




"""simple docstring"""




import unittest

from transformers import MraConfig, 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


if is_torch_available():
					import torch

					from transformers import (
					    MraForMaskedLM,
					    MraForMultipleChoice,
					    MraForQuestionAnswering,
					    MraForSequenceClassification,
					    MraForTokenClassification,
					    MraModel,
					)
					from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST






class     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, _a		, _a=2		, _a=8		, _a=True		, _a=True		, _a=True		, _a=True		, _a=99		, _a=16		, _a=5		, _a=2		, _a=36		, _a="gelu"		, _a=0.0		, _a=0.0		, _a=512		, _a=16		, _a=2		, _a=0.02		, _a=3		, _a=4		, _a=None		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					batch_size
							lowerCamelCase			      =					seq_length
							lowerCamelCase			      =					is_training
							lowerCamelCase			      =					use_input_mask
							lowerCamelCase			      =					use_token_type_ids
							lowerCamelCase			      =					use_labels
							lowerCamelCase			      =					vocab_size
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					attention_probs_dropout_prob
							lowerCamelCase			      =					max_position_embeddings
							lowerCamelCase			      =					type_vocab_size
							lowerCamelCase			      =					type_sequence_label_size
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					num_labels
							lowerCamelCase			      =					num_choices
							lowerCamelCase			      =					scope





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size )

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

							lowerCamelCase			      =					None
							if self.use_token_type_ids:
										lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.type_vocab_size )

							lowerCamelCase			      =					None
							lowerCamelCase			      =					None
							lowerCamelCase			      =					None
							if self.use_labels:
										lowerCamelCase			      =					ids_tensor([self.batch_size]		, self.type_sequence_label_size )
										lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.num_labels )
										lowerCamelCase			      =					ids_tensor([self.batch_size]		, self.num_choices )

							lowerCamelCase			      =					self.get_config()

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return MraConfig(
							    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=_a		, initializer_range=self.initializer_range		, )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_config()
							lowerCamelCase			      =					300
							return config





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							(
							    (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   
							)			      =					self.prepare_config_and_inputs()

							lowerCamelCase			      =					True
							lowerCamelCase			      =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
							lowerCamelCase			      =					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    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					MraModel(config=_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a		, attention_mask=_a		, token_type_ids=_a )
							lowerCamelCase			      =					model(_a		, token_type_ids=_a )
							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a		, _a		, _a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					True
							lowerCamelCase			      =					MraModel(_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(
							    _a		, attention_mask=_a		, token_type_ids=_a		, encoder_hidden_states=_a		, encoder_attention_mask=_a		, )
							lowerCamelCase			      =					model(
							    _a		, attention_mask=_a		, token_type_ids=_a		, encoder_hidden_states=_a		, )
							lowerCamelCase			      =					model(_a		, attention_mask=_a		, token_type_ids=_a )
							self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					MraForMaskedLM(config=_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a		, attention_mask=_a		, token_type_ids=_a		, labels=_a )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.vocab_size) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					MraForQuestionAnswering(config=_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(
							    _a		, attention_mask=_a		, token_type_ids=_a		, start_positions=_a		, end_positions=_a		, )
							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    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.num_labels
							lowerCamelCase			      =					MraForSequenceClassification(_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a		, attention_mask=_a		, token_type_ids=_a		, labels=_a )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.num_labels
							lowerCamelCase			      =					MraForTokenClassification(config=_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a		, attention_mask=_a		, token_type_ids=_a		, labels=_a )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.num_labels) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.num_choices
							lowerCamelCase			      =					MraForMultipleChoice(config=_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					input_ids.unsqueeze(1 ).expand(-1		, self.num_choices		, -1 ).contiguous()
							lowerCamelCase			      =					token_type_ids.unsqueeze(1 ).expand(-1		, self.num_choices		, -1 ).contiguous()
							lowerCamelCase			      =					input_mask.unsqueeze(1 ).expand(-1		, self.num_choices		, -1 ).contiguous()
							lowerCamelCase			      =					model(
							    _a		, attention_mask=_a		, token_type_ids=_a		, labels=_a		, )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_choices) )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.prepare_config_and_inputs()
							(
							    (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   (
							    lowerCamelCase
							)      ,   
							)			      =					config_and_inputs
							lowerCamelCase			      =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict






@require_torch
class     __magic_name__							(       UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						(
				    (
				        MraModel,
				        MraForMaskedLM,
				        MraForMultipleChoice,
				        MraForQuestionAnswering,
				        MraForSequenceClassification,
				        MraForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False

				__UpperCamelCase			  =						()





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					MraModelTester(self )
							lowerCamelCase			      =					ConfigTester(self		, config_class=_a		, hidden_size=37 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							self.config_tester.run_common_tests()





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
										lowerCamelCase			      =					type
										self.model_tester.create_and_check_model(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_a )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase			      =					MraModel.from_pretrained(_a )
										self.assertIsNotNone(_a )





				@unittest.skip(reason="""MRA does not output attentions""" )
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return






@require_torch
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					MraModel.from_pretrained("""uw-madison/mra-base-512-4""" )
							lowerCamelCase			      =					torch.arange(256 ).unsqueeze(0 )

							with torch.no_grad():
										lowerCamelCase			      =					model(_a )[0]

							lowerCamelCase			      =					torch.Size((1, 256, 768) )
							self.assertEqual(output.shape		, _a )

							lowerCamelCase			      =					torch.tensor(
							    [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]] )

							self.assertTrue(torch.allclose(output[:, :3, :3]		, _a		, atol=1e-4 ) )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4""" )
							lowerCamelCase			      =					torch.arange(256 ).unsqueeze(0 )

							with torch.no_grad():
										lowerCamelCase			      =					model(_a )[0]

							lowerCamelCase			      =					50_265

							lowerCamelCase			      =					torch.Size((1, 256, vocab_size) )
							self.assertEqual(output.shape		, _a )

							lowerCamelCase			      =					torch.tensor(
							    [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]] )

							self.assertTrue(torch.allclose(output[:, :3, :3]		, _a		, atol=1e-4 ) )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3""" )
							lowerCamelCase			      =					torch.arange(4_096 ).unsqueeze(0 )

							with torch.no_grad():
										lowerCamelCase			      =					model(_a )[0]

							lowerCamelCase			      =					50_265

							lowerCamelCase			      =					torch.Size((1, 4_096, vocab_size) )
							self.assertEqual(output.shape		, _a )

							lowerCamelCase			      =					torch.tensor(
							    [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]] )

							self.assertTrue(torch.allclose(output[:, :3, :3]		, _a		, atol=1e-4 ) )









"""simple docstring"""




def     a__ (				snake_case__				)						->					list:

			if len(snake_case__				) < 2:
						return collection

			def circle_sort_util(snake_case__       , snake_case__       , snake_case__				) -> bool:
						lowerCamelCase			      =					False

						if low == high:
									return swapped

						lowerCamelCase			      =					low
						lowerCamelCase			      =					high

						while left < right:
									if collection[left] > collection[right]:
												lowerCamelCase      ,   lowerCamelCase			      =					(
												    collection[right],
												    collection[left],
												)
												lowerCamelCase			      =					True

									left += 1
									right -= 1

						if left == right and collection[left] > collection[right + 1]:
									lowerCamelCase      ,   lowerCamelCase			      =					(
									    collection[right + 1],
									    collection[left],
									)

									lowerCamelCase			      =					True

						lowerCamelCase			      =					low + int((high - low) / 2				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , snake_case__       , snake_case__				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , mid + 1       , snake_case__				)

						return swapped or left_swap or right_swap

			lowerCamelCase			      =					True

			while is_not_sorted is True:
						lowerCamelCase			      =					circle_sort_util(snake_case__       , 0       , len(snake_case__				) - 1				)

			return collection


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



