





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__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      lowerCAmelCase__=[0.5, 0.5, 0.5]		,      lowerCAmelCase__=[0.5, 0.5, 0.5]		,      lowerCAmelCase__=True		,      lowerCAmelCase__=1 / 2_5_5		,      lowerCAmelCase__=True		,      )   ->						str:



        '''simple docstring'''




        a__	:					str										=size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
        a__	:					Tuple										=parent
        a__	:					Any										=batch_size
        a__	:					int										=num_channels
        a__	:					List[str]										=min_resolution
        a__	:					Tuple										=max_resolution
        a__	:					Tuple										=do_resize
        a__	:					Dict										=size
        a__	:					List[str]										=do_normalize
        a__	:					Optional[Any]										=image_mean
        a__	:					Tuple										=image_std
        a__	:					Dict										=do_rescale
        a__	:					List[Any]										=rescale_factor
        a__	:					Optional[Any]										=do_pad







      def    _lowercase  (    self							)   ->						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		,      lowerCAmelCase__		,      lowerCAmelCase__=False							)   ->						Any:



        '''simple docstring'''




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

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

        return expected_height, expected_width







@require_torch
@require_vision
class 				__lowerCAmelCase       (    UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					Tuple						=			DeformableDetrImageProcessor if is_vision_available() else None







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					List[str]										=DeformableDetrImageProcessingTester(self							)







      @property
      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "image_mean"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "image_std"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_normalize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_resize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_rescale"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_pad"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "size"							)							)







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Optional[int]										=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		,      lowerCAmelCase__							)

        a__	:					List[str]										=self.image_processing_class.from_dict(
            self.image_processor_dict		,      size=4_2		,      max_size=8_4		,      pad_and_return_pixel_mask=lowerCAmelCase__							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 4_2, "longest_edge": 8_4}							)
        self.assertEqual(image_processor.do_pad		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        a__	:					List[Any]										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      Image.Image							)

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

        a__					,							a__	:					List[Any]										=self.image_processor_tester.get_expected_values(lowerCAmelCase__							)

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

        # Test batched
        a__					,							a__	:					List[str]										=self.image_processor_tester.get_expected_values(lowerCAmelCase__		,      batched=lowerCAmelCase__							)

        a__	:					Optional[int]										=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,
                expected_height,
                expected_width,
            )		,      )







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					List[Any]										=self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        a__	:					int										=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
        a__	:					Union[str, Any]										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values

        a__					,							a__	:					Dict										=self.image_processor_tester.get_expected_values(lowerCAmelCase__							)

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

        # Test batched
        a__	:					Union[str, Any]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values

        a__					,							a__	:					List[str]										=self.image_processor_tester.get_expected_values(lowerCAmelCase__		,      batched=lowerCAmelCase__							)

        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'''




        a__	:					List[str]										=self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        a__	:					Union[str, Any]										=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
        a__	:					int										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values

        a__					,							a__	:					List[Any]										=self.image_processor_tester.get_expected_values(lowerCAmelCase__							)

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

        # Test batched
        a__	:					Union[str, Any]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values

        a__					,							a__	:					Tuple										=self.image_processor_tester.get_expected_values(lowerCAmelCase__		,      batched=lowerCAmelCase__							)

        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							)   ->						Optional[int]:



        '''simple docstring'''




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

        a__	:					Any										={"image_id": 3_9_7_6_9, "annotations": target}

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

        # verify pixel values
        a__	:					Tuple										=torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
        self.assertEqual(encoding["pixel_values"].shape		,      lowerCAmelCase__							)

        a__	:					Any										=torch.tensor([0.27_96, 0.31_38, 0.34_81]							)
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,      lowerCAmelCase__		,      atol=1E-4							)							)

        # verify area
        a__	:					List[Any]										=torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,      lowerCAmelCase__							)							)
        # verify boxes
        a__	:					Any										=torch.Size([6, 4]							)
        self.assertEqual(encoding["labels"][0]["boxes"].shape		,      lowerCAmelCase__							)
        a__	:					Optional[Any]										=torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,      lowerCAmelCase__		,      atol=1E-3							)							)
        # verify image_id
        a__	:					int										=torch.tensor([3_9_7_6_9]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,      lowerCAmelCase__							)							)
        # verify is_crowd
        a__	:					str										=torch.tensor([0, 0, 0, 0, 0, 0]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,      lowerCAmelCase__							)							)
        # verify class_labels
        a__	:					List[str]										=torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,      lowerCAmelCase__							)							)
        # verify orig_size
        a__	:					Optional[int]										=torch.tensor([4_8_0, 6_4_0]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,      lowerCAmelCase__							)							)
        # verify size
        a__	:					int										=torch.tensor([8_0_0, 1_0_6_6]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,      lowerCAmelCase__							)							)







      @slow
      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					List[str]										=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"		,      "r"							) as f:
          a__	:					int										=json.loads(f.read()							)

        a__	:					List[str]										={"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

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

        # encode them
        a__	:					Any										=DeformableDetrImageProcessor(format="coco_panoptic"							)
        a__	:					Optional[Any]										=image_processing(images=lowerCAmelCase__		,      annotations=lowerCAmelCase__		,      masks_path=lowerCAmelCase__		,      return_tensors="pt"							)

        # verify pixel values
        a__	:					Union[str, Any]										=torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
        self.assertEqual(encoding["pixel_values"].shape		,      lowerCAmelCase__							)

        a__	:					Union[str, Any]										=torch.tensor([0.27_96, 0.31_38, 0.34_81]							)
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,      lowerCAmelCase__		,      atol=1E-4							)							)

        # verify area
        a__	:					Dict										=torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,      lowerCAmelCase__							)							)
        # verify boxes
        a__	:					Optional[int]										=torch.Size([6, 4]							)
        self.assertEqual(encoding["labels"][0]["boxes"].shape		,      lowerCAmelCase__							)
        a__	:					Any										=torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,      lowerCAmelCase__		,      atol=1E-3							)							)
        # verify image_id
        a__	:					int										=torch.tensor([3_9_7_6_9]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,      lowerCAmelCase__							)							)
        # verify is_crowd
        a__	:					List[str]										=torch.tensor([0, 0, 0, 0, 0, 0]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,      lowerCAmelCase__							)							)
        # verify class_labels
        a__	:					Optional[Any]										=torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,      lowerCAmelCase__							)							)
        # verify masks
        a__	:					int										=8_2_2_8_7_3
        self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,      lowerCAmelCase__							)
        # verify orig_size
        a__	:					List[str]										=torch.tensor([4_8_0, 6_4_0]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,      lowerCAmelCase__							)							)
        # verify size
        a__	:					int										=torch.tensor([8_0_0, 1_0_6_6]							)
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,      lowerCAmelCase__							)							)













import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase	:	int		     =       False







class 				__lowerCAmelCase       (    unittest.TestCase):
      pass







@nightly
@require_torch_gpu
class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)

        a__	:					Optional[Any]										=torch.manual_seed(0							)
        a__	:					Optional[Any]										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="numpy"		,      ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCAmelCase__							)
          a__	:					str										=VersatileDiffusionPipeline.from_pretrained(lowerCAmelCase__		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										=generator.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="numpy"		,      ).images

        assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										="cyberpunk 2077"
        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
        a__	:					Union[str, Any]										=torch.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt=lowerCAmelCase__		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"		,      ).images

        a__	:					int										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]							)

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

        a__	:					str										="A painting of a squirrel eating a burger "
        a__	:					Optional[int]										=torch.manual_seed(0							)
        a__	:					str										=pipe.text_to_image(
            prompt=lowerCAmelCase__		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"							).images

        a__	:					Any										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Optional[int]										=np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]							)

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

        a__	:					Optional[Any]										=pipe.image_variation(lowerCAmelCase__		,      generator=lowerCAmelCase__		,      output_type="numpy"							).images

        a__	:					Union[str, Any]										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]							)

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






