





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 ConditionalDetrImageProcessor






class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase=7 ,						__lowerCAmelCase=3 ,						__lowerCAmelCase=3_0 ,						__lowerCAmelCase=4_0_0 ,						__lowerCAmelCase=True ,						__lowerCAmelCase=None ,						__lowerCAmelCase=True ,						__lowerCAmelCase=[0.5, 0.5, 0.5] ,						__lowerCAmelCase=[0.5, 0.5, 0.5] ,						__lowerCAmelCase=True ,						__lowerCAmelCase=1 / 2_5_5 ,						__lowerCAmelCase=True ,						):



				'''simple docstring'''







				lowerCamelCase__              =	size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
				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 	__lowerCamelCase       (					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 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=False							):



				'''simple docstring'''







				if not batched:
					lowerCamelCase__              =	image_inputs[0]
					if isinstance(UpperCAmelCase_ ,						Image.Image							):
						lowerCamelCase__       ,			lowerCamelCase__              =	image.size
					else:
						lowerCamelCase__       ,			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__       ,			lowerCamelCase__              =	self.get_expected_values([image]							)
						expected_values.append((expected_height, expected_width)							)
					lowerCamelCase__              =	max(UpperCAmelCase_ ,						key=lambda __lowerCAmelCase							: item[0]							)[0]
					lowerCamelCase__              =	max(UpperCAmelCase_ ,						key=lambda __lowerCAmelCase							: item[1]							)[1]

				return expected_height, expected_width






@require_torch
@require_vision
class       __A					(					__UpperCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		ConditionalDetrImageProcessor if is_vision_available() else None





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	ConditionalDetrImageProcessingTester(self							)





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	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_ ,						'''size'''							)							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.image_processing_class.from_dict(self.image_processor_dict							)
				self.assertEqual(image_processor.size ,						{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}							)
				self.assertEqual(image_processor.do_pad ,						UpperCAmelCase_							)

				lowerCamelCase__              =	self.image_processing_class.from_dict(
				    self.image_processor_dict ,						size=4_2 ,						max_size=8_4 ,						pad_and_return_pixel_mask=UpperCAmelCase_							)
				self.assertEqual(image_processor.size ,						{'''shortest_edge''': 4_2, '''longest_edge''': 8_4}							)
				self.assertEqual(image_processor.do_pad ,						UpperCAmelCase_							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				pass





			def 	__lowerCamelCase       (					self							):



				'''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=UpperCAmelCase_							)
				for image in image_inputs:
					self.assertIsInstance(UpperCAmelCase_ ,						Image.Image							)

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

				lowerCamelCase__       ,			lowerCamelCase__              =	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
				lowerCamelCase__       ,			lowerCamelCase__              =	self.image_processor_tester.get_expected_values(UpperCAmelCase_ ,						batched=UpperCAmelCase_							)

				lowerCamelCase__              =	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							):



				'''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=UpperCAmelCase_ ,						numpify=UpperCAmelCase_							)
				for image in image_inputs:
					self.assertIsInstance(UpperCAmelCase_ ,						np.ndarray							)

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

				lowerCamelCase__       ,			lowerCamelCase__              =	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
				lowerCamelCase__              =	image_processing(UpperCAmelCase_ ,						return_tensors='''pt'''							).pixel_values

				lowerCamelCase__       ,			lowerCamelCase__              =	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							):



				'''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=UpperCAmelCase_ ,						torchify=UpperCAmelCase_							)
				for image in image_inputs:
					self.assertIsInstance(UpperCAmelCase_ ,						torch.Tensor							)

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

				lowerCamelCase__       ,			lowerCamelCase__              =	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
				lowerCamelCase__              =	image_processing(UpperCAmelCase_ ,						return_tensors='''pt'''							).pixel_values

				lowerCamelCase__       ,			lowerCamelCase__              =	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							):



				'''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_9_7_6_9, '''annotations''': target}

				# encode them
				lowerCamelCase__              =	ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''							)
				lowerCamelCase__              =	image_processing(images=UpperCAmelCase_ ,						annotations=UpperCAmelCase_ ,						return_tensors='''pt'''							)

				# verify pixel values
				lowerCamelCase__              =	torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
				self.assertEqual(encoding['''pixel_values'''].shape ,						UpperCAmelCase_							)

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

				# verify area
				lowerCamelCase__              =	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,						UpperCAmelCase_							)							)
				# verify boxes
				lowerCamelCase__              =	torch.Size([6, 4]							)
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,						UpperCAmelCase_							)
				lowerCamelCase__              =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,						UpperCAmelCase_ ,						atol=1E-3							)							)
				# verify image_id
				lowerCamelCase__              =	torch.tensor([3_9_7_6_9]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,						UpperCAmelCase_							)							)
				# verify is_crowd
				lowerCamelCase__              =	torch.tensor([0, 0, 0, 0, 0, 0]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,						UpperCAmelCase_							)							)
				# verify class_labels
				lowerCamelCase__              =	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,						UpperCAmelCase_							)							)
				# verify orig_size
				lowerCamelCase__              =	torch.tensor([4_8_0, 6_4_0]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,						UpperCAmelCase_							)							)
				# verify size
				lowerCamelCase__              =	torch.tensor([8_0_0, 1_0_6_6]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,						UpperCAmelCase_							)							)





			@slow
			def 	__lowerCamelCase       (					self							):



				'''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_9_7_6_9, '''segments_info''': target}

				lowerCamelCase__              =	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

				# encode them
				lowerCamelCase__              =	ConditionalDetrImageProcessor(format='''coco_panoptic'''							)
				lowerCamelCase__              =	image_processing(images=UpperCAmelCase_ ,						annotations=UpperCAmelCase_ ,						masks_path=UpperCAmelCase_ ,						return_tensors='''pt'''							)

				# verify pixel values
				lowerCamelCase__              =	torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
				self.assertEqual(encoding['''pixel_values'''].shape ,						UpperCAmelCase_							)

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

				# verify area
				lowerCamelCase__              =	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,						UpperCAmelCase_							)							)
				# verify boxes
				lowerCamelCase__              =	torch.Size([6, 4]							)
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,						UpperCAmelCase_							)
				lowerCamelCase__              =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,						UpperCAmelCase_ ,						atol=1E-3							)							)
				# verify image_id
				lowerCamelCase__              =	torch.tensor([3_9_7_6_9]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,						UpperCAmelCase_							)							)
				# verify is_crowd
				lowerCamelCase__              =	torch.tensor([0, 0, 0, 0, 0, 0]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,						UpperCAmelCase_							)							)
				# verify class_labels
				lowerCamelCase__              =	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,						UpperCAmelCase_							)							)
				# verify masks
				lowerCamelCase__              =	8_2_2_8_7_3
				self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,						UpperCAmelCase_							)
				# verify orig_size
				lowerCamelCase__              =	torch.tensor([4_8_0, 6_4_0]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,						UpperCAmelCase_							)							)
				# verify size
				lowerCamelCase__              =	torch.tensor([8_0_0, 1_0_6_6]							)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,						UpperCAmelCase_							)							)










def lowerCAmelCase__(__snake_case							)       ->   int:







	'''simple docstring'''







	if not grid or not grid[0]:
		raise TypeError('''The grid does not contain the appropriate information'''							)

	for cell_n in range(1 ,len(grid[0]							)							):
		grid[0][cell_n] += grid[0][cell_n - 1]
	lowerCamelCase__              =	grid[0]

	for row_n in range(1 ,len(__snake_case							)							):
		lowerCamelCase__              =	grid[row_n]
		lowerCamelCase__              =	fill_row(__snake_case ,__snake_case							)
		lowerCamelCase__              =	grid[row_n]

	return grid[-1][-1]





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   list:







	'''simple docstring'''







	current_row[0] += row_above[0]
	for cell_n in range(1 ,len(__snake_case							)							):
		current_row[cell_n] += min(current_row[cell_n - 1] ,row_above[cell_n]							)

	return current_row


if __name__ == "__main__":
			import doctest

			doctest.testmod()



