

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 		__magic_name__    (							unittest.TestCase							):

   '''simple docstring'''




   def __init__(       self,					lowercase_,					lowercase_=7,					lowercase_=3,					lowercase_=18,					lowercase_=30,					lowercase_=400,					lowercase_=True,					lowercase_=None,					lowercase_=True,					lowercase_=None,					lowercase_=True,					lowercase_=[0.5, 0.5, 0.5],					lowercase_=[0.5, 0.5, 0.5],					)							->					Any:






     """simple docstring"""






     a__						=size if size is not None else {'''height''': 20, '''width''': 20}
     a__						=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

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



   def 				_UpperCAmelCase				(       self )							->					Union[str, Any]:






     """simple docstring"""






     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 		__magic_name__    (							lowerCamelCase__	, unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__				:			List[Any]												=     DeiTImageProcessor if is_vision_available() else None
   lowerCamelCase__				:			Optional[int]												=     True



   def 				_UpperCAmelCase				(       self )							->					List[Any]:






     """simple docstring"""






     a__						=DeiTImageProcessingTester(self )



   @property
   def 				_UpperCAmelCase				(       self )							->					Any:






     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()



   def 				_UpperCAmelCase				(       self )							->					str:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(lowercase_,					'''do_resize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''size''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_center_crop''' ) )
     self.assertTrue(hasattr(lowercase_,					'''center_crop''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_normalize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''image_mean''' ) )
     self.assertTrue(hasattr(lowercase_,					'''image_std''' ) )



   def 				_UpperCAmelCase				(       self )							->					str:






     """simple docstring"""






     a__						=self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size,					{'''height''': 20, '''width''': 20} )
     self.assertEqual(image_processor.crop_size,					{'''height''': 18, '''width''': 18} )

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



   def 				_UpperCAmelCase				(       self )							->					Tuple:






     """simple docstring"""






     pass



   def 				_UpperCAmelCase				(       self )							->					Optional[Any]:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     a__						=prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowercase_ )
     for image in image_inputs:
       self.assertIsInstance(lowercase_,					Image.Image )

     # Test not batched input
     a__						=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
     a__						=image_processing(lowercase_,					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 )							->					Any:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     a__						=prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowercase_,					numpify=lowercase_ )
     for image in image_inputs:
       self.assertIsInstance(lowercase_,					np.ndarray )

     # Test not batched input
     a__						=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
     a__						=image_processing(lowercase_,					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 )							->					Dict:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     a__						=prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowercase_,					torchify=lowercase_ )
     for image in image_inputs:
       self.assertIsInstance(lowercase_,					torch.Tensor )

     # Test not batched input
     a__						=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
     a__						=image_processing(lowercase_,					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     UpperCAmelCase__       (    _A   :				int = 4_00_00_00							):





  '''simple docstring'''







  try:
    a__						=int(_A							)
  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.'''							)
  a__						=1_00
  a__						=(Decimal(5							) ** Decimal(0.5							) + 1) / Decimal(2							)

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


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