'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import SwinaSRImageProcessor
 from transformers.image_transforms import get_image_size




class _lowerCAmelCase (  unittest.TestCase				):







      def __init__(     self     ,						lowercase     ,						lowercase=7     ,						lowercase=3     ,						lowercase=18     ,						lowercase=30     ,						lowercase=400     ,						lowercase=True     ,						lowercase=1 / 255     ,						lowercase=True     ,						lowercase=8     ,						):
        lowercase							:       List[Any]											=      parent
        lowercase							:       Optional[int]											=      batch_size
        lowercase							:       List[str]											=      num_channels
        lowercase							:       int											=      image_size
        lowercase							:       str											=      min_resolution
        lowercase							:       Optional[Any]											=      max_resolution
        lowercase							:       List[str]											=      do_rescale
        lowercase							:       Tuple											=      rescale_factor
        lowercase							:       str											=      do_pad
        lowercase							:       str											=      pad_size







      def     lowerCamelCase__			(     self    ):
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }







      def     lowerCamelCase__			(     self     ,						lowercase=False     ,						lowercase=False     ,						lowercase=False    ):
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
          lowercase							:       Union[str, Any]											=      []
          for i in range(self.batch_size    ):
            image_inputs.append(
                np.random.randint(
                    255     ,						size=(self.num_channels, self.max_resolution, self.max_resolution)     ,						dtype=np.uinta    )    )
        else:
          lowercase							:       Optional[int]											=      []
          for i in range(self.batch_size    ):
            lowercase							,			lowercase							:       Tuple											=      np.random.choice(np.arange(self.min_resolution     ,						self.max_resolution    )     ,						2    )
            image_inputs.append(np.random.randint(255     ,						size=(self.num_channels, width, height)     ,						dtype=np.uinta    )    )

        if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          lowercase							:       List[str]											=      [Image.fromarray(np.moveaxis(lowercase     ,						0     ,						-1    )    ) for x in image_inputs]

        if torchify:
          lowercase							:       Tuple											=      [torch.from_numpy(lowercase    ) for x in image_inputs]

        return image_inputs





@require_torch
@require_vision
class _lowerCAmelCase (  snake_case       ,      unittest.TestCase				):
      UpperCAmelCase_  :	Union[str, Any]     =					SwinaSRImageProcessor if is_vision_available() else None







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Optional[int]											=      SwinaSRImageProcessingTester(self    )







      @property
      def     lowerCamelCase__			(     self    ):
        return self.image_processor_tester.prepare_image_processor_dict()







      def     lowerCamelCase__			(     self    ):
        lowercase							:       Dict											=      self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(lowercase     ,						'''do_rescale'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''rescale_factor'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''do_pad'''    )    )
        self.assertTrue(hasattr(lowercase     ,						'''pad_size'''    )    )







      def     lowerCamelCase__			(     self    ):
        pass







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        lowercase							,			lowercase							:       Any											=      get_image_size(lowercase    )
        lowercase							:       str											=      self.image_processor_tester.pad_size

        lowercase							:       Optional[int]											=      (old_height // size + 1) * size - old_height
        lowercase							:       List[str]											=      (old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width







      def     lowerCamelCase__			(     self    ):
        # Initialize image_processor
        lowercase							:       Dict											=      self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        lowercase							:       List[str]											=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase    )
        for image in image_inputs:
          self.assertIsInstance(lowercase     ,						Image.Image    )

        # Test not batched input
        lowercase							:       Optional[int]											=      image_processor(image_inputs[0]     ,						return_tensors='''pt'''    ).pixel_values
        lowercase							,			lowercase							:       List[Any]											=      self.calculate_expected_size(np.array(image_inputs[0]    )    )
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,						)







      def     lowerCamelCase__			(     self    ):
        # Initialize image_processor
        lowercase							:       Tuple											=      self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        lowercase							:       Dict											=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase     ,						numpify=lowercase    )
        for image in image_inputs:
          self.assertIsInstance(lowercase     ,						np.ndarray    )

        # Test not batched input
        lowercase							:       str											=      image_processor(image_inputs[0]     ,						return_tensors='''pt'''    ).pixel_values
        lowercase							,			lowercase							:       Tuple											=      self.calculate_expected_size(image_inputs[0]    )
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,						)







      def     lowerCamelCase__			(     self    ):
        # Initialize image_processor
        lowercase							:       str											=      self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        lowercase							:       Any											=      self.image_processor_tester.prepare_inputs(equal_resolution=lowercase     ,						torchify=lowercase    )
        for image in image_inputs:
          self.assertIsInstance(lowercase     ,						torch.Tensor    )

        # Test not batched input
        lowercase							:       Dict											=      image_processor(image_inputs[0]     ,						return_tensors='''pt'''    ).pixel_values
        lowercase							,			lowercase							:       Tuple											=      self.calculate_expected_size(image_inputs[0]    )
        self.assertEqual(
            encoded_images.shape     ,						(
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,						)
'''simple docstring'''
def 							__UpperCamelCase   (				__UpperCAmelCase     :				int )       ->  list:
  lowercase							:       Union[str, Any]											=      [True for i in range(n + 1 )]
  lowercase							:       Dict											=      [i - 1 for i in range(n + 1 )]
  lowercase							:       Union[str, Any]											=      []
  for i in range(2   ,      n + 1 ):
    if is_prime[i]:
      primes.append(__UpperCAmelCase )
    for j in range(0   ,      len(__UpperCAmelCase ) ):
      if i * primes[j] >= n:
        break
      lowercase							:       List[str]											=      False

      if i % primes[j] == 0:
        lowercase							:       Optional[Any]											=      totients[i] * primes[j]
        break

      lowercase							:       Union[str, Any]											=      totients[i] * (primes[j] - 1)

  return totients


if __name__ == "__main__":
 import doctest

 doctest.testmod()