
"""simple docstring"""





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, 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.models.esm.modeling_esmfold import EsmForProteinFolding
class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ ,				snake_case_=13 ,				snake_case_=7 ,				snake_case_=False ,				snake_case_=True ,				snake_case_=False ,				snake_case_=False ,				snake_case_=19 ,				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 ,				)		->       Optional[int]:
       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      )		->       Optional[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      )		->       int:
       UpperCamelCase__         =    EsmConfig(
           vocab_size=33 ,				hidden_size=self.hidden_size ,				pad_token_id=1 ,				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 ,				is_folding_model=snake_case_ ,				esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False} ,				)
       return config

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Tuple:
       UpperCamelCase__         =    EsmForProteinFolding(config=snake_case_      ).float()
       model.to(snake_case_      )
       model.eval()
       UpperCamelCase__         =    model(snake_case_ ,				attention_mask=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )

       self.parent.assertEqual(result.positions.shape ,				(8, self.batch_size, self.seq_length, 14, 3)      )
       self.parent.assertEqual(result.angles.shape ,				(8, self.batch_size, self.seq_length, 7, 2)      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       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    (  _a							,	_a							,	unittest.TestCase  ):
   a      :				Tuple 						=False

   a      :				List[str] 						=(EsmForProteinFolding,) if is_torch_available() else ()
   a      :				Optional[Any] 						=()
   a      :				List[str] 						={} if is_torch_available() else {}
   a      :				Optional[Any] 						=False

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    EsmFoldModelTester(self      )
       UpperCamelCase__         =    ConfigTester(self ,				config_class=snake_case_ ,				hidden_size=37      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_      )

   @unittest.skip('Does not support attention outputs'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       pass

   @unittest.skip
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       pass

   @unittest.skip('Esm does not support embedding resizing'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       pass

   @unittest.skip('Esm does not support embedding resizing'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       pass

   @unittest.skip('ESMFold does not support passing input embeds!'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       pass

   @unittest.skip('ESMFold does not support head pruning.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       pass

   @unittest.skip('ESMFold does not support head pruning.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       pass

   @unittest.skip('ESMFold does not support head pruning.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       pass

   @unittest.skip('ESMFold does not support head pruning.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       pass

   @unittest.skip('ESMFold does not support head pruning.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       pass

   @unittest.skip('ESMFold does not output hidden states in the normal way.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       pass

   @unittest.skip('ESMfold does not output hidden states in the normal way.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       pass

   @unittest.skip('ESMFold only has one output format.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       pass

   @unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       pass

   @unittest.skip('ESMFold does not support input chunking.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       pass

   @unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       pass

   @unittest.skip('ESMFold doesn\'t support torchscript compilation.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       pass

   @unittest.skip('ESMFold doesn\'t support torchscript compilation.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       pass

   @unittest.skip('ESMFold doesn\'t support torchscript compilation.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       pass

   @unittest.skip('ESMFold doesn\'t support data parallel.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       pass







   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       pass







@require_torch
class    __lowerCamelCase    (  _a  ):

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    EsmForProteinFolding.from_pretrained('facebook/esmfold_v1'      ).float()
       model.eval()
       UpperCamelCase__         =    torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]      )
       UpperCamelCase__         =    model(snake_case_      )['positions']
       UpperCamelCase__         =    torch.tensor([2.5_828, 0.7_993, -10.9_334] ,				dtype=torch.floataa      )
       self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] ,				snake_case_ ,				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))




