code: 
  
  
  
  import random
  
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[int]       )   ->				Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						def is_sorted(lowerCAmelCase_     :      Any       ):
  												for i in range(len(lowerCAmelCase_       ) - 1       ):
  																		if collection[i] > collection[i + 1]:
  																								return False
  												return True
  
  						while not is_sorted(lowerCAmelCase_       ):
  												random.shuffle(lowerCAmelCase_       )
  						return collection
  
  
  if __name__ == "__main__":
  	__A       					=		input('''Enter numbers separated by a comma:\n''').strip()
  	__A       					=		[int(item) for item in user_input.split(''',''')]
  	print(bogo_sort(unsorted))
  
  
  
  
  
style_context: 
  
  
  
  import unittest
  
  from transformers import YosoConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  	import torch
  
  	from transformers import (
  	    YosoForMaskedLM,
  	    YosoForMultipleChoice,
  	    YosoForQuestionAnswering,
  	    YosoForSequenceClassification,
  	    YosoForTokenClassification,
  	    YosoModel,
  	)
  	from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  
  
  
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Optional[int]=13		,						__UpperCAmelCase			:		Optional[Any]=7		,						__UpperCAmelCase			:		Union[str, Any]=True		,						__UpperCAmelCase			:		Dict=True		,						__UpperCAmelCase			:		int=True		,						__UpperCAmelCase			:		Optional[int]=True		,						__UpperCAmelCase			:		Optional[Any]=99		,						__UpperCAmelCase			:		List[Any]=32		,						__UpperCAmelCase			:		int=5		,						__UpperCAmelCase			:		List[str]=4		,						__UpperCAmelCase			:		Union[str, Any]=37		,						__UpperCAmelCase			:		Any="gelu"		,						__UpperCAmelCase			:		Any=0.1		,						__UpperCAmelCase			:		List[Any]=0.1		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Tuple=16		,						__UpperCAmelCase			:		Any=2		,						__UpperCAmelCase			:		Tuple=0.02		,						__UpperCAmelCase			:		Any=3		,						__UpperCAmelCase			:		Tuple=4		,						__UpperCAmelCase			:		int=None		,						)    ->				str:
  							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			:		Tuple  )    ->				str:
  							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
  							if self.use_token_type_ids:
  													UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						self.type_vocab_size  )
  
  							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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
  							return YosoConfig(
  							    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		,						is_decoder=__UpperCAmelCase		,						initializer_range=self.initializer_range		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     self.get_config()
  							UpperCAmelCase_=     300
  							return config
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				str:
  							(
  							    (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     
  							)=     self.prepare_config_and_inputs()
  
  							UpperCAmelCase_=     True
  							UpperCAmelCase_=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
  							UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						vocab_size=2  )
  
  							return (
  							    config,
  							    input_ids,
  							    token_type_ids,
  							    input_mask,
  							    sequence_labels,
  							    token_labels,
  							    choice_labels,
  							    encoder_hidden_states,
  							    encoder_attention_mask,
  							)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Any  )    ->				List[Any]:
  							UpperCAmelCase_=     YosoModel(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase  )
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						token_type_ids=__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			:		List[Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Dict		,						)    ->				List[str]:
  							UpperCAmelCase_=     True
  							UpperCAmelCase_=     YosoModel(__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(
  							    __UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						encoder_hidden_states=__UpperCAmelCase		,						encoder_attention_mask=__UpperCAmelCase		,						)
  							UpperCAmelCase_=     model(
  							    __UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						encoder_hidden_states=__UpperCAmelCase		,						)
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase  )
  							self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		str  )    ->				List[Any]:
  							UpperCAmelCase_=     YosoForMaskedLM(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[int]  )    ->				List[Any]:
  							UpperCAmelCase_=     YosoForQuestionAnswering(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		List[str]  )    ->				str:
  							UpperCAmelCase_=     self.num_labels
  							UpperCAmelCase_=     YosoForSequenceClassification(__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						labels=__UpperCAmelCase  )
  							self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Tuple  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     self.num_labels
  							UpperCAmelCase_=     YosoForTokenClassification(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		Optional[int]  )    ->				Any:
  							UpperCAmelCase_=     self.num_choices
  							UpperCAmelCase_=     YosoForMultipleChoice(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     input_ids.unsqueeze(1  ).expand(-1		,						self.num_choices		,						-1  ).contiguous()
  							UpperCAmelCase_=     token_type_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		,						token_type_ids=__UpperCAmelCase		,						labels=__UpperCAmelCase		,						)
  							self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_choices)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Any:
  							UpperCAmelCase_=     self.prepare_config_and_inputs()
  							(
  							    (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     
  							)=     config_and_inputs
  							UpperCAmelCase_=     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
  							return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class 	lowercase		(				snake_case__				,					snake_case__				,					unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							(
  	    (
  	        YosoModel,
  	        YosoForMaskedLM,
  	        YosoForMultipleChoice,
  	        YosoForQuestionAnswering,
  	        YosoForSequenceClassification,
  	        YosoForTokenClassification,
  	    )
  	    if is_torch_available()
  	    else ()
  	)
  	a__   :      Optional[int]	     =							False
  	a__   :      Optional[Any]	     =							False
  	a__   :      Dict	     =							False
  
  	a__   :      int	     =							()
  	a__   :      int	     =							(
  	    {
  	        "feature-extraction": YosoModel,
  	        "fill-mask": YosoForMaskedLM,
  	        "question-answering": YosoForQuestionAnswering,
  	        "text-classification": YosoForSequenceClassification,
  	        "token-classification": YosoForTokenClassification,
  	        "zero-shot": YosoForSequenceClassification,
  	    }
  	    if is_torch_available()
  	    else {}
  	)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Optional[int]:
  							UpperCAmelCase_=     YosoModelTester(self  )
  							UpperCAmelCase_=     ConfigTester(self		,						config_class=__UpperCAmelCase		,						hidden_size=37  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Union[str, Any]:
  							self.config_tester.run_common_tests()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				Dict:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_model(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Any:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							for type in ["absolute", "relative_key", "relative_key_query"]:
  													UpperCAmelCase_=     type
  													self.model_tester.create_and_check_model(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				Any:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Optional[int]:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Dict:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				int:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Optional[Any]:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Optional[Any]:
  							for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  													UpperCAmelCase_=     YosoModel.from_pretrained(__UpperCAmelCase  )
  													self.assertIsNotNone(__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Tuple:
  							return
  
  
  
  
  
  
  @require_torch
  class 	lowercase		(				unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Optional[int]:
  							UpperCAmelCase_=     YosoModel.from_pretrained("""uw-madison/yoso-4096"""  )
  							UpperCAmelCase_=     torch.tensor([[0, 1, 2, 3, 4, 5]]  )
  
  							with torch.no_grad():
  													UpperCAmelCase_=     model(__UpperCAmelCase  )[0]
  
  							UpperCAmelCase_=     torch.Size((1, 6, 768)  )
  							self.assertEqual(output.shape		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     torch.tensor(
  							    [[[-0.0_611, 0.1_242, 0.0_840], [0.0_280, -0.0_048, 0.1_125], [0.0_106, 0.0_226, 0.0_751]]]  )
  
  							self.assertTrue(torch.allclose(output[:, :3, :3]		,						__UpperCAmelCase		,						atol=1E-4  )  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				List[Any]:
  							UpperCAmelCase_=     YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096"""  )
  							UpperCAmelCase_=     torch.tensor([[0, 1, 2, 3, 4, 5]]  )
  
  							with torch.no_grad():
  													UpperCAmelCase_=     model(__UpperCAmelCase  )[0]
  
  							UpperCAmelCase_=     50_265
  
  							UpperCAmelCase_=     torch.Size((1, 6, vocab_size)  )
  							self.assertEqual(output.shape		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     torch.tensor(
  							    [[[-2.1_313, -3.7_285, -2.2_407], [-2.7_047, -3.3_314, -2.6_408], [0.0_629, -2.5_166, -0.3_356]]]  )
  
  							self.assertTrue(torch.allclose(output[:, :3, :3]		,						__UpperCAmelCase		,						atol=1E-4  )  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Tuple:
  							UpperCAmelCase_=     YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096"""  )
  							UpperCAmelCase_=     torch.arange(4_096  ).unsqueeze(0  )
  
  							with torch.no_grad():
  													UpperCAmelCase_=     model(__UpperCAmelCase  )[0]
  
  							UpperCAmelCase_=     50_265
  
  							UpperCAmelCase_=     torch.Size((1, 4_096, vocab_size)  )
  							self.assertEqual(output.shape		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     torch.tensor(
  							    [[[-2.3_914, -4.3_742, -5.0_956], [-4.0_988, -4.2_384, -7.0_406], [-3.1_427, -3.7_192, -6.6_800]]]  )
  
  							self.assertTrue(torch.allclose(output[:, :3, :3]		,						__UpperCAmelCase		,						atol=1E-4  )  )
  
  
  
  
  