



'''simple docstring'''

import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
	    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    DistilBertForMaskedLM,
	    DistilBertForMultipleChoice,
	    DistilBertForQuestionAnswering,
	    DistilBertForSequenceClassification,
	    DistilBertForTokenClassification,
	    DistilBertModel,
	)

class __snake_case(				_SCREAMING_SNAKE_CASE ):


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=13     ,	A_=7     ,	A_=True     ,	A_=True     ,	A_=False     ,	A_=True     ,	A_=99     ,	A_=32     ,	A_=5     ,	A_=4     ,	A_=37     ,	A_="gelu"     ,	A_=0.1     ,	A_=0.1     ,	A_=512     ,	A_=16     ,	A_=2     ,	A_=0.0_2     ,	A_=3     ,	A_=4     ,	A_=None     ,	)     ->     int:
							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     __snake_case   ( self				)     ->     Optional[int]:
							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
							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, input_mask, sequence_labels, token_labels, choice_labels


						def     __snake_case   ( self				)     ->     Optional[int]:
							return DistilBertConfig(
							    vocab_size=self.vocab_size     ,	dim=self.hidden_size     ,	n_layers=self.num_hidden_layers     ,	n_heads=self.num_attention_heads     ,	hidden_dim=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	dropout=self.hidden_dropout_prob     ,	attention_dropout=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	initializer_range=self.initializer_range     ,	)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     str:
							lowerCAmelCase   =     DistilBertModel(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_     ,	A_				)
							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Union[str, Any]:
							lowerCAmelCase   =     DistilBertForMaskedLM(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_     ,	attention_mask=A_     ,	labels=A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.vocab_size)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     List[Any]:
							lowerCAmelCase   =     DistilBertForQuestionAnswering(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(
							    A_     ,	attention_mask=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     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Tuple:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     DistilBertForSequenceClassification(A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_     ,	attention_mask=A_     ,	labels=A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Tuple:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     DistilBertForTokenClassification(config=A_				)
							model.to(A_				)
							model.eval()

							lowerCAmelCase   =     model(A_     ,	attention_mask=A_     ,	labels=A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.num_labels)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.num_choices
							lowerCAmelCase   =     DistilBertForMultipleChoice(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     input_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_     ,	labels=A_     ,	)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_choices)				)




						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.prepare_config_and_inputs()
							((lowerCAmelCase),       (lowerCAmelCase),       (lowerCAmelCase),       (lowerCAmelCase),       (lowerCAmelCase),       (lowerCAmelCase))   =     config_and_inputs
							lowerCAmelCase   =     {"""input_ids""": input_ids, """attention_mask""": input_mask}
							return config, inputs_dict







@require_torch
class __snake_case(				_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Any									=		(
						    (
						        DistilBertModel,
						        DistilBertForMaskedLM,
						        DistilBertForMultipleChoice,
						        DistilBertForQuestionAnswering,
						        DistilBertForSequenceClassification,
						        DistilBertForTokenClassification,
						    )
						    if is_torch_available()
						    else None
						)
						UpperCAmelCase							:   Tuple									=		(
						    {
						        "feature-extraction": DistilBertModel,
						        "fill-mask": DistilBertForMaskedLM,
						        "question-answering": DistilBertForQuestionAnswering,
						        "text-classification": DistilBertForSequenceClassification,
						        "token-classification": DistilBertForTokenClassification,
						        "zero-shot": DistilBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase							:   str									=		True
						UpperCAmelCase							:   Any									=		True
						UpperCAmelCase							:   List[Any]									=		True
						UpperCAmelCase							:   Optional[Any]									=		True


						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     DistilBertModelTester(self				)
							lowerCAmelCase   =     ConfigTester(self     ,	config_class=A_     ,	dim=37				)


						def     __snake_case   ( self				)     ->     Dict:
							self.config_tester.run_common_tests()


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_model(*A_				)


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_masked_lm(*A_				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_question_answering(*A_				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_sequence_classification(*A_				)


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_token_classification(*A_				)


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_multiple_choice(*A_				)


						@slow
						def     __snake_case   ( self				)     ->     Tuple:
							for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCAmelCase   =     DistilBertModel.from_pretrained(A_				)
								self.assertIsNotNone(A_				)




						@slow
						@require_torch_gpu
						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
								# BertForMultipleChoice behaves incorrectly in JIT environments.
								if model_class == DistilBertForMultipleChoice:
									return

								lowerCAmelCase   =     True
								lowerCAmelCase   =     model_class(config=A_				)

								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_				)
								lowerCAmelCase   =     torch.jit.trace(
								    A_     ,	(inputs_dict["""input_ids"""].to("""cpu"""				), inputs_dict["""attention_mask"""].to("""cpu"""				))				)

								with tempfile.TemporaryDirectory() as tmp:
									torch.jit.save(A_     ,	os.path.join(A_     ,	"""traced_model.pt"""				)				)
									lowerCAmelCase   =     torch.jit.load(os.path.join(A_     ,	"""traced_model.pt"""				)     ,	map_location=A_				)
									loaded(inputs_dict["""input_ids"""].to(A_				)     ,	inputs_dict["""attention_mask"""].to(A_				)				)







@require_torch
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     DistilBertModel.from_pretrained("""distilbert-base-uncased"""				)
							lowerCAmelCase   =     torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]				)
							lowerCAmelCase   =     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
							with torch.no_grad():
								lowerCAmelCase   =     model(A_     ,	attention_mask=A_				)[0]
							lowerCAmelCase   =     torch.Size((1, 11, 768)				)
							self.assertEqual(output.shape     ,	A_				)
							lowerCAmelCase   =     torch.tensor(
							    [[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]				)

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




'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list							)      ->						list:







	"""simple docstring"""

	if len(_SCREAMING_SNAKE_CASE							) < 2:
		return collection

	def circle_sort_util(_SCREAMING_SNAKE_CASE					:						list							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						int							) -> 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(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		mid + 1							,		_SCREAMING_SNAKE_CASE							)

		return swapped or left_swap or right_swap

	lowerCAmelCase   =     True

	while is_not_sorted is True:
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		0							,		len(_SCREAMING_SNAKE_CASE							) - 1							)

	return collection


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