



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 ViltImageProcessor

class 			__snake_case   ( unittest.TestCase):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_=7	,	a_=3	,	a_=18	,	a_=30	,	a_=400	,	a_=True	,	a_=None	,	a_=2	,	a_=True	,	a_=[0.5, 0.5, 0.5]	,	a_=[0.5, 0.5, 0.5]	,	):
											a__					   =		size if size is not None else {"""shortest_edge""": 30}
											a__					   =		parent
											a__					   =		batch_size
											a__					   =		num_channels
											a__					   =		image_size
											a__					   =		min_resolution
											a__					   =		max_resolution
											a__					   =		do_resize
											a__					   =		size
											a__					   =		size_divisor
											a__					   =		do_normalize
											a__					   =		image_mean
											a__					   =		image_std


							def _a   (				self		):
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "size_divisor": self.size_divisor,
											}







							def _a   (				self	,	a_	,	a_=False		):
											if not batched:
															a__					   =		self.size["""shortest_edge"""]
															a__					   =		image_inputs[0]
															if isinstance(a_	,	Image.Image		):
																			a__   ,      a__					   =		image.size
															else:
																			a__   ,      a__					   =		image.shape[1], image.shape[2]
															a__					   =		size / min(a_	,	a_		)
															if h < w:
																			a__   ,      a__					   =		size, scale * w
															else:
																			a__   ,      a__					   =		scale * h, size

															a__					   =		int((1_333 / 800) * size		)
															if max(a_	,	a_		) > max_size:
																			a__					   =		max_size / max(a_	,	a_		)
																			a__					   =		newh * scale
																			a__					   =		neww * scale

															a__   ,      a__					   =		int(newh + 0.5		), int(neww + 0.5		)
															a__   ,      a__					   =		(
															    newh // self.size_divisor * self.size_divisor,
															    neww // self.size_divisor * self.size_divisor,
															)

											else:
															a__					   =		[]
															for image in image_inputs:
																			a__   ,      a__					   =		self.get_expected_values([image]		)
																			expected_values.append((expected_height, expected_width)		)
															a__					   =		max(a_	,	key=lambda a_		: item[0]		)[0]
															a__					   =		max(a_	,	key=lambda a_		: item[1]		)[1]

											return expected_height, expected_width




@require_torch
@require_vision
class 			__snake_case   ( SCREAMING_SNAKE_CASE      ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__					:   List[str]       =       ViltImageProcessor if is_vision_available() else None


							def _a   (				self		):
											a__					   =		ViltImageProcessingTester(self		)


							@property
							def _a   (				self		):
											return self.image_processor_tester.prepare_image_processor_dict()


							def _a   (				self		):
											a__					   =		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_	,	"""size"""		)		)
											self.assertTrue(hasattr(a_	,	"""size_divisor"""		)		)


							def _a   (				self		):
											a__					   =		self.image_processing_class.from_dict(self.image_processor_dict		)
											self.assertEqual(image_processor.size	,	{"""shortest_edge""": 30}		)

											a__					   =		self.image_processing_class.from_dict(self.image_processor_dict	,	size=42		)
											self.assertEqual(image_processor.size	,	{"""shortest_edge""": 42}		)


							def _a   (				self		):
											pass


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

											# Test not batched input
											a__					   =		image_processing(image_inputs[0]	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)


							def _a   (				self		):
											# Initialize image_processing
											a__					   =		self.image_processing_class(**self.image_processor_dict		)
											# create random numpy tensors
											a__					   =		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
											a__					   =		image_processing(image_inputs[0]	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)







							def _a   (				self		):
											# Initialize image_processing
											a__					   =		self.image_processing_class(**self.image_processor_dict		)
											# create random PyTorch tensors
											a__					   =		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
											a__					   =		image_processing(image_inputs[0]	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)






from __future__ import annotations

def       A_   (						__a  :					int		):

				"""simple docstring"""
				a__					   =		{1}
				a__					   =		[1]
				for _ in range(2			,		depth + 1		):
								a__					   =		[*previous_coefficients, 0]
								a__					   =		[0, *previous_coefficients]
								a__					   =		[]
								for x, y in zip(__a			,		__a		):
												coefficients.add(x + y		)
												previous_coefficients.append(x + y		)
				return coefficients

def       A_   (						__a  :					set[int]		):

				"""simple docstring"""
				a__					   =		set()
				for number in unique_coefficients:
								a__					   =		2
								a__					   =		number
								while divisor**2 <= copy_number:
												a__					   =		0
												while copy_number % divisor == 0:
																copy_number //= divisor
																multiplicity += 1
												if multiplicity >= 2:
																non_squarefrees.add(__a		)
																break
												divisor += 1

				return unique_coefficients.difference(__a		)

def       A_   (						__a  :					int = 51		):

				"""simple docstring"""
				a__					   =		get_pascal_triangle_unique_coefficients(__a		)
				a__					   =		get_squarefrees(__a		)
				return sum(__a		)


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

