


"""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 DetaImageProcessor


class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=7       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=30       ,		UpperCAmelCase__=400       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=[0.5, 0.5, 0.5]       ,		UpperCAmelCase__=[0.5, 0.5, 0.5]       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=1 / 255       ,		UpperCAmelCase__=True       ,		):
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								SCREAMING_SNAKE_CASE__     							=				size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
								SCREAMING_SNAKE_CASE__     							=				parent
								SCREAMING_SNAKE_CASE__     							=				batch_size
								SCREAMING_SNAKE_CASE__     							=				num_channels
								SCREAMING_SNAKE_CASE__     							=				min_resolution
								SCREAMING_SNAKE_CASE__     							=				max_resolution
								SCREAMING_SNAKE_CASE__     							=				do_resize
								SCREAMING_SNAKE_CASE__     							=				size
								SCREAMING_SNAKE_CASE__     							=				do_normalize
								SCREAMING_SNAKE_CASE__     							=				image_mean
								SCREAMING_SNAKE_CASE__     							=				image_std
								SCREAMING_SNAKE_CASE__     							=				do_rescale
								SCREAMING_SNAKE_CASE__     							=				rescale_factor
								SCREAMING_SNAKE_CASE__     							=				do_pad



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

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

								return expected_height, expected_width


@require_torch
@require_vision
class 			lowerCamelCase_		(			lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Optional[int]      							=					DetaImageProcessor if is_vision_available() else None



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				DetaImageProcessingTester(self     )



				@property
				def   lowerCAmelCase__		(						self     ):
								return self.image_processor_tester.prepare_image_processor_dict()



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.image_processing_class(**self.image_processor_dict     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"image_mean"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"image_std"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"do_normalize"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"do_resize"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"do_rescale"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"do_pad"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"size"     )     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				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       ,		UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								pass



				def   lowerCAmelCase__		(						self     ):
								# Initialize image_processing
								SCREAMING_SNAKE_CASE__     							=				self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								SCREAMING_SNAKE_CASE__     							=				prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=UpperCAmelCase__     )
								for image in image_inputs:
												self.assertIsInstance(UpperCAmelCase__       ,		Image.Image     )

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

								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processor_tester.get_expected_values(UpperCAmelCase__     )

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

								# Test batched
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processor_tester.get_expected_values(UpperCAmelCase__       ,		batched=UpperCAmelCase__     )

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

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

								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processor_tester.get_expected_values(UpperCAmelCase__     )

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

								# Test batched
								SCREAMING_SNAKE_CASE__     							=				image_processing(UpperCAmelCase__       ,		return_tensors="pt"     ).pixel_values

								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processor_tester.get_expected_values(UpperCAmelCase__       ,		batched=UpperCAmelCase__     )

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



				def   lowerCAmelCase__		(						self     ):
								# Initialize image_processing
								SCREAMING_SNAKE_CASE__     							=				self.image_processing_class(**self.image_processor_dict     )
								# create random PyTorch tensors
								SCREAMING_SNAKE_CASE__     							=				prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=UpperCAmelCase__       ,		torchify=UpperCAmelCase__     )
								for image in image_inputs:
												self.assertIsInstance(UpperCAmelCase__       ,		torch.Tensor     )

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

								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processor_tester.get_expected_values(UpperCAmelCase__     )

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

								# Test batched
								SCREAMING_SNAKE_CASE__     							=				image_processing(UpperCAmelCase__       ,		return_tensors="pt"     ).pixel_values

								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processor_tester.get_expected_values(UpperCAmelCase__       ,		batched=UpperCAmelCase__     )

								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     ):
								# prepare image and target
								SCREAMING_SNAKE_CASE__     							=				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__     							=				json.loads(f.read()     )

								SCREAMING_SNAKE_CASE__     							=				{"image_id": 3_9769, "annotations": target}

								# encode them
								SCREAMING_SNAKE_CASE__     							=				DetaImageProcessor()
								SCREAMING_SNAKE_CASE__     							=				image_processing(images=UpperCAmelCase__       ,		annotations=UpperCAmelCase__       ,		return_tensors="pt"     )

								# verify pixel values
								SCREAMING_SNAKE_CASE__     							=				torch.Size([1, 3, 800, 1066]     )
								self.assertEqual(encoding["pixel_values"].shape       ,		UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,		UpperCAmelCase__       ,		atol=1e-4     )     )

								# verify area
								SCREAMING_SNAKE_CASE__     							=				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"]       ,		UpperCAmelCase__     )     )
								# verify boxes
								SCREAMING_SNAKE_CASE__     							=				torch.Size([6, 4]     )
								self.assertEqual(encoding["labels"][0]["boxes"].shape       ,		UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,		UpperCAmelCase__       ,		atol=1e-3     )     )
								# verify image_id
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([3_9769]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,		UpperCAmelCase__     )     )
								# verify is_crowd
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([0, 0, 0, 0, 0, 0]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,		UpperCAmelCase__     )     )
								# verify class_labels
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([75, 75, 63, 65, 17, 17]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,		UpperCAmelCase__     )     )
								# verify orig_size
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([480, 640]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,		UpperCAmelCase__     )     )
								# verify size
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([800, 1066]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,		UpperCAmelCase__     )     )






				@slow
				def   lowerCAmelCase__		(						self     ):
								# prepare image, target and masks_path
								SCREAMING_SNAKE_CASE__     							=				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__     							=				json.loads(f.read()     )

								SCREAMING_SNAKE_CASE__     							=				{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

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

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

								# verify pixel values
								SCREAMING_SNAKE_CASE__     							=				torch.Size([1, 3, 800, 1066]     )
								self.assertEqual(encoding["pixel_values"].shape       ,		UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,		UpperCAmelCase__       ,		atol=1e-4     )     )

								# verify area
								SCREAMING_SNAKE_CASE__     							=				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"]       ,		UpperCAmelCase__     )     )
								# verify boxes
								SCREAMING_SNAKE_CASE__     							=				torch.Size([6, 4]     )
								self.assertEqual(encoding["labels"][0]["boxes"].shape       ,		UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,		UpperCAmelCase__       ,		atol=1e-3     )     )
								# verify image_id
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([3_9769]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,		UpperCAmelCase__     )     )
								# verify is_crowd
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([0, 0, 0, 0, 0, 0]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,		UpperCAmelCase__     )     )
								# verify class_labels
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([17, 17, 63, 75, 75, 93]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,		UpperCAmelCase__     )     )
								# verify masks
								SCREAMING_SNAKE_CASE__     							=				82_2873
								self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,		UpperCAmelCase__     )
								# verify orig_size
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([480, 640]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,		UpperCAmelCase__     )     )
								# verify size
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([800, 1066]     )
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,		UpperCAmelCase__     )     )




"""simple docstring"""






from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					CustomTokenizer
				pass
