code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import inspect
  import unittest
  
  from transformers import MobileViTConfig
  from transformers.file_utils import is_tf_available, is_vision_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  							import numpy as np
  							import tensorflow as tf
  
  							from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
  							from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
  							from PIL import Image
  
  							from transformers import MobileViTImageProcessor
  
  
  
  
  class      __lowercase	(	__snake_case					):
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]						)   -> str:
  							_lowercase					:  List[Any]    			=      self.config_class(**self.inputs_dict						)
  							self.parent.assertTrue(hasattr(snake_case			,  "hidden_sizes"						)						)
  							self.parent.assertTrue(hasattr(snake_case			,  "neck_hidden_sizes"						)						)
  							self.parent.assertTrue(hasattr(snake_case			,  "num_attention_heads"						)						)
  
  
  
  
  class      __lowercase	:
  
  
  
  
  
  
  	def __init__(self							:   int			,  snake_case							:   Tuple			,  snake_case							:   Dict=13			,  snake_case							:   Tuple=32			,  snake_case							:   Any=2			,  snake_case							:   Tuple=3			,  snake_case							:   Any=640			,  snake_case							:   Union[str, Any]=4			,  snake_case							:   Dict="silu"			,  snake_case							:   int=3			,  snake_case							:   Optional[Any]=32			,  snake_case							:   Dict=0.1			,  snake_case							:   List[str]=0.1			,  snake_case							:   Any=0.1			,  snake_case							:   List[str]=0.02			,  snake_case							:   Optional[Any]=True			,  snake_case							:   Optional[int]=True			,  snake_case							:   Any=10			,  snake_case							:   List[Any]=None			,  )   -> Any:
  							_lowercase					:  List[Any]    			=      parent
  							_lowercase					:  Optional[Any]    			=      batch_size
  							_lowercase					:  Union[str, Any]    			=      image_size
  							_lowercase					:  int    			=      patch_size
  							_lowercase					:  Optional[int]    			=      num_channels
  							_lowercase					:  Dict    			=      last_hidden_size
  							_lowercase					:  int    			=      num_attention_heads
  							_lowercase					:  Tuple    			=      hidden_act
  							_lowercase					:  Any    			=      conv_kernel_size
  							_lowercase					:  List[Any]    			=      output_stride
  							_lowercase					:  Optional[Any]    			=      hidden_dropout_prob
  							_lowercase					:  List[Any]    			=      attention_probs_dropout_prob
  							_lowercase					:  Optional[int]    			=      classifier_dropout_prob
  							_lowercase					:  str    			=      use_labels
  							_lowercase					:  str    			=      is_training
  							_lowercase					:  List[str]    			=      num_labels
  							_lowercase					:  str    			=      initializer_range
  							_lowercase					:  Optional[Any]    			=      scope
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]						)   -> Optional[int]:
  							_lowercase					:  int    			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
  
  							_lowercase					:  Dict    			=      None
  							_lowercase					:  Optional[Any]    			=      None
  							if self.use_labels:
  													_lowercase					:  Any    			=      ids_tensor([self.batch_size]			,  self.num_labels						)
  													_lowercase					:  List[str]    			=      ids_tensor([self.batch_size, self.image_size, self.image_size]			,  self.num_labels						)
  
  							_lowercase					:  List[Any]    			=      self.get_config()
  
  							return config, pixel_values, labels, pixel_labels
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> Any:
  							return MobileViTConfig(
  							    image_size=self.image_size			,  patch_size=self.patch_size			,  num_channels=self.num_channels			,  num_attention_heads=self.num_attention_heads			,  hidden_act=self.hidden_act			,  conv_kernel_size=self.conv_kernel_size			,  output_stride=self.output_stride			,  hidden_dropout_prob=self.hidden_dropout_prob			,  attention_probs_dropout_prob=self.attention_probs_dropout_prob			,  classifier_dropout_prob=self.classifier_dropout_prob			,  initializer_range=self.initializer_range			,  )
  
  
  
  
  
  
  	def 			_a(self							:   int			,  snake_case							:   List[str]			,  snake_case							:   int			,  snake_case							:   str			,  snake_case							:   Dict						)   -> str:
  							_lowercase					:  Dict    			=      TFMobileViTModel(config=snake_case						)
  							_lowercase					:  int    			=      model(snake_case			,  training=snake_case						)
  							_lowercase					:  Dict    			=      self.image_size // self.output_stride
  							self.parent.assertEqual(
  							    result.last_hidden_state.shape			,  (self.batch_size, self.last_hidden_size, expected_height, expected_width)						)
  
  
  
  
  
  
  	def 			_a(self							:   str			,  snake_case							:   Optional[Any]			,  snake_case							:   List[str]			,  snake_case							:   Union[str, Any]			,  snake_case							:   Any						)   -> List[str]:
  							_lowercase					:  Optional[int]    			=      self.num_labels
  							_lowercase					:  Optional[int]    			=      TFMobileViTForImageClassification(snake_case						)
  							_lowercase					:  int    			=      model(snake_case			,  labels=snake_case			,  training=snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]			,  snake_case							:   Tuple			,  snake_case							:   Optional[Any]			,  snake_case							:   Any			,  snake_case							:   List[str]						)   -> Union[str, Any]:
  							_lowercase					:  Any    			=      self.num_labels
  							_lowercase					:  Optional[int]    			=      TFMobileViTForSemanticSegmentation(snake_case						)
  							_lowercase					:  Optional[Any]    			=      self.image_size // self.output_stride
  
  							_lowercase					:  int    			=      model(snake_case			,  training=snake_case						)
  							self.parent.assertEqual(
  							    result.logits.shape			,  (self.batch_size, self.num_labels, expected_height, expected_width)						)
  
  							_lowercase					:  List[str]    			=      model(snake_case			,  labels=snake_case			,  training=snake_case						)
  							self.parent.assertEqual(
  							    result.logits.shape			,  (self.batch_size, self.num_labels, expected_height, expected_width)						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> List[str]:
  							_lowercase					:  List[Any]    			=      self.prepare_config_and_inputs()
  							_lowercase					:  List[str]    			=      config_and_inputs
  							_lowercase					:  Optional[Any]    			=      {"pixel_values": pixel_values}
  							return config, inputs_dict
  
  
  
  
  @require_tf
  class      __lowercase	(	__snake_case      ,   __snake_case      ,   unittest.TestCase					):
  	_A   			=      (
  	    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
  	    if is_tf_available()
  	    else ()
  	)
  	_A   			=      (
  	    {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
  	    if is_tf_available()
  	    else {}
  	)
  
  	_A   			=      False
  	_A   			=      False
  	_A   			=      False
  	_A   			=      False
  	_A   			=      False
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]						)   -> int:
  							_lowercase					:  str    			=      TFMobileViTModelTester(self						)
  							_lowercase					:  Dict    			=      TFMobileViTConfigTester(self			,  config_class=snake_case			,  has_text_modality=snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]						)   -> Optional[int]:
  							self.config_tester.run_common_tests()
  
  
  
  
  
  
  	@unittest.skip(reason="MobileViT does not use inputs_embeds"						)
  	def 			_a(self							:   str						)   -> List[str]:
  							pass
  
  
  
  
  
  
  	@unittest.skip(reason="MobileViT does not support input and output embeddings"						)
  	def 			_a(self							:   List[str]						)   -> List[Any]:
  							pass
  
  
  
  
  
  
  	@unittest.skip(reason="MobileViT does not output attentions"						)
  	def 			_a(self							:   Optional[int]						)   -> Any:
  							pass
  
  
  
  
  
  
  	def 			_a(self							:   str						)   -> str:
  							_lowercase					:  Optional[int]    			=      self.model_tester.prepare_config_and_inputs_for_common()
  
  							for model_class in self.all_model_classes:
  													_lowercase					:  Union[str, Any]    			=      model_class(snake_case						)
  													_lowercase					:  List[Any]    			=      inspect.signature(model.call						)
  													# signature.parameters is an OrderedDict => so arg_names order is deterministic
  													_lowercase					:  str    			=      [*signature.parameters.keys()]
  
  													_lowercase					:  int    			=      ["pixel_values"]
  													self.assertListEqual(arg_names[:1]			,  snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> List[Any]:
  							_lowercase					:  List[Any]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_model(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]						)   -> Tuple:
  							def check_hidden_states_output(snake_case							:   Optional[int]			,  snake_case							:   Tuple			,  snake_case							:   List[str]						):
  													_lowercase					:  Dict    			=      model_class(snake_case						)
  
  													_lowercase					:  Dict    			=      model(**self._prepare_for_class(snake_case			,  snake_case						)						)
  
  													_lowercase					:  str    			=      outputs.hidden_states
  
  													_lowercase					:  Optional[Any]    			=      5
  													self.assertEqual(len(snake_case						)			,  snake_case						)
  
  													# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
  													# with the width and height being successively divided by 2.
  													_lowercase					:  List[str]    			=      2
  													for i in range(len(snake_case						)						):
  																			self.assertListEqual(
  																			    list(hidden_states[i].shape[-2:]						)			,  [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]			,  )
  																			divisor *= 2
  
  													self.assertEqual(self.model_tester.output_stride			,  divisor // 2						)
  
  							_lowercase					:  str    			=      self.model_tester.prepare_config_and_inputs_for_common()
  
  							for model_class in self.all_model_classes:
  													_lowercase					:  List[Any]    			=      True
  													check_hidden_states_output(snake_case			,  snake_case			,  snake_case						)
  
  													# check that output_hidden_states also work using config
  													del inputs_dict["output_hidden_states"]
  													_lowercase					:  Dict    			=      True
  
  													check_hidden_states_output(snake_case			,  snake_case			,  snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Dict						)   -> Optional[Any]:
  							_lowercase					:  Optional[int]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_image_classification(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> Any:
  							_lowercase					:  str    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_semantic_segmentation(*snake_case						)
  
  
  
  
  
  
  	@unittest.skipIf(
  	    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0			,  reason="TF does not support backprop for grouped convolutions on CPU."			,  )
  	def 			_a(self							:   Union[str, Any]						)   -> Union[str, Any]:
  							super().test_dataset_conversion()
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]			,  snake_case							:   Dict			,  snake_case							:   Dict			,  snake_case							:   Union[str, Any]=2e-1			,  snake_case							:   Tuple=2e-1						)   -> str:
  							self.assertTrue(np.allclose(snake_case			,  snake_case			,  atol=snake_case			,  rtol=snake_case						)						)
  
  
  
  
  
  
  	@unittest.skipIf(
  	    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0			,  reason="TF does not support backprop for grouped convolutions on CPU."			,  )
  	@slow
  	def 			_a(self							:   Optional[Any]						)   -> Any:
  							_lowercase					:  Optional[int]    			=      self.model_tester.prepare_config_and_inputs_for_common()
  
  							for model_class in self.all_model_classes:
  													# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
  													if model_class.__name__ != "TFMobileViTModel":
  																			_lowercase					:  Any    			=      model_class(snake_case						)
  																			if getattr(snake_case			,  "hf_compute_loss"			,  snake_case						):
  																									super().test_keras_fit()
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]						)   -> Any:
  							_lowercase					:  str    			=      self.model_tester.prepare_config_and_inputs_for_common()
  							for model_class in self.all_model_classes:
  													# set an ignore index to correctly test the masked loss used in
  													# `TFMobileViTForSemanticSegmentation`.
  													if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
  																			_lowercase					:  Optional[int]    			=      5
  
  													_lowercase					:  int    			=      model_class(snake_case						)
  													if getattr(snake_case			,  "hf_compute_loss"			,  snake_case						):
  																			# The number of elements in the loss should be the same as the number of elements in the label
  																			_lowercase					:  Optional[int]    			=      self._prepare_for_class(inputs_dict.copy()			,  snake_case			,  return_labels=snake_case						)
  																			_lowercase					:  Tuple    			=      prepared_for_class[
  																			    sorted(prepared_for_class.keys() - inputs_dict.keys()			,  reverse=snake_case						)[0]
  																			]
  																			_lowercase					:  List[str]    			=      added_label.shape.as_list()[:1]
  
  																			# Test that model correctly compute the loss with kwargs
  																			_lowercase					:  Union[str, Any]    			=      self._prepare_for_class(inputs_dict.copy()			,  snake_case			,  return_labels=snake_case						)
  																			_lowercase					:  List[Any]    			=      {"input_ids", "pixel_values", "input_features"}
  																			_lowercase					:  Any    			=      possible_input_names.intersection(set(snake_case						)						).pop()
  																			_lowercase					:  Optional[int]    			=      prepared_for_class.pop(snake_case						)
  
  																			_lowercase					:  Optional[Any]    			=      model(snake_case			,  **snake_case						)[0]
  																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)
  
  																			# Test that model correctly compute the loss when we mask some positions
  																			_lowercase					:  Any    			=      self._prepare_for_class(inputs_dict.copy()			,  snake_case			,  return_labels=snake_case						)
  																			_lowercase					:  List[str]    			=      {"input_ids", "pixel_values", "input_features"}
  																			_lowercase					:  int    			=      possible_input_names.intersection(set(snake_case						)						).pop()
  																			_lowercase					:  Union[str, Any]    			=      prepared_for_class.pop(snake_case						)
  																			if "labels" in prepared_for_class:
  																									_lowercase					:  Optional[Any]    			=      prepared_for_class["labels"].numpy()
  																									if len(labels.shape						) > 1 and labels.shape[1] != 1:
  																															# labels[0] = -100
  																															_lowercase					:  Dict    			=      tf.convert_to_tensor(snake_case						)
  																															_lowercase					:  Union[str, Any]    			=      model(snake_case			,  **snake_case						)[0]
  																															self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)
  																															self.assertTrue(not np.any(np.isnan(loss.numpy()						)						)						)
  
                  # Test that model correctly compute the loss with a dict
  																			_lowercase					:  List[str]    			=      self._prepare_for_class(inputs_dict.copy()			,  snake_case			,  return_labels=snake_case						)
  																			_lowercase					:  Optional[int]    			=      model(snake_case						)[0]
  																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)
  
  																			# Test that model correctly compute the loss with a tuple
  																			_lowercase					:  List[Any]    			=      self._prepare_for_class(inputs_dict.copy()			,  snake_case			,  return_labels=snake_case						)
  
  																			# Get keys that were added with the _prepare_for_class function
  																			_lowercase					:  Dict    			=      prepared_for_class.keys() - inputs_dict.keys()
  																			_lowercase					:  Optional[Any]    			=      inspect.signature(model.call						).parameters
  																			_lowercase					:  Union[str, Any]    			=      list(signature.keys()						)
  
  																			# Create a dictionary holding the location of the tensors in the tuple
  																			_lowercase					:  Any    			=      {0: input_name}
  																			for label_key in label_keys:
  																									_lowercase					:  str    			=      signature_names.index(snake_case						)
  																									_lowercase					:  Optional[int]    			=      label_key
  																			_lowercase					:  Union[str, Any]    			=      sorted(tuple_index_mapping.items()						)
  																			# Initialize a list with their default values, update the values and convert to a tuple
  																			_lowercase					:  Optional[Any]    			=      []
  
  																			for name in signature_names:
  																									if name != "kwargs":
  																															list_input.append(signature[name].default						)
  
  																			for index, value in sorted_tuple_index_mapping:
  																									_lowercase					:  List[Any]    			=      prepared_for_class[value]
  
  																			_lowercase					:  List[Any]    			=      tuple(snake_case						)
  
  																			# Send to model
  																			_lowercase					:  List[str]    			=      model(tuple_input[:-1]						)[0]
  
  																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)
  
  
  
  
  
  
  	@slow
  	def 			_a(self							:   Any						)   -> Optional[Any]:
  							for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  													_lowercase					:  List[str]    			=      TFMobileViTModel.from_pretrained(snake_case						)
  													self.assertIsNotNone(snake_case						)
  
  
  
  def 		UpperCamelCase    (					)      -> Tuple:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  List[str]    			=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
  						return image
  
  
  
  
  @require_tf
  class      __lowercase	(	unittest.TestCase					):
  
  
  
  
  
  
  	@slow
  	def 			_a(self							:   str						)   -> List[Any]:
  							_lowercase					:  Optional[Any]    			=      TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"						)
  
  							_lowercase					:  Optional[int]    			=      MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"						)
  							_lowercase					:  Optional[int]    			=      prepare_img()
  							_lowercase					:  Optional[int]    			=      image_processor(images=snake_case			,  return_tensors="tf"						)
  
  							# forward pass
  							_lowercase					:  Tuple    			=      model(**snake_case			,  training=snake_case						)
  
  							# verify the logits
  							_lowercase					:  List[str]    			=      tf.TensorShape((1, 1000)						)
  							self.assertEqual(outputs.logits.shape			,  snake_case						)
  
  							_lowercase					:  List[str]    			=      tf.constant([-1.93_64, -1.23_27, -0.46_53]						)
  
  							tf.debugging.assert_near(outputs.logits[0, :3]			,  snake_case			,  atol=1e-4			,  rtol=1e-04						)
  
  
  
  
  
  
  	@slow
  	def 			_a(self							:   List[str]						)   -> Tuple:
  							# `from_pt` will be removed
  							_lowercase					:  int    			=      TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"						)
  
  							_lowercase					:  Optional[int]    			=      MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"						)
  
  							_lowercase					:  List[str]    			=      prepare_img()
  							_lowercase					:  Optional[Any]    			=      image_processor(images=snake_case			,  return_tensors="tf"						)
  
  							# forward pass
  							_lowercase					:  Any    			=      model(inputs.pixel_values			,  training=snake_case						)
  							_lowercase					:  Union[str, Any]    			=      outputs.logits
  
  							# verify the logits
  							_lowercase					:  int    			=      tf.TensorShape((1, 21, 32, 32)						)
  							self.assertEqual(logits.shape			,  snake_case						)
  
  							_lowercase					:  str    			=      tf.constant(
  							    [
  							        [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
  							        [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
  							        [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
  							    ]						)
  
  							tf.debugging.assert_near(logits[0, :3, :3, :3]			,  snake_case			,  rtol=1e-4			,  atol=1e-4						)
  
  
  
  
  
style_context: 
  import math
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> list:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if n <= 0:
  												raise ValueError("Only positive integers have prime factors"     )
  						_lowercase					:  Optional[int]    			=      []
  						while n % 2 == 0:
  												pf.append(2     )
  												_lowercase					:  Tuple    			=      int(n / 2     )
  						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
  												while n % i == 0:
  																		pf.append(_UpperCAmelCase     )
  																		_lowercase					:  Optional[int]    			=      int(n / i     )
  						if n > 2:
  												pf.append(_UpperCAmelCase     )
  						return pf
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if n <= 0:
  												raise ValueError("Only positive numbers are accepted"     )
  						_lowercase					:  str    			=      1
  						_lowercase					:  Tuple    			=      1
  						while n % 2 == 0:
  												temp += 1
  												_lowercase					:  List[Any]    			=      int(n / 2     )
  						div *= temp
  						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
  												_lowercase					:  Optional[Any]    			=      1
  												while n % i == 0:
  																		temp += 1
  																		_lowercase					:  Optional[Any]    			=      int(n / i     )
  												div *= temp
  						if n > 1:
  												div *= 2
  						return div
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if n <= 0:
  												raise ValueError("Only positive numbers are accepted"     )
  						_lowercase					:  int    			=      1
  						_lowercase					:  List[str]    			=      1
  						while n % 2 == 0:
  												temp += 1
  												_lowercase					:  str    			=      int(n / 2     )
  						if temp > 1:
  												s *= (2**temp - 1) / (2 - 1)
  						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
  												_lowercase					:  int    			=      1
  												while n % i == 0:
  																		temp += 1
  																		_lowercase					:  List[Any]    			=      int(n / i     )
  												if temp > 1:
  																		s *= (i**temp - 1) / (i - 1)
  						return int(_UpperCAmelCase     )
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  int    			=      n
  						for x in set(prime_factors(_UpperCAmelCase     )     ):
  												s *= (x - 1) / x
  						return int(_UpperCAmelCase     )
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  