'''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 lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def __init__(      self			:       Any  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       Dict=7  ,							UpperCAmelCase__			:       Optional[int]=3  ,							UpperCAmelCase__			:       Dict=30  ,							UpperCAmelCase__			:       List[str]=400  ,							UpperCAmelCase__			:       Union[str, Any]=True  ,							UpperCAmelCase__			:       List[Any]=None  ,							UpperCAmelCase__			:       int=True  ,							UpperCAmelCase__			:       Any=[0.5, 0.5, 0.5]  ,							UpperCAmelCase__			:       str=[0.5, 0.5, 0.5]  ,							UpperCAmelCase__			:       Dict=True  ,							UpperCAmelCase__			:       Optional[int]=1 / 255  ,							UpperCAmelCase__			:       str=True  ,							)				->List[str]:
        UpperCAmelCase_            =  size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
        UpperCAmelCase_            =  parent
        UpperCAmelCase_            =  batch_size
        UpperCAmelCase_            =  num_channels
        UpperCAmelCase_            =  min_resolution
        UpperCAmelCase_            =  max_resolution
        UpperCAmelCase_            =  do_resize
        UpperCAmelCase_            =  size
        UpperCAmelCase_            =  do_normalize
        UpperCAmelCase_            =  image_mean
        UpperCAmelCase_            =  image_std
        UpperCAmelCase_            =  do_rescale
        UpperCAmelCase_            =  rescale_factor
        UpperCAmelCase_            =  do_pad






  def  lowerCAmelCase__ (      self			:       str  )				->List[str]:
        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			:       Union[str, Any]  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Tuple=False  )				->List[Any]:







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

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

        return expected_height, expected_width







@require_torch
@require_vision
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	DeformableDetrImageProcessor if is_vision_available() else None






  def  lowerCAmelCase__ (      self			:       str  )				->Optional[int]:
        UpperCAmelCase_            =  DeformableDetrImageProcessingTester(self  )






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






  def  lowerCAmelCase__ (      self			:       Dict  )				->List[Any]:
        UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       Tuple  )				->int:
        UpperCAmelCase_            =  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__  )

        UpperCAmelCase_            =  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			:       List[Any]  )				->List[str]:
        pass






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Dict:
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCAmelCase_            =  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_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  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_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )

        UpperCAmelCase_            =  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			:       Any  )				->Union[str, Any]:
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        UpperCAmelCase_            =  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_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  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_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  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			:       Dict  )				->List[str]:
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        UpperCAmelCase_            =  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_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  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_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  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			:       Optional[int]  )				->Dict:
        UpperCAmelCase_            =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
        with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''  ,							'''r'''  ) as f:
              UpperCAmelCase_            =  json.loads(f.read()  )

        UpperCAmelCase_            =  {'''image_id''': 3_9769, '''annotations''': target}

        # encode them
        UpperCAmelCase_            =  DeformableDetrImageProcessor()
        UpperCAmelCase_            =  image_processing(images=UpperCAmelCase__  ,							annotations=UpperCAmelCase__  ,							return_tensors='''pt'''  )

        # verify pixel values
        UpperCAmelCase_            =  torch.Size([1, 3, 800, 1066]  )
        self.assertEqual(encoding['''pixel_values'''].shape  ,							UpperCAmelCase__  )

        UpperCAmelCase_            =  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_            =  torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']  ,							UpperCAmelCase__  )  )
        # verify boxes
        UpperCAmelCase_            =  torch.Size([6, 4]  )
        self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  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_            =  torch.tensor([3_9769]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,							UpperCAmelCase__  )  )
        # verify is_crowd
        UpperCAmelCase_            =  torch.tensor([0, 0, 0, 0, 0, 0]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,							UpperCAmelCase__  )  )
        # verify class_labels
        UpperCAmelCase_            =  torch.tensor([75, 75, 63, 65, 17, 17]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,							UpperCAmelCase__  )  )
        # verify orig_size
        UpperCAmelCase_            =  torch.tensor([480, 640]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,							UpperCAmelCase__  )  )
        # verify size
        UpperCAmelCase_            =  torch.tensor([800, 1066]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,							UpperCAmelCase__  )  )






  @slow
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[Any]:
        UpperCAmelCase_            =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
        with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''  ,							'''r'''  ) as f:
              UpperCAmelCase_            =  json.loads(f.read()  )

        UpperCAmelCase_            =  {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

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

        # encode them
        UpperCAmelCase_            =  DeformableDetrImageProcessor(format='''coco_panoptic'''  )
        UpperCAmelCase_            =  image_processing(images=UpperCAmelCase__  ,							annotations=UpperCAmelCase__  ,							masks_path=UpperCAmelCase__  ,							return_tensors='''pt'''  )

        # verify pixel values
        UpperCAmelCase_            =  torch.Size([1, 3, 800, 1066]  )
        self.assertEqual(encoding['''pixel_values'''].shape  ,							UpperCAmelCase__  )

        UpperCAmelCase_            =  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_            =  torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']  ,							UpperCAmelCase__  )  )
        # verify boxes
        UpperCAmelCase_            =  torch.Size([6, 4]  )
        self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  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_            =  torch.tensor([3_9769]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,							UpperCAmelCase__  )  )
        # verify is_crowd
        UpperCAmelCase_            =  torch.tensor([0, 0, 0, 0, 0, 0]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,							UpperCAmelCase__  )  )
        # verify class_labels
        UpperCAmelCase_            =  torch.tensor([17, 17, 63, 75, 75, 93]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,							UpperCAmelCase__  )  )
        # verify masks
        UpperCAmelCase_            =  82_2873
        self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()  ,							UpperCAmelCase__  )
        # verify orig_size
        UpperCAmelCase_            =  torch.tensor([480, 640]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,							UpperCAmelCase__  )  )
        # verify size
        UpperCAmelCase_            =  torch.tensor([800, 1066]  )
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,							UpperCAmelCase__  )  )



'''simple docstring'''
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  list[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  ''''''
      for word_or_phrase in separated:
            if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
                  raise Exception('''join() accepts only strings to be joined'''					)
            joined += word_or_phrase + separator
      return joined.strip(_UpperCamelCase					)


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

      testmod()





