional[int]      							=_in_place_partition(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							__UpperCAmelCase				,							p - 1					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							p + 1				,							__UpperCAmelCase					)
			return count







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=0
			snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=a[end]
			snake_case:    List[str]      							=a[pivot]
			snake_case:    Dict      							=temp
			snake_case:    Union[str, Any]      							=start - 1
			for index in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									snake_case:    Optional[Any]      							=new_pivot_index + 1
									snake_case:    Dict      							=a[new_pivot_index]
									snake_case:    Union[str, Any]      							=a[index]
									snake_case:    List[Any]      							=temp

			snake_case:    Union[str, Any]      							=a[new_pivot_index + 1]
			snake_case:    Tuple      							=a[end]
			snake_case:    Union[str, Any]      							=temp
			return new_pivot_index + 1, count


a							   =				TemporaryFile()
a							   =				100  # 1000 elements are to be sorted


a   ,      a							   =				0, 1  # mean and standard deviation
a							   =				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
a							   =				np.load(outfile)
a							   =				len(M) - 1
a							   =				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)







print(z)




