


'''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 DetrImageProcessor
class _lowerCamelCase			(		unittest.TestCase				):



		'''simple docstring'''







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







		def   __lowerCAmelCase						(   self						:		Tuple     )    ->					int:
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_rescale": self.do_rescale,
			    "rescale_factor": self.rescale_factor,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_pad": self.do_pad,
			}







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

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

			return expected_height, expected_width







@require_torch
@require_vision
class _lowerCamelCase			(		__UpperCamelCase						,	unittest.TestCase				):



		'''simple docstring'''







		A_      :     Optional[Any]        =							DetrImageProcessor if is_vision_available() else None







		def   __lowerCAmelCase						(   self						:		Dict     )    ->					Tuple:
			__magic_name__			:       str	      =	DetrImageProcessingTester(self     )







		@property
		def   __lowerCAmelCase						(   self						:		Any     )    ->					Dict:
			return self.image_processor_tester.prepare_image_processor_dict()







		def   __lowerCAmelCase						(   self						:		Dict     )    ->					List[str]:
			__magic_name__			:       Optional[Any]	      =	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_rescale'     )     )
			self.assertTrue(hasattr(_A    ,	'rescale_factor'     )     )
			self.assertTrue(hasattr(_A    ,	'do_resize'     )     )
			self.assertTrue(hasattr(_A    ,	'size'     )     )
			self.assertTrue(hasattr(_A    ,	'do_pad'     )     )







		def   __lowerCAmelCase						(   self						:		Any     )    ->					Dict:
			__magic_name__			:       List[Any]	      =	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    ,	_A     )

			__magic_name__			:       List[str]	      =	self.image_processing_class.from_dict(
			    self.image_processor_dict    ,	size=42    ,	max_size=84    ,	pad_and_return_pixel_mask=_A     )
			self.assertEqual(image_processor.size    ,	{'shortest_edge': 42, 'longest_edge': 84}     )
			self.assertEqual(image_processor.do_pad    ,	_A     )







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







		def   __lowerCAmelCase						(   self						:		List[Any]     )    ->					str:
			# Initialize image_processing
			__magic_name__			:       Dict	      =	self.image_processing_class(**self.image_processor_dict     )
			# create random PIL images
			__magic_name__			:       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
			__magic_name__			:       Any	      =	image_processing(image_inputs[0]    ,	return_tensors='pt'     ).pixel_values

			__magic_name__      ,							__magic_name__			:       Any	      =	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
			__magic_name__      ,							__magic_name__			:       List[str]	      =	self.image_processor_tester.get_expected_values(_A    ,	batched=_A     )

			__magic_name__			:       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,
			        expected_height,
			        expected_width,
			    )    ,	)







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

			__magic_name__      ,							__magic_name__			:       Optional[Any]	      =	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
			__magic_name__			:       List[Any]	      =	image_processing(_A    ,	return_tensors='pt'     ).pixel_values

			__magic_name__      ,							__magic_name__			:       List[Any]	      =	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   __lowerCAmelCase						(   self						:		Tuple     )    ->					int:
			# Initialize image_processing
			__magic_name__			:       Tuple	      =	self.image_processing_class(**self.image_processor_dict     )
			# create random PyTorch tensors
			__magic_name__			:       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
			__magic_name__			:       Union[str, Any]	      =	image_processing(image_inputs[0]    ,	return_tensors='pt'     ).pixel_values

			__magic_name__      ,							__magic_name__			:       int	      =	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
			__magic_name__			:       Any	      =	image_processing(_A    ,	return_tensors='pt'     ).pixel_values

			__magic_name__      ,							__magic_name__			:       List[str]	      =	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,
			    )    ,	)







		@slow
		def   __lowerCAmelCase						(   self						:		Union[str, Any]     )    ->					Union[str, Any]:
			# prepare image and target
			__magic_name__			:       str	      =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
			with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,	'r'     ) as f:
				__magic_name__			:       Dict	      =	json.loads(f.read()     )

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

			# encode them
			__magic_name__			:       Tuple	      =	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'     )
			__magic_name__			:       List[Any]	      =	image_processing(images=_A    ,	annotations=_A    ,	return_tensors='pt'     )

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

			__magic_name__			:       Optional[Any]	      =	torch.tensor([0.2796, 0.3138, 0.3481]     )
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,	_A    ,	atol=1E-4     )     )

			# verify area
			__magic_name__			:       Optional[int]	      =	torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,	_A     )     )
			# verify boxes
			__magic_name__			:       str	      =	torch.Size([6, 4]     )
			self.assertEqual(encoding['labels'][0]['boxes'].shape    ,	_A     )
			__magic_name__			:       List[str]	      =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,	_A    ,	atol=1E-3     )     )
			# verify image_id
			__magic_name__			:       Dict	      =	torch.tensor([39769]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,	_A     )     )
			# verify is_crowd
			__magic_name__			:       Union[str, Any]	      =	torch.tensor([0, 0, 0, 0, 0, 0]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,	_A     )     )
			# verify class_labels
			__magic_name__			:       Tuple	      =	torch.tensor([75, 75, 63, 65, 17, 17]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,	_A     )     )
			# verify orig_size
			__magic_name__			:       List[Any]	      =	torch.tensor([480, 640]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,	_A     )     )
			# verify size
			__magic_name__			:       Optional[int]	      =	torch.tensor([800, 1066]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,	_A     )     )







		@slow
		def   __lowerCAmelCase						(   self						:		Dict     )    ->					Optional[Any]:
			# prepare image, target and masks_path
			__magic_name__			:       int	      =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
			with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,	'r'     ) as f:
				__magic_name__			:       Optional[int]	      =	json.loads(f.read()     )

			__magic_name__			:       int	      =	{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

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

			# encode them
			__magic_name__			:       Tuple	      =	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'     )
			__magic_name__			:       Dict	      =	image_processing(images=_A    ,	annotations=_A    ,	masks_path=_A    ,	return_tensors='pt'     )

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

			__magic_name__			:       Optional[int]	      =	torch.tensor([0.2796, 0.3138, 0.3481]     )
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,	_A    ,	atol=1E-4     )     )

			# verify area
			__magic_name__			:       int	      =	torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,	_A     )     )
			# verify boxes
			__magic_name__			:       str	      =	torch.Size([6, 4]     )
			self.assertEqual(encoding['labels'][0]['boxes'].shape    ,	_A     )
			__magic_name__			:       List[Any]	      =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,	_A    ,	atol=1E-3     )     )
			# verify image_id
			__magic_name__			:       List[Any]	      =	torch.tensor([39769]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,	_A     )     )
			# verify is_crowd
			__magic_name__			:       List[str]	      =	torch.tensor([0, 0, 0, 0, 0, 0]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,	_A     )     )
			# verify class_labels
			__magic_name__			:       Dict	      =	torch.tensor([17, 17, 63, 75, 75, 93]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,	_A     )     )
			# verify masks
			__magic_name__			:       Tuple	      =	822873
			self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,	_A     )
			# verify orig_size
			__magic_name__			:       List[Any]	      =	torch.tensor([480, 640]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,	_A     )     )
			# verify size
			__magic_name__			:       List[Any]	      =	torch.tensor([800, 1066]     )
			self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,	_A     )     )

from __future__ import annotations

import math





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5     ,		int(math.sqrt(__lowerCAmelCase				) + 1				)     ,		6				):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					str(__lowerCAmelCase				)
    snake_case__    =					[n]
    for i in range(1     ,		len(__lowerCAmelCase				)				):
        list_nums.append(int(str_num[i:]				)				)
        list_nums.append(int(str_num[:-i]				)				)
    return list_nums





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if len(str(__lowerCAmelCase				)				) > 3:
        if not is_prime(int(str(__lowerCAmelCase				)[-3:]				)				) or not is_prime(int(str(__lowerCAmelCase				)[:3]				)				):
            return False
    return True





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase = 11				):
    snake_case__    =					[]
    snake_case__    =					13
    while len(__lowerCAmelCase				) != count:
        if validate(__lowerCAmelCase				):
            snake_case__    =					list_truncated_nums(__lowerCAmelCase				)
            if all(is_prime(__lowerCAmelCase				) for i in list_nums				):
                list_truncated_primes.append(__lowerCAmelCase				)
        num += 2
    return list_truncated_primes





def 						SCREAMING_SNAKE_CASE__     (   ):
    return sum(compute_truncated_primes(11				)				)


if __name__ == "__main__":
     print(F'''{sum(compute_truncated_primes(11)) = }''')



