


'''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 	a       (       unittest.TestCase    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__=7 ,			__magic_name__=3 ,			__magic_name__=30 ,			__magic_name__=4_00 ,			__magic_name__=True ,			__magic_name__=None ,			__magic_name__=True ,			__magic_name__=1 / 2_55 ,			__magic_name__=True ,			__magic_name__=[0.5, 0.5, 0.5] ,			__magic_name__=[0.5, 0.5, 0.5] ,			__magic_name__=True ,			)   ->							Any:
      _a      =							size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
      _a      =							parent
      _a      =							batch_size
      _a      =							num_channels
      _a      =							min_resolution
      _a      =							max_resolution
      _a      =							do_resize
      _a      =							size
      _a      =							do_rescale
      _a      =							rescale_factor
      _a      =							do_normalize
      _a      =							image_mean
      _a      =							image_std
      _a      =							do_pad
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      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 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=False  )   ->							List[str]:


      if not batched:
           _a      =							image_inputs[0]
           if isinstance(_A ,			Image.Image  ):
                _a      =							image.size
           else:
                _a      =							image.shape[1], image.shape[2]
           if w < h:
                _a      =							int(self.size['shortest_edge'] * h / w  )
                _a      =							self.size['shortest_edge']
           elif w > h:
                _a      =							self.size['shortest_edge']
                _a      =							int(self.size['shortest_edge'] * w / h  )
           else:
                _a      =							self.size['shortest_edge']
                _a      =							self.size['shortest_edge']

      else:
           _a      =							[]
           for image in image_inputs:
                _a      =							self.get_expected_values([image]  )
                expected_values.append((expected_height, expected_width)  )
           _a      =							max(_A ,			key=lambda __magic_name__  : item[0]  )[0]
           _a      =							max(_A ,			key=lambda __magic_name__  : item[1]  )[1]

      return expected_height, expected_width





@require_torch
@require_vision
class 	a       (       snake_case_					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		DetrImageProcessor if is_vision_available() else None
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							DetrImageProcessingTester(self  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return self.image_processor_tester.prepare_image_processor_dict()
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							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 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							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 ,			_A  )

      _a      =							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 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      pass
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _a      =							prepare_image_inputs(self.image_processor_tester ,			equal_resolution=_A  )
      for image in image_inputs:
           self.assertIsInstance(_A ,			Image.Image  )

      # Test not batched input
      _a      =							image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values

      _a      =							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
      _a      =							self.image_processor_tester.get_expected_values(_A ,			batched=_A  )

      _a      =							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 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _a      =							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
      _a      =							image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values

      _a      =							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
      _a      =							image_processing(_A ,			return_tensors='pt'  ).pixel_values

      _a      =							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 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _a      =							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
      _a      =							image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values

      _a      =							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
      _a      =							image_processing(_A ,			return_tensors='pt'  ).pixel_values

      _a      =							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 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' ,			'r'  ) as f:
           _a      =							json.loads(f.read()  )

      _a      =							{'image_id': 3_97_69, 'annotations': target}

      # encode them
      _a      =							DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'  )
      _a      =							image_processing(images=_A ,			annotations=_A ,			return_tensors='pt'  )

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

      _a      =							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] ,			_A ,			atol=1e-4  )  )

      # verify area
      _a      =							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'] ,			_A  )  )
      # verify boxes
      _a      =							torch.Size([6, 4]  )
      self.assertEqual(encoding['labels'][0]['boxes'].shape ,			_A  )
      _a      =							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] ,			_A ,			atol=1e-3  )  )
      # verify image_id
      _a      =							torch.tensor([3_97_69]  )
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,			_A  )  )
      # verify is_crowd
      _a      =							torch.tensor([0, 0, 0, 0, 0, 0]  )
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,			_A  )  )
      # verify class_labels
      _a      =							torch.tensor([75, 75, 63, 65, 17, 17]  )
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,			_A  )  )
      # verify orig_size
      _a      =							torch.tensor([4_80, 6_40]  )
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,			_A  )  )
      # verify size
      _a      =							torch.tensor([8_00, 10_66]  )
      self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,			_A  )  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' ,			'r'  ) as f:
           _a      =							json.loads(f.read()  )

      _a      =							{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

      _a      =							pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'  )

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

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

      _a      =							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] ,			_A ,			atol=1e-4  )  )

      # verify area
      _a      =							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'] ,			_A  )  )
      # verify boxes
      _a      =							torch.Size([6, 4]  )
      self.assertEqual(encoding['labels'][0]['boxes'].shape ,			_A  )
      _a      =							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] ,			_A ,			atol=1e-3  )  )
      # verify image_id
      _a      =							torch.tensor([3_97_69]  )
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,			_A  )  )
      # verify is_crowd
      _a      =							torch.tensor([0, 0, 0, 0, 0, 0]  )
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,			_A  )  )
      # verify class_labels
      _a      =							torch.tensor([17, 17, 63, 75, 75, 93]  )
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,			_A  )  )
      # verify masks
      _a      =							82_28_73
      self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,			_A  )
      # verify orig_size
      _a      =							torch.tensor([4_80, 6_40]  )
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,			_A  )  )
      # verify size
      _a      =							torch.tensor([8_00, 10_66]  )
      self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,			_A  )  )





'''simple docstring'''






from timeit import timeit






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     if number < 0:
          raise ValueError('the value of input must not be negative'						)
     _a      =							0
     while number:
          number &= number - 1
          result += 1
     return result






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     if number < 0:
          raise ValueError('the value of input must not be negative'						)
     _a      =							0
     while number:
          if number % 2 == 1:
               result += 1
          number >>= 1
     return result






def 		_A			()				->					None:




     '''simple docstring'''



     def do_benchmark(lowerCAmelCase__					:int						) -> None:
          _a      =							'import __main__ as z'
          print(f'Benchmark when {number = }:'						)
          print(f'{get_set_bits_count_using_modulo_operator(lowerCAmelCase__						) = }'						)
          _a      =							timeit('z.get_set_bits_count_using_modulo_operator(25)'			,  setup=lowerCAmelCase__						)
          print(f'timeit() runs in {timing} seconds'						)
          print(f'{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase__						) = }'						)
          _a      =							timeit(
              'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'			,  setup=lowerCAmelCase__			,  )
          print(f'timeit() runs in {timing} seconds'						)

     for number in (25, 37, 58, 0):
          do_benchmark(lowerCAmelCase__						)
          print()


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()

