





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        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def __init__(				self							:List[str]      ,   snake_case							:Dict      ,   snake_case							:Optional[int]=7      ,   snake_case							:Any=3      ,   snake_case							:int=30      ,   snake_case							:int=400      ,   snake_case							:Optional[Any]=True      ,   snake_case							:int=None      ,   snake_case							:str=True      ,   snake_case							:Optional[int]=[0.5, 0.5, 0.5]      ,   snake_case							:Any=[0.5, 0.5, 0.5]      ,   snake_case							:Dict=True      ,   snake_case							:Any=1 / 255      ,   snake_case							:Optional[int]=True      ,   ):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
        A_       :					Any        =      parent
        A_       :					str        =      batch_size
        A_       :					int        =      num_channels
        A_       :					str        =      min_resolution
        A_       :					int        =      max_resolution
        A_       :					Union[str, Any]        =      do_resize
        A_       :					Any        =      size
        A_       :					Any        =      do_normalize
        A_       :					int        =      image_mean
        A_       :					int        =      image_std
        A_       :					List[str]        =      do_rescale
        A_       :					str        =      rescale_factor
        A_       :					Union[str, Any]        =      do_pad



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''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     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   snake_case							:Union[str, Any]      ,   snake_case							:str=False	):





        '''simple docstring'''




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

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

        return expected_height, expected_width




@require_torch
@require_vision
class        __magic_name__	(				lowerCamelCase__     ,					unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  DetaImageProcessor if is_vision_available() else None



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        A_       :					List[Any]        =      DetaImageProcessingTester(self	)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()



     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''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     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        A_       :					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      ,   snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        pass



     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        A_       :					List[Any]        =      self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        A_       :					List[str]        =      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_       :					int        =      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_   ,    A_       :					str        =      self.image_processor_tester.get_expected_values(snake_case      ,   batched=snake_case	)

        A_       :					int        =      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     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					Dict        =      self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        A_       :					int        =      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_       :					Optional[int]        =      image_processing(image_inputs[0]      ,   return_tensors="pt"	).pixel_values

        A_   ,    A_       :					str        =      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_       :					Union[str, Any]        =      image_processing(snake_case      ,   return_tensors="pt"	).pixel_values

        A_   ,    A_       :					Dict        =      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     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




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

        A_   ,    A_       :					Tuple        =      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     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




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

        A_       :					List[str]        =      {"image_id": 39_769, "annotations": target}

        # encode them
        A_       :					List[Any]        =      DetaImageProcessor()
        A_       :					Optional[int]        =      image_processing(images=snake_case      ,   annotations=snake_case      ,   return_tensors="pt"	)

        # verify pixel values
        A_       :					List[str]        =      torch.Size([1, 3, 800, 1_066]	)
        self.assertEqual(encoding["pixel_values"].shape      ,   snake_case	)

        A_       :					Any        =      torch.tensor([0.2796, 0.3138, 0.3481]	)
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,   snake_case      ,   atol=1e-4	)	)

        # verify area
        A_       :					Optional[Any]        =      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
        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_       :					Tuple        =      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,   snake_case      ,   atol=1e-3	)	)
        # verify image_id
        A_       :					Tuple        =      torch.tensor([39_769]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,   snake_case	)	)
        # verify is_crowd
        A_       :					Optional[Any]        =      torch.tensor([0, 0, 0, 0, 0, 0]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,   snake_case	)	)
        # verify class_labels
        A_       :					Union[str, Any]        =      torch.tensor([75, 75, 63, 65, 17, 17]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,   snake_case	)	)
        # verify orig_size
        A_       :					List[Any]        =      torch.tensor([480, 640]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,   snake_case	)	)
        # verify size
        A_       :					Tuple        =      torch.tensor([800, 1_066]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,   snake_case	)	)





     @slow
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_       :					Any        =      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_       :					str        =      {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

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

        # encode them
        A_       :					Any        =      DetaImageProcessor(format="coco_panoptic"	)
        A_       :					str        =      image_processing(images=snake_case      ,   annotations=snake_case      ,   masks_path=snake_case      ,   return_tensors="pt"	)

        # verify pixel values
        A_       :					Optional[Any]        =      torch.Size([1, 3, 800, 1_066]	)
        self.assertEqual(encoding["pixel_values"].shape      ,   snake_case	)

        A_       :					Optional[Any]        =      torch.tensor([0.2796, 0.3138, 0.3481]	)
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,   snake_case      ,   atol=1e-4	)	)

        # verify area
        A_       :					Optional[int]        =      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,   snake_case	)	)
        # verify boxes
        A_       :					List[Any]        =      torch.Size([6, 4]	)
        self.assertEqual(encoding["labels"][0]["boxes"].shape      ,   snake_case	)
        A_       :					Union[str, Any]        =      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,   snake_case      ,   atol=1e-3	)	)
        # verify image_id
        A_       :					Optional[Any]        =      torch.tensor([39_769]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,   snake_case	)	)
        # verify is_crowd
        A_       :					Optional[Any]        =      torch.tensor([0, 0, 0, 0, 0, 0]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,   snake_case	)	)
        # verify class_labels
        A_       :					List[str]        =      torch.tensor([17, 17, 63, 75, 75, 93]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,   snake_case	)	)
        # verify masks
        A_       :					List[Any]        =      822_873
        self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,   snake_case	)
        # verify orig_size
        A_       :					str        =      torch.tensor([480, 640]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,   snake_case	)	)
        # verify size
        A_       :					List[str]        =      torch.tensor([800, 1_066]	)
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,   snake_case	)	)







def   __snake_case    (       _lowerCAmelCase			:			int						)			->    bool:
   if num < 0:
      return False

   A_       :					int        =      num
   A_       :					int        =      0
   while num > 0:
      A_       :					str        =      rev_num * 10 + (num % 10)
      num //= 10

   return num_copy == rev_num


if __name__ == "__main__":
 import doctest

 doctest.testmod()
