






"""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 ViTImageProcessor


class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__=7       ,  snake_case__=3       ,  snake_case__=18       ,  snake_case__=30       ,  snake_case__=400       ,  snake_case__=True       ,  snake_case__=None       ,  snake_case__=True       ,  snake_case__=[0.5, 0.5, 0.5]       ,  snake_case__=[0.5, 0.5, 0.5]       ,  ):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       size if size is not None else {"height": 18, "width": 18}
												lowercase__				:      str=       parent
												lowercase__				:      Any=       batch_size
												lowercase__				:      str=       num_channels
												lowercase__				:      Union[str, Any]=       image_size
												lowercase__				:      Union[str, Any]=       min_resolution
												lowercase__				:      Tuple=       max_resolution
												lowercase__				:      Union[str, Any]=       do_resize
												lowercase__				:      Optional[int]=       size
												lowercase__				:      Optional[int]=       do_normalize
												lowercase__				:      Optional[int]=       image_mean
												lowercase__				:      str=       image_std




						def 		UpperCAmelCase_       (   self			):
												'''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,
												}


@require_torch
@require_vision
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					ViTImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       ViTImageProcessingTester(self			)




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      int=       self.image_processing_class(**self.image_processor_dict			)
												self.assertTrue(hasattr(snake_case__       ,  "image_mean"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_std"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_normalize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_resize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "size"			)			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       self.image_processing_class.from_dict(self.image_processor_dict			)
												self.assertEqual(image_processor.size       ,  {"height": 18, "width": 18}			)

												lowercase__				:      Optional[int]=       self.image_processing_class.from_dict(self.image_processor_dict       ,  size=42			)
												self.assertEqual(image_processor.size       ,  {"height": 42, "width": 42}			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      Optional[Any]=       self.image_processing_class(**self.image_processor_dict			)
												# create random PIL images
												lowercase__				:      Optional[Any]=       prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  Image.Image			)

												# Test not batched input
												lowercase__				:      Optional[int]=       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.size["height"],
												        self.image_processor_tester.size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      int=       image_processing(snake_case__       ,  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.size["height"],
												        self.image_processor_tester.size["width"],
												    )       ,  )




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      int=       self.image_processing_class(**self.image_processor_dict			)
												# create random numpy tensors
												lowercase__				:      Optional[int]=       prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=snake_case__       ,  numpify=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  np.ndarray			)

												# Test not batched input
												lowercase__				:      Union[str, Any]=       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.size["height"],
												        self.image_processor_tester.size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      List[str]=       image_processing(snake_case__       ,  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.size["height"],
												        self.image_processor_tester.size["width"],
												    )       ,  )




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      str=       self.image_processing_class(**self.image_processor_dict			)
												# create random PyTorch tensors
												lowercase__				:      Tuple=       prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=snake_case__       ,  torchify=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  torch.Tensor			)

												# Test not batched input
												lowercase__				:      Optional[int]=       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.size["height"],
												        self.image_processor_tester.size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      int=       image_processing(snake_case__       ,  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.size["height"],
												        self.image_processor_tester.size["width"],
												    )       ,  )












def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		int:

    """simple docstring"""


    __UpperCamelCase     						=  1.0
    for i in range(1    ,   r + 1					):
        ret *= (n + 1 - i) / i
    return round(lowercase_					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return choose(8 + n    ,   lowercase_					) + choose(9 + n    ,   lowercase_					) - 10




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return sum(non_bouncy_exact(lowercase_					) for i in range(1    ,   n + 1					)					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_ = 1_00					)				->		int:

    """simple docstring"""


    return non_bouncy_upto(lowercase_					)


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






