

'''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					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       List[Any]=7					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=3					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=30					,							__SCREAMING_SNAKE_CASE					:       int=400					,							__SCREAMING_SNAKE_CASE					:       int=True					,							__SCREAMING_SNAKE_CASE					:       str=None					,							__SCREAMING_SNAKE_CASE					:       int=True					,							__SCREAMING_SNAKE_CASE					:       Dict=[0.5, 0.5, 0.5]					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=[0.5, 0.5, 0.5]					,							__SCREAMING_SNAKE_CASE					:       Tuple=True					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=1 / 255					,							__SCREAMING_SNAKE_CASE					:       Dict=True					,							)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
      __SCREAMING_SNAKE_CASE     =      parent
      __SCREAMING_SNAKE_CASE     =      batch_size
      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      min_resolution
      __SCREAMING_SNAKE_CASE     =      max_resolution
      __SCREAMING_SNAKE_CASE     =      do_resize
      __SCREAMING_SNAKE_CASE     =      size
      __SCREAMING_SNAKE_CASE     =      do_normalize
      __SCREAMING_SNAKE_CASE     =      image_mean
      __SCREAMING_SNAKE_CASE     =      image_std
      __SCREAMING_SNAKE_CASE     =      do_rescale
      __SCREAMING_SNAKE_CASE     =      rescale_factor
      __SCREAMING_SNAKE_CASE     =      do_pad
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[str]:


      """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       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       Any=False			)  -> int:


      """simple docstring"""




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

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

      return expected_height, expected_width







@require_torch
@require_vision
class 						lowerCAmelCase__   (      a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      DetaImageProcessor if is_vision_available() else None
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      DetaImageProcessingTester(self			)
 @property
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Tuple:


      """simple docstring"""




      return self.image_processor_tester.prepare_image_processor_dict()
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							"""image_mean"""			)			)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							"""image_std"""			)			)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							"""do_normalize"""			)			)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							"""do_resize"""			)			)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							"""do_rescale"""			)			)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							"""do_pad"""			)			)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							"""size"""			)			)
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      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					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Dict:


      """simple docstring"""




      pass
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Optional[int]:


      """simple docstring"""




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

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

      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE			)

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

      # Test batched
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE					,							batched=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      image_processing(__SCREAMING_SNAKE_CASE					,							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					:       List[str]			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE     =      prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__SCREAMING_SNAKE_CASE					,							numpify=__SCREAMING_SNAKE_CASE			)
      for image in image_inputs:
           self.assertIsInstance(__SCREAMING_SNAKE_CASE					,							np.ndarray			)

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

      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE			)

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

      # Test batched
      __SCREAMING_SNAKE_CASE     =      image_processing(__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			).pixel_values

      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE					,							batched=__SCREAMING_SNAKE_CASE			)

      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			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE     =      prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__SCREAMING_SNAKE_CASE					,							torchify=__SCREAMING_SNAKE_CASE			)
      for image in image_inputs:
           self.assertIsInstance(__SCREAMING_SNAKE_CASE					,							torch.Tensor			)

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

      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE			)

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

      # Test batched
      __SCREAMING_SNAKE_CASE     =      image_processing(__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			).pixel_values

      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE					,							batched=__SCREAMING_SNAKE_CASE			)

      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]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      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     =      json.loads(f.read()			)

      __SCREAMING_SNAKE_CASE     =      {"""image_id""": 39_769, """annotations""": target}

      # encode them
      __SCREAMING_SNAKE_CASE     =      DetaImageProcessor()
      __SCREAMING_SNAKE_CASE     =      image_processing(images=__SCREAMING_SNAKE_CASE					,							annotations=__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			)

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

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

      # verify area
      __SCREAMING_SNAKE_CASE     =      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]					,							__SCREAMING_SNAKE_CASE			)			)
      # verify boxes
      __SCREAMING_SNAKE_CASE     =      torch.Size([6, 4]			)
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,							__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)
      # verify image_id
      __SCREAMING_SNAKE_CASE     =      torch.tensor([39_769]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,							__SCREAMING_SNAKE_CASE			)			)
      # verify is_crowd
      __SCREAMING_SNAKE_CASE     =      torch.tensor([0, 0, 0, 0, 0, 0]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,							__SCREAMING_SNAKE_CASE			)			)
      # verify class_labels
      __SCREAMING_SNAKE_CASE     =      torch.tensor([75, 75, 63, 65, 17, 17]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,							__SCREAMING_SNAKE_CASE			)			)
      # verify orig_size
      __SCREAMING_SNAKE_CASE     =      torch.tensor([480, 640]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,							__SCREAMING_SNAKE_CASE			)			)
      # verify size
      __SCREAMING_SNAKE_CASE     =      torch.tensor([800, 1_066]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,							__SCREAMING_SNAKE_CASE			)			)


 @slow
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      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     =      json.loads(f.read()			)

      __SCREAMING_SNAKE_CASE     =      {"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

      __SCREAMING_SNAKE_CASE     =      pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""			)

      # encode them
      __SCREAMING_SNAKE_CASE     =      DetaImageProcessor(format="""coco_panoptic"""			)
      __SCREAMING_SNAKE_CASE     =      image_processing(images=__SCREAMING_SNAKE_CASE					,							annotations=__SCREAMING_SNAKE_CASE					,							masks_path=__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			)

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

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

      # verify area
      __SCREAMING_SNAKE_CASE     =      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]					,							__SCREAMING_SNAKE_CASE			)			)
      # verify boxes
      __SCREAMING_SNAKE_CASE     =      torch.Size([6, 4]			)
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,							__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)
      # verify image_id
      __SCREAMING_SNAKE_CASE     =      torch.tensor([39_769]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,							__SCREAMING_SNAKE_CASE			)			)
      # verify is_crowd
      __SCREAMING_SNAKE_CASE     =      torch.tensor([0, 0, 0, 0, 0, 0]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,							__SCREAMING_SNAKE_CASE			)			)
      # verify class_labels
      __SCREAMING_SNAKE_CASE     =      torch.tensor([17, 17, 63, 75, 75, 93]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,							__SCREAMING_SNAKE_CASE			)			)
      # verify masks
      __SCREAMING_SNAKE_CASE     =      822_873
      self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()					,							__SCREAMING_SNAKE_CASE			)
      # verify orig_size
      __SCREAMING_SNAKE_CASE     =      torch.tensor([480, 640]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,							__SCREAMING_SNAKE_CASE			)			)
      # verify size
      __SCREAMING_SNAKE_CASE     =      torch.tensor([800, 1_066]			)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,							__SCREAMING_SNAKE_CASE			)			)









'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      n
      __SCREAMING_SNAKE_CASE     =      [None] * self.n
      __SCREAMING_SNAKE_CASE     =      0  # index of the first element
      __SCREAMING_SNAKE_CASE     =      0
      __SCREAMING_SNAKE_CASE     =      0
 def __len__(		self					:       Tuple			)  -> int:


      """simple docstring"""




      return self.size
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> bool:


      """simple docstring"""




      return self.size == 0
 def       UpperCAmelCase__		(		self					:       Any			)  -> int:


      """simple docstring"""




      return False if self.is_empty() else self.array[self.front]
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> Dict:


      """simple docstring"""




      if self.size >= self.n:
           raise Exception("""QUEUE IS FULL"""			)

      __SCREAMING_SNAKE_CASE     =      data
      __SCREAMING_SNAKE_CASE     =      (self.rear + 1) % self.n
      self.size += 1
      return self


 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[Any]:


      """simple docstring"""




      if self.size == 0:
           raise Exception("""UNDERFLOW"""			)

      __SCREAMING_SNAKE_CASE     =      self.array[self.front]
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      (self.front + 1) % self.n
      self.size -= 1
      return temp






