



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    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def __init__(self				,			__a				,			__a=7				,			__a=3				,			__a=30				,			__a=4_00				,			__a=True				,			__a=None				,			__a=True				,			__a=[0.5, 0.5, 0.5]				,			__a=[0.5, 0.5, 0.5]				,			__a=True				,			__a=1 / 2_55				,			__a=True				,			)     ->   Optional[int]:






           '''simple docstring'''



           lowerCamelCase								=		size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
           lowerCamelCase								=		parent
           lowerCamelCase								=		batch_size
           lowerCamelCase								=		num_channels
           lowerCamelCase								=		min_resolution
           lowerCamelCase								=		max_resolution
           lowerCamelCase								=		do_resize
           lowerCamelCase								=		size
           lowerCamelCase								=		do_normalize
           lowerCamelCase								=		image_mean
           lowerCamelCase								=		image_std
           lowerCamelCase								=		do_rescale
           lowerCamelCase								=		rescale_factor
           lowerCamelCase								=		do_pad







     def 						_a      (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 						_a      (self				,			__a				,			__a=False    )     ->   Any:






           '''simple docstring'''



           if not batched:
                 lowerCamelCase								=		image_inputs[0]
                 if isinstance(__a				,			Image.Image    ):
                       lowerCamelCase								=		image.size
                 else:
                       lowerCamelCase								=		image.shape[1], image.shape[2]
                 if w < h:
                       lowerCamelCase								=		int(self.size["shortest_edge"] * h / w    )
                       lowerCamelCase								=		self.size['shortest_edge']
                 elif w > h:
                       lowerCamelCase								=		self.size['shortest_edge']
                       lowerCamelCase								=		int(self.size["shortest_edge"] * w / h    )
                 else:
                       lowerCamelCase								=		self.size['shortest_edge']
                       lowerCamelCase								=		self.size['shortest_edge']

           else:
                 lowerCamelCase								=		[]
                 for image in image_inputs:
                       lowerCamelCase								=		self.get_expected_values([image]    )
                       expected_values.append((expected_height, expected_width)    )
                 lowerCamelCase								=		max(__a				,			key=lambda __a    : item[0]    )[0]
                 lowerCamelCase								=		max(__a				,			key=lambda __a    : item[1]    )[1]

           return expected_height, expected_width





@require_torch
@require_vision
class    lowerCamelCase__  (						UpperCamelCase_       ,    unittest.TestCase):
     """simple docstring"""



     _A       =			DetaImageProcessor if is_vision_available() else None







     def 						_a      (self    )     ->   int:






           '''simple docstring'''



           lowerCamelCase								=		DetaImageProcessingTester(self    )







     @property
     def 						_a      (self    )     ->   Union[str, Any]:






           '''simple docstring'''



           return self.image_processor_tester.prepare_image_processor_dict()







     def 						_a      (self    )     ->   Optional[int]:






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(**self.image_processor_dict    )
           self.assertTrue(hasattr(__a				,			"image_mean"    )    )
           self.assertTrue(hasattr(__a				,			"image_std"    )    )
           self.assertTrue(hasattr(__a				,			"do_normalize"    )    )
           self.assertTrue(hasattr(__a				,			"do_resize"    )    )
           self.assertTrue(hasattr(__a				,			"do_rescale"    )    )
           self.assertTrue(hasattr(__a				,			"do_pad"    )    )
           self.assertTrue(hasattr(__a				,			"size"    )    )







     def 						_a      (self    )     ->   int:






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class.from_dict(self.image_processor_dict    )
           self.assertEqual(image_processor.size				,			{"shortest_edge": 18, "longest_edge": 13_33}    )
           self.assertEqual(image_processor.do_pad				,			__a    )







     def 						_a      (self    )     ->   List[str]:






           '''simple docstring'''



           pass







     def 						_a      (self    )     ->   int:






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(**self.image_processor_dict    )
           # create random PIL images
           lowerCamelCase								=		prepare_image_inputs(self.image_processor_tester				,			equal_resolution=__a    )
           for image in image_inputs:
                 self.assertIsInstance(__a				,			Image.Image    )

           # Test not batched input
           lowerCamelCase								=		image_processing(image_inputs[0]				,			return_tensors="pt"    ).pixel_values

           lowerCamelCase								=		self.image_processor_tester.get_expected_values(__a    )

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

           # Test batched
           lowerCamelCase								=		self.image_processor_tester.get_expected_values(__a				,			batched=__a    )

           lowerCamelCase								=		image_processing(__a				,			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 						_a      (self    )     ->   Tuple:






           '''simple docstring'''



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

           # Test not batched input
           lowerCamelCase								=		image_processing(image_inputs[0]				,			return_tensors="pt"    ).pixel_values

           lowerCamelCase								=		self.image_processor_tester.get_expected_values(__a    )

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

           # Test batched
           lowerCamelCase								=		image_processing(__a				,			return_tensors="pt"    ).pixel_values

           lowerCamelCase								=		self.image_processor_tester.get_expected_values(__a				,			batched=__a    )

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







     def 						_a      (self    )     ->   List[str]:






           '''simple docstring'''



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

           # Test not batched input
           lowerCamelCase								=		image_processing(image_inputs[0]				,			return_tensors="pt"    ).pixel_values

           lowerCamelCase								=		self.image_processor_tester.get_expected_values(__a    )

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

           # Test batched
           lowerCamelCase								=		image_processing(__a				,			return_tensors="pt"    ).pixel_values

           lowerCamelCase								=		self.image_processor_tester.get_expected_values(__a				,			batched=__a    )

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







     @slow
     def 						_a      (self    )     ->   List[Any]:






           '''simple docstring'''



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

           lowerCamelCase								=		{'image_id': 3_97_69, 'annotations': target}

           # encode them
           lowerCamelCase								=		DetaImageProcessor()
           lowerCamelCase								=		image_processing(images=__a				,			annotations=__a				,			return_tensors="pt"    )

           # verify pixel values
           lowerCamelCase								=		torch.Size([1, 3, 8_00, 10_66]    )
           self.assertEqual(encoding["pixel_values"].shape				,			__a    )

           lowerCamelCase								=		torch.tensor([0.2796, 0.3138, 0.3481]    )
           self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,			__a				,			atol=1E-4    )    )

           # verify area
           lowerCamelCase								=		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"]				,			__a    )    )
           # verify boxes
           lowerCamelCase								=		torch.Size([6, 4]    )
           self.assertEqual(encoding["labels"][0]["boxes"].shape				,			__a    )
           lowerCamelCase								=		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,			__a				,			atol=1E-3    )    )
           # verify image_id
           lowerCamelCase								=		torch.tensor([3_97_69]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,			__a    )    )
           # verify is_crowd
           lowerCamelCase								=		torch.tensor([0, 0, 0, 0, 0, 0]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,			__a    )    )
           # verify class_labels
           lowerCamelCase								=		torch.tensor([75, 75, 63, 65, 17, 17]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,			__a    )    )
           # verify orig_size
           lowerCamelCase								=		torch.tensor([4_80, 6_40]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,			__a    )    )
           # verify size
           lowerCamelCase								=		torch.tensor([8_00, 10_66]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,			__a    )    )







     @slow
     def 						_a      (self    )     ->   Dict:






           '''simple docstring'''



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

           lowerCamelCase								=		{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

           lowerCamelCase								=		pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"    )

           # encode them
           lowerCamelCase								=		DetaImageProcessor(format="coco_panoptic"    )
           lowerCamelCase								=		image_processing(images=__a				,			annotations=__a				,			masks_path=__a				,			return_tensors="pt"    )

           # verify pixel values
           lowerCamelCase								=		torch.Size([1, 3, 8_00, 10_66]    )
           self.assertEqual(encoding["pixel_values"].shape				,			__a    )

           lowerCamelCase								=		torch.tensor([0.2796, 0.3138, 0.3481]    )
           self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,			__a				,			atol=1E-4    )    )

           # verify area
           lowerCamelCase								=		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"]				,			__a    )    )
           # verify boxes
           lowerCamelCase								=		torch.Size([6, 4]    )
           self.assertEqual(encoding["labels"][0]["boxes"].shape				,			__a    )
           lowerCamelCase								=		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,			__a				,			atol=1E-3    )    )
           # verify image_id
           lowerCamelCase								=		torch.tensor([3_97_69]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,			__a    )    )
           # verify is_crowd
           lowerCamelCase								=		torch.tensor([0, 0, 0, 0, 0, 0]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,			__a    )    )
           # verify class_labels
           lowerCamelCase								=		torch.tensor([17, 17, 63, 75, 75, 93]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,			__a    )    )
           # verify masks
           lowerCamelCase								=		82_28_73
           self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,			__a    )
           # verify orig_size
           lowerCamelCase								=		torch.tensor([4_80, 6_40]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,			__a    )    )
           # verify size
           lowerCamelCase								=		torch.tensor([8_00, 10_66]    )
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,			__a    )    )




a_						:     List[str]	     =		'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

a_						:     Any	     =		[{'type': 'code', 'content': INSTALL_CONTENT}]
a_						:     str	     =		{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}