




"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor






class lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase=7    , lowercase=3    , lowercase=30    , lowercase=400    , lowercase=True    , lowercase=None    , lowercase=True    , lowercase=[0.5, 0.5, 0.5]    , lowercase=[0.5, 0.5, 0.5]    , lowercase=True    , lowercase=1 / 255    , lowercase=True    , ):
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							_lowerCamelCase	:     Optional[Any]       =       size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
							_lowerCamelCase	:     Union[str, Any]       =       parent
							_lowerCamelCase	:     Union[str, Any]       =       batch_size
							_lowerCamelCase	:     str       =       num_channels
							_lowerCamelCase	:     int       =       min_resolution
							_lowerCamelCase	:     int       =       max_resolution
							_lowerCamelCase	:     List[Any]       =       do_resize
							_lowerCamelCase	:     List[str]       =       size
							_lowerCamelCase	:     Any       =       do_normalize
							_lowerCamelCase	:     Union[str, Any]       =       image_mean
							_lowerCamelCase	:     Optional[Any]       =       image_std
							_lowerCamelCase	:     Optional[Any]       =       do_rescale
							_lowerCamelCase	:     Union[str, Any]       =       rescale_factor
							_lowerCamelCase	:     Dict       =       do_pad
			def    A_       (    self    ):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}






			def    A_       (    self    , lowercase    , lowercase=False    ):
							if not batched:
											_lowerCamelCase	:     List[Any]       =       image_inputs[0]
											if isinstance(lowercase    , Image.Image    ):
															_lowerCamelCase,					_lowerCamelCase	:     Optional[int]       =       image.size
											else:
															_lowerCamelCase,					_lowerCamelCase	:     Tuple       =       image.shape[1], image.shape[2]
											if w < h:
															_lowerCamelCase	:     Optional[int]       =       int(self.size['shortest_edge'] * h / w    )
															_lowerCamelCase	:     Any       =       self.size['shortest_edge']
											elif w > h:
															_lowerCamelCase	:     Tuple       =       self.size['shortest_edge']
															_lowerCamelCase	:     Optional[Any]       =       int(self.size['shortest_edge'] * w / h    )
											else:
															_lowerCamelCase	:     Optional[int]       =       self.size['shortest_edge']
															_lowerCamelCase	:     Union[str, Any]       =       self.size['shortest_edge']

							else:
											_lowerCamelCase	:     int       =       []
											for image in image_inputs:
															_lowerCamelCase,					_lowerCamelCase	:     int       =       self.get_expected_values([image]    )
															expected_values.append((expected_height, expected_width)    )
											_lowerCamelCase	:     Optional[Any]       =       max(lowercase    , key=lambda lowercase    : item[0]    )[0]
											_lowerCamelCase	:     Union[str, Any]       =       max(lowercase    , key=lambda lowercase    : item[1]    )[1]

							return expected_height, expected_width






@require_torch
@require_vision
class lowerCAmelCase__      (	lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     ConditionalDetrImageProcessor if is_vision_available() else None
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       ConditionalDetrImageProcessingTester(self    )
			@property
			def    A_       (    self    ):
							return self.image_processor_tester.prepare_image_processor_dict()
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(lowercase    , 'image_mean'    )    )
							self.assertTrue(hasattr(lowercase    , 'image_std'    )    )
							self.assertTrue(hasattr(lowercase    , 'do_normalize'    )    )
							self.assertTrue(hasattr(lowercase    , 'do_resize'    )    )
							self.assertTrue(hasattr(lowercase    , 'size'    )    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       self.image_processing_class.from_dict(self.image_processor_dict    )
							self.assertEqual(image_processor.size    , {'shortest_edge': 18, 'longest_edge': 1333}    )
							self.assertEqual(image_processor.do_pad    , lowercase    )

							_lowerCamelCase	:     str       =       self.image_processing_class.from_dict(
							    self.image_processor_dict    , size=42    , max_size=84    , pad_and_return_pixel_mask=lowercase    )
							self.assertEqual(image_processor.size    , {'shortest_edge': 42, 'longest_edge': 84}    )
							self.assertEqual(image_processor.do_pad    , lowercase    )
			def    A_       (    self    ):
							pass
			def    A_       (    self    ):
							# Initialize image_processing
							_lowerCamelCase	:     str       =       self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							_lowerCamelCase	:     Optional[int]       =       prepare_image_inputs(self.image_processor_tester    , equal_resolution=lowercase    )
							for image in image_inputs:
											self.assertIsInstance(lowercase    , Image.Image    )

							# Test not batched input
							_lowerCamelCase	:     List[Any]       =       image_processing(image_inputs[0]    , return_tensors='pt'    ).pixel_values

							_lowerCamelCase,					_lowerCamelCase	:     List[str]       =       self.image_processor_tester.get_expected_values(lowercase    )

							self.assertEqual(
							    encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

							# Test batched
							_lowerCamelCase,					_lowerCamelCase	:     Any       =       self.image_processor_tester.get_expected_values(lowercase    , batched=lowercase    )

							_lowerCamelCase	:     Dict       =       image_processing(lowercase    , return_tensors='pt'    ).pixel_values
							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
							_lowerCamelCase	:     Dict       =       self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							_lowerCamelCase	:     Optional[Any]       =       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
							_lowerCamelCase	:     Union[str, Any]       =       image_processing(image_inputs[0]    , return_tensors='pt'    ).pixel_values

							_lowerCamelCase,					_lowerCamelCase	:     Any       =       self.image_processor_tester.get_expected_values(lowercase    )

							self.assertEqual(
							    encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

							# Test batched
							_lowerCamelCase	:     int       =       image_processing(lowercase    , return_tensors='pt'    ).pixel_values

							_lowerCamelCase,					_lowerCamelCase	:     Tuple       =       self.image_processor_tester.get_expected_values(lowercase    , batched=lowercase    )

							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
							_lowerCamelCase	:     Dict       =       self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							_lowerCamelCase	:     Optional[Any]       =       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
							_lowerCamelCase	:     str       =       image_processing(image_inputs[0]    , return_tensors='pt'    ).pixel_values

							_lowerCamelCase,					_lowerCamelCase	:     Optional[int]       =       self.image_processor_tester.get_expected_values(lowercase    )

							self.assertEqual(
							    encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

							# Test batched
							_lowerCamelCase	:     int       =       image_processing(lowercase    , return_tensors='pt'    ).pixel_values

							_lowerCamelCase,					_lowerCamelCase	:     Tuple       =       self.image_processor_tester.get_expected_values(lowercase    , batched=lowercase    )

							self.assertEqual(
							    encoded_images.shape    , (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    , )
			@slow
			def    A_       (    self    ):
							# prepare image and target
							_lowerCamelCase	:     Dict       =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    , 'r'    ) as f:
											_lowerCamelCase	:     Dict       =       json.loads(f.read()    )

							_lowerCamelCase	:     Optional[Any]       =       {'image_id': 39769, 'annotations': target}

							# encode them
							_lowerCamelCase	:     Optional[Any]       =       ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'    )
							_lowerCamelCase	:     List[Any]       =       image_processing(images=lowercase    , annotations=lowercase    , return_tensors='pt'    )

							# verify pixel values
							_lowerCamelCase	:     Optional[int]       =       torch.Size([1, 3, 800, 1066]    )
							self.assertEqual(encoding['pixel_values'].shape    , lowercase    )

							_lowerCamelCase	:     Optional[int]       =       torch.tensor([0.27_96, 0.31_38, 0.34_81]    )
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    , lowercase    , atol=1E-4    )    )

							# verify area
							_lowerCamelCase	:     Union[str, Any]       =       torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']    , lowercase    )    )
							# verify boxes
							_lowerCamelCase	:     Dict       =       torch.Size([6, 4]    )
							self.assertEqual(encoding['labels'][0]['boxes'].shape    , lowercase    )
							_lowerCamelCase	:     Optional[Any]       =       torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    , lowercase    , atol=1E-3    )    )
							# verify image_id
							_lowerCamelCase	:     Any       =       torch.tensor([39769]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    , lowercase    )    )
							# verify is_crowd
							_lowerCamelCase	:     str       =       torch.tensor([0, 0, 0, 0, 0, 0]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    , lowercase    )    )
							# verify class_labels
							_lowerCamelCase	:     Union[str, Any]       =       torch.tensor([75, 75, 63, 65, 17, 17]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    , lowercase    )    )
							# verify orig_size
							_lowerCamelCase	:     List[Any]       =       torch.tensor([480, 640]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    , lowercase    )    )
							# verify size
							_lowerCamelCase	:     Optional[int]       =       torch.tensor([800, 1066]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']    , lowercase    )    )






			@slow
			def    A_       (    self    ):
							# prepare image, target and masks_path
							_lowerCamelCase	:     List[Any]       =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
							with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    , 'r'    ) as f:
											_lowerCamelCase	:     List[str]       =       json.loads(f.read()    )

							_lowerCamelCase	:     Optional[int]       =       {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

							_lowerCamelCase	:     Optional[Any]       =       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'    )

							# encode them
							_lowerCamelCase	:     Any       =       ConditionalDetrImageProcessor(format='coco_panoptic'    )
							_lowerCamelCase	:     Tuple       =       image_processing(images=lowercase    , annotations=lowercase    , masks_path=lowercase    , return_tensors='pt'    )

							# verify pixel values
							_lowerCamelCase	:     Any       =       torch.Size([1, 3, 800, 1066]    )
							self.assertEqual(encoding['pixel_values'].shape    , lowercase    )

							_lowerCamelCase	:     Tuple       =       torch.tensor([0.27_96, 0.31_38, 0.34_81]    )
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    , lowercase    , atol=1E-4    )    )

							# verify area
							_lowerCamelCase	:     Optional[Any]       =       torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']    , lowercase    )    )
							# verify boxes
							_lowerCamelCase	:     Tuple       =       torch.Size([6, 4]    )
							self.assertEqual(encoding['labels'][0]['boxes'].shape    , lowercase    )
							_lowerCamelCase	:     Optional[Any]       =       torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    , lowercase    , atol=1E-3    )    )
							# verify image_id
							_lowerCamelCase	:     Dict       =       torch.tensor([39769]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    , lowercase    )    )
							# verify is_crowd
							_lowerCamelCase	:     List[Any]       =       torch.tensor([0, 0, 0, 0, 0, 0]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    , lowercase    )    )
							# verify class_labels
							_lowerCamelCase	:     str       =       torch.tensor([17, 17, 63, 75, 75, 93]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    , lowercase    )    )
							# verify masks
							_lowerCamelCase	:     str       =       822873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()    , lowercase    )
							# verify orig_size
							_lowerCamelCase	:     Union[str, Any]       =       torch.tensor([480, 640]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    , lowercase    )    )
							# verify size
							_lowerCamelCase	:     List[Any]       =       torch.tensor([800, 1066]    )
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']    , lowercase    )    )





"""simple docstring"""






from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     int       =       int(number**0.5						)
				return number == sq * sq







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						):
				_lowerCamelCase	:     int       =       x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
				_lowerCamelCase	:     int       =       x_den * y_den * z_den
				_lowerCamelCase	:     int       =       gcd(lowercase__				,       lowercase__						)
				top //= hcf
				bottom //= hcf
				return top, bottom







def 	_snake_case     (							lowercase__ = 35						):
				_lowerCamelCase	:     set       =       set()
				_lowerCamelCase	:     int
				_lowerCamelCase	:     Fraction       =       Fraction(0						)
				_lowerCamelCase	:     tuple[int, int]

				for x_num in range(1				,       order + 1						):
								for x_den in range(x_num + 1				,       order + 1						):
												for y_num in range(1				,       order + 1						):
																for y_den in range(y_num + 1				,       order + 1						):
																				# n=1
																				_lowerCamelCase	:     int       =       x_num * y_den + x_den * y_num
																				_lowerCamelCase	:     List[Any]       =       x_den * y_den
																				_lowerCamelCase	:     int       =       gcd(lowercase__				,       lowercase__						)
																				z_num //= hcf
																				z_den //= hcf
																				if 0 < z_num < z_den <= order:
																								_lowerCamelCase	:     Optional[Any]       =       add_three(
																								    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)
																								unique_s.add(lowercase__						)

																				# n=2
																				_lowerCamelCase	:     Tuple       =       (
																				    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
																				)
																				_lowerCamelCase	:     Dict       =       x_den * x_den * y_den * y_den
																				if is_sq(lowercase__						) and is_sq(lowercase__						):
																								_lowerCamelCase	:     Dict       =       int(sqrt(lowercase__						)						)
																								_lowerCamelCase	:     Optional[Any]       =       int(sqrt(lowercase__						)						)
																								_lowerCamelCase	:     List[Any]       =       gcd(lowercase__				,       lowercase__						)
																								z_num //= hcf
																								z_den //= hcf
																								if 0 < z_num < z_den <= order:
																												_lowerCamelCase	:     Union[str, Any]       =       add_three(
																												    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)
																												unique_s.add(lowercase__						)

                    # n=-1
																				_lowerCamelCase	:     List[str]       =       x_num * y_num
																				_lowerCamelCase	:     int       =       x_den * y_num + x_num * y_den
																				_lowerCamelCase	:     Dict       =       gcd(lowercase__				,       lowercase__						)
																				z_num //= hcf
																				z_den //= hcf
																				if 0 < z_num < z_den <= order:
																								_lowerCamelCase	:     List[str]       =       add_three(
																								    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)
																								unique_s.add(lowercase__						)

																				# n=2
																				_lowerCamelCase	:     Tuple       =       x_num * x_num * y_num * y_num
																				_lowerCamelCase	:     List[str]       =       (
																				    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
																				)
																				if is_sq(lowercase__						) and is_sq(lowercase__						):
																								_lowerCamelCase	:     Optional[int]       =       int(sqrt(lowercase__						)						)
																								_lowerCamelCase	:     Optional[int]       =       int(sqrt(lowercase__						)						)
																								_lowerCamelCase	:     Dict       =       gcd(lowercase__				,       lowercase__						)
																								z_num //= hcf
																								z_den //= hcf
																								if 0 < z_num < z_den <= order:
																												_lowerCamelCase	:     int       =       add_three(
																												    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)
																												unique_s.add(lowercase__						)

				for num, den in unique_s:
								total += Fraction(lowercase__				,       lowercase__						)

				return total.denominator + total.numerator


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