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 GLPNImageProcessor





class UpperCAmelCase		(   unittest.TestCase    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      lowercase=7						,      lowercase=3						,      lowercase=1_8						,      lowercase=3_0						,      lowercase=4_0_0						,      lowercase=True						,      lowercase=3_2						,      lowercase=True						,      ):



						"""simple docstring"""





						A_						:							Any      = parent
						A_						:							str      = batch_size
						A_						:							int      = num_channels
						A_						:							Tuple      = image_size
						A_						:							Union[str, Any]      = min_resolution
						A_						:							List[str]      = max_resolution
						A_						:							Optional[int]      = do_resize
						A_						:							List[str]      = size_divisor
						A_						:							List[str]      = do_rescale







				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return {
						    "do_resize": self.do_resize,
						    "size_divisor": self.size_divisor,
						    "do_rescale": self.do_rescale,
						}





@require_torch
@require_vision
class UpperCAmelCase		(   __A   ,			unittest.TestCase    ):


				'''simple docstring'''






				lowerCamelCase_        =   GLPNImageProcessor if is_vision_available() else None
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Dict      = GLPNImageProcessingTester(self   )
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self.image_processor_tester.prepare_image_processor_dict()
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							List[str]      = self.image_processing_class(**self.image_processor_dict   )
						self.assertTrue(hasattr(lowercase						,      'do_resize'   )   )
						self.assertTrue(hasattr(lowercase						,      'size_divisor'   )   )
						self.assertTrue(hasattr(lowercase						,      'resample'   )   )
						self.assertTrue(hasattr(lowercase						,      'do_rescale'   )   )
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						pass
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Optional[Any]      = self.image_processing_class(**self.image_processor_dict   )
						# create random PIL images
						A_						:							Tuple      = prepare_image_inputs(self.image_processor_tester						,      equal_resolution=lowercase   )
						for image in image_inputs:
								self.assertIsInstance(lowercase						,      Image.Image   )

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						A_						:							List[str]      = image_processing(image_inputs[0]						,      return_tensors='pt'   ).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Any      = self.image_processing_class(**self.image_processor_dict   )
						# create random numpy tensors
						A_						:							Dict      = 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 (GLPNImageProcessor doesn't support batching)
						A_						:							Any      = image_processing(image_inputs[0]						,      return_tensors='pt'   ).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )







				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Dict      = self.image_processing_class(**self.image_processor_dict   )
						# create random PyTorch tensors
						A_						:							int      = 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 (GLPNImageProcessor doesn't support batching)
						A_						:							List[Any]      = image_processing(image_inputs[0]						,      return_tensors='pt'   ).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )










def     lowercase	(    a = 50 ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[1] * (length + 1)

		for row_length in range(3     , length + 1 ):
				for block_length in range(3     , row_length + 1 ):
						for block_start in range(row_length - block_length ):
								ways_number[row_length] += ways_number[
								    row_length - block_start - block_length - 1
								]

						ways_number[row_length] += 1

		return ways_number[length]


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






