

"""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 DeformableDetrImageProcessor







class   __lowercase      (     unittest.TestCase       ):

  def __init__(	self		:      List[Any]       ,A		:      Optional[Any]       ,A		:      List[Any]=7       ,A		:      List[Any]=3       ,A		:      Optional[Any]=30       ,A		:      List[str]=400       ,A		:      Any=True       ,A		:      str=None       ,A		:      str=True       ,A		:      int=[0.5, 0.5, 0.5]       ,A		:      List[Any]=[0.5, 0.5, 0.5]       ,A		:      Any=True       ,A		:      Dict=1 / 255       ,A		:      str=True       ,):






   '''simple docstring'''
   # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
   UpperCAmelCase__       :	List[Any]        =       size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
   UpperCAmelCase__       :	Dict        =       parent
   UpperCAmelCase__       :	Tuple        =       batch_size
   UpperCAmelCase__       :	Optional[int]        =       num_channels
   UpperCAmelCase__       :	Optional[int]        =       min_resolution
   UpperCAmelCase__       :	Union[str, Any]        =       max_resolution
   UpperCAmelCase__       :	Optional[Any]        =       do_resize
   UpperCAmelCase__       :	str        =       size
   UpperCAmelCase__       :	Dict        =       do_normalize
   UpperCAmelCase__       :	int        =       image_mean
   UpperCAmelCase__       :	Dict        =       image_std
   UpperCAmelCase__       :	str        =       do_rescale
   UpperCAmelCase__       :	List[Any]        =       rescale_factor
   UpperCAmelCase__       :	Union[str, Any]        =       do_pad

  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''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		:      str       ,A		:      int       ,A		:      Optional[int]=False     ):






   '''simple docstring'''
   if not batched:
    UpperCAmelCase__       :	List[Any]        =       image_inputs[0]
    if isinstance(A       ,Image.Image     ):
     UpperCAmelCase__ ,   UpperCAmelCase__       :	Union[str, Any]        =       image.size
    else:
     UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[int]        =       image.shape[1], image.shape[2]
    if w < h:
     UpperCAmelCase__       :	Tuple        =       int(self.size["""shortest_edge"""] * h / w     )
     UpperCAmelCase__       :	Any        =       self.size["""shortest_edge"""]
    elif w > h:
     UpperCAmelCase__       :	Tuple        =       self.size["""shortest_edge"""]
     UpperCAmelCase__       :	Optional[Any]        =       int(self.size["""shortest_edge"""] * w / h     )
    else:
     UpperCAmelCase__       :	Optional[int]        =       self.size["""shortest_edge"""]
     UpperCAmelCase__       :	List[str]        =       self.size["""shortest_edge"""]

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

   return expected_height, expected_width







@require_torch
@require_vision
class   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  snake_case_												=							DeformableDetrImageProcessor if is_vision_available() else None

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       DeformableDetrImageProcessingTester(self     )

  @property
  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   return self.image_processor_tester.prepare_image_processor_dict()

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       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_resize"""     )     )
   self.assertTrue(hasattr(A       ,"""do_rescale"""     )     )
   self.assertTrue(hasattr(A       ,"""do_pad"""     )     )
   self.assertTrue(hasattr(A       ,"""size"""     )     )

  def  __lowercase							(	self		:      Optional[int]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       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       ,A     )

   UpperCAmelCase__       :	int        =       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  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   pass

  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   # Initialize image_processing
   UpperCAmelCase__       :	Optional[int]        =       self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   UpperCAmelCase__       :	List[str]        =       prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A     )
   for image in image_inputs:
    self.assertIsInstance(A       ,Image.Image     )

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

   UpperCAmelCase__ ,   UpperCAmelCase__       :	List[Any]        =       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
   UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[Any]        =       self.image_processor_tester.get_expected_values(A       ,batched=A     )

   UpperCAmelCase__       :	Union[str, Any]        =       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  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   # Initialize image_processing
   UpperCAmelCase__       :	Tuple        =       self.image_processing_class(**self.image_processor_dict     )
   # create random numpy tensors
   UpperCAmelCase__       :	Union[str, Any]        =       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
   UpperCAmelCase__       :	int        =       image_processing(image_inputs[0]       ,return_tensors="""pt"""     ).pixel_values

   UpperCAmelCase__ ,   UpperCAmelCase__       :	Dict        =       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
   UpperCAmelCase__       :	Dict        =       image_processing(A       ,return_tensors="""pt"""     ).pixel_values

   UpperCAmelCase__ ,   UpperCAmelCase__       :	List[str]        =       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  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   # Initialize image_processing
   UpperCAmelCase__       :	Tuple        =       self.image_processing_class(**self.image_processor_dict     )
   # create random PyTorch tensors
   UpperCAmelCase__       :	Union[str, Any]        =       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
   UpperCAmelCase__       :	int        =       image_processing(image_inputs[0]       ,return_tensors="""pt"""     ).pixel_values

   UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[int]        =       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
   UpperCAmelCase__       :	Optional[Any]        =       image_processing(A       ,return_tensors="""pt"""     ).pixel_values

   UpperCAmelCase__ ,   UpperCAmelCase__       :	List[str]        =       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  __lowercase							(	self		:      Dict     ):






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

   UpperCAmelCase__       :	Union[str, Any]        =       {"""image_id""": 39_769, """annotations""": target}

   # encode them
   UpperCAmelCase__       :	str        =       DeformableDetrImageProcessor()
   UpperCAmelCase__       :	Union[str, Any]        =       image_processing(images=A       ,annotations=A       ,return_tensors="""pt"""     )

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

   UpperCAmelCase__       :	Tuple        =       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
   UpperCAmelCase__       :	int        =       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"""]       ,A     )     )
   # verify boxes
   UpperCAmelCase__       :	Any        =       torch.Size([6, 4]     )
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,A     )
   UpperCAmelCase__       :	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]       ,A       ,atol=1e-3     )     )
   # verify image_id
   UpperCAmelCase__       :	Dict        =       torch.tensor([39_769]     )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,A     )     )
   # verify is_crowd
   UpperCAmelCase__       :	Tuple        =       torch.tensor([0, 0, 0, 0, 0, 0]     )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,A     )     )
   # verify class_labels
   UpperCAmelCase__       :	Optional[Any]        =       torch.tensor([75, 75, 63, 65, 17, 17]     )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,A     )     )
   # verify orig_size
   UpperCAmelCase__       :	int        =       torch.tensor([480, 640]     )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,A     )     )
   # verify size
   UpperCAmelCase__       :	int        =       torch.tensor([800, 1_066]     )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,A     )     )




  @slow
  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   # prepare image, target and masks_path
   UpperCAmelCase__       :	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__       :	Optional[Any]        =       json.loads(f.read()     )

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

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

   # encode them
   UpperCAmelCase__       :	Union[str, Any]        =       DeformableDetrImageProcessor(format="""coco_panoptic"""     )
   UpperCAmelCase__       :	Tuple        =       image_processing(images=A       ,annotations=A       ,masks_path=A       ,return_tensors="""pt"""     )

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

   UpperCAmelCase__       :	Tuple        =       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
   UpperCAmelCase__       :	List[str]        =       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"""]       ,A     )     )
   # verify boxes
   UpperCAmelCase__       :	Tuple        =       torch.Size([6, 4]     )
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,A     )
   UpperCAmelCase__       :	Union[str, Any]        =       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
   UpperCAmelCase__       :	Tuple        =       torch.tensor([39_769]     )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,A     )     )
   # verify is_crowd
   UpperCAmelCase__       :	Optional[int]        =       torch.tensor([0, 0, 0, 0, 0, 0]     )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,A     )     )
   # verify class_labels
   UpperCAmelCase__       :	str        =       torch.tensor([17, 17, 63, 75, 75, 93]     )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,A     )     )
   # verify masks
   UpperCAmelCase__       :	Any        =       822_873
   self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,A     )
   # verify orig_size
   UpperCAmelCase__       :	List[Any]        =       torch.tensor([480, 640]     )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,A     )     )
   # verify size
   UpperCAmelCase__       :	str        =       torch.tensor([800, 1_066]     )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,A     )     )








"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Any    							=      int(A_					)
	if decimal in (0, 1):  # Exit cases for the recursion
		return str(A_					)
	lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      divmod(A_		,	2					)
	return binary_recursive(A_					) + str(A_					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Union[str, Any]    							=      str(A_					).strip()
	if not number:
		raise ValueError('''No input value was provided'''					)
	lowerCAmelCase__      :		Any    							=      '''-''' if number.startswith('''-'''					) else ''''''
	lowerCAmelCase__      :		Dict    							=      number.lstrip('''-'''					)
	if not number.isnumeric():
		raise ValueError('''Input value is not an integer'''					)
	return f'{negative}0b{binary_recursive(int(A_					)					)}'


if __name__ == "__main__":
							from doctest import testmod

							testmod()




