






"""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 DeiTImageProcessor

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=None				,						_lowerCamelCase=True				,						_lowerCamelCase=None				,						_lowerCamelCase=True				,						_lowerCamelCase=[0.5, 0.5, 0.5]				,						_lowerCamelCase=[0.5, 0.5, 0.5]				,						):
      lowercase				       =			size if size is not None else {'height': 2_0, 'width': 2_0}
      lowercase				       =			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			num_channels
      lowercase				       =			image_size
      lowercase				       =			min_resolution
      lowercase				       =			max_resolution
      lowercase				       =			do_resize
      lowercase				       =			size
      lowercase				       =			do_center_crop
      lowercase				       =			crop_size
      lowercase				       =			do_normalize
      lowercase				       =			image_mean
      lowercase				       =			image_std







    def      UpperCamelCase_ (     self			):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
      }


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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			DeiTImageProcessingTester(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'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_center_crop'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'center_crop'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_normalize'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'image_mean'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'image_std'			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size				,						{'height': 2_0, 'width': 2_0}			)
      self.assertEqual(image_processor.crop_size				,						{'height': 1_8, 'width': 1_8}			)

      lowercase				       =			self.image_processing_class.from_dict(self.image_processor_dict				,						size=4_2				,						crop_size=8_4			)
      self.assertEqual(image_processor.size				,						{'height': 4_2, 'width': 4_2}			)
      self.assertEqual(image_processor.crop_size				,						{'height': 8_4, 'width': 8_4}			)







    def      UpperCamelCase_ (     self			):
      pass







    def      UpperCamelCase_ (     self			):
      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
      lowercase				       =			image_processing(image_inputs[0]				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)

      # Test batched
      lowercase				       =			image_processing(_lowerCamelCase				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)







    def      UpperCamelCase_ (     self			):
      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
      lowercase				       =			image_processing(image_inputs[0]				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)

      # Test batched
      lowercase				       =			image_processing(_lowerCamelCase				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)







    def      UpperCamelCase_ (     self			):
      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
      lowercase				       =			image_processing(image_inputs[0]				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)

      # Test batched
      lowercase				       =			image_processing(_lowerCamelCase				,						return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape				,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,						)









import math
from decimal import Decimal, getcontext


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase = 4_000_000       ):

       try:
              _UpperCamelCase														=    int(lowerCAmelCase       )
       except (TypeError, ValueError):
              raise TypeError('''Parameter n must be int or castable to int.'''       )
       if n <= 0:
              raise ValueError('''Parameter n must be greater than or equal to one.'''       )
       _UpperCamelCase														=    100
       _UpperCamelCase														=    (Decimal(5       ) ** Decimal(0.5       ) + 1) / Decimal(2       )

       _UpperCamelCase														=    (math.floor(math.log(n * (phi + 2)     ,							lowerCAmelCase       ) - 1       ) // 3) * 3 + 2
       _UpperCamelCase														=    Decimal(round(phi ** Decimal(index + 1       )       )       ) / (phi + 2)
       _UpperCamelCase														=    num // 2
       return int(lowerCAmelCase       )


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






