
"""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    __lowerCamelCase    (  snake_case__  ):

   def __init__(					self ,				snake_case_ ,				snake_case_=13 ,				snake_case_=7 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=False ,				snake_case_=True ,				snake_case_=99 ,				snake_case_=32 ,				snake_case_=5 ,				snake_case_=4 ,				snake_case_=37 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=512 ,				snake_case_=16 ,				snake_case_=2 ,				snake_case_=0.02 ,				snake_case_=3 ,				snake_case_=4 ,				snake_case_=None ,				)		->       Any:
       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__         =    scope

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

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

       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, input_mask, sequence_labels, token_labels, choice_labels

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       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 ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    DistilBertModel(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       UpperCamelCase__         =    model(UpperCAmelCase_ ,				UpperCAmelCase_      )
       UpperCamelCase__         =    model(UpperCAmelCase_      )
       self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       int:
       UpperCamelCase__         =    DistilBertForMaskedLM(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       UpperCamelCase__         =    model(UpperCAmelCase_ ,				attention_mask=UpperCAmelCase_ ,				labels=UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Optional[Any]:
       UpperCamelCase__         =    DistilBertForQuestionAnswering(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       UpperCamelCase__         =    model(
           UpperCAmelCase_ ,				attention_mask=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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Dict:
       UpperCamelCase__         =    self.num_labels
       UpperCamelCase__         =    DistilBertForSequenceClassification(UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       UpperCamelCase__         =    model(UpperCAmelCase_ ,				attention_mask=UpperCAmelCase_ ,				labels=UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Dict:
       UpperCamelCase__         =    self.num_labels
       UpperCamelCase__         =    DistilBertForTokenClassification(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Tuple:
       UpperCamelCase__         =    self.num_choices
       UpperCamelCase__         =    DistilBertForMultipleChoice(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       UpperCamelCase__         =    input_ids.unsqueeze(1      ).expand(-1 ,				self.num_choices ,				-1      ).contiguous()
       UpperCamelCase__         =    input_mask.unsqueeze(1      ).expand(-1 ,				self.num_choices ,				-1      ).contiguous()
       UpperCamelCase__         =    model(
           UpperCAmelCase_ ,				attention_mask=UpperCAmelCase_ ,				labels=UpperCAmelCase_ ,				)
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    self.prepare_config_and_inputs()
       ((UpperCamelCase__)       ,						(UpperCamelCase__)       ,						(UpperCamelCase__)       ,						(UpperCamelCase__)       ,						(UpperCamelCase__)       ,						(UpperCamelCase__))         =    config_and_inputs
       UpperCamelCase__         =    {'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_torch
class    __lowerCamelCase    (  snake_case__							,	snake_case__							,	unittest.TestCase  ):
   a      :				str 						=(
       (
           DistilBertModel,
           DistilBertForMaskedLM,
           DistilBertForMultipleChoice,
           DistilBertForQuestionAnswering,
           DistilBertForSequenceClassification,
           DistilBertForTokenClassification,
       )
       if is_torch_available()
       else None
   )
   a      :				Tuple 						=(
       {
           """feature-extraction""": DistilBertModel,
           """fill-mask""": DistilBertForMaskedLM,
           """question-answering""": DistilBertForQuestionAnswering,
           """text-classification""": DistilBertForSequenceClassification,
           """token-classification""": DistilBertForTokenClassification,
           """zero-shot""": DistilBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   a      :				List[str] 						=True
   a      :				Tuple 						=True
   a      :				Dict 						=True
   a      :				Union[str, Any] 						=True

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    DistilBertModelTester(self      )
       UpperCamelCase__         =    ConfigTester(self ,				config_class=UpperCAmelCase_ ,				dim=37      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       self.config_tester.run_common_tests()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_model(*UpperCAmelCase_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCAmelCase_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCAmelCase_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCAmelCase_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCAmelCase_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCAmelCase_      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:



       for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__         =    DistilBertModel.from_pretrained(UpperCAmelCase_      )
           self.assertIsNotNone(UpperCAmelCase_      )







   @slow
   @require_torch_gpu
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__       ,						UpperCamelCase__         =    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

           UpperCamelCase__         =    True
           UpperCamelCase__         =    model_class(config=UpperCAmelCase_      )

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

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







@require_torch
class    __lowerCamelCase    (  unittest.TestCase  ):

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    DistilBertModel.from_pretrained('distilbert-base-uncased'      )
       UpperCamelCase__         =    torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
       UpperCamelCase__         =    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
       with torch.no_grad():
           UpperCamelCase__         =    model(UpperCAmelCase_ ,				attention_mask=UpperCAmelCase_      )[0]
       UpperCamelCase__         =    torch.Size((1, 11, 768)      )
       self.assertEqual(output.shape ,				UpperCAmelCase_      )
       UpperCamelCase__         =    torch.tensor(
           [[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]]      )

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












"""simple docstring"""





def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	list[int]:
    """simple docstring"""

    UpperCamelCase__         =    len(SCREAMING_SNAKE_CASE					)
    for i in range(SCREAMING_SNAKE_CASE					):
        for j in range(i + 1 ,	SCREAMING_SNAKE_CASE					):
            if numbers[j] < numbers[i]:
                UpperCamelCase__       ,						UpperCamelCase__         =    numbers[j], numbers[i]
    return numbers


if __name__ == "__main__":
  A__					: Union[str, Any]=	input("""Enter numbers separated by a comma:\n""").strip()
  A__					: List[Any]=	[int(item) for item in user_input.split(""",""")]
  print(exchange_sort(unsorted))




