






'''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      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def __init__(      self	:							Tuple					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Dict=7					,      __lowercase	:							List[str]=3					,      __lowercase	:							str=30					,      __lowercase	:							int=4_00					,      __lowercase	:							Optional[int]=True					,      __lowercase	:							Tuple=None					,      __lowercase	:							List[Any]=True					,      __lowercase	:							Dict=[0.5, 0.5, 0.5]					,      __lowercase	:							Optional[Any]=[0.5, 0.5, 0.5]					,      __lowercase	:							Optional[int]=True					,      __lowercase	:							List[str]=1 / 2_55					,      __lowercase	:							int=True					,      )	->						Dict:
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       SCREAMING_SNAKE_CASE__     :				int									=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
       SCREAMING_SNAKE_CASE__     :				int									=parent
       SCREAMING_SNAKE_CASE__     :				List[Any]									=batch_size
       SCREAMING_SNAKE_CASE__     :				List[str]									=num_channels
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=min_resolution
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=max_resolution
       SCREAMING_SNAKE_CASE__     :				List[str]									=do_resize
       SCREAMING_SNAKE_CASE__     :				Tuple									=size
       SCREAMING_SNAKE_CASE__     :				Any									=do_normalize
       SCREAMING_SNAKE_CASE__     :				int									=image_mean
       SCREAMING_SNAKE_CASE__     :				str									=image_std
       SCREAMING_SNAKE_CASE__     :				List[Any]									=do_rescale
       SCREAMING_SNAKE_CASE__     :				List[str]									=rescale_factor
       SCREAMING_SNAKE_CASE__     :				List[str]									=do_pad




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

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

       return expected_height, expected_width






@require_torch
@require_vision
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							ConditionalDetrImageProcessor if is_vision_available() else None




 def      __magic_name__	(      self	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[str]									=ConditionalDetrImageProcessingTester(self      )




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						List[Any]:
       return self.image_processor_tester.prepare_image_processor_dict()




 def      __magic_name__	(      self	:							Any      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=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      __magic_name__	(      self	:							Optional[Any]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				int									=self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size					,      {'''shortest_edge''': 18, '''longest_edge''': 13_33}      )
       self.assertEqual(image_processor.do_pad					,      __lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=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      __magic_name__	(      self	:							Optional[int]      )	->						List[Any]:
       pass




 def      __magic_name__	(      self	:							Any      )	->						Any:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__     :				Dict									=self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       SCREAMING_SNAKE_CASE__     :				Any									=prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowercase      )
       for image in image_inputs:
             self.assertIsInstance(__lowercase					,      Image.Image      )

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

       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				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
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				int									=self.image_processor_tester.get_expected_values(__lowercase					,      batched=__lowercase      )

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

       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[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
       SCREAMING_SNAKE_CASE__     :				str									=image_processing(__lowercase					,      return_tensors='''pt'''      ).pixel_values

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

       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				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
       SCREAMING_SNAKE_CASE__     :				Dict									=image_processing(__lowercase					,      return_tensors='''pt'''      ).pixel_values

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

       SCREAMING_SNAKE_CASE__     :				int									={'''image_id''': 3_97_69, '''annotations''': target}

       # encode them
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=image_processing(images=__lowercase					,      annotations=__lowercase					,      return_tensors='''pt'''      )

       # verify pixel values
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.Size([1, 3, 8_00, 10_66]      )
       self.assertEqual(encoding['''pixel_values'''].shape					,      __lowercase      )

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

       # verify area
       SCREAMING_SNAKE_CASE__     :				Dict									=torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					,      __lowercase      )      )
       # verify boxes
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.Size([6, 4]      )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					,      __lowercase					,      atol=1e-3      )      )
       # verify image_id
       SCREAMING_SNAKE_CASE__     :				int									=torch.tensor([3_97_69]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					,      __lowercase      )      )
       # verify is_crowd
       SCREAMING_SNAKE_CASE__     :				Any									=torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					,      __lowercase      )      )
       # verify class_labels
       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.tensor([75, 75, 63, 65, 17, 17]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					,      __lowercase      )      )
       # verify orig_size
       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.tensor([4_80, 6_40]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					,      __lowercase      )      )
       # verify size
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.tensor([8_00, 10_66]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					,      __lowercase      )      )




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

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									={'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

       SCREAMING_SNAKE_CASE__     :				Any									=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

       # encode them
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=ConditionalDetrImageProcessor(format='''coco_panoptic'''      )
       SCREAMING_SNAKE_CASE__     :				str									=image_processing(images=__lowercase					,      annotations=__lowercase					,      masks_path=__lowercase					,      return_tensors='''pt'''      )

       # verify pixel values
       SCREAMING_SNAKE_CASE__     :				Dict									=torch.Size([1, 3, 8_00, 10_66]      )
       self.assertEqual(encoding['''pixel_values'''].shape					,      __lowercase      )

       SCREAMING_SNAKE_CASE__     :				List[str]									=torch.tensor([0.2796, 0.3138, 0.3481]      )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]					,      __lowercase					,      atol=1e-4      )      )

       # verify area
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					,      __lowercase      )      )
       # verify boxes
       SCREAMING_SNAKE_CASE__     :				str									=torch.Size([6, 4]      )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					,      __lowercase					,      atol=1e-3      )      )
       # verify image_id
       SCREAMING_SNAKE_CASE__     :				int									=torch.tensor([3_97_69]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					,      __lowercase      )      )
       # verify is_crowd
       SCREAMING_SNAKE_CASE__     :				Any									=torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					,      __lowercase      )      )
       # verify class_labels
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.tensor([17, 17, 63, 75, 75, 93]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					,      __lowercase      )      )
       # verify masks
       SCREAMING_SNAKE_CASE__     :				Tuple									=82_28_73
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()					,      __lowercase      )
       # verify orig_size
       SCREAMING_SNAKE_CASE__     :				List[str]									=torch.tensor([4_80, 6_40]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					,      __lowercase      )      )
       # verify size
       SCREAMING_SNAKE_CASE__     :				str									=torch.tensor([8_00, 10_66]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					,      __lowercase      )      )







'''simple docstring'''
from math import isqrt





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=[True] * max_number
      for i in range(2,	isqrt(max_number - 1							) + 1							):
            if is_prime[i]:
                  for j in range(i**2,	UpperCamelCase__,	UpperCamelCase__							):
                        SCREAMING_SNAKE_CASE__     :				Any									=False

      return [i for i in range(2,	UpperCamelCase__							) if is_prime[i]]





def  _a(  UpperCamelCase__				:						int = 1_0**8							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=calculate_prime_numbers(max_number // 2							)

      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(UpperCamelCase__							) - 1
      while left <= right:
            while prime_numbers[left] * prime_numbers[right] >= max_number:
                  right -= 1
            semiprimes_count += right - left + 1
            left += 1

      return semiprimes_count


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