

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 LevitImageProcessor






class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			def __init__(			self							: Any      ,      a							: Optional[int]      ,      a							: str=7      ,      a							: Dict=3      ,      a							: Optional[Any]=18      ,      a							: Optional[Any]=30      ,      a							: str=400      ,      a							: Dict=True      ,      a							: int=None      ,      a							: List[Any]=True      ,      a							: str=None      ,      a							: Any=True      ,      a							: Union[str, Any]=[0.5, 0.5, 0.5]      ,      a							: Optional[Any]=[0.5, 0.5, 0.5]      ,      )							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       size if size is not None else {"shortest_edge": 18}
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       crop_size if crop_size is not None else {"height": 18, "width": 18}
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       parent
				SCREAMING_SNAKE_CASE   :      int				     =       batch_size
				SCREAMING_SNAKE_CASE   :      int				     =       num_channels
				SCREAMING_SNAKE_CASE   :      Any				     =       image_size
				SCREAMING_SNAKE_CASE   :      str				     =       min_resolution
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max_resolution
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       do_resize
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       size
				SCREAMING_SNAKE_CASE   :      Any				     =       do_center_crop
				SCREAMING_SNAKE_CASE   :      Tuple				     =       crop_size
				SCREAMING_SNAKE_CASE   :      Tuple				     =       do_normalize
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       image_mean
				SCREAMING_SNAKE_CASE   :      Any				     =       image_std






			def 							__UpperCamelCase (			self							: List[Any]	)							->       str:

				"""simple docstring"""

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






@require_torch
@require_vision
class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =LevitImageProcessor if is_vision_available() else None
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       LevitImageProcessingTester(self	)
			@property
			def 							__UpperCamelCase (			self							: Tuple	)							->       Dict:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__UpperCamelCase (			self							: Dict	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(a      ,      "image_mean"	)	)
				self.assertTrue(hasattr(a      ,      "image_std"	)	)
				self.assertTrue(hasattr(a      ,      "do_normalize"	)	)
				self.assertTrue(hasattr(a      ,      "do_resize"	)	)
				self.assertTrue(hasattr(a      ,      "do_center_crop"	)	)
				self.assertTrue(hasattr(a      ,      "size"	)	)
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.image_processing_class.from_dict(self.image_processor_dict	)
				self.assertEqual(image_processor.size      ,      {"shortest_edge": 18}	)
				self.assertEqual(image_processor.crop_size      ,      {"height": 18, "width": 18}	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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							: Optional[int]	)							->       str:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Any	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=a	)
				for image in image_inputs:
					self.assertIsInstance(a      ,      Image.Image	)

				# Test not batched input
				SCREAMING_SNAKE_CASE   :      List[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.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )

				# Test batched
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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							: int	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE   :      Dict				     =       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
				SCREAMING_SNAKE_CASE   :      Dict				     =       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
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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							: Any	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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
				SCREAMING_SNAKE_CASE   :      List[str]				     =       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
				SCREAMING_SNAKE_CASE   :      int				     =       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  lowerCamelCase__ (					_a = 50):
	SCREAMING_SNAKE_CASE   :      int				     =       [1] * (length + 1)

	for row_length in range(length + 1):
		for tile_length in range(2							,			5):
			for tile_start in range(row_length - tile_length + 1):
				ways_number[row_length] += ways_number[
				    row_length - tile_start - tile_length
				]

	return ways_number[length]


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