






"""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, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import GLPNImageProcessor

class        a     (   unittest.TestCase	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=3_2							,		_lowerCamelCase=True							,		):
      lowercase							    =       parent
      lowercase							    =       batch_size
      lowercase							    =       num_channels
      lowercase							    =       image_size
      lowercase							    =       min_resolution
      lowercase							    =       max_resolution
      lowercase							    =       do_resize
      lowercase							    =       size_divisor
      lowercase							    =       do_rescale







    def 			UpperCamelCase_      (			self       ):
      return {
          "do_resize": self.do_resize,
          "size_divisor": self.size_divisor,
          "do_rescale": self.do_rescale,
      }


@require_torch
@require_vision
class        a     (   a_,				unittest.TestCase	):
    UpperCAmelCase_ :     Optional[Any]	     =GLPNImageProcessor if is_vision_available() else None







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       GLPNImageProcessingTester(self       )







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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_resize'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'size_divisor'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'resample'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_rescale'       )       )







    def 			UpperCamelCase_      (			self       ):
      pass







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		Image.Image       )

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      lowercase							    =       image_processing(image_inputs[0]							,		return_tensors='pt'       ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0       )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0       )







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		numpify=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		np.ndarray       )

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      lowercase							    =       image_processing(image_inputs[0]							,		return_tensors='pt'       ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0       )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0       )







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		torchify=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		torch.Tensor       )

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      lowercase							    =       image_processing(image_inputs[0]							,		return_tensors='pt'       ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0       )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0       )










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int = 4_00_00_00    ):





  '''simple docstring'''





  lowercase							    =       []
  lowercase    ,     lowercase							    =       0, 1
  while b <= n:
    if b % 2 == 0:
      even_fibs.append(__snake_case    )
    lowercase    ,     lowercase							    =       b, a + b
  return sum(__snake_case    )


if __name__ == "__main__":
    print(F'''{solution() = }''')


