
'''simple docstring'''






import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     DistilBertForMaskedLM,
     DistilBertForMultipleChoice,
     DistilBertForQuestionAnswering,
     DistilBertForSequenceClassification,
     DistilBertForTokenClassification,
     DistilBertModel,
 )






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




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






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

   __lowercase													=				None
   if self.use_input_mask:
    __lowercase													=				random_attention_mask([self.batch_size, self.seq_length]    )

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

   __lowercase													=				self.get_config()

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   return DistilBertConfig(
       vocab_size=self.vocab_size      ,dim=self.hidden_size      ,n_layers=self.num_hidden_layers      ,n_heads=self.num_attention_heads      ,hidden_dim=self.intermediate_size      ,hidden_act=self.hidden_act      ,dropout=self.hidden_dropout_prob      ,attention_dropout=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,initializer_range=self.initializer_range      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[Any]      ,lowercase__     :  str      ,lowercase__     :  Tuple      ,lowercase__     :  List[str]    ):
   __lowercase													=				DistilBertModel(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Optional[Any]    ):
   __lowercase													=				DistilBertForMaskedLM(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Any      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  int    ):
   __lowercase													=				DistilBertForQuestionAnswering(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(
       lowercase__      ,attention_mask=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       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Optional[int]      ,lowercase__     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  int    ):
   __lowercase													=				self.num_labels
   __lowercase													=				DistilBertForSequenceClassification(lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  Any      ,lowercase__     :  Optional[int]      ,lowercase__     :  Tuple      ,lowercase__     :  Any      ,lowercase__     :  int      ,lowercase__     :  Union[str, Any]    ):
   __lowercase													=				self.num_labels
   __lowercase													=				DistilBertForTokenClassification(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()

   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  str    ):
   __lowercase													=				self.num_choices
   __lowercase													=				DistilBertForMultipleChoice(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				input_ids.unsqueeze(1    ).expand(-1      ,self.num_choices      ,-1    ).contiguous()
   __lowercase													=				input_mask.unsqueeze(1    ).expand(-1      ,self.num_choices      ,-1    ).contiguous()
   __lowercase													=				model(
       lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__      ,)
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.prepare_config_and_inputs()
   ((__lowercase)						,			(__lowercase)						,			(__lowercase)						,			(__lowercase)						,			(__lowercase)						,			(__lowercase))													=				config_and_inputs
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_torch
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						str           =    (
      (
          DistilBertModel,
          DistilBertForMaskedLM,
          DistilBertForMultipleChoice,
          DistilBertForQuestionAnswering,
          DistilBertForSequenceClassification,
          DistilBertForTokenClassification,
      )
      if is_torch_available()
      else None
  )
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    (
      {
          'feature-extraction': DistilBertModel,
          'fill-mask': DistilBertForMaskedLM,
          'question-answering': DistilBertForQuestionAnswering,
          'text-classification': DistilBertForSequenceClassification,
          'token-classification': DistilBertForTokenClassification,
          'zero-shot': DistilBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					:						int           =    True
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    True
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    True
  SCREAMING_SNAKE_CASE					:						Any           =    True






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				DistilBertModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__      ,dim=3_7    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_for_masked_lm(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_for_question_answering(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_for_token_classification(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowercase__    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase													=				DistilBertModel.from_pretrained(lowercase__    )
    self.assertIsNotNone(lowercase__    )






  @slow
  @require_torch_gpu
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
    # BertForMultipleChoice behaves incorrectly in JIT environments.
    if model_class == DistilBertForMultipleChoice:
     return

    __lowercase													=				True
    __lowercase													=				model_class(config=lowercase__    )

    __lowercase													=				self._prepare_for_class(lowercase__      ,lowercase__    )
    __lowercase													=				torch.jit.trace(
        lowercase__      ,(inputs_dict['''input_ids'''].to('''cpu'''    ), inputs_dict['''attention_mask'''].to('''cpu'''    ))    )

    with tempfile.TemporaryDirectory() as tmp:
     torch.jit.save(lowercase__      ,os.path.join(lowercase__      ,'''traced_model.pt'''    )    )
     __lowercase													=				torch.jit.load(os.path.join(lowercase__      ,'''traced_model.pt'''    )      ,map_location=lowercase__    )
     loaded(inputs_dict['''input_ids'''].to(lowercase__    )      ,inputs_dict['''attention_mask'''].to(lowercase__    )    )






@require_torch
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				DistilBertModel.from_pretrained('''distilbert-base-uncased'''    )
   __lowercase													=				torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]    )
   __lowercase													=				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
   with torch.no_grad():
    __lowercase													=				model(lowercase__      ,attention_mask=lowercase__    )[0]
   __lowercase													=				torch.Size((1, 1_1, 7_6_8)    )
   self.assertEqual(output.shape      ,lowercase__    )
   __lowercase													=				torch.tensor(
       [[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]    )

   self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,lowercase__      ,atol=1e-4    )    )




'''simple docstring'''






def 	_A  (					):






 """simple docstring"""



 for n in range(1  ,       1000000   ):
  yield n * (n + 1) // 2


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				1
 __lowercase													=				2
 while i * i <= n:
  __lowercase													=				0
  while n % i == 0:
   n //= i
   multiplicity += 1
  divisors_count *= multiplicity + 1
  i += 1
 if n > 1:
  divisors_count *= 2
 return divisors_count


def 	_A  (					):






 """simple docstring"""



 return next(i for i in triangle_number_generator() if count_divisors(A__   ) > 500   )


if __name__ == "__main__":
 print(solution())

