

'''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 ViltImageProcessor



class        lowercase    (	unittest.TestCase			):


							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=2     ,      _snake_case=True     ,      _snake_case=[0.5, 0.5, 0.5]     ,      _snake_case=[0.5, 0.5, 0.5]     ,      )				->					int:
								UpperCAmelCase_		:   Dict													=       size if size is not None else {'shortest_edge': 30}
								UpperCAmelCase_		:   Dict													=       parent
								UpperCAmelCase_		:   List[Any]													=       batch_size
								UpperCAmelCase_		:   Dict													=       num_channels
								UpperCAmelCase_		:   Optional[Any]													=       image_size
								UpperCAmelCase_		:   int													=       min_resolution
								UpperCAmelCase_		:   Dict													=       max_resolution
								UpperCAmelCase_		:   List[Any]													=       do_resize
								UpperCAmelCase_		:   List[str]													=       size
								UpperCAmelCase_		:   List[str]													=       size_divisor
								UpperCAmelCase_		:   Dict													=       do_normalize
								UpperCAmelCase_		:   Optional[int]													=       image_mean
								UpperCAmelCase_		:   Any													=       image_std


							def     _snake_case				(      self)				->					Dict:
								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     _snake_case				(      self     ,      _snake_case     ,      _snake_case=False)				->					Optional[Any]:
								if not batched:
									UpperCAmelCase_		:   Union[str, Any]													=       self.size['shortest_edge']
									UpperCAmelCase_		:   str													=       image_inputs[0]
									if isinstance(_snake_case     ,      Image.Image):
										UpperCAmelCase_						,     UpperCAmelCase_		:   Optional[int]													=       image.size
									else:
										UpperCAmelCase_						,     UpperCAmelCase_		:   int													=       image.shape[1], image.shape[2]
									UpperCAmelCase_		:   Union[str, Any]													=       size / min(_snake_case     ,      _snake_case)
									if h < w:
										UpperCAmelCase_						,     UpperCAmelCase_		:   Any													=       size, scale * w
									else:
										UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       scale * h, size

									UpperCAmelCase_		:   Union[str, Any]													=       int((1333 / 800) * size)
									if max(_snake_case     ,      _snake_case) > max_size:
										UpperCAmelCase_		:   Union[str, Any]													=       max_size / max(_snake_case     ,      _snake_case)
										UpperCAmelCase_		:   List[Any]													=       newh * scale
										UpperCAmelCase_		:   Dict													=       neww * scale

									UpperCAmelCase_						,     UpperCAmelCase_		:   Optional[int]													=       int(newh + 0.5), int(neww + 0.5)
									UpperCAmelCase_						,     UpperCAmelCase_		:   int													=       (
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

								else:
									UpperCAmelCase_		:   List[str]													=       []
									for image in image_inputs:
										UpperCAmelCase_						,     UpperCAmelCase_		:   int													=       self.get_expected_values([image])
										expected_values.append((expected_height, expected_width))
									UpperCAmelCase_		:   List[Any]													=       max(_snake_case     ,      key=lambda _snake_case: item[0])[0]
									UpperCAmelCase_		:   Dict													=       max(_snake_case     ,      key=lambda _snake_case: item[1])[1]

								return expected_height, expected_width



@require_torch
@require_vision
class        lowercase    (	a_,       unittest.TestCase			):
							_lowerCamelCase		:   Tuple= ViltImageProcessor if is_vision_available() else None


							def     _snake_case				(      self)				->					Any:
								UpperCAmelCase_		:   Tuple													=       ViltImageProcessingTester(self)


							@property
							def     _snake_case				(      self)				->					List[str]:
								return self.image_processor_tester.prepare_image_processor_dict()


							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   Optional[Any]													=       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'))
								self.assertTrue(hasattr(_snake_case     ,      'size_divisor'))


							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   Union[str, Any]													=       self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size     ,      {'shortest_edge': 30})

								UpperCAmelCase_		:   int													=       self.image_processing_class.from_dict(self.image_processor_dict     ,      size=42)
								self.assertEqual(image_processor.size     ,      {'shortest_edge': 42})


							def     _snake_case				(      self)				->					List[str]:
								pass


							def     _snake_case				(      self)				->					Any:
								# Initialize image_processing
								UpperCAmelCase_		:   Optional[Any]													=       self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								UpperCAmelCase_		:   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
								UpperCAmelCase_		:   Optional[Any]													=       image_processing(image_inputs[0]     ,      return_tensors='pt').pixel_values

								UpperCAmelCase_						,     UpperCAmelCase_		:   str													=       self.image_processor_tester.get_expected_values(_snake_case)
								self.assertEqual(
								    encoded_images.shape     ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,      )

								# Test batched
								UpperCAmelCase_		:   List[Any]													=       image_processing(_snake_case     ,      return_tensors='pt').pixel_values

								UpperCAmelCase_						,     UpperCAmelCase_		:   List[str]													=       self.image_processor_tester.get_expected_values(_snake_case     ,      batched=_snake_case)
								self.assertEqual(
								    encoded_images.shape     ,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )     ,      )


							def     _snake_case				(      self)				->					Optional[Any]:
								# Initialize image_processing
								UpperCAmelCase_		:   List[Any]													=       self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								UpperCAmelCase_		:   List[str]													=       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
								UpperCAmelCase_		:   Dict													=       image_processing(image_inputs[0]     ,      return_tensors='pt').pixel_values

								UpperCAmelCase_						,     UpperCAmelCase_		:   Optional[Any]													=       self.image_processor_tester.get_expected_values(_snake_case)
								self.assertEqual(
								    encoded_images.shape     ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,      )

								# Test batched
								UpperCAmelCase_		:   Any													=       image_processing(_snake_case     ,      return_tensors='pt').pixel_values

								UpperCAmelCase_						,     UpperCAmelCase_		:   Tuple													=       self.image_processor_tester.get_expected_values(_snake_case     ,      batched=_snake_case)
								self.assertEqual(
								    encoded_images.shape     ,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )     ,      )





							def     _snake_case				(      self)				->					Optional[int]:
								# Initialize image_processing
								UpperCAmelCase_		:   str													=       self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								UpperCAmelCase_		:   Any													=       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
								UpperCAmelCase_		:   List[str]													=       image_processing(image_inputs[0]     ,      return_tensors='pt').pixel_values

								UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       self.image_processor_tester.get_expected_values(_snake_case)
								self.assertEqual(
								    encoded_images.shape     ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,      )

								# Test batched
								UpperCAmelCase_		:   Any													=       image_processing(_snake_case     ,      return_tensors='pt').pixel_values

								UpperCAmelCase_						,     UpperCAmelCase_		:   List[str]													=       self.image_processor_tester.get_expected_values(_snake_case     ,      batched=_snake_case)
								self.assertEqual(
								    encoded_images.shape     ,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )     ,      )









'''simple docstring'''


from itertools import combinations, product







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	UpperCAmelCase_		:   int													=       xa * xa + ya * ya
	UpperCAmelCase_		:   List[str]													=       xa * xa + ya * ya
	UpperCAmelCase_		:   Optional[Any]													=       (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase = 5_0	)     ->     int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1	) ,repeat=2	) ,2	)
	    if is_right(*UpperCamelCase ,*UpperCamelCase	)	)


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






