



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       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							str     ,  UpperCamelCase :							str     ,  UpperCamelCase :							int=1_3     ,  UpperCamelCase :							List[Any]=7     ,  UpperCamelCase :							int=True     ,  UpperCamelCase :							Optional[int]=True     ,  UpperCamelCase :							List[Any]=True     ,  UpperCamelCase :							List[Any]=True     ,  UpperCamelCase :							Any=9_9     ,  UpperCamelCase :							str=3_2     ,  UpperCamelCase :							List[Any]=5     ,  UpperCamelCase :							int=4     ,  UpperCamelCase :							List[str]=3_7     ,  UpperCamelCase :							List[Any]="gelu"     ,  UpperCamelCase :							List[Any]=0.1     ,  UpperCamelCase :							str=0.1     ,  UpperCamelCase :							Dict=5_1_2     ,  UpperCamelCase :							Any=1_6     ,  UpperCamelCase :							str=2     ,  UpperCamelCase :							List[Any]=0.0_2     ,  UpperCamelCase :							List[str]=False     ,  UpperCamelCase :							List[str]=True     ,  UpperCamelCase :							Union[str, Any]="None"     ,  UpperCamelCase :							Union[str, Any]=3     ,  UpperCamelCase :							Optional[int]=4     ,  UpperCamelCase :							List[str]=None     ,  )->str:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	parent
      __SCREAMING_SNAKE_CASE       :    int       =	batch_size
      __SCREAMING_SNAKE_CASE       :    Any       =	seq_length
      __SCREAMING_SNAKE_CASE       :    List[str]       =	is_training
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	use_input_mask
      __SCREAMING_SNAKE_CASE       :    Any       =	use_token_type_ids
      __SCREAMING_SNAKE_CASE       :    List[str]       =	use_labels
      __SCREAMING_SNAKE_CASE       :    List[str]       =	vocab_size
      __SCREAMING_SNAKE_CASE       :    Tuple       =	hidden_size
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	num_hidden_layers
      __SCREAMING_SNAKE_CASE       :    List[str]       =	num_attention_heads
      __SCREAMING_SNAKE_CASE       :    List[str]       =	intermediate_size
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	hidden_act
      __SCREAMING_SNAKE_CASE       :    Any       =	hidden_dropout_prob
      __SCREAMING_SNAKE_CASE       :    List[str]       =	attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE       :    Dict       =	max_position_embeddings
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	type_vocab_size
      __SCREAMING_SNAKE_CASE       :    str       =	type_sequence_label_size
      __SCREAMING_SNAKE_CASE       :    Any       =	initializer_range
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	num_labels
      __SCREAMING_SNAKE_CASE       :    Any       =	num_choices
      __SCREAMING_SNAKE_CASE       :    Any       =	relative_attention
      __SCREAMING_SNAKE_CASE       :    str       =	position_biased_input
      __SCREAMING_SNAKE_CASE       :    str       =	pos_att_type
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	scope





     def 	__snake_case    (       self :							Dict				)->str:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size				)

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

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

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

      __SCREAMING_SNAKE_CASE       :    str       =	self.get_config()

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





     def 	__snake_case    (       self :							List[Any]				)->Tuple:
      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 	__snake_case    (       self :							Optional[Any]				)->int:
      __SCREAMING_SNAKE_CASE       :    str       =	self.get_config()
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	3_0_0
      return config





     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							Any				)->Tuple:
      self.parent.assertListEqual(list(result.loss.size()				)     ,  []				)





     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							int     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							str				)->Any:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	DebertaModel(config=UpperCamelCase_				)
      model.to(UpperCamelCase_				)
      model.eval()
      __SCREAMING_SNAKE_CASE       :    Tuple       =	model(UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_				)[0]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	model(UpperCamelCase_     ,  token_type_ids=UpperCamelCase_				)[0]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	model(UpperCamelCase_				)[0]

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





     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							str     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							List[Any]				)->str:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	DebertaForMaskedLM(config=UpperCamelCase_				)
      model.to(UpperCamelCase_				)
      model.eval()
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	model(UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_     ,  labels=UpperCamelCase_				)

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





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							str     ,  UpperCamelCase :							str     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							List[Any]				)->str:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.num_labels
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	DebertaForSequenceClassification(UpperCamelCase_				)
      model.to(UpperCamelCase_				)
      model.eval()
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	model(UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_     ,  labels=UpperCamelCase_				)
      self.parent.assertListEqual(list(result.logits.size()				)     ,  [self.batch_size, self.num_labels]				)
      self.check_loss_output(UpperCamelCase_				)





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							Union[str, Any]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							str				)->Dict:
      __SCREAMING_SNAKE_CASE       :    str       =	self.num_labels
      __SCREAMING_SNAKE_CASE       :    Tuple       =	DebertaForTokenClassification(config=UpperCamelCase_				)
      model.to(UpperCamelCase_				)
      model.eval()
      __SCREAMING_SNAKE_CASE       :    int       =	model(UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_     ,  labels=UpperCamelCase_				)
      self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.num_labels)				)





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							List[str]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Dict       =	DebertaForQuestionAnswering(config=UpperCamelCase_				)
      model.to(UpperCamelCase_				)
      model.eval()
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	model(
          UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_     ,  start_positions=UpperCamelCase_     ,  end_positions=UpperCamelCase_     ,  )
      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 	__snake_case    (       self :							List[Any]				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.prepare_config_and_inputs()
      (
          __SCREAMING_SNAKE_CASE    
      )       :    int       =	config_and_inputs
      __SCREAMING_SNAKE_CASE       :    Dict       =	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict






@require_torch
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			(
         (
             DebertaModel,
             DebertaForMaskedLM,
             DebertaForSequenceClassification,
             DebertaForTokenClassification,
             DebertaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     lowerCAmelCase					=			(
         {
             """feature-extraction""": DebertaModel,
             """fill-mask""": DebertaForMaskedLM,
             """question-answering""": DebertaForQuestionAnswering,
             """text-classification""": DebertaForSequenceClassification,
             """token-classification""": DebertaForTokenClassification,
             """zero-shot""": DebertaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     lowerCAmelCase					=			True
     lowerCAmelCase					=			False
     lowerCAmelCase					=			False
     lowerCAmelCase					=			False
     lowerCAmelCase					=			False





     def 	__snake_case    (       self :							List[Any]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    str       =	DebertaModelTester(self				)
      __SCREAMING_SNAKE_CASE       :    str       =	ConfigTester(self     ,  config_class=UpperCamelCase_     ,  hidden_size=3_7				)





     def 	__snake_case    (       self :							int				)->Union[str, Any]:
      self.config_tester.run_common_tests()





     def 	__snake_case    (       self :							List[str]				)->str:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*UpperCamelCase_				)





     def 	__snake_case    (       self :							Optional[Any]				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCamelCase_				)





     def 	__snake_case    (       self :							int				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCamelCase_				)





     def 	__snake_case    (       self :							Union[str, Any]				)->Any:
      __SCREAMING_SNAKE_CASE       :    str       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*UpperCamelCase_				)





     def 	__snake_case    (       self :							List[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*UpperCamelCase_				)






     @slow
     def 	__snake_case    (       self :							int				)->Optional[int]:





      for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __SCREAMING_SNAKE_CASE       :    Tuple       =	DebertaModel.from_pretrained(UpperCamelCase_				)
       self.assertIsNotNone(UpperCamelCase_				)






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





     @unittest.skip(reason="Model not available yet"				)
     def 	__snake_case    (       self :							Optional[int]				)->Union[str, Any]:
      pass






     @slow
     def 	__snake_case    (       self :							Optional[int]				)->Any:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	DebertaModel.from_pretrained("microsoft/deberta-base"				)

      __SCREAMING_SNAKE_CASE       :    Any       =	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]]				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
      with torch.no_grad():
       __SCREAMING_SNAKE_CASE       :    List[str]       =	model(UpperCamelCase_     ,  attention_mask=UpperCamelCase_				)[0]
      # compare the actual values for a slice.
      __SCREAMING_SNAKE_CASE       :    Dict       =	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]     ,  UpperCamelCase_     ,  atol=1E-4				)     ,  F"""{output[:, 1:4, 1:4]}"""				)











from math import sqrt






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5		,	int(sqrt(__lowerCamelCase     ) + 1     )		,	6     ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int = 10001     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	0
 __SCREAMING_SNAKE_CASE       :    Dict       =	1
 while count != nth and number < 3:
  number += 1
  if is_prime(__lowerCamelCase     ):
   count += 1
 while count != nth:
  number += 2
  if is_prime(__lowerCamelCase     ):
   count += 1
 return number


if __name__ == "__main__":
      print(f'''{solution() = }''')






