





"""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  _UpperCAmelCase  (   unittest.TestCase		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_=7					,      snake_case_=3					,      snake_case_=3_0					,      snake_case_=4_0_0					,      snake_case_=True					,      snake_case_=None					,      snake_case_=True					,      snake_case_=[0.5, 0.5, 0.5]					,      snake_case_=[0.5, 0.5, 0.5]					,      snake_case_=True					,      snake_case_=1 / 2_5_5					,      snake_case_=True					,      ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self  ):

        """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 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=False  ):

        """simple docstring"""

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

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

        return expected_height, expected_width


@require_torch
@require_vision
class  _UpperCAmelCase  (   UpperCAmelCase__			,   unittest.TestCase		):



      '''simple docstring'''
      lowercase_      : List[str]	    =   DeformableDetrImageProcessor if is_vision_available() else None

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Dict										=  DeformableDetrImageProcessingTester(self  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return self.image_processor_tester.prepare_image_processor_dict()

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(snake_case_					,      'image_mean'  )  )
        self.assertTrue(hasattr(snake_case_					,      'image_std'  )  )
        self.assertTrue(hasattr(snake_case_					,      'do_normalize'  )  )
        self.assertTrue(hasattr(snake_case_					,      'do_resize'  )  )
        self.assertTrue(hasattr(snake_case_					,      'do_rescale'  )  )
        self.assertTrue(hasattr(snake_case_					,      'do_pad'  )  )
        self.assertTrue(hasattr(snake_case_					,      'size'  )  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[str]										=  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					,      snake_case_  )

        A_  :       List[Any]										=  self.image_processing_class.from_dict(
            self.image_processor_dict					,      size=4_2					,      max_size=8_4					,      pad_and_return_pixel_mask=snake_case_  )
        self.assertEqual(image_processor.size					,      {'shortest_edge': 4_2, 'longest_edge': 8_4}  )
        self.assertEqual(image_processor.do_pad					,      snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Tuple										=  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        A_  :       Union[str, Any]										=  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=snake_case_  )
        for image in image_inputs:
          self.assertIsInstance(snake_case_					,      Image.Image  )

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

        A_						,       A_  :       Optional[int]										=  self.image_processor_tester.get_expected_values(snake_case_  )

        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(snake_case_					,      batched=snake_case_  )

        A_  :       str										=  image_processing(snake_case_					,      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  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        A_  :       List[str]										=  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=snake_case_					,      numpify=snake_case_  )
        for image in image_inputs:
          self.assertIsInstance(snake_case_					,      np.ndarray  )

        # 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(snake_case_  )

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

        # Test batched
        A_  :       Dict										=  image_processing(snake_case_					,      return_tensors='pt'  ).pixel_values

        A_						,       A_  :       List[str]										=  self.image_processor_tester.get_expected_values(snake_case_					,      batched=snake_case_  )

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

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Optional[int]										=  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        A_  :       Any										=  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=snake_case_					,      torchify=snake_case_  )
        for image in image_inputs:
          self.assertIsInstance(snake_case_					,      torch.Tensor  )

        # Test not batched input
        A_  :       Optional[int]										=  image_processing(image_inputs[0]					,      return_tensors='pt'  ).pixel_values

        A_						,       A_  :       Tuple										=  self.image_processor_tester.get_expected_values(snake_case_  )

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

        # Test batched
        A_  :       List[Any]										=  image_processing(snake_case_					,      return_tensors='pt'  ).pixel_values

        A_						,       A_  :       Optional[int]										=  self.image_processor_tester.get_expected_values(snake_case_					,      batched=snake_case_  )

        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  ):

        """simple docstring"""

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

        A_  :       Union[str, Any]										=  {'image_id': 3_9_7_6_9, 'annotations': target}

        # encode them
        A_  :       Optional[int]										=  DeformableDetrImageProcessor()
        A_  :       Optional[Any]										=  image_processing(images=snake_case_					,      annotations=snake_case_					,      return_tensors='pt'  )

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

        A_  :       str										=  torch.tensor([0.27_96, 0.31_38, 0.34_81]  )
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]					,      snake_case_					,      atol=1E-4  )  )

        # verify area
        A_  :       Optional[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']					,      snake_case_  )  )
        # verify boxes
        A_  :       Optional[Any]										=  torch.Size([6, 4]  )
        self.assertEqual(encoding['labels'][0]['boxes'].shape					,      snake_case_  )
        A_  :       List[str]										=  torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]					,      snake_case_					,      atol=1E-3  )  )
        # verify image_id
        A_  :       Union[str, Any]										=  torch.tensor([3_9_7_6_9]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,      snake_case_  )  )
        # verify is_crowd
        A_  :       Optional[int]										=  torch.tensor([0, 0, 0, 0, 0, 0]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,      snake_case_  )  )
        # verify class_labels
        A_  :       Dict										=  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,      snake_case_  )  )
        # verify orig_size
        A_  :       str										=  torch.tensor([4_8_0, 6_4_0]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,      snake_case_  )  )
        # verify size
        A_  :       List[Any]										=  torch.tensor([8_0_0, 1_0_6_6]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,      snake_case_  )  )

      @slow
      def 					lowerCamelCase_			(     self  ):

        """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_  :       Optional[Any]										=  {'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

        A_  :       Optional[int]										=  pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'  )

        # encode them
        A_  :       Any										=  DeformableDetrImageProcessor(format='coco_panoptic'  )
        A_  :       Union[str, Any]										=  image_processing(images=snake_case_					,      annotations=snake_case_					,      masks_path=snake_case_					,      return_tensors='pt'  )

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

        A_  :       int										=  torch.tensor([0.27_96, 0.31_38, 0.34_81]  )
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]					,      snake_case_					,      atol=1E-4  )  )

        # verify area
        A_  :       List[Any]										=  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']					,      snake_case_  )  )
        # verify boxes
        A_  :       Union[str, Any]										=  torch.Size([6, 4]  )
        self.assertEqual(encoding['labels'][0]['boxes'].shape					,      snake_case_  )
        A_  :       Optional[int]										=  torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]					,      snake_case_					,      atol=1E-3  )  )
        # verify image_id
        A_  :       Any										=  torch.tensor([3_9_7_6_9]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,      snake_case_  )  )
        # verify is_crowd
        A_  :       Union[str, Any]										=  torch.tensor([0, 0, 0, 0, 0, 0]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,      snake_case_  )  )
        # verify class_labels
        A_  :       Optional[int]										=  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,      snake_case_  )  )
        # verify masks
        A_  :       Tuple										=  8_2_2_8_7_3
        self.assertEqual(encoding['labels'][0]['masks'].sum().item()					,      snake_case_  )
        # verify orig_size
        A_  :       List[Any]										=  torch.tensor([4_8_0, 6_4_0]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,      snake_case_  )  )
        # verify size
        A_  :       Dict										=  torch.tensor([8_0_0, 1_0_6_6]  )
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,      snake_case_  )  )






"""simple docstring"""




import torch

from diffusers import DiffusionPipeline

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        super().__init__()

        self.register_modules(unet=snake_case_					,      scheduler=snake_case_  )

      def __call__(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  torch.randn(
            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      )
        A_  :       List[str]										=  1

        A_  :       List[str]										=  self.unet(snake_case_					,      snake_case_  ).sample
        A_  :       Optional[int]										=  self.scheduler.step(snake_case_					,      snake_case_					,      snake_case_  ).prev_sample

        A_  :       List[Any]										=  scheduler_output - scheduler_output + torch.ones_like(snake_case_  )

        return result