




'''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   __UpperCamelCase       ( unittest.TestCase				):






       def __init__(							self,		lowerCAmelCase,		lowerCAmelCase=7,		lowerCAmelCase=3,		lowerCAmelCase=30,		lowerCAmelCase=400,		lowerCAmelCase=True,		lowerCAmelCase=None,		lowerCAmelCase=True,		lowerCAmelCase=[0.5, 0.5, 0.5],		lowerCAmelCase=[0.5, 0.5, 0.5],		lowerCAmelCase=True,		lowerCAmelCase=1 / 255,		lowerCAmelCase=True,		):



              """simple docstring"""







              lowerCamelCase_							=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
              lowerCamelCase_							=parent
              lowerCamelCase_							=batch_size
              lowerCamelCase_							=num_channels
              lowerCamelCase_							=min_resolution
              lowerCamelCase_							=max_resolution
              lowerCamelCase_							=do_resize
              lowerCamelCase_							=size
              lowerCamelCase_							=do_normalize
              lowerCamelCase_							=image_mean
              lowerCamelCase_							=image_std
              lowerCamelCase_							=do_rescale
              lowerCamelCase_							=rescale_factor
              lowerCamelCase_							=do_pad






       def      lowercase__     (							self  ):



              """simple docstring"""







              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      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase=False  ):



              """simple docstring"""







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

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

              return expected_height, expected_width





@require_torch
@require_vision
class   __UpperCamelCase       ( lowerCamelCase__			,					unittest.TestCase				):
       lowercase	:    List[str]      						=DetaImageProcessor if is_vision_available() else None






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=DetaImageProcessingTester(self  )






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return self.image_processor_tester.prepare_image_processor_dict()






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=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_resize'''  )  )
              self.assertTrue(hasattr(lowerCAmelCase,		'''do_rescale'''  )  )
              self.assertTrue(hasattr(lowerCAmelCase,		'''do_pad'''  )  )
              self.assertTrue(hasattr(lowerCAmelCase,		'''size'''  )  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=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  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              pass






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.image_processing_class(**self.image_processor_dict  )
              # create random PIL images
              lowerCamelCase_							=prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCAmelCase  )
              for image in image_inputs:
                     self.assertIsInstance(lowerCAmelCase,		Image.Image  )

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

              lowerCamelCase_,      lowerCamelCase_							=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
              lowerCamelCase_,      lowerCamelCase_							=self.image_processor_tester.get_expected_values(lowerCAmelCase,		batched=lowerCAmelCase  )

              lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.image_processing_class(**self.image_processor_dict  )
              # create random numpy tensors
              lowerCamelCase_							=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
              lowerCamelCase_							=image_processing(image_inputs[0],		return_tensors='''pt'''  ).pixel_values

              lowerCamelCase_,      lowerCamelCase_							=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
              lowerCamelCase_							=image_processing(lowerCAmelCase,		return_tensors='''pt'''  ).pixel_values

              lowerCamelCase_,      lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.image_processing_class(**self.image_processor_dict  )
              # create random PyTorch tensors
              lowerCamelCase_							=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
              lowerCamelCase_							=image_processing(image_inputs[0],		return_tensors='''pt'''  ).pixel_values

              lowerCamelCase_,      lowerCamelCase_							=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
              lowerCamelCase_							=image_processing(lowerCAmelCase,		return_tensors='''pt'''  ).pixel_values

              lowerCamelCase_,      lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
              with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''',		'''r'''  ) as f:
                     lowerCamelCase_							=json.loads(f.read()  )

              lowerCamelCase_							={'''image_id''': 39_769, '''annotations''': target}

              # encode them
              lowerCamelCase_							=DetaImageProcessor()
              lowerCamelCase_							=image_processing(images=lowerCAmelCase,		annotations=lowerCAmelCase,		return_tensors='''pt'''  )

              # verify pixel values
              lowerCamelCase_							=torch.Size([1, 3, 800, 1_066]  )
              self.assertEqual(encoding['''pixel_values'''].shape,		lowerCAmelCase  )

              lowerCamelCase_							=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
              lowerCamelCase_							=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
              lowerCamelCase_							=torch.Size([6, 4]  )
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,		lowerCAmelCase  )
              lowerCamelCase_							=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
              lowerCamelCase_							=torch.tensor([39_769]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],		lowerCAmelCase  )  )
              # verify is_crowd
              lowerCamelCase_							=torch.tensor([0, 0, 0, 0, 0, 0]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],		lowerCAmelCase  )  )
              # verify class_labels
              lowerCamelCase_							=torch.tensor([75, 75, 63, 65, 17, 17]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],		lowerCAmelCase  )  )
              # verify orig_size
              lowerCamelCase_							=torch.tensor([480, 640]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],		lowerCAmelCase  )  )
              # verify size
              lowerCamelCase_							=torch.tensor([800, 1_066]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],		lowerCAmelCase  )  )






       @slow
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
              with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''',		'''r'''  ) as f:
                     lowerCamelCase_							=json.loads(f.read()  )

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

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

              # encode them
              lowerCamelCase_							=DetaImageProcessor(format='''coco_panoptic'''  )
              lowerCamelCase_							=image_processing(images=lowerCAmelCase,		annotations=lowerCAmelCase,		masks_path=lowerCAmelCase,		return_tensors='''pt'''  )

              # verify pixel values
              lowerCamelCase_							=torch.Size([1, 3, 800, 1_066]  )
              self.assertEqual(encoding['''pixel_values'''].shape,		lowerCAmelCase  )

              lowerCamelCase_							=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
              lowerCamelCase_							=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
              lowerCamelCase_							=torch.Size([6, 4]  )
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,		lowerCAmelCase  )
              lowerCamelCase_							=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
              lowerCamelCase_							=torch.tensor([39_769]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],		lowerCAmelCase  )  )
              # verify is_crowd
              lowerCamelCase_							=torch.tensor([0, 0, 0, 0, 0, 0]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],		lowerCAmelCase  )  )
              # verify class_labels
              lowerCamelCase_							=torch.tensor([17, 17, 63, 75, 75, 93]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],		lowerCAmelCase  )  )
              # verify masks
              lowerCamelCase_							=822_873
              self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item(),		lowerCAmelCase  )
              # verify orig_size
              lowerCamelCase_							=torch.tensor([480, 640]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],		lowerCAmelCase  )  )
              # verify size
              lowerCamelCase_							=torch.tensor([800, 1_066]  )
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],		lowerCAmelCase  )  )





'''simple docstring'''





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_:     Optional[int]  =Lock()





def 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Dict					,    snake_case_       : Optional[int]					,    snake_case_       : Tuple					,    snake_case_       : Any					,    snake_case_       : Optional[Any]					,    snake_case_       : Union[str, Any]			)    ->      Optional[Any]:


     '''simple docstring'''




     global process_lock

     # we perform n swaps since after n swaps we know we are sorted
     # we *could* stop early if we are sorted already, but it takes as long to
     # find out we are sorted as it does to sort the list with this algorithm
     for i in range(0					,    10			):
          if (i + position) % 2 == 0 and r_send is not None:
               # send your value to your right neighbor
               process_lock.acquire()
               r_send[1].send(snake_case_			)
               process_lock.release()

               # receive your right neighbor's value
               process_lock.acquire()
               UpperCAmelCase_           =   rr_cv[0].recv()
               process_lock.release()

               # take the lower value since you are on the left
               UpperCAmelCase_           =   min(snake_case_					,    snake_case_			)
          elif (i + position) % 2 != 0 and l_send is not None:
               # send your value to your left neighbor
               process_lock.acquire()
               l_send[1].send(snake_case_			)
               process_lock.release()

               # receive your left neighbor's value
               process_lock.acquire()
               UpperCAmelCase_           =   lr_cv[0].recv()
               process_lock.release()

               # take the higher value since you are on the right
               UpperCAmelCase_           =   max(snake_case_					,    snake_case_			)
    # after all swaps are performed, send the values back to main
     result_pipe[1].send(snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   []
     UpperCAmelCase_           =   []
     # initialize the list of pipes where the values will be retrieved
     for _ in arr:
          result_pipe.append(Pipe()			)
     # creates the processes
     # the first and last process only have one neighbor so they are made outside
     # of the loop
     UpperCAmelCase_           =   Pipe()
     UpperCAmelCase_           =   Pipe()
     process_array_.append(
         Process(
             target=snake_case_					,    args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])					,    )			)
     UpperCAmelCase_           =   temp_rs
     UpperCAmelCase_           =   temp_rr

     for i in range(1					,    len(snake_case_			) - 1			):
          UpperCAmelCase_           =   Pipe()
          UpperCAmelCase_           =   Pipe()
          process_array_.append(
              Process(
                  target=snake_case_					,    args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])					,    )			)
          UpperCAmelCase_           =   temp_rs
          UpperCAmelCase_           =   temp_rr

     process_array_.append(
         Process(
             target=snake_case_					,    args=(
                 len(snake_case_			) - 1,
                 arr[len(snake_case_			) - 1],
                 temp_ls,
                 None,
                 temp_lr,
                 None,
                 result_pipe[len(snake_case_			) - 1],
             )					,    )			)

     # start the processes
     for p in process_array_:
          p.start()

     # wait for the processes to end and write their values to the list
     for p in range(0					,    len(snake_case_			)			):
          UpperCAmelCase_           =   result_pipe[p][0].recv()
          process_array_[p].join()
     return arr





def 	lowerCAmelCase_				(					)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   list(range(10					,    0					,    -1			)			)
     print("Initial List"			)
     print(*snake_case_			)
     UpperCAmelCase_           =   odd_even_transposition(snake_case_			)
     print("Sorted List\n"			)
     print(*snake_case_			)


if __name__ == "__main__":
    main()




