



'''simple docstring'''

import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import MaskFormerSwinBackbone
	from transformers.models.maskformer import MaskFormerSwinModel

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=13     ,	A_=32     ,	A_=2     ,	A_=3     ,	A_=16     ,	A_=[1, 2, 1]     ,	A_=[2, 2, 4]     ,	A_=2     ,	A_=2.0     ,	A_=True     ,	A_=0.0     ,	A_=0.0     ,	A_=0.1     ,	A_="gelu"     ,	A_=False     ,	A_=True     ,	A_=0.0_2     ,	A_=1e-5     ,	A_=True     ,	A_=None     ,	A_=True     ,	A_=10     ,	A_=8     ,	A_=["stage1", "stage2", "stage3"]     ,	A_=[1, 2, 3]     ,	)     ->     List[str]:
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     image_size
							lowerCAmelCase   =     patch_size
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     embed_dim
							lowerCAmelCase   =     depths
							lowerCAmelCase   =     num_heads
							lowerCAmelCase   =     window_size
							lowerCAmelCase   =     mlp_ratio
							lowerCAmelCase   =     qkv_bias
							lowerCAmelCase   =     hidden_dropout_prob
							lowerCAmelCase   =     attention_probs_dropout_prob
							lowerCAmelCase   =     drop_path_rate
							lowerCAmelCase   =     hidden_act
							lowerCAmelCase   =     use_absolute_embeddings
							lowerCAmelCase   =     patch_norm
							lowerCAmelCase   =     layer_norm_eps
							lowerCAmelCase   =     initializer_range
							lowerCAmelCase   =     is_training
							lowerCAmelCase   =     scope
							lowerCAmelCase   =     use_labels
							lowerCAmelCase   =     type_sequence_label_size
							lowerCAmelCase   =     encoder_stride
							lowerCAmelCase   =     out_features
							lowerCAmelCase   =     out_indices


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

							lowerCAmelCase   =     None
							if self.use_labels:
								lowerCAmelCase   =     ids_tensor([self.batch_size]     ,	self.type_sequence_label_size				)

							lowerCAmelCase   =     self.get_config()

							return config, pixel_values, labels


						def     __snake_case   ( self				)     ->     str:
							return MaskFormerSwinConfig(
							    image_size=self.image_size     ,	patch_size=self.patch_size     ,	num_channels=self.num_channels     ,	embed_dim=self.embed_dim     ,	depths=self.depths     ,	num_heads=self.num_heads     ,	window_size=self.window_size     ,	mlp_ratio=self.mlp_ratio     ,	qkv_bias=self.qkv_bias     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	drop_path_rate=self.drop_path_rate     ,	hidden_act=self.hidden_act     ,	use_absolute_embeddings=self.use_absolute_embeddings     ,	path_norm=self.patch_norm     ,	layer_norm_eps=self.layer_norm_eps     ,	initializer_range=self.initializer_range     ,	encoder_stride=self.encoder_stride     ,	out_features=self.out_features     ,	out_indices=self.out_indices     ,	)


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

							lowerCAmelCase   =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
							lowerCAmelCase   =     int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

							self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, expected_seq_len, expected_dim)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     Optional[Any]:
							lowerCAmelCase   =     MaskFormerSwinBackbone(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_				)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps				)     ,	len(config.out_features				)				)
							self.parent.assertListEqual(list(result.feature_maps[0].shape				)     ,	[13, 16, 16, 16]				)

							# verify channels
							self.parent.assertEqual(len(model.channels				)     ,	len(config.out_features				)				)
							self.parent.assertListEqual(model.channels     ,	[16, 32, 64]				)

							# verify ValueError
							with self.parent.assertRaises(A_				):
								lowerCAmelCase   =     ["""stem"""]
								lowerCAmelCase   =     MaskFormerSwinBackbone(config=A_				)




						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     self.prepare_config_and_inputs()
							lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     config_and_inputs
							lowerCAmelCase   =     {"""pixel_values""": pixel_values}
							return config, inputs_dict







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


						'''simple docstring'''



						UpperCAmelCase							:   int									=		(
						    (
						        MaskFormerSwinModel,
						        MaskFormerSwinBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase							:   str									=		{"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
						UpperCAmelCase							:   Optional[int]									=		False
						UpperCAmelCase							:   Union[str, Any]									=		False
						UpperCAmelCase							:   Dict									=		False
						UpperCAmelCase							:   Optional[int]									=		False
						UpperCAmelCase							:   Union[str, Any]									=		False


						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     MaskFormerSwinModelTester(self				)
							lowerCAmelCase   =     ConfigTester(self     ,	config_class=A_     ,	embed_dim=37				)


						@require_torch_multi_gpu
						@unittest.skip(
						    reason=(
						        """`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
						        """ `nn.DataParallel`"""
						    )				)
						def     __snake_case   ( self				)     ->     List[Any]:
							pass


						def     __snake_case   ( self				)     ->     Tuple:
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()


						def     __snake_case   ( self				)     ->     List[Any]:
							return


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_				)


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


						@unittest.skip("""Swin does not use inputs_embeds"""				)
						def     __snake_case   ( self				)     ->     str:
							pass


						@unittest.skip("""Swin does not support feedforward chunking"""				)
						def     __snake_case   ( self				)     ->     Optional[int]:
							pass


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     model_class(A_				)
								self.assertIsInstance(model.get_input_embeddings()     ,	(nn.Module)				)
								lowerCAmelCase   =     model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(A_     ,	nn.Linear				)				)


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     model_class(A_				)
								lowerCAmelCase   =     inspect.signature(model.forward				)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								lowerCAmelCase   =     [*signature.parameters.keys()]

								lowerCAmelCase   =     ["""pixel_values"""]
								self.assertListEqual(arg_names[:1]     ,	A_				)


						@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions"""				)
						def     __snake_case   ( self				)     ->     int:
							pass


						@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone"""				)
						def     __snake_case   ( self				)     ->     List[Any]:
							pass


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Union[str, Any]:
							lowerCAmelCase   =     model_class(A_				)
							model.to(A_				)
							model.eval()

							with torch.no_grad():
								lowerCAmelCase   =     model(**self._prepare_for_class(A_     ,	A_				)				)

							lowerCAmelCase   =     outputs.hidden_states

							lowerCAmelCase   =     getattr(
							    self.model_tester     ,	"""expected_num_hidden_layers"""     ,	len(self.model_tester.depths				) + 1				)
							self.assertEqual(len(A_				)     ,	A_				)

							# Swin has a different seq_length
							lowerCAmelCase   =     (
							    config.patch_size
							    if isinstance(config.patch_size     ,	collections.abc.Iterable				)
							    else (config.patch_size, config.patch_size)
							)

							lowerCAmelCase   =     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]				)     ,	[num_patches, self.model_tester.embed_dim]     ,	)


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()

							lowerCAmelCase   =     (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size     ,	collections.abc.Iterable				)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     True
								self.check_hidden_states_output(A_     ,	A_     ,	A_     ,	A_				)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								lowerCAmelCase   =     True

								self.check_hidden_states_output(A_     ,	A_     ,	A_     ,	A_				)


						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase   =     3

							lowerCAmelCase   =     (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size     ,	collections.abc.Iterable				)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							lowerCAmelCase   =     (
							    config.patch_size
							    if isinstance(config.patch_size     ,	collections.abc.Iterable				)
							    else (config.patch_size, config.patch_size)
							)

							lowerCAmelCase   =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							lowerCAmelCase   =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     True
								self.check_hidden_states_output(A_     ,	A_     ,	A_     ,	(padded_height, padded_width)				)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								lowerCAmelCase   =     True
								self.check_hidden_states_output(A_     ,	A_     ,	A_     ,	(padded_height, padded_width)				)


						@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints"""				)
						def     __snake_case   ( self				)     ->     List[str]:
							pass


						@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""				)
						def     __snake_case   ( self				)     ->     Any:
							pass


						@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""				)
						def     __snake_case   ( self				)     ->     List[str]:
							pass




						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()

							def set_nan_tensor_to_zero(A_				):
								lowerCAmelCase   =     0
								return t

							def check_equivalence(A_     ,	A_     ,	A_     ,	A_={}				):
								with torch.no_grad():
									lowerCAmelCase   =     model(**A_     ,	return_dict=A_     ,	**A_				)
									lowerCAmelCase   =     model(**A_     ,	return_dict=A_     ,	**A_				).to_tuple()

									def recursive_check(A_     ,	A_				):
										if isinstance(A_     ,	(List, Tuple)				):
											for tuple_iterable_value, dict_iterable_value in zip(A_     ,	A_				):
												recursive_check(A_     ,	A_				)
										elif isinstance(A_     ,	A_				):
											for tuple_iterable_value, dict_iterable_value in zip(
											    tuple_object.values()     ,	dict_object.values()				):
												recursive_check(A_     ,	A_				)
										elif tuple_object is None:
											return
										else:
											self.assertTrue(
											    torch.allclose(
											        set_nan_tensor_to_zero(A_				)     ,	set_nan_tensor_to_zero(A_				)     ,	atol=1e-5				)     ,	msg=(
											        """Tuple and dict output are not equal. Difference:"""
											        f' {torch.max(torch.abs(tuple_object - dict_object				)				)}. Tuple has `nan`:'
											        f' {torch.isnan(A_				).any()} and `inf`: {torch.isinf(A_				)}. Dict has'
											        f' `nan`: {torch.isnan(A_				).any()} and `inf`: {torch.isinf(A_				)}.'
											    )     ,	)

									recursive_check(A_     ,	A_				)

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     model_class(A_				)
								model.to(A_				)
								model.eval()

								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_				)
								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_				)
								check_equivalence(A_     ,	A_     ,	A_				)

								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_     ,	return_labels=A_				)
								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_     ,	return_labels=A_				)
								check_equivalence(A_     ,	A_     ,	A_				)

								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_				)
								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_				)
								check_equivalence(A_     ,	A_     ,	A_     ,	{"""output_hidden_states""": True}				)

								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_     ,	return_labels=A_				)
								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_     ,	return_labels=A_				)
								check_equivalence(A_     ,	A_     ,	A_     ,	{"""output_hidden_states""": True}				)







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


						'''simple docstring'''



						UpperCAmelCase							:   int									=		(MaskFormerSwinBackbone,) if is_torch_available() else ()
						UpperCAmelCase							:   str									=		MaskFormerSwinConfig


						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     MaskFormerSwinModelTester(self				)




						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase   =     inputs_dict["""pixel_values"""].shape[0]

							for backbone_class in self.all_model_classes:
								lowerCAmelCase   =     backbone_class(A_				)
								backbone.to(A_				)
								backbone.eval()

								lowerCAmelCase   =     backbone(**A_				)

								# Test default outputs and verify feature maps
								self.assertIsInstance(outputs.feature_maps     ,	A_				)
								self.assertTrue(len(outputs.feature_maps				) == len(backbone.channels				)				)
								for feature_map, n_channels in zip(outputs.feature_maps     ,	backbone.channels				):
									self.assertTrue(feature_map.shape[:2]     ,	(batch_size, n_channels)				)
								self.assertIsNone(outputs.hidden_states				)
								self.assertIsNone(outputs.attentions				)

								# Test output_hidden_states=True
								lowerCAmelCase   =     backbone(**A_     ,	output_hidden_states=A_				)
								self.assertIsNotNone(outputs.hidden_states				)
								self.assertTrue(len(outputs.hidden_states				)     ,	len(backbone.stage_names				)				)
								# We skip the stem layer
								for hidden_states, n_channels in zip(outputs.hidden_states[1:]     ,	backbone.channels				):
									for hidden_state in hidden_states:
										# Hidden states are in the format (batch_size, (height * width), n_channels)
										lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     hidden_state.shape
										self.assertTrue((h_batch_size, h_n_channels)     ,	(batch_size, n_channels)				)

            # Test output_attentions=True
								if self.has_attentions:
									lowerCAmelCase   =     backbone(**A_     ,	output_attentions=A_				)
									self.assertIsNotNone(outputs.attentions				)




'''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))