

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							):




  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							,     )						->				str:
        '''simple docstring'''






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




  def 						A__  (       self      )						->				Optional[int]:
        '''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 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase=False      )						->				Optional[int]:
        '''simple docstring'''






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

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

        return expected_height, expected_width





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




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        _lowercase			 =DeformableDetrImageProcessingTester(self      )




  @property
  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =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 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =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_      )

        _lowercase			 =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 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        pass




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






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

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

        _lowercase			 =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
        _lowercase			 =self.image_processor_tester.get_expected_values(a_							,     batched=a_      )

        _lowercase			 =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 						A__  (       self      )						->				int:
        '''simple docstring'''






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

        _lowercase			 =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
        _lowercase			 =image_processing(a_							,     return_tensors='pt'      ).pixel_values

        _lowercase			 =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 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






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

        _lowercase			 =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
        _lowercase			 =image_processing(a_							,     return_tensors='pt'      ).pixel_values

        _lowercase			 =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 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






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

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

        # encode them
        _lowercase			 =DeformableDetrImageProcessor()
        _lowercase			 =image_processing(images=a_							,     annotations=a_							,     return_tensors='pt'      )

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

        _lowercase			 =torch.tensor([0.2796, 0.3138, 0.3481]      )
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]							,     a_							,     atol=1e-4      )      )

        # verify area
        _lowercase			 =torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']							,     a_      )      )
        # verify boxes
        _lowercase			 =torch.Size([6, 4]      )
        self.assertEqual(encoding['labels'][0]['boxes'].shape							,     a_      )
        _lowercase			 =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]							,     a_							,     atol=1e-3      )      )
        # verify image_id
        _lowercase			 =torch.tensor([39_769]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']							,     a_      )      )
        # verify is_crowd
        _lowercase			 =torch.tensor([0, 0, 0, 0, 0, 0]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']							,     a_      )      )
        # verify class_labels
        _lowercase			 =torch.tensor([75, 75, 63, 65, 17, 17]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']							,     a_      )      )
        # verify orig_size
        _lowercase			 =torch.tensor([480, 640]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']							,     a_      )      )
        # verify size
        _lowercase			 =torch.tensor([800, 1_066]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']							,     a_      )      )




  @slow
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






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

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

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

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

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

        _lowercase			 =torch.tensor([0.2796, 0.3138, 0.3481]      )
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]							,     a_							,     atol=1e-4      )      )

        # verify area
        _lowercase			 =torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']							,     a_      )      )
        # verify boxes
        _lowercase			 =torch.Size([6, 4]      )
        self.assertEqual(encoding['labels'][0]['boxes'].shape							,     a_      )
        _lowercase			 =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]							,     a_							,     atol=1e-3      )      )
        # verify image_id
        _lowercase			 =torch.tensor([39_769]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']							,     a_      )      )
        # verify is_crowd
        _lowercase			 =torch.tensor([0, 0, 0, 0, 0, 0]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']							,     a_      )      )
        # verify class_labels
        _lowercase			 =torch.tensor([17, 17, 63, 75, 75, 93]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']							,     a_      )      )
        # verify masks
        _lowercase			 =822_873
        self.assertEqual(encoding['labels'][0]['masks'].sum().item()							,     a_      )
        # verify orig_size
        _lowercase			 =torch.tensor([480, 640]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']							,     a_      )      )
        # verify size
        _lowercase			 =torch.tensor([800, 1_066]      )
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']							,     a_      )      )





import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
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 BeitImageProcessor





class 				__lowerCAmelCase  (      unittest.TestCase							):




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase=7							,     lowerCAmelCase=3							,     lowerCAmelCase=18							,     lowerCAmelCase=30							,     lowerCAmelCase=400							,     lowerCAmelCase=True							,     lowerCAmelCase=None							,     lowerCAmelCase=True							,     lowerCAmelCase=None							,     lowerCAmelCase=True							,     lowerCAmelCase=[0.5, 0.5, 0.5]							,     lowerCAmelCase=[0.5, 0.5, 0.5]							,     lowerCAmelCase=False							,     )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =size if size is not None else {'height': 20, 'width': 20}
        _lowercase			 =crop_size if crop_size is not None else {'height': 18, 'width': 18}
        _lowercase			 =parent
        _lowercase			 =batch_size
        _lowercase			 =num_channels
        _lowercase			 =image_size
        _lowercase			 =min_resolution
        _lowercase			 =max_resolution
        _lowercase			 =do_resize
        _lowercase			 =size
        _lowercase			 =do_center_crop
        _lowercase			 =crop_size
        _lowercase			 =do_normalize
        _lowercase			 =image_mean
        _lowercase			 =image_std
        _lowercase			 =do_reduce_labels




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_reduce_labels": self.do_reduce_labels,
        }
def   a   (    )					->   Tuple:





      """simple docstring"""



      _lowercase			 =load_dataset('hf-internal-testing/fixtures_ade20k'   ,     split='test'  )

      _lowercase			 =Image.open(dataset[0]['file']  )
      _lowercase			 =Image.open(dataset[1]['file']  )

      return image, map
def   a   (    )					->   Union[str, Any]:





      """simple docstring"""



      _lowercase			 =load_dataset('hf-internal-testing/fixtures_ade20k'   ,     split='test'  )

      _lowercase			 =Image.open(ds[0]['file']  )
      _lowercase			 =Image.open(ds[1]['file']  )
      _lowercase			 =Image.open(ds[2]['file']  )
      _lowercase			 =Image.open(ds[3]['file']  )

      return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						BeitImageProcessor if is_vision_available() else None




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =BeitImageProcessingTester(self      )




  @property
  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_resize'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'size'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_center_crop'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'center_crop'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_normalize'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'image_mean'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'image_std'      )      )




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class.from_dict(self.image_processor_dict      )
        self.assertEqual(image_processor.size							,     {'height': 20, 'width': 20}      )
        self.assertEqual(image_processor.crop_size							,     {'height': 18, 'width': 18}      )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase      )

        _lowercase			 =self.image_processing_class.from_dict(
            self.image_processor_dict							,     size=42							,     crop_size=84							,     reduce_labels=lowerCAmelCase      )
        self.assertEqual(image_processor.size							,     {'height': 42, 'width': 42}      )
        self.assertEqual(image_processor.crop_size							,     {'height': 84, 'width': 84}      )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase      )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        pass




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






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

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =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,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random numpy tensors
        _lowercase			 =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
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =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,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        _lowercase			 =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
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =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,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase							,     torchify=lowerCAmelCase      )
        _lowercase			 =[]
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     torch.Tensor      )
              maps.append(torch.zeros(image.shape[-2:]      ).long()      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     maps[0]							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                1,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test not batched input (PIL images)
        _lowercase , _lowercase			 =prepare_semantic_single_inputs()

        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                1,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test batched input (PIL images)
        _lowercase , _lowercase			 =prepare_semantic_batch_inputs()

        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                2,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )




  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        _lowercase , _lowercase			 =prepare_semantic_single_inputs()
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 150      )

        _lowercase			 =True
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )


