

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 YolosImageProcessor
class  _A		(  unittest.TestCase):

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=7 ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=30 ,					_SCREAMING_SNAKE_CASE=400 ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,					_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=1 / 255 ,					_SCREAMING_SNAKE_CASE=True ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
        SCREAMING_SNAKE_CASE_				:  Dict           =     parent
        SCREAMING_SNAKE_CASE_				:  int           =     batch_size
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     num_channels
        SCREAMING_SNAKE_CASE_				:  List[str]           =     min_resolution
        SCREAMING_SNAKE_CASE_				:  Tuple           =     max_resolution
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     do_resize
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     size
        SCREAMING_SNAKE_CASE_				:  int           =     do_normalize
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     image_mean
        SCREAMING_SNAKE_CASE_				:  List[str]           =     image_std
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     do_rescale
        SCREAMING_SNAKE_CASE_				:  Tuple           =     rescale_factor
        SCREAMING_SNAKE_CASE_				:  Tuple           =     do_pad

  def  UpperCAmelCase    (				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  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=False					):





        """simple docstring"""







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

        else:
              SCREAMING_SNAKE_CASE_				:  Any           =     []
              for image in image_inputs:
                    SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_expected_values([image]					)
                    expected_values.append((expected_height, expected_width)					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     max(_SCREAMING_SNAKE_CASE ,					key=lambda _SCREAMING_SNAKE_CASE					: item[0]					)[0]
              SCREAMING_SNAKE_CASE_				:  Any           =     max(_SCREAMING_SNAKE_CASE ,					key=lambda _SCREAMING_SNAKE_CASE					: item[1]					)[1]

        return expected_height, expected_width

@require_torch
@require_vision
class  _A		(  __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  YolosImageProcessor if is_vision_available() else None

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     YolosImageProcessingTester(self					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.image_processor_tester.prepare_image_processor_dict()

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'image_mean'					)					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'image_std'					)					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'do_normalize'					)					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'do_resize'					)					)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,					'size'					)					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.image_processing_class.from_dict(self.image_processor_dict					)
        self.assertEqual(image_processor.size ,					{'shortest_edge': 18, 'longest_edge': 1333}					)
        self.assertEqual(image_processor.do_pad ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     self.image_processing_class.from_dict(
            self.image_processor_dict ,					size=42 ,					max_size=84 ,					pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE					)
        self.assertEqual(image_processor.size ,					{'shortest_edge': 42, 'longest_edge': 84}					)
        self.assertEqual(image_processor.do_pad ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

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

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE					)

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

        # Test batched
        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Dict           =     self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,					batched=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  int           =     image_processing(_SCREAMING_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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        SCREAMING_SNAKE_CASE_				:  Dict           =     prepare_image_inputs(self.image_processor_tester ,					equal_resolution=_SCREAMING_SNAKE_CASE ,					numpify=_SCREAMING_SNAKE_CASE					)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE ,					np.ndarray					)

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

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  str           =     self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE					)

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

        # Test batched
        SCREAMING_SNAKE_CASE_				:  str           =     image_processing(_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					).pixel_values

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,					batched=_SCREAMING_SNAKE_CASE					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE_				:  Tuple           =     prepare_image_inputs(self.image_processor_tester ,					equal_resolution=_SCREAMING_SNAKE_CASE ,					torchify=_SCREAMING_SNAKE_CASE					)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE ,					torch.Tensor					)

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

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE					)

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

        # Test batched
        SCREAMING_SNAKE_CASE_				:  Any           =     image_processing(_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					).pixel_values

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  int           =     self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,					batched=_SCREAMING_SNAKE_CASE					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     self.image_processing_class(**self.image_processor_dict					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.image_processing_class(do_resize=_SCREAMING_SNAKE_CASE ,					do_normalize=_SCREAMING_SNAKE_CASE ,					do_rescale=_SCREAMING_SNAKE_CASE					)
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     prepare_image_inputs(self.image_processor_tester ,					equal_resolution=_SCREAMING_SNAKE_CASE ,					torchify=_SCREAMING_SNAKE_CASE					)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE ,					torch.Tensor					)

        # Test whether the method "pad" and calling the image processor return the same tensors
        SCREAMING_SNAKE_CASE_				:  List[str]           =     image_processing_a.pad(_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					)
        SCREAMING_SNAKE_CASE_				:  Any           =     image_processing_a(_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					)

        self.assertTrue(
            torch.allclose(encoded_images_with_method['pixel_values'] ,					encoded_images['pixel_values'] ,					atol=1e-4					)					)

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_				:  Dict           =     {'image_id': 3_9769, 'annotations': target}

        # encode them
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     YolosImageProcessor.from_pretrained('hustvl/yolos-small'					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     image_processing(images=_SCREAMING_SNAKE_CASE ,					annotations=_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					)

        # verify pixel values
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.Size([1, 3, 800, 1066]					)
        self.assertEqual(encoding['pixel_values'].shape ,					_SCREAMING_SNAKE_CASE					)

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

        # verify area
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,					_SCREAMING_SNAKE_CASE					)					)
        # verify boxes
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.Size([6, 4]					)
        self.assertEqual(encoding['labels'][0]['boxes'].shape ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,					_SCREAMING_SNAKE_CASE ,					atol=1e-3					)					)
        # verify image_id
        SCREAMING_SNAKE_CASE_				:  Dict           =     torch.tensor([3_9769]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,					_SCREAMING_SNAKE_CASE					)					)
        # verify is_crowd
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.tensor([0, 0, 0, 0, 0, 0]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,					_SCREAMING_SNAKE_CASE					)					)
        # verify class_labels
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.tensor([75, 75, 63, 65, 17, 17]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,					_SCREAMING_SNAKE_CASE					)					)
        # verify orig_size
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.tensor([480, 640]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,					_SCREAMING_SNAKE_CASE					)					)
        # verify size
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.tensor([800, 1066]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,					_SCREAMING_SNAKE_CASE					)					)



  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  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:
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     json.loads(f.read()					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

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

        # encode them
        SCREAMING_SNAKE_CASE_				:  Any           =     YolosImageProcessor(format='coco_panoptic'					)
        SCREAMING_SNAKE_CASE_				:  str           =     image_processing(images=_SCREAMING_SNAKE_CASE ,					annotations=_SCREAMING_SNAKE_CASE ,					masks_path=_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					)

        # verify pixel values
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Size([1, 3, 800, 1066]					)
        self.assertEqual(encoding['pixel_values'].shape ,					_SCREAMING_SNAKE_CASE					)

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

        # verify area
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,					_SCREAMING_SNAKE_CASE					)					)
        # verify boxes
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.Size([6, 4]					)
        self.assertEqual(encoding['labels'][0]['boxes'].shape ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,					_SCREAMING_SNAKE_CASE ,					atol=1e-3					)					)
        # verify image_id
        SCREAMING_SNAKE_CASE_				:  int           =     torch.tensor([3_9769]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,					_SCREAMING_SNAKE_CASE					)					)
        # verify is_crowd
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.tensor([0, 0, 0, 0, 0, 0]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,					_SCREAMING_SNAKE_CASE					)					)
        # verify class_labels
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.tensor([17, 17, 63, 75, 75, 93]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,					_SCREAMING_SNAKE_CASE					)					)
        # verify masks
        SCREAMING_SNAKE_CASE_				:  Dict           =     82_2873
        self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,					_SCREAMING_SNAKE_CASE					)
        # verify orig_size
        SCREAMING_SNAKE_CASE_				:  int           =     torch.tensor([480, 640]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,					_SCREAMING_SNAKE_CASE					)					)
        # verify size
        SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.tensor([800, 1066]					)
        self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,					_SCREAMING_SNAKE_CASE					)					)









import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
    import torch

    from transformers import AutoModelForImageClassification

if is_vision_available():
    from transformers import AutoImageProcessor
@require_torch
@require_vision
class  _A		(  unittest.TestCase):

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
        SCREAMING_SNAKE_CASE_				:  Any           =     AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
        model.to(_SCREAMING_SNAKE_CASE					)

        from datasets import load_dataset

        SCREAMING_SNAKE_CASE_				:  str           =     load_dataset('nielsr/rvlcdip-demo'					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     dataset['train'][0]['image'].convert('RGB'					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     image_processor(_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					).to(_SCREAMING_SNAKE_CASE					)

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_				:  int           =     model(**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Any           =     outputs.logits

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     torch.Size((1, 16)					)
        self.assertEqual(logits.shape ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.tensor(
            [-0.4158, -0.4092, -0.4347] ,					device=_SCREAMING_SNAKE_CASE ,					dtype=torch.float ,					)
        self.assertTrue(torch.allclose(logits[0, :3] ,					_SCREAMING_SNAKE_CASE ,					atol=1e-4					)					)






