





def 		lowerCAmelCase  (	UpperCamelCase__   :   list					)      ->    list:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   False
						while is_sorted is False:  # Until all the indices are traversed keep looping
												__SCREAMING_SNAKE_CASE:				Any           =   True
												for i in range(0       ,			len(UpperCamelCase__					) - 1       ,			2					):  # iterating over all even indices
																		if input_list[i] > input_list[i + 1]:
																								__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[int]           =   input_list[i + 1], input_list[i]
																								# swapping if elements not in order
																								__SCREAMING_SNAKE_CASE:				Tuple           =   False

												for i in range(1       ,			len(UpperCamelCase__					) - 1       ,			2					):  # iterating over all odd indices
																		if input_list[i] > input_list[i + 1]:
																								__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Dict           =   input_list[i + 1], input_list[i]
																								# swapping if elements not in order
																								__SCREAMING_SNAKE_CASE:				List[Any]           =   False
						return input_list


if __name__ == "__main__":
							print("""Enter list to be sorted""")
							lowerCAmelCase							:       int					 =	[int(x) for x in input().split()]
							# inputing elements of the list in one line
							lowerCAmelCase							:       Optional[Any]					 =	odd_even_sort(input_list)
							print("""The sorted list is""")
							print(sorted_list)










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 import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
							from transformers.models.esm.modeling_esm import (
							    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    EsmEmbeddings,
							    create_position_ids_from_input_ids,
							)





class 				a			:






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=13			, _lowerCAmelCase=7			, _lowerCAmelCase=False			, _lowerCAmelCase=True			, _lowerCAmelCase=False			, _lowerCAmelCase=True			, _lowerCAmelCase=33			, _lowerCAmelCase=32			, _lowerCAmelCase=5			, _lowerCAmelCase=4			, _lowerCAmelCase=37			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=512			, _lowerCAmelCase=16			, _lowerCAmelCase=2			, _lowerCAmelCase=0.02			, _lowerCAmelCase=3			, _lowerCAmelCase=4			, _lowerCAmelCase=None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   parent
										__SCREAMING_SNAKE_CASE:				Any           =   batch_size
										__SCREAMING_SNAKE_CASE:				Dict           =   seq_length
										__SCREAMING_SNAKE_CASE:				List[Any]           =   is_training
										__SCREAMING_SNAKE_CASE:				List[Any]           =   use_input_mask
										__SCREAMING_SNAKE_CASE:				str           =   use_token_type_ids
										__SCREAMING_SNAKE_CASE:				Any           =   use_labels
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   vocab_size
										__SCREAMING_SNAKE_CASE:				Dict           =   hidden_size
										__SCREAMING_SNAKE_CASE:				Dict           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				str           =   intermediate_size
										__SCREAMING_SNAKE_CASE:				str           =   hidden_act
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				Any           =   max_position_embeddings
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   type_vocab_size
										__SCREAMING_SNAKE_CASE:				List[Any]           =   type_sequence_label_size
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   initializer_range
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   num_labels
										__SCREAMING_SNAKE_CASE:				List[str]           =   num_choices
										__SCREAMING_SNAKE_CASE:				List[Any]           =   scope






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   ids_tensor([self.batch_size, self.seq_length]			, self.vocab_size					)

										__SCREAMING_SNAKE_CASE:				int           =   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
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   None
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   None
										if self.use_labels:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   ids_tensor([self.batch_size]			, self.type_sequence_label_size					)
																__SCREAMING_SNAKE_CASE:				List[str]           =   ids_tensor([self.batch_size, self.seq_length]			, self.num_labels					)
																__SCREAMING_SNAKE_CASE:				str           =   ids_tensor([self.batch_size]			, self.num_choices					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_config()

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return EsmConfig(
										    vocab_size=self.vocab_size			, 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			, )






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   EsmModel(config=_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   model(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   model(_lowerCAmelCase					)

										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			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   EsmForMaskedLM(config=_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase			, labels=_lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.vocab_size)					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   self.num_labels
										__SCREAMING_SNAKE_CASE:				List[Any]           =   EsmForTokenClassification(config=_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()
										__SCREAMING_SNAKE_CASE:				int           =   model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase			, labels=_lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.num_labels)					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.prepare_config_and_inputs()
										(
										    (
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,
										):				int           =   config_and_inputs
										__SCREAMING_SNAKE_CASE:				Tuple           =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict





@require_torch
class 				a			(      __lowercase   ,__lowercase   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     False

				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     (
				    (
				        EsmForMaskedLM,
				        EsmModel,
				        EsmForSequenceClassification,
				        EsmForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__     :   int		      =     ()
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     (
				    {
				        '''feature-extraction''': EsmModel,
				        '''fill-mask''': EsmForMaskedLM,
				        '''text-classification''': EsmForSequenceClassification,
				        '''token-classification''': EsmForTokenClassification,
				        '''zero-shot''': EsmForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE__     :   Any		      =     True






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   EsmModelTester(self					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   ConfigTester(self			, config_class=_lowerCAmelCase			, hidden_size=37					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										self.config_tester.run_common_tests()






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																__SCREAMING_SNAKE_CASE:				Any           =   type
																self.model_tester.create_and_check_model(*_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase					)






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   EsmModel.from_pretrained(_lowerCAmelCase					)
																self.assertIsNotNone(_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.model_tester.prepare_config_and_inputs()[0]
										__SCREAMING_SNAKE_CASE:				int           =   EsmEmbeddings(config=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   torch.as_tensor([[12, 31, 13, model.padding_idx]]					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.as_tensor(
										    [
										        [
										            0 + model.padding_idx + 1,
										            1 + model.padding_idx + 1,
										            2 + model.padding_idx + 1,
										            model.padding_idx,
										        ]
										    ]					)
										__SCREAMING_SNAKE_CASE:				Any           =   create_position_ids_from_input_ids(_lowerCAmelCase			, model.padding_idx					)
										self.assertEqual(position_ids.shape			, expected_positions.shape					)
										self.assertTrue(torch.all(torch.eq(_lowerCAmelCase			, _lowerCAmelCase					)					)					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   self.model_tester.prepare_config_and_inputs()[0]
										__SCREAMING_SNAKE_CASE:				Dict           =   EsmEmbeddings(config=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.empty(2			, 4			, 30					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   [
										    0 + embeddings.padding_idx + 1,
										    1 + embeddings.padding_idx + 1,
										    2 + embeddings.padding_idx + 1,
										    3 + embeddings.padding_idx + 1,
										]
										__SCREAMING_SNAKE_CASE:				str           =   torch.as_tensor([expected_single_positions, expected_single_positions]					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   embeddings.create_position_ids_from_inputs_embeds(_lowerCAmelCase					)
										self.assertEqual(position_ids.shape			, expected_positions.shape					)
										self.assertTrue(torch.all(torch.eq(_lowerCAmelCase			, _lowerCAmelCase					)					)					)






				@unittest.skip('''Esm does not support embedding resizing'''					)
				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass






				@unittest.skip('''Esm does not support embedding resizing'''					)
				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass






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

										"""simple docstring"""




										pass





@require_torch
class 				a			(      __lowercase			):






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										with torch.no_grad():
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)
																model.eval()
																__SCREAMING_SNAKE_CASE:				Tuple           =   torch.tensor([[0, 1, 2, 3, 4, 5]]					)
																__SCREAMING_SNAKE_CASE:				str           =   model(_lowerCAmelCase					)[0]

																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   33

																__SCREAMING_SNAKE_CASE:				Dict           =   torch.Size((1, 6, vocab_size)					)
																self.assertEqual(output.shape			, _lowerCAmelCase					)

																__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.tensor(
																    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]					)
																self.assertTrue(torch.allclose(output[:, :3, :3]			, _lowerCAmelCase			, atol=1e-4					)					)






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										with torch.no_grad():
																__SCREAMING_SNAKE_CASE:				Dict           =   EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)
																model.eval()

																__SCREAMING_SNAKE_CASE:				Dict           =   torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]					)
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model(_lowerCAmelCase					)[0]
																# compare the actual values for a slice.
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.tensor(
																    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]					)
																self.assertTrue(torch.allclose(output[:, :3, :3]			, _lowerCAmelCase			, atol=1e-4					)					)



