

'''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 DetrImageProcessor




class        A_ (				unittest.TestCase							):






     '''simple docstring'''



     def __init__(		self :     str     ,			lowercase_ :     Optional[Any]     ,			lowercase_ :     List[Any]=7     ,			lowercase_ :     Optional[Any]=3     ,			lowercase_ :     Dict=30     ,			lowercase_ :     Optional[int]=400     ,			lowercase_ :     List[Any]=True     ,			lowercase_ :     str=None     ,			lowercase_ :     List[Any]=True     ,			lowercase_ :     Any=1 / 255     ,			lowercase_ :     Optional[Any]=True     ,			lowercase_ :     List[str]=[0.5, 0.5, 0.5]     ,			lowercase_ :     Union[str, Any]=[0.5, 0.5, 0.5]     ,			lowercase_ :     Dict=True     ,			)						->	Optional[int]:
      # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
      UpperCAmelCase       :	Any											= size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
      UpperCAmelCase       :	Tuple											= parent
      UpperCAmelCase       :	Any											= batch_size
      UpperCAmelCase       :	str											= num_channels
      UpperCAmelCase       :	List[str]											= min_resolution
      UpperCAmelCase       :	List[str]											= max_resolution
      UpperCAmelCase       :	Tuple											= do_resize
      UpperCAmelCase       :	Dict											= size
      UpperCAmelCase       :	Optional[Any]											= do_rescale
      UpperCAmelCase       :	Union[str, Any]											= rescale_factor
      UpperCAmelCase       :	Dict											= do_normalize
      UpperCAmelCase       :	Union[str, Any]											= image_mean
      UpperCAmelCase       :	Dict											= image_std
      UpperCAmelCase       :	int											= do_pad


     def    UpperCAmelCase_       (		self :     Union[str, Any]       )						->	Optional[Any]:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_pad": self.do_pad,
      }




     def    UpperCAmelCase_       (		self :     str     ,			lowercase_ :     List[Any]     ,			lowercase_ :     Dict=False       )						->	Optional[int]:
      if not batched:
       UpperCAmelCase       :	Tuple											= image_inputs[0]
       if isinstance(lowerCAmelCase_     ,			Image.Image       ):
        UpperCAmelCase					, UpperCAmelCase       :	Tuple											= image.size
       else:
        UpperCAmelCase					, UpperCAmelCase       :	Optional[Any]											= image.shape[1], image.shape[2]
       if w < h:
        UpperCAmelCase       :	Tuple											= int(self.size['shortest_edge'] * h / w       )
        UpperCAmelCase       :	Union[str, Any]											= self.size['shortest_edge']
       elif w > h:
        UpperCAmelCase       :	Optional[int]											= self.size['shortest_edge']
        UpperCAmelCase       :	str											= int(self.size['shortest_edge'] * w / h       )
       else:
        UpperCAmelCase       :	Optional[int]											= self.size['shortest_edge']
        UpperCAmelCase       :	int											= self.size['shortest_edge']

      else:
       UpperCAmelCase       :	Optional[int]											= []
       for image in image_inputs:
        UpperCAmelCase					, UpperCAmelCase       :	str											= self.get_expected_values([image]       )
        expected_values.append((expected_height, expected_width)       )
       UpperCAmelCase       :	Tuple											= max(lowerCAmelCase_     ,			key=lambda lowercase_       : item[0]       )[0]
       UpperCAmelCase       :	int											= max(lowerCAmelCase_     ,			key=lambda lowercase_       : item[1]       )[1]

      return expected_height, expected_width




@require_torch
@require_vision
class        A_ (				_UpperCamelCase       ,   unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:	Any  		=  DetrImageProcessor if is_vision_available() else None


     def    UpperCAmelCase_       (		self :     List[Any]       )						->	int:
      UpperCAmelCase       :	str											= DetrImageProcessingTester(self       )


     @property
     def    UpperCAmelCase_       (		self :     Any       )						->	List[str]:
      return self.image_processor_tester.prepare_image_processor_dict()


     def    UpperCAmelCase_       (		self :     List[Any]       )						->	int:
      UpperCAmelCase       :	str											= 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_rescale'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'rescale_factor'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'do_resize'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'size'       )       )
      self.assertTrue(hasattr(lowerCAmelCase_     ,			'do_pad'       )       )


     def    UpperCAmelCase_       (		self :     str       )						->	Optional[Any]:
      UpperCAmelCase       :	List[Any]											= 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     ,			lowerCAmelCase_       )

      UpperCAmelCase       :	List[Any]											= self.image_processing_class.from_dict(
          self.image_processor_dict     ,			size=42     ,			max_size=84     ,			pad_and_return_pixel_mask=lowerCAmelCase_       )
      self.assertEqual(image_processor.size     ,			{'shortest_edge': 42, 'longest_edge': 84}       )
      self.assertEqual(image_processor.do_pad     ,			lowerCAmelCase_       )


     def    UpperCAmelCase_       (		self :     Tuple       )						->	Optional[Any]:
      pass


     def    UpperCAmelCase_       (		self :     int       )						->	Any:
      # Initialize image_processing
      UpperCAmelCase       :	Optional[int]											= self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      UpperCAmelCase       :	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
      UpperCAmelCase       :	Optional[Any]											= image_processing(image_inputs[0]     ,			return_tensors='pt'       ).pixel_values

      UpperCAmelCase					, UpperCAmelCase       :	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
      UpperCAmelCase					, UpperCAmelCase       :	int											= self.image_processor_tester.get_expected_values(lowerCAmelCase_     ,			batched=lowerCAmelCase_       )

      UpperCAmelCase       :	Union[str, Any]											= 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    UpperCAmelCase_       (		self :     Dict       )						->	List[Any]:
      # Initialize image_processing
      UpperCAmelCase       :	Tuple											= self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      UpperCAmelCase       :	List[str]											= 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
      UpperCAmelCase       :	str											= image_processing(image_inputs[0]     ,			return_tensors='pt'       ).pixel_values

      UpperCAmelCase					, UpperCAmelCase       :	Union[str, 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
      UpperCAmelCase       :	Optional[int]											= image_processing(lowerCAmelCase_     ,			return_tensors='pt'       ).pixel_values

      UpperCAmelCase					, UpperCAmelCase       :	Optional[int]											= 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    UpperCAmelCase_       (		self :     Union[str, Any]       )						->	Dict:
      # Initialize image_processing
      UpperCAmelCase       :	Union[str, Any]											= self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      UpperCAmelCase       :	List[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
      UpperCAmelCase       :	Any											= image_processing(image_inputs[0]     ,			return_tensors='pt'       ).pixel_values

      UpperCAmelCase					, UpperCAmelCase       :	Optional[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
      UpperCAmelCase       :	str											= image_processing(lowerCAmelCase_     ,			return_tensors='pt'       ).pixel_values

      UpperCAmelCase					, UpperCAmelCase       :	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,
          )     ,			)


     @slow
     def    UpperCAmelCase_       (		self :     Tuple       )						->	str:
      # prepare image and target
      UpperCAmelCase       :	Optional[Any]											= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
      with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,			'r'       ) as f:
       UpperCAmelCase       :	Tuple											= json.loads(f.read()       )

      UpperCAmelCase       :	Optional[int]											= {'image_id': 39_769, 'annotations': target}

      # encode them
      UpperCAmelCase       :	Any											= DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'       )
      UpperCAmelCase       :	int											= image_processing(images=lowerCAmelCase_     ,			annotations=lowerCAmelCase_     ,			return_tensors='pt'       )

      # verify pixel values
      UpperCAmelCase       :	Union[str, Any]											= torch.Size([1, 3, 800, 1_066]       )
      self.assertEqual(encoding['pixel_values'].shape     ,			lowerCAmelCase_       )

      UpperCAmelCase       :	int											= torch.tensor([0.2796, 0.3138, 0.3481]       )
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,			lowerCAmelCase_     ,			atol=1E-4       )       )

      # verify area
      UpperCAmelCase       :	Dict											= 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']     ,			lowerCAmelCase_       )       )
      # verify boxes
      UpperCAmelCase       :	Any											= torch.Size([6, 4]       )
      self.assertEqual(encoding['labels'][0]['boxes'].shape     ,			lowerCAmelCase_       )
      UpperCAmelCase       :	Optional[int]											= torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,			lowerCAmelCase_     ,			atol=1E-3       )       )
      # verify image_id
      UpperCAmelCase       :	Optional[int]											= torch.tensor([39_769]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,			lowerCAmelCase_       )       )
      # verify is_crowd
      UpperCAmelCase       :	int											= torch.tensor([0, 0, 0, 0, 0, 0]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,			lowerCAmelCase_       )       )
      # verify class_labels
      UpperCAmelCase       :	List[str]											= torch.tensor([75, 75, 63, 65, 17, 17]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,			lowerCAmelCase_       )       )
      # verify orig_size
      UpperCAmelCase       :	List[Any]											= torch.tensor([480, 640]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,			lowerCAmelCase_       )       )
      # verify size
      UpperCAmelCase       :	List[str]											= torch.tensor([800, 1_066]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,			lowerCAmelCase_       )       )




     @slow
     def    UpperCAmelCase_       (		self :     Optional[int]       )						->	str:
      # prepare image, target and masks_path
      UpperCAmelCase       :	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:
       UpperCAmelCase       :	str											= json.loads(f.read()       )

      UpperCAmelCase       :	Optional[int]											= {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

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

      # encode them
      UpperCAmelCase       :	Union[str, Any]											= DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'       )
      UpperCAmelCase       :	int											= image_processing(images=lowerCAmelCase_     ,			annotations=lowerCAmelCase_     ,			masks_path=lowerCAmelCase_     ,			return_tensors='pt'       )

      # verify pixel values
      UpperCAmelCase       :	Dict											= torch.Size([1, 3, 800, 1_066]       )
      self.assertEqual(encoding['pixel_values'].shape     ,			lowerCAmelCase_       )

      UpperCAmelCase       :	Dict											= torch.tensor([0.2796, 0.3138, 0.3481]       )
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,			lowerCAmelCase_     ,			atol=1E-4       )       )

      # verify area
      UpperCAmelCase       :	Dict											= 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']     ,			lowerCAmelCase_       )       )
      # verify boxes
      UpperCAmelCase       :	Dict											= torch.Size([6, 4]       )
      self.assertEqual(encoding['labels'][0]['boxes'].shape     ,			lowerCAmelCase_       )
      UpperCAmelCase       :	str											= torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,			lowerCAmelCase_     ,			atol=1E-3       )       )
      # verify image_id
      UpperCAmelCase       :	Any											= torch.tensor([39_769]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,			lowerCAmelCase_       )       )
      # verify is_crowd
      UpperCAmelCase       :	List[str]											= torch.tensor([0, 0, 0, 0, 0, 0]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,			lowerCAmelCase_       )       )
      # verify class_labels
      UpperCAmelCase       :	List[str]											= torch.tensor([17, 17, 63, 75, 75, 93]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,			lowerCAmelCase_       )       )
      # verify masks
      UpperCAmelCase       :	Optional[Any]											= 822_873
      self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,			lowerCAmelCase_       )
      # verify orig_size
      UpperCAmelCase       :	Any											= torch.tensor([480, 640]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,			lowerCAmelCase_       )       )
      # verify size
      UpperCAmelCase       :	List[Any]											= torch.tensor([800, 1_066]       )
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,			lowerCAmelCase_       )       )











def   a_    ( lowerCAmelCase_      :			str,	lowerCAmelCase_      :			int	):
   return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase_	) - ngram_size + 1	)]


if __name__ == "__main__":
     from doctest import testmod

     testmod()

