





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 MobileNetVaImageProcessor

class 			__A(    unittest.TestCase			):

     def __init__(   self,							A,							A=7,							A=3,							A=18,							A=30,							A=400,							A=True,							A=None,							A=True,							A=None,							):





            """simple docstring"""

            _UpperCamelCase														=    size if size is not None else {'''shortest_edge''': 20}
            _UpperCamelCase														=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
            _UpperCamelCase														=    parent
            _UpperCamelCase														=    batch_size
            _UpperCamelCase														=    num_channels
            _UpperCamelCase														=    image_size
            _UpperCamelCase														=    min_resolution
            _UpperCamelCase														=    max_resolution
            _UpperCamelCase														=    do_resize
            _UpperCamelCase														=    size
            _UpperCamelCase														=    do_center_crop
            _UpperCamelCase														=    crop_size



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
            }






@require_torch
@require_vision
class 			__A(    __UpperCAmelCase    ,      unittest.TestCase			):
     __A							=							MobileNetVaImageProcessor if is_vision_available() else None

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    MobileNetVaImageProcessingTester(self   )

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self.image_processor_tester.prepare_image_processor_dict()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(A,							'''do_resize'''   )   )
            self.assertTrue(hasattr(A,							'''size'''   )   )
            self.assertTrue(hasattr(A,							'''do_center_crop'''   )   )
            self.assertTrue(hasattr(A,							'''center_crop'''   )   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

            _UpperCamelCase														=    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   ):





            """simple docstring"""

            pass

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

            # Test not batched input
            _UpperCamelCase														=    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
            _UpperCamelCase														=    image_processing(A,							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   ):





            """simple docstring"""

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

            # Test not batched input
            _UpperCamelCase														=    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
            _UpperCamelCase														=    image_processing(A,							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   ):





            """simple docstring"""

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

            # Test not batched input
            _UpperCamelCase														=    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
            _UpperCamelCase														=    image_processing(A,							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() = }''')






