
"""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   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





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



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

          else:
               UpperCAmelCase__							: Any 							=						[]
               for image in image_inputs:
                    UpperCAmelCase__							: Dict 							=						self.get_expected_values([image]  )
                    expected_values.append((expected_height, expected_width)  )
               UpperCAmelCase__							: Dict 							=						max(__UpperCamelCase ,			key=lambda __UpperCamelCase  : item[0]  )[0]
               UpperCAmelCase__							: Tuple 							=						max(__UpperCamelCase ,			key=lambda __UpperCamelCase  : item[1]  )[1]

          return expected_height, expected_width







@require_torch
@require_vision
class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							ConditionalDetrImageProcessor if is_vision_available() else None



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: str 							=						ConditionalDetrImageProcessingTester(self  )



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



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: int 							=						self.image_processing_class(**self.image_processor_dict  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"image_mean"  )  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"image_std"  )  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"do_normalize"  )  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"do_resize"  )  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"size"  )  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Optional[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 ,			__UpperCamelCase  )

          UpperCAmelCase__							: Tuple 							=						self.image_processing_class.from_dict(
              self.image_processor_dict ,			size=42 ,			max_size=84 ,			pad_and_return_pixel_mask=__UpperCamelCase  )
          self.assertEqual(image_processor.size ,			{"shortest_edge": 42, "longest_edge": 84}  )
          self.assertEqual(image_processor.do_pad ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					str:
          pass



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          # Initialize image_processing
          UpperCAmelCase__							: int 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random PIL images
          UpperCAmelCase__							: Dict 							=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__UpperCamelCase  )
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase ,			Image.Image  )

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

          UpperCAmelCase__							: Any 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase  )

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

          # Test batched
          UpperCAmelCase__							: List[str] 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase ,			batched=__UpperCamelCase  )

          UpperCAmelCase__							: Optional[Any] 							=						image_processing(__UpperCamelCase ,			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  )->					Union[str, Any]:
          # Initialize image_processing
          UpperCAmelCase__							: Tuple 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random numpy tensors
          UpperCAmelCase__							: List[str] 							=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__UpperCamelCase ,			numpify=__UpperCamelCase  )
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase ,			np.ndarray  )

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

          UpperCAmelCase__							: List[str] 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase  )

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

          # Test batched
          UpperCAmelCase__							: Optional[int] 							=						image_processing(__UpperCamelCase ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							: Tuple 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase ,			batched=__UpperCamelCase  )

          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]:
          # Initialize image_processing
          UpperCAmelCase__							: int 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random PyTorch tensors
          UpperCAmelCase__							: List[str] 							=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__UpperCamelCase ,			torchify=__UpperCamelCase  )
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase ,			torch.Tensor  )

          # Test not batched input
          UpperCAmelCase__							: Dict 							=						image_processing(image_inputs[0] ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							: int 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase  )

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

          # Test batched
          UpperCAmelCase__							: Union[str, Any] 							=						image_processing(__UpperCamelCase ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							: int 							=						self.image_processor_tester.get_expected_values(__UpperCamelCase ,			batched=__UpperCamelCase  )

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

          UpperCAmelCase__							: Union[str, Any] 							=						{"image_id": 3_97_69, "annotations": target}

          # encode them
          UpperCAmelCase__							: Any 							=						ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"  )
          UpperCAmelCase__							: List[Any] 							=						image_processing(images=__UpperCamelCase ,			annotations=__UpperCamelCase ,			return_tensors="pt"  )

          # verify pixel values
          UpperCAmelCase__							: List[str] 							=						torch.Size([1, 3, 8_00, 10_66]  )
          self.assertEqual(encoding["pixel_values"].shape ,			__UpperCamelCase  )

          UpperCAmelCase__							: Tuple 							=						torch.tensor([0.2796, 0.3138, 0.3481]  )
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,			__UpperCamelCase ,			atol=1E-4  )  )

          # verify area
          UpperCAmelCase__							: Optional[Any] 							=						torch.tensor([5887.9600, 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"] ,			__UpperCamelCase  )  )
          # verify boxes
          UpperCAmelCase__							: List[Any] 							=						torch.Size([6, 4]  )
          self.assertEqual(encoding["labels"][0]["boxes"].shape ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,			__UpperCamelCase ,			atol=1E-3  )  )
          # verify image_id
          UpperCAmelCase__							: Union[str, Any] 							=						torch.tensor([3_97_69]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,			__UpperCamelCase  )  )
          # verify is_crowd
          UpperCAmelCase__							: Optional[int] 							=						torch.tensor([0, 0, 0, 0, 0, 0]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,			__UpperCamelCase  )  )
          # verify class_labels
          UpperCAmelCase__							: List[Any] 							=						torch.tensor([75, 75, 63, 65, 17, 17]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,			__UpperCamelCase  )  )
          # verify orig_size
          UpperCAmelCase__							: Any 							=						torch.tensor([4_80, 6_40]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,			__UpperCamelCase  )  )
          # verify size
          UpperCAmelCase__							: Tuple 							=						torch.tensor([8_00, 10_66]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,			__UpperCamelCase  )  )



     @slow
     def  lowerCAmelCase__       (      self  )->					Tuple:
          # prepare image, target and masks_path
          UpperCAmelCase__							: 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:
               UpperCAmelCase__							: Dict 							=						json.loads(f.read()  )

          UpperCAmelCase__							: Tuple 							=						{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

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

          # encode them
          UpperCAmelCase__							: str 							=						ConditionalDetrImageProcessor(format="coco_panoptic"  )
          UpperCAmelCase__							: List[Any] 							=						image_processing(images=__UpperCamelCase ,			annotations=__UpperCamelCase ,			masks_path=__UpperCamelCase ,			return_tensors="pt"  )

          # verify pixel values
          UpperCAmelCase__							: Optional[int] 							=						torch.Size([1, 3, 8_00, 10_66]  )
          self.assertEqual(encoding["pixel_values"].shape ,			__UpperCamelCase  )

          UpperCAmelCase__							: Any 							=						torch.tensor([0.2796, 0.3138, 0.3481]  )
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,			__UpperCamelCase ,			atol=1E-4  )  )

          # verify area
          UpperCAmelCase__							: str 							=						torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5879.6562, 7634.1147]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,			__UpperCamelCase  )  )
          # verify boxes
          UpperCAmelCase__							: List[Any] 							=						torch.Size([6, 4]  )
          self.assertEqual(encoding["labels"][0]["boxes"].shape ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,			__UpperCamelCase ,			atol=1E-3  )  )
          # verify image_id
          UpperCAmelCase__							: str 							=						torch.tensor([3_97_69]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,			__UpperCamelCase  )  )
          # verify is_crowd
          UpperCAmelCase__							: Tuple 							=						torch.tensor([0, 0, 0, 0, 0, 0]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,			__UpperCamelCase  )  )
          # verify class_labels
          UpperCAmelCase__							: List[Any] 							=						torch.tensor([17, 17, 63, 75, 75, 93]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,			__UpperCamelCase  )  )
          # verify masks
          UpperCAmelCase__							: Optional[int] 							=						82_28_73
          self.assertEqual(encoding["labels"][0]["masks"].sum().item() ,			__UpperCamelCase  )
          # verify orig_size
          UpperCAmelCase__							: List[str] 							=						torch.tensor([4_80, 6_40]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,			__UpperCamelCase  )  )
          # verify size
          UpperCAmelCase__							: str 							=						torch.tensor([8_00, 10_66]  )
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,			__UpperCamelCase  )  )


"""simple docstring"""




from timeit import timeit



def 							a__ (				lowerCAmelCase	:       int		):






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



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if number < 0:
          raise ValueError("the value of input must not be negative"		)
     UpperCAmelCase__							: Union[str, Any] 							=						0
     while number:
          if number % 2 == 1:
               result += 1
          number >>= 1
     return result



def 							a__ (				):






     '''simple docstring'''

     def do_benchmark(lowerCAmelCase	:       int		) -> None:
          UpperCAmelCase__							: Dict 							=						"import __main__ as z"
          print(F"Benchmark when {number = }:"		)
          print(F"{get_set_bits_count_using_modulo_operator(lowerCAmelCase		) = }"		)
          UpperCAmelCase__							: Tuple 							=						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		) = }"		)
          UpperCAmelCase__							: Any 							=						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()



