



'''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 DetaImageProcessor






class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




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




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

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

      return expected_height, expected_width







@require_torch
@require_vision
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				str           =				DetaImageProcessor if is_vision_available() else None




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  str											= DetaImageProcessingTester(self				)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		int:
      return self.image_processor_tester.prepare_image_processor_dict()




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  Union[str, Any]											= 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  ,"""do_rescale"""				)				)
      self.assertTrue(hasattr(__UpperCAmelCase  ,"""do_pad"""				)				)
      self.assertTrue(hasattr(__UpperCAmelCase  ,"""size"""				)				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      lowerCAmelCase__			:  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  ,__UpperCAmelCase				)




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




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

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

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

      lowerCAmelCase__			:  int											= 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 							UpperCAmelCase_     (   self				)   ->		Tuple:
      # Initialize image_processing
      lowerCAmelCase__			:  Optional[int]											= self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      lowerCAmelCase__			:  int											= 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
      lowerCAmelCase__			:  Tuple											= image_processing(image_inputs[0]  ,return_tensors="""pt"""				).pixel_values

      lowerCAmelCase__				,       lowerCAmelCase__			:  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
      lowerCAmelCase__			:  int											= image_processing(__UpperCAmelCase  ,return_tensors="""pt"""				).pixel_values

      lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= 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 							UpperCAmelCase_     (   self				)   ->		str:
      # Initialize image_processing
      lowerCAmelCase__			:  Union[str, Any]											= self.image_processing_class(**self.image_processor_dict				)
      # create random PyTorch tensors
      lowerCAmelCase__			:  Optional[Any]											= 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
      lowerCAmelCase__			:  Dict											= image_processing(image_inputs[0]  ,return_tensors="""pt"""				).pixel_values

      lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, 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
      lowerCAmelCase__			:  List[Any]											= image_processing(__UpperCAmelCase  ,return_tensors="""pt"""				).pixel_values

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

      lowerCAmelCase__			:  Any											= {"""image_id""": 3_9769, """annotations""": target}

      # encode them
      lowerCAmelCase__			:  Union[str, Any]											= DetaImageProcessor()
      lowerCAmelCase__			:  str											= image_processing(images=__UpperCAmelCase  ,annotations=__UpperCAmelCase  ,return_tensors="""pt"""				)

      # verify pixel values
      lowerCAmelCase__			:  Union[str, Any]											= torch.Size([1, 3, 800, 1066]				)
      self.assertEqual(encoding["""pixel_values"""].shape  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  List[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]  ,__UpperCAmelCase  ,atol=1E-4				)				)

      # verify area
      lowerCAmelCase__			:  Dict											= 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"""]  ,__UpperCAmelCase				)				)
      # verify boxes
      lowerCAmelCase__			:  Tuple											= torch.Size([6, 4]				)
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= 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]  ,__UpperCAmelCase  ,atol=1E-3				)				)
      # verify image_id
      lowerCAmelCase__			:  int											= torch.tensor([3_9769]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,__UpperCAmelCase				)				)
      # verify is_crowd
      lowerCAmelCase__			:  Tuple											= torch.tensor([0, 0, 0, 0, 0, 0]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,__UpperCAmelCase				)				)
      # verify class_labels
      lowerCAmelCase__			:  Union[str, Any]											= torch.tensor([75, 75, 63, 65, 17, 17]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,__UpperCAmelCase				)				)
      # verify orig_size
      lowerCAmelCase__			:  List[str]											= torch.tensor([480, 640]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,__UpperCAmelCase				)				)
      # verify size
      lowerCAmelCase__			:  List[Any]											= torch.tensor([800, 1066]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,__UpperCAmelCase				)				)






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

      lowerCAmelCase__			:  str											= {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

      lowerCAmelCase__			:  Dict											= pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

      # encode them
      lowerCAmelCase__			:  Dict											= DetaImageProcessor(format="""coco_panoptic"""				)
      lowerCAmelCase__			:  Optional[Any]											= image_processing(images=__UpperCAmelCase  ,annotations=__UpperCAmelCase  ,masks_path=__UpperCAmelCase  ,return_tensors="""pt"""				)

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

      lowerCAmelCase__			:  int											= 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]  ,__UpperCAmelCase  ,atol=1E-4				)				)

      # verify area
      lowerCAmelCase__			:  Optional[int]											= 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"""]  ,__UpperCAmelCase				)				)
      # verify boxes
      lowerCAmelCase__			:  Optional[int]											= torch.Size([6, 4]				)
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,__UpperCAmelCase				)
      lowerCAmelCase__			:  Optional[int]											= 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]  ,__UpperCAmelCase  ,atol=1E-3				)				)
      # verify image_id
      lowerCAmelCase__			:  str											= torch.tensor([3_9769]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,__UpperCAmelCase				)				)
      # verify is_crowd
      lowerCAmelCase__			:  Union[str, Any]											= torch.tensor([0, 0, 0, 0, 0, 0]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,__UpperCAmelCase				)				)
      # verify class_labels
      lowerCAmelCase__			:  Tuple											= torch.tensor([17, 17, 63, 75, 75, 93]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,__UpperCAmelCase				)				)
      # verify masks
      lowerCAmelCase__			:  List[str]											= 82_2873
      self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()  ,__UpperCAmelCase				)
      # verify orig_size
      lowerCAmelCase__			:  Optional[int]											= torch.tensor([480, 640]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,__UpperCAmelCase				)				)
      # verify size
      lowerCAmelCase__			:  Any											= torch.tensor([800, 1066]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,__UpperCAmelCase				)				)







'''simple docstring'''





def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase = 1000      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= -1
    lowerCAmelCase__			:  Optional[Any]											= 0
    for a in range(1							,   n // 3      ):
        # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
        lowerCAmelCase__			:  Optional[Any]											= (n * n - 2 * a * n) // (2 * n - 2 * a)
        lowerCAmelCase__			:  Tuple											= n - a - b
        if c * c == (a * a + b * b):
            lowerCAmelCase__			:  int											= a * b * c
            if candidate >= product:
                lowerCAmelCase__			:  Any											= candidate
    return product


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


