





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, prepare_video_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import VideoMAEImageProcessor



class 				_lowerCAmelCase			(	unittest.TestCase							):


  '''simple docstring'''







  def __init__(					self					:    Union[str, Any]					, UpperCamelCase					:    Optional[int]					, UpperCamelCase					:    Union[str, Any]=7					, UpperCamelCase					:    Union[str, Any]=3					, UpperCamelCase					:    Any=10					, UpperCamelCase					:    Optional[Any]=18					, UpperCamelCase					:    Dict=30					, UpperCamelCase					:    Dict=4_00					, UpperCamelCase					:    List[str]=True					, UpperCamelCase					:    List[str]=None					, UpperCamelCase					:    List[Any]=True					, UpperCamelCase					:    Any=[0.5, 0.5, 0.5]					, UpperCamelCase					:    List[Any]=[0.5, 0.5, 0.5]					, UpperCamelCase					:    Optional[int]=None					, ):


    '''simple docstring'''





    _snake_case      : int														=		size if size is not None else {'shortest_edge': 18}
    _snake_case      : Union[str, Any]														=		crop_size if crop_size is not None else {'height': 18, 'width': 18}

    _snake_case      : Dict														=		parent
    _snake_case      : Any														=		batch_size
    _snake_case      : Dict														=		num_channels
    _snake_case      : Any														=		num_frames
    _snake_case      : Optional[int]														=		image_size
    _snake_case      : List[str]														=		min_resolution
    _snake_case      : List[Any]														=		max_resolution
    _snake_case      : str														=		do_resize
    _snake_case      : Tuple														=		size
    _snake_case      : Optional[Any]														=		do_normalize
    _snake_case      : Optional[Any]														=		image_mean
    _snake_case      : Optional[int]														=		image_std
    _snake_case      : str														=		crop_size







  def UpperCamelCase_       (					self					:    Optional[Any]							):


    '''simple docstring'''





    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "size": self.size,
        "crop_size": self.crop_size,
    }




@require_torch
@require_vision
class 				_lowerCAmelCase			(	UpperCAmelCase_							,			unittest.TestCase							):


  '''simple docstring'''
  a_							:				str					    =VideoMAEImageProcessor if is_vision_available() else None







  def UpperCamelCase_       (					self					:    Optional[Any]							):


    '''simple docstring'''





    _snake_case      : List[Any]														=		VideoMAEImageProcessingTester(self							)







  @property
  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    return self.image_processor_tester.prepare_image_processor_dict()







  def UpperCamelCase_       (					self					:    Optional[Any]							):


    '''simple docstring'''





    _snake_case      : List[str]														=		self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(__UpperCAmelCase					, 'image_mean'							)							)
    self.assertTrue(hasattr(__UpperCAmelCase					, 'image_std'							)							)
    self.assertTrue(hasattr(__UpperCAmelCase					, 'do_normalize'							)							)
    self.assertTrue(hasattr(__UpperCAmelCase					, 'do_resize'							)							)
    self.assertTrue(hasattr(__UpperCAmelCase					, 'do_center_crop'							)							)
    self.assertTrue(hasattr(__UpperCAmelCase					, 'size'							)							)







  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    _snake_case      : List[Any]														=		self.image_processing_class.from_dict(self.image_processor_dict							)
    self.assertEqual(image_processor.size					, {'shortest_edge': 18}							)
    self.assertEqual(image_processor.crop_size					, {'height': 18, 'width': 18}							)

    _snake_case      : int														=		self.image_processing_class.from_dict(self.image_processor_dict					, size=42					, crop_size=84							)
    self.assertEqual(image_processor.size					, {'shortest_edge': 42}							)
    self.assertEqual(image_processor.crop_size					, {'height': 84, 'width': 84}							)







  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    pass







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


    '''simple docstring'''





    _snake_case      : Union[str, Any]														=		self.image_processing_class(**self.image_processor_dict							)
    # create random PIL videos
    _snake_case      : List[Any]														=		prepare_video_inputs(self.image_processor_tester					, equal_resolution=__UpperCAmelCase							)
    for video in video_inputs:
      self.assertIsInstance(__UpperCAmelCase					, __UpperCAmelCase							)
      self.assertIsInstance(video[0]					, Image.Image							)

    # Test not batched input
    _snake_case      : Optional[int]														=		image_processing(video_inputs[0]					, return_tensors='pt'							).pixel_values
    self.assertEqual(
        encoded_videos.shape					, (
            1,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )					, )

    # Test batched
    _snake_case      : List[Any]														=		image_processing(__UpperCAmelCase					, return_tensors='pt'							).pixel_values
    self.assertEqual(
        encoded_videos.shape					, (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )					, )







  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    _snake_case      : Union[str, Any]														=		self.image_processing_class(**self.image_processor_dict							)
    # create random numpy tensors
    _snake_case      : Union[str, Any]														=		prepare_video_inputs(self.image_processor_tester					, equal_resolution=__UpperCAmelCase					, numpify=__UpperCAmelCase							)
    for video in video_inputs:
      self.assertIsInstance(__UpperCAmelCase					, __UpperCAmelCase							)
      self.assertIsInstance(video[0]					, np.ndarray							)

    # Test not batched input
    _snake_case      : Tuple														=		image_processing(video_inputs[0]					, return_tensors='pt'							).pixel_values
    self.assertEqual(
        encoded_videos.shape					, (
            1,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )					, )

    # Test batched
    _snake_case      : Union[str, Any]														=		image_processing(__UpperCAmelCase					, return_tensors='pt'							).pixel_values
    self.assertEqual(
        encoded_videos.shape					, (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )					, )







  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    _snake_case      : int														=		self.image_processing_class(**self.image_processor_dict							)
    # create random PyTorch tensors
    _snake_case      : str														=		prepare_video_inputs(self.image_processor_tester					, equal_resolution=__UpperCAmelCase					, torchify=__UpperCAmelCase							)
    for video in video_inputs:
      self.assertIsInstance(__UpperCAmelCase					, __UpperCAmelCase							)
      self.assertIsInstance(video[0]					, torch.Tensor							)

    # Test not batched input
    _snake_case      : Union[str, Any]														=		image_processing(video_inputs[0]					, return_tensors='pt'							).pixel_values
    self.assertEqual(
        encoded_videos.shape					, (
            1,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )					, )

    # Test batched
    _snake_case      : List[Any]														=		image_processing(__UpperCAmelCase					, return_tensors='pt'							).pixel_values
    self.assertEqual(
        encoded_videos.shape					, (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )					, )






"""simple docstring"""


def     _lowerCamelCase(__UpperCamelCase       )				-> list:
	_lowerCAmelCase								=[True for i in range(n + 1       )]
	_lowerCAmelCase								=[i - 1 for i in range(n + 1       )]
	_lowerCAmelCase								=[]
	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
			_lowerCAmelCase								=False

			if i % primes[j] == 0:
				_lowerCAmelCase								=totients[i] * primes[j]
				break

			_lowerCAmelCase								=totients[i] * (primes[j] - 1)

	return totients


if __name__ == "__main__":
			import doctest

			doctest.testmod()



