


'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class      lowerCamelCase_  (      __a	,			__a	,			__a	,			unittest.TestCase ):
      lowerCAmelCase__							=	StableDiffusionInpaintPipeline
      lowerCAmelCase__							=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      lowerCAmelCase__							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      lowerCAmelCase__							=	frozenset(
          [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      lowerCAmelCase__							=	frozenset([] )






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          torch.manual_seed(0					)
          UpperCAmelCase__     :						List[str]						=							UNetaDConditionModel(
              block_out_channels=(32, 64)		,		layers_per_block=2		,		sample_size=32		,		in_channels=9		,		out_channels=4		,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,		cross_attention_dim=32		,		attention_head_dim=(2, 4)		,		use_linear_projection=_A		,		)
          UpperCAmelCase__     :						int						=							PNDMScheduler(skip_prk_steps=_A					)
          torch.manual_seed(0					)
          UpperCAmelCase__     :						str						=							AutoencoderKL(
              block_out_channels=[32, 64]		,		in_channels=3		,		out_channels=3		,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,		latent_channels=4		,		sample_size=128		,		)
          torch.manual_seed(0					)
          UpperCAmelCase__     :						Optional[Any]						=							CLIPTextConfig(
              bos_token_id=0		,		eos_token_id=2		,		hidden_size=32		,		intermediate_size=37		,		layer_norm_eps=1e-05		,		num_attention_heads=4		,		num_hidden_layers=5		,		pad_token_id=1		,		vocab_size=1_000		,		hidden_act='''gelu'''		,		projection_dim=512		,		)
          UpperCAmelCase__     :						Union[str, Any]						=							CLIPTextModel(_A					)
          UpperCAmelCase__     :						List[Any]						=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

          UpperCAmelCase__     :						str						=							{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''safety_checker''': None,
              '''feature_extractor''': None,
          }
          return components






      def lowercase_				(   self							:       str		,		_A							:       Dict		,		_A							:       Any=0					):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							floats_tensor((1, 3, 32, 32)		,		rng=random.Random(_A					)					).to(_A					)
          UpperCAmelCase__     :						Dict						=							image.cpu().permute(0		,		2		,		3		,		1					)[0]
          UpperCAmelCase__     :						Optional[int]						=							Image.fromarray(np.uinta(_A					)					).convert('''RGB'''					).resize((64, 64)					)
          UpperCAmelCase__     :						int						=							Image.fromarray(np.uinta(image + 4					)					).convert('''RGB'''					).resize((64, 64)					)
          if str(_A					).startswith('''mps'''					):
              UpperCAmelCase__     :						List[Any]						=							torch.manual_seed(_A					)
          else:
              UpperCAmelCase__     :						str						=							torch.Generator(device=_A					).manual_seed(_A					)
          UpperCAmelCase__     :						Optional[int]						=							{
              '''prompt''': '''A painting of a squirrel eating a burger''',
              '''image''': init_image,
              '''mask_image''': mask_image,
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 6.0,
              '''output_type''': '''numpy''',
          }
          return inputs






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


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase__     :						Tuple						=							self.get_dummy_components()
          UpperCAmelCase__     :						str						=							StableDiffusionInpaintPipeline(**_A					)
          UpperCAmelCase__     :						List[str]						=							sd_pipe.to(_A					)
          sd_pipe.set_progress_bar_config(disable=_A					)

          UpperCAmelCase__     :						Dict						=							self.get_dummy_inputs(_A					)
          UpperCAmelCase__     :						Any						=							sd_pipe(**_A					).images
          UpperCAmelCase__     :						Dict						=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCAmelCase__     :						int						=							np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          super().test_inference_batch_single_identical(expected_max_diff=3e-3					)




@slow
@require_torch_gpu
class      lowerCamelCase_  (      unittest.TestCase ):






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/sd2-inpaint/init_image.png'''					)
          UpperCAmelCase__     :						Any						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''					)
          UpperCAmelCase__     :						List[Any]						=							load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
              '''/yellow_cat_sitting_on_a_park_bench.npy'''					)

          UpperCAmelCase__     :						Dict						=							'''stabilityai/stable-diffusion-2-inpainting'''
          UpperCAmelCase__     :						Optional[int]						=							StableDiffusionInpaintPipeline.from_pretrained(_A		,		safety_checker=_A					)
          pipe.to(_A					)
          pipe.set_progress_bar_config(disable=_A					)
          pipe.enable_attention_slicing()

          UpperCAmelCase__     :						Tuple						=							'''Face of a yellow cat, high resolution, sitting on a park bench'''

          UpperCAmelCase__     :						str						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						str						=							pipe(
              prompt=_A		,		image=_A		,		mask_image=_A		,		generator=_A		,		output_type='''np'''		,		)
          UpperCAmelCase__     :						int						=							output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image					).max() < 9e-3






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/sd2-inpaint/init_image.png'''					)
          UpperCAmelCase__     :						Any						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''					)
          UpperCAmelCase__     :						Union[str, Any]						=							load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
              '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''					)

          UpperCAmelCase__     :						Tuple						=							'''stabilityai/stable-diffusion-2-inpainting'''
          UpperCAmelCase__     :						Any						=							StableDiffusionInpaintPipeline.from_pretrained(
              _A		,		torch_dtype=torch.floataa		,		safety_checker=_A		,		)
          pipe.to(_A					)
          pipe.set_progress_bar_config(disable=_A					)
          pipe.enable_attention_slicing()

          UpperCAmelCase__     :						Tuple						=							'''Face of a yellow cat, high resolution, sitting on a park bench'''

          UpperCAmelCase__     :						Union[str, Any]						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						Optional[Any]						=							pipe(
              prompt=_A		,		image=_A		,		mask_image=_A		,		generator=_A		,		output_type='''np'''		,		)
          UpperCAmelCase__     :						Tuple						=							output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image					).max() < 5e-1






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          UpperCAmelCase__     :						Union[str, Any]						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/sd2-inpaint/init_image.png'''					)
          UpperCAmelCase__     :						Dict						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''					)

          UpperCAmelCase__     :						Optional[Any]						=							'''stabilityai/stable-diffusion-2-inpainting'''
          UpperCAmelCase__     :						str						=							PNDMScheduler.from_pretrained(_A		,		subfolder='''scheduler'''					)
          UpperCAmelCase__     :						Optional[Any]						=							StableDiffusionInpaintPipeline.from_pretrained(
              _A		,		safety_checker=_A		,		scheduler=_A		,		torch_dtype=torch.floataa		,		)
          pipe.to(_A					)
          pipe.set_progress_bar_config(disable=_A					)
          pipe.enable_attention_slicing(1					)
          pipe.enable_sequential_cpu_offload()

          UpperCAmelCase__     :						Optional[int]						=							'''Face of a yellow cat, high resolution, sitting on a park bench'''

          UpperCAmelCase__     :						Tuple						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						Any						=							pipe(
              prompt=_A		,		image=_A		,		mask_image=_A		,		generator=_A		,		num_inference_steps=2		,		output_type='''np'''		,		)

          UpperCAmelCase__     :						int						=							torch.cuda.max_memory_allocated()
          # make sure that less than 2.65 GB is allocated
          assert mem_bytes < 2.6_5 * 10**9


"""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 ,							lowercase_ ,							lowercase_=7 ,							lowercase_=3 ,							lowercase_=3_0 ,							lowercase_=4_0_0 ,							lowercase_=True ,							lowercase_=None ,							lowercase_=True ,							lowercase_=[0.5, 0.5, 0.5] ,							lowercase_=[0.5, 0.5, 0.5] ,							lowercase_=True ,							lowercase_=1 / 2_5_5 ,							lowercase_=True ,							)    ->       Union[str, Any]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          UpperCAmelCase          =       size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
          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      a_   (				self				)    ->       int:
          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 ,							lowercase_ ,							lowercase_=False				)    ->       Union[str, Any]:
          if not batched:
              UpperCAmelCase          =       image_inputs[0]
              if isinstance(lowercase_ ,							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(lowercase_ ,							key=lambda lowercase_				: item[0]				)[0]
              UpperCAmelCase          =       max(lowercase_ ,							key=lambda lowercase_				: item[1]				)[1]

          return expected_height, expected_width

@require_torch
@require_vision
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						DetaImageProcessor if is_vision_available() else None




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       DetaImageProcessingTester(self				)




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




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.image_processing_class(**self.image_processor_dict				)
          self.assertTrue(hasattr(lowercase_ ,							'image_mean'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'image_std'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'do_normalize'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'do_resize'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'do_rescale'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'do_pad'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'size'				)				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       self.image_processing_class.from_dict(self.image_processor_dict				)
          self.assertEqual(image_processor.size ,							{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}				)
          self.assertEqual(image_processor.do_pad ,							lowercase_				)




      def      a_   (				self				)    ->       int:
          pass




      def      a_   (				self				)    ->       Union[str, Any]:
          # Initialize image_processing
          UpperCAmelCase          =       self.image_processing_class(**self.image_processor_dict				)
          # create random PIL images
          UpperCAmelCase          =       prepare_image_inputs(self.image_processor_tester ,							equal_resolution=lowercase_				)
          for image in image_inputs:
              self.assertIsInstance(lowercase_ ,							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(lowercase_				)

          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(lowercase_ ,							batched=lowercase_				)

          UpperCAmelCase          =       image_processing(lowercase_ ,							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				)    ->       Tuple:
          # Initialize image_processing
          UpperCAmelCase          =       self.image_processing_class(**self.image_processor_dict				)
          # create random numpy tensors
          UpperCAmelCase          =       prepare_image_inputs(self.image_processor_tester ,							equal_resolution=lowercase_ ,							numpify=lowercase_				)
          for image in image_inputs:
              self.assertIsInstance(lowercase_ ,							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(lowercase_				)

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

          # Test batched
          UpperCAmelCase          =       image_processing(lowercase_ ,							return_tensors='pt'				).pixel_values

          UpperCAmelCase      ,				UpperCAmelCase          =       self.image_processor_tester.get_expected_values(lowercase_ ,							batched=lowercase_				)

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




      def      a_   (				self				)    ->       Optional[Any]:
          # Initialize image_processing
          UpperCAmelCase          =       self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          UpperCAmelCase          =       prepare_image_inputs(self.image_processor_tester ,							equal_resolution=lowercase_ ,							torchify=lowercase_				)
          for image in image_inputs:
              self.assertIsInstance(lowercase_ ,							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(lowercase_				)

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

          # Test batched
          UpperCAmelCase          =       image_processing(lowercase_ ,							return_tensors='pt'				).pixel_values

          UpperCAmelCase      ,				UpperCAmelCase          =       self.image_processor_tester.get_expected_values(lowercase_ ,							batched=lowercase_				)

          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				)    ->       str:
          # prepare image and target
          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_9_7_6_9, 'annotations': target}

          # encode them
          UpperCAmelCase          =       DetaImageProcessor()
          UpperCAmelCase          =       image_processing(images=lowercase_ ,							annotations=lowercase_ ,							return_tensors='pt'				)

          # verify pixel values
          UpperCAmelCase          =       torch.Size([1, 3, 8_0_0, 1_0_6_6]				)
          self.assertEqual(encoding['pixel_values'].shape ,							lowercase_				)

          UpperCAmelCase          =       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] ,							lowercase_ ,							atol=1E-4				)				)

          # verify area
          UpperCAmelCase          =       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'] ,							lowercase_				)				)
          # verify boxes
          UpperCAmelCase          =       torch.Size([6, 4]				)
          self.assertEqual(encoding['labels'][0]['boxes'].shape ,							lowercase_				)
          UpperCAmelCase          =       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] ,							lowercase_ ,							atol=1E-3				)				)
          # verify image_id
          UpperCAmelCase          =       torch.tensor([3_9_7_6_9]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,							lowercase_				)				)
          # verify is_crowd
          UpperCAmelCase          =       torch.tensor([0, 0, 0, 0, 0, 0]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,							lowercase_				)				)
          # verify class_labels
          UpperCAmelCase          =       torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,							lowercase_				)				)
          # verify orig_size
          UpperCAmelCase          =       torch.tensor([4_8_0, 6_4_0]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,							lowercase_				)				)
          # verify size
          UpperCAmelCase          =       torch.tensor([8_0_0, 1_0_6_6]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,							lowercase_				)				)




      @slow
      def      a_   (				self				)    ->       Any:
          # prepare image, target and masks_path
          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_9_7_6_9, 'segments_info': target}

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

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

          # verify pixel values
          UpperCAmelCase          =       torch.Size([1, 3, 8_0_0, 1_0_6_6]				)
          self.assertEqual(encoding['pixel_values'].shape ,							lowercase_				)

          UpperCAmelCase          =       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] ,							lowercase_ ,							atol=1E-4				)				)

          # verify area
          UpperCAmelCase          =       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'] ,							lowercase_				)				)
          # verify boxes
          UpperCAmelCase          =       torch.Size([6, 4]				)
          self.assertEqual(encoding['labels'][0]['boxes'].shape ,							lowercase_				)
          UpperCAmelCase          =       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] ,							lowercase_ ,							atol=1E-3				)				)
          # verify image_id
          UpperCAmelCase          =       torch.tensor([3_9_7_6_9]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,							lowercase_				)				)
          # verify is_crowd
          UpperCAmelCase          =       torch.tensor([0, 0, 0, 0, 0, 0]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,							lowercase_				)				)
          # verify class_labels
          UpperCAmelCase          =       torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,							lowercase_				)				)
          # verify masks
          UpperCAmelCase          =       8_2_2_8_7_3
          self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,							lowercase_				)
          # verify orig_size
          UpperCAmelCase          =       torch.tensor([4_8_0, 6_4_0]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,							lowercase_				)				)
          # verify size
          UpperCAmelCase          =       torch.tensor([8_0_0, 1_0_6_6]				)
          self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,							lowercase_				)				)



