

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  a     :


						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_=0.02  ,    A_=1e-5  ,    A_=True  ,    A_=None  ,    A_=True  ,    A_=10  ,    A_=8  ,    A_=["stage1", "stage2"]  ,    A_=[1, 2]  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			parent
										_UpperCAmelCase				:						Optional[Any]       						=			batch_size
										_UpperCAmelCase				:						Tuple       						=			image_size
										_UpperCAmelCase				:						str       						=			patch_size
										_UpperCAmelCase				:						Dict       						=			num_channels
										_UpperCAmelCase				:						int       						=			embed_dim
										_UpperCAmelCase				:						Any       						=			depths
										_UpperCAmelCase				:						Tuple       						=			num_heads
										_UpperCAmelCase				:						str       						=			window_size
										_UpperCAmelCase				:						Dict       						=			mlp_ratio
										_UpperCAmelCase				:						List[Any]       						=			qkv_bias
										_UpperCAmelCase				:						Any       						=			hidden_dropout_prob
										_UpperCAmelCase				:						Optional[Any]       						=			attention_probs_dropout_prob
										_UpperCAmelCase				:						List[str]       						=			drop_path_rate
										_UpperCAmelCase				:						Tuple       						=			hidden_act
										_UpperCAmelCase				:						int       						=			use_absolute_embeddings
										_UpperCAmelCase				:						Dict       						=			patch_norm
										_UpperCAmelCase				:						Optional[Any]       						=			layer_norm_eps
										_UpperCAmelCase				:						Any       						=			initializer_range
										_UpperCAmelCase				:						str       						=			is_training
										_UpperCAmelCase				:						Tuple       						=			scope
										_UpperCAmelCase				:						Union[str, Any]       						=			use_labels
										_UpperCAmelCase				:						Dict       						=			type_sequence_label_size
										_UpperCAmelCase				:						Tuple       						=			encoder_stride
										_UpperCAmelCase				:						Union[str, Any]       						=			out_features
										_UpperCAmelCase				:						Dict       						=			out_indices


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

										_UpperCAmelCase				:						List[Any]       						=			None
										if self.use_labels:
														_UpperCAmelCase				:						Tuple       						=			ids_tensor([self.batch_size]  ,    self.type_sequence_label_size		)

										_UpperCAmelCase				:						int       						=			self.get_config()

										return config, pixel_values, labels


						def 				_UpperCAmelCase (				self		):







										'''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 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			SwinModel(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase				:						int       						=			model(A_		)

										_UpperCAmelCase				:						str       						=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
										_UpperCAmelCase				:						str       						=			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 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			SwinBackbone(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase				:						str       						=			model(A_		)

										# 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				:						Optional[Any]       						=			None
										_UpperCAmelCase				:						Any       						=			SwinBackbone(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase				:						List[Any]       						=			model(A_		)

										# 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 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			SwinForMaskedImageModeling(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase				:						List[Any]       						=			model(A_		)
										self.parent.assertEqual(
										    result.logits.shape  ,    (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

										# test greyscale images
										_UpperCAmelCase				:						Optional[Any]       						=			1
										_UpperCAmelCase				:						Any       						=			SwinForMaskedImageModeling(A_		)
										model.to(A_		)
										model.eval()

										_UpperCAmelCase				:						Optional[int]       						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
										_UpperCAmelCase				:						str       						=			model(A_		)
										self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, 1, self.image_size, self.image_size)		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			self.type_sequence_label_size
										_UpperCAmelCase				:						str       						=			SwinForImageClassification(A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase				:						List[str]       						=			model(A_  ,    labels=A_		)
										self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.type_sequence_label_size)		)

										# test greyscale images
										_UpperCAmelCase				:						Optional[int]       						=			1
										_UpperCAmelCase				:						List[Any]       						=			SwinForImageClassification(A_		)
										model.to(A_		)
										model.eval()

										_UpperCAmelCase				:						Optional[int]       						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
										_UpperCAmelCase				:						Dict       						=			model(A_		)
										self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.type_sequence_label_size)		)





						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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







@require_torch
class  a     (		UpperCAmelCase							,       UpperCAmelCase							,       unittest.TestCase ):
						_lowercase			  =	(
						    (
						        SwinModel,
						        SwinBackbone,
						        SwinForImageClassification,
						        SwinForMaskedImageModeling,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowercase			  =	(
						    {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
						    if is_torch_available()
						    else {}
						)
						_lowercase			  =	True

						_lowercase			  =	False
						_lowercase			  =	False
						_lowercase			  =	False


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			SwinModelTester(self		)
										_UpperCAmelCase				:						List[str]       						=			ConfigTester(self  ,    config_class=A_  ,    embed_dim=37		)


						def 				_UpperCAmelCase (				self		):







										'''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 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_		)


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







										'''simple docstring'''


										pass


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										super().test_training_gradient_checkpointing()


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*A_		)


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







										'''simple docstring'''


										pass


						@unittest.skip(reason="Swin Transformer does not use feedforward chunking"		)
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										pass


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_UpperCAmelCase				:						List[Any]       						=			model_class(A_		)
														self.assertIsInstance(model.get_input_embeddings()  ,    (nn.Module)		)
														_UpperCAmelCase				:						List[str]       						=			model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(A_  ,    nn.Linear		)		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Dict       						=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_UpperCAmelCase				:						str       						=			model_class(A_		)
														_UpperCAmelCase				:						Union[str, Any]       						=			inspect.signature(model.forward		)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_UpperCAmelCase				:						Optional[int]       						=			[*signature.parameters.keys()]

														_UpperCAmelCase				:						List[Any]       						=			["pixel_values"]
														self.assertListEqual(arg_names[:1]  ,    A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[Any]       						=			self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCAmelCase				:						List[str]       						=			True

										for model_class in self.all_model_classes:
														_UpperCAmelCase				:						Optional[Any]       						=			True
														_UpperCAmelCase				:						str       						=			False
														_UpperCAmelCase				:						Any       						=			True
														_UpperCAmelCase				:						Tuple       						=			model_class(A_		)
														model.to(A_		)
														model.eval()
														with torch.no_grad():
																		_UpperCAmelCase				:						str       						=			model(**self._prepare_for_class(A_  ,    A_		)		)
														_UpperCAmelCase				:						Optional[int]       						=			outputs.attentions
														_UpperCAmelCase				:						Dict       						=			len(self.model_tester.depths		)
														self.assertEqual(len(A_		)  ,    A_		)

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

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

														# Check attention is always last and order is fine
														_UpperCAmelCase				:						Dict       						=			True
														_UpperCAmelCase				:						Optional[int]       						=			True
														_UpperCAmelCase				:						Union[str, Any]       						=			model_class(A_		)
														model.to(A_		)
														model.eval()
														with torch.no_grad():
																		_UpperCAmelCase				:						Optional[int]       						=			model(**self._prepare_for_class(A_  ,    A_		)		)

														# also another +1 for reshaped_hidden_states
														_UpperCAmelCase				:						Union[str, Any]       						=			1 if model_class.__name__ == "SwinBackbone" else 2
														self.assertEqual(out_len + added_hidden_states  ,    len(A_		)		)

														_UpperCAmelCase				:						List[Any]       						=			outputs.attentions

														self.assertEqual(len(A_		)  ,    A_		)

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


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			model_class(A_		)
										model.to(A_		)
										model.eval()

										with torch.no_grad():
														_UpperCAmelCase				:						Optional[Any]       						=			model(**self._prepare_for_class(A_  ,    A_		)		)

										_UpperCAmelCase				:						int       						=			outputs.hidden_states

										_UpperCAmelCase				:						Any       						=			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
										_UpperCAmelCase				:						Any       						=			(
										    config.patch_size
										    if isinstance(config.patch_size  ,    collections.abc.Iterable		)
										    else (config.patch_size, config.patch_size)
										)

										_UpperCAmelCase				:						int       						=			(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				:						List[str]       						=			outputs.reshaped_hidden_states
														self.assertEqual(len(A_		)  ,    A_		)

														_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			reshaped_hidden_states[0].shape
														_UpperCAmelCase				:						Optional[Any]       						=			(
														    reshaped_hidden_states[0].view(A_  ,    A_  ,    height * width		).permute(0  ,    2  ,    1		)
														)
														self.assertListEqual(
														    list(reshaped_hidden_states.shape[-2:]		)  ,    [num_patches, self.model_tester.embed_dim]  ,    )


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[Any]       						=			self.model_tester.prepare_config_and_inputs_for_common()

										_UpperCAmelCase				:						Optional[Any]       						=			(
										    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				:						Dict       						=			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"]
														_UpperCAmelCase				:						str       						=			True

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


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCAmelCase				:						Dict       						=			3

										_UpperCAmelCase				:						Any       						=			(
										    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				:						Dict       						=			(
										    config.patch_size
										    if isinstance(config.patch_size  ,    collections.abc.Iterable		)
										    else (config.patch_size, config.patch_size)
										)

										_UpperCAmelCase				:						int       						=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
										_UpperCAmelCase				:						Optional[Any]       						=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

										for model_class in self.all_model_classes:
														_UpperCAmelCase				:						Tuple       						=			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"]
														_UpperCAmelCase				:						List[str]       						=			True
														self.check_hidden_states_output(A_  ,    A_  ,    A_  ,    (padded_height, padded_width)		)


						@slow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase				:						List[str]       						=			SwinModel.from_pretrained(A_		)
														self.assertIsNotNone(A_		)





						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[int]       						=			self.model_tester.prepare_config_and_inputs_for_common()

										_UpperCAmelCase				:						List[str]       						=			_config_zero_init(A_		)
										for model_class in self.all_model_classes:
														_UpperCAmelCase				:						List[str]       						=			model_class(config=A_		)
														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  a     (		unittest.TestCase ):


						@cached_property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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





						@slow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"		).to(A_		)
										_UpperCAmelCase				:						Tuple       						=			self.default_image_processor

										_UpperCAmelCase				:						Tuple       						=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
										_UpperCAmelCase				:						str       						=			image_processor(images=A_  ,    return_tensors="pt"		).to(A_		)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase				:						str       						=			model(**A_		)

										# verify the logits
										_UpperCAmelCase				:						List[str]       						=			torch.Size((1, 1000)		)
										self.assertEqual(outputs.logits.shape  ,    A_		)
										_UpperCAmelCase				:						Optional[int]       						=			torch.tensor([-0.09_48, -0.64_54, -0.09_21]		).to(A_		)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,    A_  ,    atol=1e-4		)		)







@require_torch
class  a     (		unittest.TestCase							,       UpperCAmelCase ):
						_lowercase			  =	(SwinBackbone,) if is_torch_available() else ()
						_lowercase			  =	SwinConfig


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			SwinModelTester(self		)





import math

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				list:
				if n <= 0:
								raise ValueError("Only positive integers have prime factors"       )
				_UpperCAmelCase				:						Union[str, Any]       						=			[]
				while n % 2 == 0:
								pf.append(2       )
								_UpperCAmelCase				:						Tuple       						=			int(n / 2       )
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								while n % i == 0:
												pf.append(lowerCAmelCase       )
												_UpperCAmelCase				:						Optional[int]       						=			int(n / i       )
				if n > 2:
								pf.append(lowerCAmelCase       )
				return pf

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				if n <= 0:
								raise ValueError("Only positive numbers are accepted"       )
				_UpperCAmelCase				:						Dict       						=			1
				_UpperCAmelCase				:						str       						=			1
				while n % 2 == 0:
								temp += 1
								_UpperCAmelCase				:						List[str]       						=			int(n / 2       )
				div *= temp
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								_UpperCAmelCase				:						Any       						=			1
								while n % i == 0:
												temp += 1
												_UpperCAmelCase				:						Any       						=			int(n / i       )
								div *= temp
				if n > 1:
								div *= 2
				return div

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				if n <= 0:
								raise ValueError("Only positive numbers are accepted"       )
				_UpperCAmelCase				:						Optional[Any]       						=			1
				_UpperCAmelCase				:						Union[str, Any]       						=			1
				while n % 2 == 0:
								temp += 1
								_UpperCAmelCase				:						Optional[Any]       						=			int(n / 2       )
				if temp > 1:
								s *= (2**temp - 1) / (2 - 1)
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								_UpperCAmelCase				:						List[str]       						=			1
								while n % i == 0:
												temp += 1
												_UpperCAmelCase				:						List[Any]       						=			int(n / i       )
								if temp > 1:
												s *= (i**temp - 1) / (i - 1)
				return int(lowerCAmelCase       )

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				_UpperCAmelCase				:						Any       						=			n
				for x in set(prime_factors(lowerCAmelCase       )       ):
								s *= (x - 1) / x
				return int(lowerCAmelCase       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()


