





import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
				import torch
				from torch import nn

				from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
				from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	int			,       __lowercase     :	Optional[int]			,       __lowercase     :	Optional[int]=13			,       __lowercase     :	Tuple=32			,       __lowercase     :	int=2			,       __lowercase     :	Dict=3			,       __lowercase     :	Any=16			,       __lowercase     :	Optional[int]=[1, 2, 1]			,       __lowercase     :	Dict=[2, 2, 4]			,       __lowercase     :	Union[str, Any]=2			,       __lowercase     :	str=2.0			,       __lowercase     :	Dict=True			,       __lowercase     :	Any=0.0			,       __lowercase     :	Optional[int]=0.0			,       __lowercase     :	List[str]=0.1			,       __lowercase     :	str="gelu"			,       __lowercase     :	Optional[int]=False			,       __lowercase     :	Any=True			,       __lowercase     :	Optional[Any]=0.02			,       __lowercase     :	List[str]=1e-5			,       __lowercase     :	Any=True			,       __lowercase     :	str=None			,       __lowercase     :	List[Any]=True			,       __lowercase     :	List[str]=10			,       __lowercase     :	Dict=8			,       __lowercase     :	Optional[Any]=["stage1", "stage2"]			,       __lowercase     :	Optional[Any]=[1, 2]			,       ):






						'''simple docstring'''







						UpperCAmelCase_		    =  parent
						UpperCAmelCase_		    =  batch_size
						UpperCAmelCase_		    =  image_size
						UpperCAmelCase_		    =  patch_size
						UpperCAmelCase_		    =  num_channels
						UpperCAmelCase_		    =  embed_dim
						UpperCAmelCase_		    =  depths
						UpperCAmelCase_		    =  num_heads
						UpperCAmelCase_		    =  window_size
						UpperCAmelCase_		    =  mlp_ratio
						UpperCAmelCase_		    =  qkv_bias
						UpperCAmelCase_		    =  hidden_dropout_prob
						UpperCAmelCase_		    =  attention_probs_dropout_prob
						UpperCAmelCase_		    =  drop_path_rate
						UpperCAmelCase_		    =  hidden_act
						UpperCAmelCase_		    =  use_absolute_embeddings
						UpperCAmelCase_		    =  patch_norm
						UpperCAmelCase_		    =  layer_norm_eps
						UpperCAmelCase_		    =  initializer_range
						UpperCAmelCase_		    =  is_training
						UpperCAmelCase_		    =  scope
						UpperCAmelCase_		    =  use_labels
						UpperCAmelCase_		    =  type_sequence_label_size
						UpperCAmelCase_		    =  encoder_stride
						UpperCAmelCase_		    =  out_features
						UpperCAmelCase_		    =  out_indices





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

						UpperCAmelCase_		    =  self.get_config()

						return config, pixel_values, labels





					def 							SCREAMING_SNAKE_CASE				(    self     :	str		):






						'''simple docstring'''







						return SwinConfig(
						    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 							SCREAMING_SNAKE_CASE				(    self     :	Any			,       __lowercase     :	Tuple			,       __lowercase     :	List[str]			,       __lowercase     :	List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  SwinModel(config=__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_		    =  model(__lowercase		)

						UpperCAmelCase_		    =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
						UpperCAmelCase_		    =  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 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]			,       __lowercase     :	List[str]			,       __lowercase     :	List[Any]			,       __lowercase     :	Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  SwinBackbone(config=__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_		    =  model(__lowercase		)

						# verify hidden states
						self.parent.assertEqual(len(result.feature_maps		)			,       len(config.out_features		)		)
						self.parent.assertListEqual(list(result.feature_maps[0].shape		)			,       [self.batch_size, model.channels[0], 16, 16]		)

						# verify channels
						self.parent.assertEqual(len(model.channels		)			,       len(config.out_features		)		)

						# verify backbone works with out_features=None
						UpperCAmelCase_		    =  None
						UpperCAmelCase_		    =  SwinBackbone(config=__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_		    =  model(__lowercase		)

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps		)			,       1		)
						self.parent.assertListEqual(list(result.feature_maps[0].shape		)			,       [self.batch_size, model.channels[-1], 4, 4]		)

						# verify channels
						self.parent.assertEqual(len(model.channels		)			,       1		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict			,       __lowercase     :	Union[str, Any]			,       __lowercase     :	str			,       __lowercase     :	int		):






						'''simple docstring'''







						UpperCAmelCase_		    =  SwinForMaskedImageModeling(config=__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_		    =  model(__lowercase		)
						self.parent.assertEqual(
						    result.logits.shape			,       (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

						# test greyscale images
						UpperCAmelCase_		    =  1
						UpperCAmelCase_		    =  SwinForMaskedImageModeling(__lowercase		)
						model.to(__lowercase		)
						model.eval()

						UpperCAmelCase_		    =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						UpperCAmelCase_		    =  model(__lowercase		)
						self.parent.assertEqual(result.logits.shape			,       (self.batch_size, 1, self.image_size, self.image_size)		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[str]			,       __lowercase     :	int			,       __lowercase     :	str			,       __lowercase     :	List[str]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.type_sequence_label_size
						UpperCAmelCase_		    =  SwinForImageClassification(__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_		    =  model(__lowercase			,       labels=__lowercase		)
						self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.type_sequence_label_size)		)

						# test greyscale images
						UpperCAmelCase_		    =  1
						UpperCAmelCase_		    =  SwinForImageClassification(__lowercase		)
						model.to(__lowercase		)
						model.eval()

						UpperCAmelCase_		    =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						UpperCAmelCase_		    =  model(__lowercase		)
						self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.type_sequence_label_size)		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	int		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.prepare_config_and_inputs()
						(
						    (
						    UpperCAmelCase_	
						)    ,						(
						    UpperCAmelCase_	
						)    ,						(
						    UpperCAmelCase_	
						)    ,							
						)		    =  config_and_inputs
						UpperCAmelCase_		    =  {"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_torch
class 	_UpperCamelCase	(							snake_case_    ,							snake_case_    ,							unittest.TestCase	):







					'''simple docstring'''

					lowerCamelCase  :  int           =							(
					    (
					        SwinModel,
					        SwinBackbone,
					        SwinForImageClassification,
					        SwinForMaskedImageModeling,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCamelCase  :  Optional[Any]           =							(
					    {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
					    if is_torch_available()
					    else {}
					)
					lowerCamelCase  :  str           =							True

					lowerCamelCase  :  Dict           =							False
					lowerCamelCase  :  List[Any]           =							False
					lowerCamelCase  :  Optional[int]           =							False





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						UpperCAmelCase_		    =  SwinModelTester(self		)
						UpperCAmelCase_		    =  ConfigTester(self			,       config_class=__lowercase			,       embed_dim=37		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Any		):






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    self     :	Any		):






						'''simple docstring'''







						return





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase		)





					@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""		)
					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict		):






						'''simple docstring'''







						pass





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						super().test_training_gradient_checkpointing()





					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*__lowercase		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[str]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__lowercase		)





					@unittest.skip(reason="""Swin does not use inputs_embeds"""		)
					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						pass





					@unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""		)
					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]		):






						'''simple docstring'''







						pass





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs_for_common()

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





					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict		):






						'''simple docstring'''







						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs_for_common()

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

							UpperCAmelCase_		    =  ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]			,       __lowercase		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_		    =  True

						for model_class in self.all_model_classes:
							UpperCAmelCase_		    =  True
							UpperCAmelCase_		    =  False
							UpperCAmelCase_		    =  True
							UpperCAmelCase_		    =  model_class(__lowercase		)
							model.to(__lowercase		)
							model.eval()
							with torch.no_grad():
								UpperCAmelCase_		    =  model(**self._prepare_for_class(__lowercase			,       __lowercase		)		)
							UpperCAmelCase_		    =  outputs.attentions
							UpperCAmelCase_		    =  len(self.model_tester.depths		)
							self.assertEqual(len(__lowercase		)			,       __lowercase		)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							UpperCAmelCase_		    =  True
							UpperCAmelCase_		    =  config.window_size**2
							UpperCAmelCase_		    =  model_class(__lowercase		)
							model.to(__lowercase		)
							model.eval()
							with torch.no_grad():
								UpperCAmelCase_		    =  model(**self._prepare_for_class(__lowercase			,       __lowercase		)		)
							UpperCAmelCase_		    =  outputs.attentions
							self.assertEqual(len(__lowercase		)			,       __lowercase		)

							self.assertListEqual(
							    list(attentions[0].shape[-3:]		)			,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,       )
							UpperCAmelCase_		    =  len(__lowercase		)

							# Check attention is always last and order is fine
							UpperCAmelCase_		    =  True
							UpperCAmelCase_		    =  True
							UpperCAmelCase_		    =  model_class(__lowercase		)
							model.to(__lowercase		)
							model.eval()
							with torch.no_grad():
								UpperCAmelCase_		    =  model(**self._prepare_for_class(__lowercase			,       __lowercase		)		)

							# also another +1 for reshaped_hidden_states
							UpperCAmelCase_		    =  1 if model_class.__name__ == """SwinBackbone""" else 2
							self.assertEqual(out_len + added_hidden_states			,       len(__lowercase		)		)

							UpperCAmelCase_		    =  outputs.attentions

							self.assertEqual(len(__lowercase		)			,       __lowercase		)

							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]		)			,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,       )





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]			,       __lowercase     :	Tuple			,       __lowercase     :	int			,       __lowercase     :	Tuple			,       __lowercase     :	Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  model_class(__lowercase		)
						model.to(__lowercase		)
						model.eval()

						with torch.no_grad():
							UpperCAmelCase_		    =  model(**self._prepare_for_class(__lowercase			,       __lowercase		)		)

						UpperCAmelCase_		    =  outputs.hidden_states

						UpperCAmelCase_		    =  getattr(
						    self.model_tester			,       """expected_num_hidden_layers"""			,       len(self.model_tester.depths		) + 1		)
						self.assertEqual(len(__lowercase		)			,       __lowercase		)

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

						UpperCAmelCase_		    =  (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]			,       )

						if not model_class.__name__ == "SwinBackbone":
							UpperCAmelCase_		    =  outputs.reshaped_hidden_states
							self.assertEqual(len(__lowercase		)			,       __lowercase		)

							UpperCAmelCase_    ,						UpperCAmelCase_    ,						UpperCAmelCase_    ,						UpperCAmelCase_		    =  reshaped_hidden_states[0].shape
							UpperCAmelCase_		    =  (
							    reshaped_hidden_states[0].view(__lowercase			,       __lowercase			,       height * width		).permute(0			,       2			,       1		)
							)
							self.assertListEqual(
							    list(reshaped_hidden_states.shape[-2:]		)			,       [num_patches, self.model_tester.embed_dim]			,       )





					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict		):






						'''simple docstring'''







						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase_		    =  (
						    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:
							UpperCAmelCase_		    =  True
							self.check_hidden_states_output(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase		)

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

							self.check_hidden_states_output(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_		    =  3

						UpperCAmelCase_		    =  (
						    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)
						)
						UpperCAmelCase_		    =  (
						    config.patch_size
						    if isinstance(config.patch_size			,       collections.abc.Iterable		)
						    else (config.patch_size, config.patch_size)
						)

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

						for model_class in self.all_model_classes:
							UpperCAmelCase_		    =  True
							self.check_hidden_states_output(__lowercase			,       __lowercase			,       __lowercase			,       (padded_height, padded_width)		)

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





					@slow
					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''simple docstring'''







						for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_		    =  SwinModel.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase_		    =  _config_zero_init(__lowercase		)
						for model_class in self.all_model_classes:
							UpperCAmelCase_		    =  model_class(config=__lowercase		)
							for name, param in model.named_parameters():
								if "embeddings" not in name and param.requires_grad:
									self.assertIn(
									    ((param.data.mean() * 1e9).round() / 1e9).item()			,       [0.0, 1.0]			,       msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""			,       )







@require_vision
@require_torch
class 	_UpperCamelCase	(							unittest.TestCase	):







					'''simple docstring'''

					@cached_property
					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						return (
						    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""		)
						    if is_vision_available()
						    else None
						)





					@slow
					def 							SCREAMING_SNAKE_CASE				(    self     :	int		):






						'''simple docstring'''







						UpperCAmelCase_		    =  SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""		).to(__lowercase		)
						UpperCAmelCase_		    =  self.default_image_processor

						UpperCAmelCase_		    =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
						UpperCAmelCase_		    =  image_processor(images=__lowercase			,       return_tensors="""pt"""		).to(__lowercase		)

						# forward pass
						with torch.no_grad():
							UpperCAmelCase_		    =  model(**__lowercase		)

						# verify the logits
						UpperCAmelCase_		    =  torch.Size((1, 10_00)		)
						self.assertEqual(outputs.logits.shape			,       __lowercase		)
						UpperCAmelCase_		    =  torch.tensor([-0.0948, -0.6454, -0.0921]		).to(__lowercase		)
						self.assertTrue(torch.allclose(outputs.logits[0, :3]			,       __lowercase			,       atol=1e-4		)		)







@require_torch
class 	_UpperCamelCase	(							unittest.TestCase    ,							snake_case_	):







					'''simple docstring'''

					lowerCamelCase  :  str           =							(SwinBackbone,) if is_torch_available() else ()
					lowerCamelCase  :  Tuple           =							SwinConfig





					def 							SCREAMING_SNAKE_CASE				(    self     :	int		):






						'''simple docstring'''







						UpperCAmelCase_		    =  SwinModelTester(self		)






from __future__ import annotations

from random import randrange




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list  ):







	'''simple docstring'''



	if len(__lowerCamelCase  ) < 2:
		return collection
	lowercase_			 = randrange(len(__lowerCamelCase  )  )  # Use random element as pivot
	lowercase_			 = collection[pivot_index]
	lowercase_			 = []  # All elements greater than pivot
	lowercase_			 = []  # All elements less than or equal to pivot

	for element in collection[:pivot_index]:
		(greater if element > pivot else lesser).append(__lowerCamelCase  )

	for element in collection[pivot_index + 1 :]:
		(greater if element > pivot else lesser).append(__lowerCamelCase  )

	return [*quick_sort(__lowerCamelCase  ), pivot, *quick_sort(__lowerCamelCase  )]


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

