




import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    MobileBertForMaskedLM,
						    MobileBertForMultipleChoice,
						    MobileBertForNextSentencePrediction,
						    MobileBertForPreTraining,
						    MobileBertForQuestionAnswering,
						    MobileBertForSequenceClassification,
						    MobileBertForTokenClassification,
						    MobileBertModel,
						)







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__      ,snake_case__=13      ,snake_case__=7      ,snake_case__=True      ,snake_case__=True      ,snake_case__=True      ,snake_case__=True      ,snake_case__=99      ,snake_case__=64      ,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      ,):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      parent
							SCREAMING_SNAKE_CASE_   : str					  =      batch_size
							SCREAMING_SNAKE_CASE_   : int					  =      seq_length
							SCREAMING_SNAKE_CASE_   : int					  =      is_training
							SCREAMING_SNAKE_CASE_   : str					  =      use_input_mask
							SCREAMING_SNAKE_CASE_   : List[str]					  =      use_token_type_ids
							SCREAMING_SNAKE_CASE_   : Dict					  =      use_labels
							SCREAMING_SNAKE_CASE_   : str					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      hidden_size
							SCREAMING_SNAKE_CASE_   : Dict					  =      embedding_size
							SCREAMING_SNAKE_CASE_   : Tuple					  =      num_hidden_layers
							SCREAMING_SNAKE_CASE_   : Any					  =      num_attention_heads
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      intermediate_size
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : Tuple					  =      hidden_dropout_prob
							SCREAMING_SNAKE_CASE_   : str					  =      attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      max_position_embeddings
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      type_vocab_size
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      type_sequence_label_size
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : Tuple					  =      num_labels
							SCREAMING_SNAKE_CASE_   : int					  =      num_choices
							SCREAMING_SNAKE_CASE_   : int					  =      scope






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : 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_   : Tuple					  =      random_attention_mask([self.batch_size, self.seq_length] )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      None
							if self.use_token_type_ids:
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      None
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      None
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      None
							if self.use_labels:
										SCREAMING_SNAKE_CASE_   : str					  =      ids_tensor([self.batch_size]      ,self.type_sequence_label_size )
										SCREAMING_SNAKE_CASE_   : Tuple					  =      ids_tensor([self.batch_size, self.seq_length]      ,self.num_labels )
										SCREAMING_SNAKE_CASE_   : Dict					  =      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 ):
							return MobileBertConfig(
							    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      ,embedding_size=self.embedding_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      ,is_decoder=snake_case__      ,initializer_range=self.initializer_range      ,)






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : str					  =      MobileBertModel(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : Dict					  =      model(snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      model(snake_case__      ,token_type_ids=snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      model(snake_case__ )

							self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size) )
							self.parent.assertEqual(result.pooler_output.shape      ,(self.batch_size, self.hidden_size) )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      MobileBertForMaskedLM(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : Any					  =      model(snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__      ,labels=snake_case__ )
							self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size) )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Any					  =      MobileBertForNextSentencePrediction(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : Any					  =      model(
							    snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__      ,labels=snake_case__      ,)
							self.parent.assertEqual(result.logits.shape      ,(self.batch_size, 2) )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Any					  =      MobileBertForPreTraining(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      model(
							    snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__      ,labels=snake_case__      ,next_sentence_label=snake_case__      ,)
							self.parent.assertEqual(result.prediction_logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size) )
							self.parent.assertEqual(result.seq_relationship_logits.shape      ,(self.batch_size, 2) )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      MobileBertForQuestionAnswering(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : str					  =      model(
							    snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__      ,start_positions=snake_case__      ,end_positions=snake_case__      ,)
							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      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.num_labels
							SCREAMING_SNAKE_CASE_   : Dict					  =      MobileBertForSequenceClassification(snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : int					  =      model(snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__      ,labels=snake_case__ )
							self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels) )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.num_labels
							SCREAMING_SNAKE_CASE_   : Any					  =      MobileBertForTokenClassification(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model(snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__      ,labels=snake_case__ )
							self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels) )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : str					  =      self.num_choices
							SCREAMING_SNAKE_CASE_   : int					  =      MobileBertForMultipleChoice(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : List[str]					  =      input_ids.unsqueeze(1 ).expand(-1      ,self.num_choices      ,-1 ).contiguous()
							SCREAMING_SNAKE_CASE_   : str					  =      token_type_ids.unsqueeze(1 ).expand(-1      ,self.num_choices      ,-1 ).contiguous()
							SCREAMING_SNAKE_CASE_   : str					  =      input_mask.unsqueeze(1 ).expand(-1      ,self.num_choices      ,-1 ).contiguous()
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model(
							    snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__      ,labels=snake_case__      ,)
							self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices) )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      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_
							)  ,    
							)   : Dict					  =      config_and_inputs
							SCREAMING_SNAKE_CASE_   : Any					  =      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_torch
class 				lowerCAmelCase_       (					lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase							):
				__a     :       Any						=     (
				    (
				        MobileBertModel,
				        MobileBertForMaskedLM,
				        MobileBertForMultipleChoice,
				        MobileBertForNextSentencePrediction,
				        MobileBertForPreTraining,
				        MobileBertForQuestionAnswering,
				        MobileBertForSequenceClassification,
				        MobileBertForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				__a     :       Optional[Any]						=     (
				    {
				        "feature-extraction": MobileBertModel,
				        "fill-mask": MobileBertForMaskedLM,
				        "question-answering": MobileBertForQuestionAnswering,
				        "text-classification": MobileBertForSequenceClassification,
				        "token-classification": MobileBertForTokenClassification,
				        "zero-shot": MobileBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__a     :       str						=     True






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__=False ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      super()._prepare_for_class(snake_case__      ,snake_case__      ,return_labels=snake_case__ )

							if return_labels:
										if model_class in get_values(snake_case__ ):
													SCREAMING_SNAKE_CASE_   : Tuple					  =      torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.seq_length)      ,dtype=torch.long      ,device=snake_case__ )
													SCREAMING_SNAKE_CASE_   : Dict					  =      torch.zeros(
													    self.model_tester.batch_size      ,dtype=torch.long      ,device=snake_case__ )
							return inputs_dict






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      MobileBertModelTester(self )
							SCREAMING_SNAKE_CASE_   : int					  =      ConfigTester(self      ,config_class=snake_case__      ,hidden_size=37 )






				def 		snake_case	(			self ):
							self.config_tester.run_common_tests()






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_model(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case__ )





def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  )       ->  Tuple:







			"""simple docstring"""





			return torch.tensor(
			    lowerCamelCase_  ,							dtype=torch.long  ,							device=lowerCamelCase_  ,							)


UpperCamelCase__ :   Dict						=							1e-3







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






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      MobileBertModel.from_pretrained('google/mobilebert-uncased' ).to(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]] )
							with torch.no_grad():
										SCREAMING_SNAKE_CASE_   : str					  =      model(snake_case__ )[0]
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      torch.Size((1, 9, 512) )
							self.assertEqual(output.shape      ,snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      torch.tensor(
							    [
							        [
							            [-2.473_6526E07, 8.269_1656E04, 1.652_1838E05],
							            [-5.754_1704E-01, 3.905_6022E00, 4.401_1507E00],
							            [2.604_7359E00, 1.567_7652E00, -1.732_4188E-01],
							        ]
							    ]      ,device=snake_case__      ,)

							# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
							# ~1 difference, it's therefore not a good idea to measure using addition.
							# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
							# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
							SCREAMING_SNAKE_CASE_   : int					  =      torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )

							self.assertTrue(lower_bound and upper_bound )











"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Tuple				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							len(snake_case				)
     for i in range(length - 1				):
          UpperCAmelCase__						:    Any							=							i
          for k in range(i + 1			,					snake_case				):
               if collection[k] < collection[least]:
                    UpperCAmelCase__						:    Union[str, Any]							=							k
          if least != i:
               UpperCAmelCase__							,				UpperCAmelCase__						:    Union[str, Any]							=							(collection[i], collection[least])
     return collection


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


