


import math




def        snake_case_					(  snake_case					)  ->    list[int]:
    lowercase__:       Optional[Any]        =       []
    lowercase__:       Union[str, Any]        =       2
    lowercase__:       Any        =       int(math.sqrt(snake_case					)					)  # Size of every segment
    lowercase__:       Dict        =       [True] * (end + 1)
    lowercase__:       List[str]        =       []

    while start <= end:
        if temp[start] is True:
            in_prime.append(snake_case					)
            for i in range(start * start      ,							end + 1      ,							snake_case					):
                lowercase__:       Union[str, Any]        =       False
        start += 1
    prime += in_prime

    lowercase__:       List[Any]        =       end + 1
    lowercase__:       int        =       min(2 * end      ,							snake_case					)

    while low <= n:
        lowercase__:       int        =       [True] * (high - low + 1)
        for each in in_prime:
            lowercase__:       int        =       math.floor(low / each					) * each
            if t < low:
                t += each

            for j in range(snake_case      ,							high + 1      ,							snake_case					):
                lowercase__:       List[str]        =       False

        for j in range(len(snake_case					)					):
            if temp[j] is True:
                prime.append(j + low					)

        lowercase__:       Optional[int]        =       high + 1
        lowercase__:       Union[str, Any]        =       min(high + end      ,							snake_case					)

    return prime




print(sieve(10**6))






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   __a      (   unittest.TestCase				):
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=7    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=30    ,			lowerCAmelCase__=400    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=1 / 255    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=[0.5, 0.5, 0.5]    ,			lowerCAmelCase__=[0.5, 0.5, 0.5]    ,			lowerCAmelCase__=True    ,			) ->    List[str]:




        '''simple docstring'''
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        lowercase__:       Dict        =       size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
        lowercase__:       Tuple        =       parent
        lowercase__:       Optional[Any]        =       batch_size
        lowercase__:       Any        =       num_channels
        lowercase__:       str        =       min_resolution
        lowercase__:       Dict        =       max_resolution
        lowercase__:       Any        =       do_resize
        lowercase__:       str        =       size
        lowercase__:       Any        =       do_rescale
        lowercase__:       Union[str, Any]        =       rescale_factor
        lowercase__:       Optional[int]        =       do_normalize
        lowercase__:       Union[str, Any]        =       image_mean
        lowercase__:       List[str]        =       image_std
        lowercase__:       Optional[Any]        =       do_pad
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=False			) ->    int:




        '''simple docstring'''
        if not batched:
               lowercase__:       List[Any]        =       image_inputs[0]
               if isinstance(lowerCAmelCase__    ,			Image.Image			):
                      lowercase__      ,						lowercase__:       List[str]        =       image.size
               else:
                      lowercase__      ,						lowercase__:       str        =       image.shape[1], image.shape[2]
               if w < h:
                      lowercase__:       Optional[int]        =       int(self.size['shortest_edge'] * h / w			)
                      lowercase__:       int        =       self.size['shortest_edge']
               elif w > h:
                      lowercase__:       Tuple        =       self.size['shortest_edge']
                      lowercase__:       int        =       int(self.size['shortest_edge'] * w / h			)
               else:
                      lowercase__:       Tuple        =       self.size['shortest_edge']
                      lowercase__:       Optional[Any]        =       self.size['shortest_edge']

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

        return expected_height, expected_width





@require_torch
@require_vision
class   __a      (   __UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Tuple       					=			DetrImageProcessor if is_vision_available() else None
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       DetrImageProcessingTester(self			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        return self.image_processor_tester.prepare_image_processor_dict()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       Dict        =       self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'image_mean'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'image_std'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_normalize'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_rescale'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'rescale_factor'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_resize'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'size'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_pad'			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       int        =       self.image_processing_class.from_dict(self.image_processor_dict			)
        self.assertEqual(image_processor.size    ,			{'shortest_edge': 18, 'longest_edge': 1_333}			)
        self.assertEqual(image_processor.do_pad    ,			lowerCAmelCase__			)

        lowercase__:       str        =       self.image_processing_class.from_dict(
            self.image_processor_dict    ,			size=42    ,			max_size=84    ,			pad_and_return_pixel_mask=lowerCAmelCase__			)
        self.assertEqual(image_processor.size    ,			{'shortest_edge': 42, 'longest_edge': 84}			)
        self.assertEqual(image_processor.do_pad    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       Dict        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowercase__:       str        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			Image.Image			)

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

        lowercase__      ,						lowercase__:       Tuple        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__			)

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

        # Test batched
        lowercase__      ,						lowercase__:       Union[str, Any]        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__    ,			batched=lowerCAmelCase__			)

        lowercase__:       Dict        =       image_processing(lowerCAmelCase__    ,			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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       Union[str, Any]        =       self.image_processing_class(**self.image_processor_dict			)
        # create random numpy tensors
        lowercase__:       Optional[int]        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__    ,			numpify=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			np.ndarray			)

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

        lowercase__      ,						lowercase__:       Optional[int]        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__			)

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

        # Test batched
        lowercase__:       Union[str, Any]        =       image_processing(lowerCAmelCase__    ,			return_tensors='pt'			).pixel_values

        lowercase__      ,						lowercase__:       Dict        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__    ,			batched=lowerCAmelCase__			)

        self.assertEqual(
            encoded_images.shape    ,			(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       Tuple        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        lowercase__:       Union[str, Any]        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__    ,			torchify=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			torch.Tensor			)

        # Test not batched input
        lowercase__:       Optional[int]        =       image_processing(image_inputs[0]    ,			return_tensors='pt'			).pixel_values

        lowercase__      ,						lowercase__:       Dict        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__			)

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

        # Test batched
        lowercase__:       Optional[Any]        =       image_processing(lowerCAmelCase__    ,			return_tensors='pt'			).pixel_values

        lowercase__      ,						lowercase__:       Optional[Any]        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__    ,			batched=lowerCAmelCase__			)

        self.assertEqual(
            encoded_images.shape    ,			(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        # prepare image and target
        lowercase__:       Any        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
        with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,			'r'			) as f:
               lowercase__:       Optional[int]        =       json.loads(f.read()			)

        lowercase__:       Optional[Any]        =       {'image_id': 39_769, 'annotations': target}

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

        # verify pixel values
        lowercase__:       Optional[int]        =       torch.Size([1, 3, 800, 1_066]			)
        self.assertEqual(encoding['pixel_values'].shape    ,			lowerCAmelCase__			)

        lowercase__:       Optional[Any]        =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,			lowerCAmelCase__    ,			atol=1E-4			)			)

        # verify area
        lowercase__:       List[Any]        =       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,			lowerCAmelCase__			)			)
        # verify boxes
        lowercase__:       Union[str, Any]        =       torch.Size([6, 4]			)
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,			lowerCAmelCase__			)
        lowercase__:       int        =       torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,			lowerCAmelCase__    ,			atol=1E-3			)			)
        # verify image_id
        lowercase__:       List[Any]        =       torch.tensor([39_769]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,			lowerCAmelCase__			)			)
        # verify is_crowd
        lowercase__:       Union[str, Any]        =       torch.tensor([0, 0, 0, 0, 0, 0]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,			lowerCAmelCase__			)			)
        # verify class_labels
        lowercase__:       List[Any]        =       torch.tensor([75, 75, 63, 65, 17, 17]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,			lowerCAmelCase__			)			)
        # verify orig_size
        lowercase__:       Tuple        =       torch.tensor([480, 640]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,			lowerCAmelCase__			)			)
        # verify size
        lowercase__:       Tuple        =       torch.tensor([800, 1_066]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,			lowerCAmelCase__			)			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        # prepare image, target and masks_path
        lowercase__:       List[str]        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
        with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,			'r'			) as f:
               lowercase__:       Tuple        =       json.loads(f.read()			)

        lowercase__:       Tuple        =       {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

        lowercase__:       List[str]        =       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

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

        # verify pixel values
        lowercase__:       Any        =       torch.Size([1, 3, 800, 1_066]			)
        self.assertEqual(encoding['pixel_values'].shape    ,			lowerCAmelCase__			)

        lowercase__:       Any        =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,			lowerCAmelCase__    ,			atol=1E-4			)			)

        # verify area
        lowercase__:       str        =       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,			lowerCAmelCase__			)			)
        # verify boxes
        lowercase__:       Dict        =       torch.Size([6, 4]			)
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,			lowerCAmelCase__			)
        lowercase__:       str        =       torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,			lowerCAmelCase__    ,			atol=1E-3			)			)
        # verify image_id
        lowercase__:       Optional[int]        =       torch.tensor([39_769]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,			lowerCAmelCase__			)			)
        # verify is_crowd
        lowercase__:       List[Any]        =       torch.tensor([0, 0, 0, 0, 0, 0]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,			lowerCAmelCase__			)			)
        # verify class_labels
        lowercase__:       Optional[int]        =       torch.tensor([17, 17, 63, 75, 75, 93]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,			lowerCAmelCase__			)			)
        # verify masks
        lowercase__:       str        =       822_873
        self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,			lowerCAmelCase__			)
        # verify orig_size
        lowercase__:       Optional[int]        =       torch.tensor([480, 640]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,			lowerCAmelCase__			)			)
        # verify size
        lowercase__:       Optional[int]        =       torch.tensor([800, 1_066]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,			lowerCAmelCase__			)			)


