





import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
														from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
														from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
							from PIL import Image




def    __UpperCAmelCase(      lowercase_ ,    lowercase_="shi-labs/oneformer_demo"      ):
							with open(hf_hub_download(lowercase_ ,    lowercase_ ,    repo_type='''dataset'''      ) ,    '''r'''      ) as f:
														_lowerCamelCase							:     Dict	=		json.load(lowercase_      )
							_lowerCamelCase							:     str	=		{}
							_lowerCamelCase							:     Any	=		[]
							_lowerCamelCase							:     Dict	=		[]
							for key, info in class_info.items():
														_lowerCamelCase							:     Tuple	=		info['''name''']
														class_names.append(info['''name''']      )
														if info["isthing"]:
																					thing_ids.append(int(lowercase_      )      )
							_lowerCamelCase							:     List[Any]	=		thing_ids
							_lowerCamelCase							:     Any	=		class_names
							return metadata







class 			__A       (      unittest.TestCase  ):




						"""simple docstring"""







						def __init__(		self      ,							a__      ,							a__=7      ,							a__=3      ,							a__=30      ,							a__=400      ,							a__=None      ,							a__=True      ,							a__=True      ,							a__=[0.5, 0.5, 0.5]      ,							a__=[0.5, 0.5, 0.5]      ,							a__=10      ,							a__=False      ,							a__=255      ,							a__="shi-labs/oneformer_demo"      ,							a__="ade20k_panoptic.json"      ,							a__=10      ,							):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[Any]	=		parent
													_lowerCamelCase							:     Optional[Any]	=		batch_size
													_lowerCamelCase							:     Optional[int]	=		num_channels
													_lowerCamelCase							:     Optional[Any]	=		min_resolution
													_lowerCamelCase							:     Union[str, Any]	=		max_resolution
													_lowerCamelCase							:     Tuple	=		do_resize
													_lowerCamelCase							:     Optional[Any]	=		{'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
													_lowerCamelCase							:     Union[str, Any]	=		do_normalize
													_lowerCamelCase							:     Dict	=		image_mean
													_lowerCamelCase							:     Dict	=		image_std
													_lowerCamelCase							:     Any	=		class_info_file
													_lowerCamelCase							:     Union[str, Any]	=		prepare_metadata(a__      ,							a__)
													_lowerCamelCase							:     List[str]	=		num_text
													_lowerCamelCase							:     Dict	=		repo_path

													# for the post_process_functions
													_lowerCamelCase							:     Optional[int]	=		2
													_lowerCamelCase							:     Dict	=		10
													_lowerCamelCase							:     Optional[Any]	=		10
													_lowerCamelCase							:     Dict	=		3
													_lowerCamelCase							:     Optional[Any]	=		4
													_lowerCamelCase							:     Union[str, Any]	=		num_labels
													_lowerCamelCase							:     str	=		do_reduce_labels
													_lowerCamelCase							:     List[Any]	=		ignore_index


						def 				__snake_case			(		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,
													    "num_labels": self.num_labels,
													    "do_reduce_labels": self.do_reduce_labels,
													    "ignore_index": self.ignore_index,
													    "class_info_file": self.class_info_file,
													    "metadata": self.metadata,
													    "num_text": self.num_text,
													}


						def 				__snake_case			(		self      ,							a__      ,							a__=False):



													"""simple docstring"""





													if not batched:
																				_lowerCamelCase							:     Tuple	=		image_inputs[0]
																				if isinstance(a__      ,							Image.Image):
																											_lowerCamelCase							:     Any	=		image.size
																				else:
																											_lowerCamelCase							:     Optional[int]	=		image.shape[1], image.shape[2]
																				if w < h:
																											_lowerCamelCase							:     Any	=		int(self.size['''shortest_edge'''] * h / w)
																											_lowerCamelCase							:     Any	=		self.size['''shortest_edge''']
																				elif w > h:
																											_lowerCamelCase							:     Tuple	=		self.size['''shortest_edge''']
																											_lowerCamelCase							:     Union[str, Any]	=		int(self.size['''shortest_edge'''] * w / h)
																				else:
																											_lowerCamelCase							:     Optional[Any]	=		self.size['''shortest_edge''']
																											_lowerCamelCase							:     Any	=		self.size['''shortest_edge''']

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

													return expected_height, expected_width



						def 				__snake_case			(		self):



													"""simple docstring"""





													return OneFormerForUniversalSegmentationOutput(
													    # +1 for null class
													    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1))      ,							masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width))      ,							)







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




						"""simple docstring"""







						UpperCAmelCase__						=      OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
						# only for test_image_processing_common.test_image_proc_to_json_string
						UpperCAmelCase__						=      image_processing_class


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Union[str, Any]	=		OneFormerImageProcessorTester(self)


						@property
						def 				__snake_case			(		self):



													"""simple docstring"""





													return self.image_processing_tester.prepare_image_processor_dict()


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     List[str]	=		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__      ,							'''size'''))
													self.assertTrue(hasattr(a__      ,							'''ignore_index'''))
													self.assertTrue(hasattr(a__      ,							'''class_info_file'''))
													self.assertTrue(hasattr(a__      ,							'''num_text'''))
													self.assertTrue(hasattr(a__      ,							'''repo_path'''))
													self.assertTrue(hasattr(a__      ,							'''metadata'''))
													self.assertTrue(hasattr(a__      ,							'''do_reduce_labels'''))


						def 				__snake_case			(		self):



													"""simple docstring"""





													pass


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     List[str]	=		self.image_processing_class(**self.image_processor_dict)
													# create random PIL images
													_lowerCamelCase							:     List[Any]	=		prepare_image_inputs(self.image_processing_tester      ,							equal_resolution=a__)
													for image in image_inputs:
																				self.assertIsInstance(a__      ,							Image.Image)

													# Test not batched input
													_lowerCamelCase							:     int	=		image_processor(image_inputs[0]      ,							['''semantic''']      ,							return_tensors='''pt''').pixel_values

													_lowerCamelCase							:     Union[str, Any]	=		self.image_processing_tester.get_expected_values(a__)

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

													# Test batched
													_lowerCamelCase							:     Optional[int]	=		self.image_processing_tester.get_expected_values(a__      ,							batched=a__)

													_lowerCamelCase							:     List[str]	=		image_processor(
													    a__      ,							['''semantic'''] * len(a__)      ,							return_tensors='''pt''').pixel_values
													self.assertEqual(
													    encoded_images.shape      ,							(
													        self.image_processing_tester.batch_size,
													        self.image_processing_tester.num_channels,
													        expected_height,
													        expected_width,
													    )      ,							)


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[int]	=		self.image_processing_class(**self.image_processor_dict)
													# create random numpy tensors
													_lowerCamelCase							:     Union[str, Any]	=		prepare_image_inputs(self.image_processing_tester      ,							equal_resolution=a__      ,							numpify=a__)
													for image in image_inputs:
																				self.assertIsInstance(a__      ,							np.ndarray)

													# Test not batched input
													_lowerCamelCase							:     str	=		image_processor(image_inputs[0]      ,							['''semantic''']      ,							return_tensors='''pt''').pixel_values

													_lowerCamelCase							:     Optional[Any]	=		self.image_processing_tester.get_expected_values(a__)

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

													# Test batched
													_lowerCamelCase							:     Union[str, Any]	=		self.image_processing_tester.get_expected_values(a__      ,							batched=a__)

													_lowerCamelCase							:     List[Any]	=		image_processor(
													    a__      ,							['''semantic'''] * len(a__)      ,							return_tensors='''pt''').pixel_values
													self.assertEqual(
													    encoded_images.shape      ,							(
													        self.image_processing_tester.batch_size,
													        self.image_processing_tester.num_channels,
													        expected_height,
													        expected_width,
													    )      ,							)


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Any	=		self.image_processing_class(**self.image_processor_dict)
													# create random PyTorch tensors
													_lowerCamelCase							:     List[str]	=		prepare_image_inputs(self.image_processing_tester      ,							equal_resolution=a__      ,							torchify=a__)
													for image in image_inputs:
																				self.assertIsInstance(a__      ,							torch.Tensor)

													# Test not batched input
													_lowerCamelCase							:     Dict	=		image_processor(image_inputs[0]      ,							['''semantic''']      ,							return_tensors='''pt''').pixel_values

													_lowerCamelCase							:     List[str]	=		self.image_processing_tester.get_expected_values(a__)

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

													# Test batched
													_lowerCamelCase							:     Union[str, Any]	=		self.image_processing_tester.get_expected_values(a__      ,							batched=a__)

													_lowerCamelCase							:     Union[str, Any]	=		image_processor(
													    a__      ,							['''semantic'''] * len(a__)      ,							return_tensors='''pt''').pixel_values
													self.assertEqual(
													    encoded_images.shape      ,							(
													        self.image_processing_tester.batch_size,
													        self.image_processing_tester.num_channels,
													        expected_height,
													        expected_width,
													    )      ,							)


						def 				__snake_case			(		self      ,							a__=False      ,							a__=False      ,							a__="np"):



													"""simple docstring"""





													_lowerCamelCase							:     List[str]	=		self.image_processing_class(**self.image_processor_dict)
													# prepare image and target
													_lowerCamelCase							:     Any	=		self.image_processing_tester.num_labels
													_lowerCamelCase							:     Optional[Any]	=		None
													_lowerCamelCase							:     Union[str, Any]	=		None
													_lowerCamelCase							:     Tuple	=		prepare_image_inputs(self.image_processing_tester      ,							equal_resolution=a__)
													if with_segmentation_maps:
																				_lowerCamelCase							:     Tuple	=		num_labels
																				if is_instance_map:
																											_lowerCamelCase							:     Optional[Any]	=		list(range(a__)) * 2
																											_lowerCamelCase							:     str	=		dict(enumerate(a__))
																				_lowerCamelCase							:     Union[str, Any]	=		[
																				    np.random.randint(0      ,							high * 2      ,							(img.size[1], img.size[0])).astype(np.uinta) for img in image_inputs
																				]
																				if segmentation_type == "pil":
																											_lowerCamelCase							:     int	=		[Image.fromarray(a__) for annotation in annotations]

													_lowerCamelCase							:     Optional[Any]	=		image_processor(
													    a__      ,							['''semantic'''] * len(a__)      ,							a__      ,							return_tensors='''pt'''      ,							instance_id_to_semantic_id=a__      ,							pad_and_return_pixel_mask=a__      ,							)

													return inputs


						def 				__snake_case			(		self):



													"""simple docstring"""





													pass


						def 				__snake_case			(		self):



													"""simple docstring"""





													def common(a__=False      ,							a__=None):
																				_lowerCamelCase							:     Dict	=		self.comm_get_image_processor_inputs(
																				    with_segmentation_maps=a__      ,							is_instance_map=a__      ,							segmentation_type=a__)

																				_lowerCamelCase							:     str	=		inputs['''mask_labels''']
																				_lowerCamelCase							:     Tuple	=		inputs['''class_labels''']
																				_lowerCamelCase							:     Union[str, Any]	=		inputs['''pixel_values''']
																				_lowerCamelCase							:     Tuple	=		inputs['''text_inputs''']

																				# check the batch_size
																				for mask_label, class_label, text_input in zip(a__      ,							a__      ,							a__):
																											self.assertEqual(mask_label.shape[0]      ,							class_label.shape[0])
																											# this ensure padding has happened
																											self.assertEqual(mask_label.shape[1:]      ,							pixel_values.shape[2:])
																											self.assertEqual(len(a__)      ,							self.image_processing_tester.num_text)

													common()
													common(is_instance_map=a__)
													common(is_instance_map=a__      ,							segmentation_type='''pil''')
													common(is_instance_map=a__      ,							segmentation_type='''pil''')


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[int]	=		np.zeros((20, 50))
													_lowerCamelCase							:     List[Any]	=		1
													_lowerCamelCase							:     List[Any]	=		1
													_lowerCamelCase							:     str	=		1

													_lowerCamelCase							:     Any	=		binary_mask_to_rle(a__)
													self.assertEqual(len(a__)      ,							4)
													self.assertEqual(rle[0]      ,							21)
													self.assertEqual(rle[1]      ,							45)


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[Any]	=		self.image_processing_class(
													    num_labels=self.image_processing_tester.num_classes      ,							max_seq_length=77      ,							task_seq_length=77      ,							class_info_file='''ade20k_panoptic.json'''      ,							num_text=self.image_processing_tester.num_text      ,							repo_path='''shi-labs/oneformer_demo'''      ,							)
													_lowerCamelCase							:     Optional[Any]	=		self.image_processing_tester.get_fake_oneformer_outputs()

													_lowerCamelCase							:     Union[str, Any]	=		fature_extractor.post_process_semantic_segmentation(a__)

													self.assertEqual(len(a__)      ,							self.image_processing_tester.batch_size)
													self.assertEqual(
													    segmentation[0].shape      ,							(
													        self.image_processing_tester.height,
													        self.image_processing_tester.width,
													    )      ,							)

													_lowerCamelCase							:     Any	=		[(1, 4) for i in range(self.image_processing_tester.batch_size)]
													_lowerCamelCase							:     Optional[int]	=		fature_extractor.post_process_semantic_segmentation(a__      ,							target_sizes=a__)

													self.assertEqual(segmentation[0].shape      ,							target_sizes[0])


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Dict	=		self.image_processing_class(
													    num_labels=self.image_processing_tester.num_classes      ,							max_seq_length=77      ,							task_seq_length=77      ,							class_info_file='''ade20k_panoptic.json'''      ,							num_text=self.image_processing_tester.num_text      ,							repo_path='''shi-labs/oneformer_demo'''      ,							)
													_lowerCamelCase							:     List[str]	=		self.image_processing_tester.get_fake_oneformer_outputs()
													_lowerCamelCase							:     List[Any]	=		image_processor.post_process_instance_segmentation(a__      ,							threshold=0)

													self.assertTrue(len(a__) == self.image_processing_tester.batch_size)
													for el in segmentation:
																				self.assertTrue('''segmentation''' in el)
																				self.assertTrue('''segments_info''' in el)
																				self.assertEqual(type(el['''segments_info'''])      ,							a__)
																				self.assertEqual(
																				    el['''segmentation'''].shape      ,							(self.image_processing_tester.height, self.image_processing_tester.width))



						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Any	=		self.image_processing_class(
													    num_labels=self.image_processing_tester.num_classes      ,							max_seq_length=77      ,							task_seq_length=77      ,							class_info_file='''ade20k_panoptic.json'''      ,							num_text=self.image_processing_tester.num_text      ,							repo_path='''shi-labs/oneformer_demo'''      ,							)
													_lowerCamelCase							:     Tuple	=		self.image_processing_tester.get_fake_oneformer_outputs()
													_lowerCamelCase							:     Optional[Any]	=		image_processor.post_process_panoptic_segmentation(a__      ,							threshold=0)

													self.assertTrue(len(a__) == self.image_processing_tester.batch_size)
													for el in segmentation:
																				self.assertTrue('''segmentation''' in el)
																				self.assertTrue('''segments_info''' in el)
																				self.assertEqual(type(el['''segments_info'''])      ,							a__)
																				self.assertEqual(
																				    el['''segmentation'''].shape      ,							(self.image_processing_tester.height, self.image_processing_tester.width))








import unittest

from knapsack import knapsack as k







class 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		0
             _lowerCamelCase							:     int	=		[0]
             _lowerCamelCase							:     Optional[int]	=		[0]
             _lowerCamelCase							:     Dict	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							0)

             _lowerCamelCase							:     str	=		[60]
             _lowerCamelCase							:     List[str]	=		[10]
             _lowerCamelCase							:     List[Any]	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							0)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		3
             _lowerCamelCase							:     List[Any]	=		[1, 2, 3]
             _lowerCamelCase							:     Dict	=		[3, 2, 1]
             _lowerCamelCase							:     Any	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							5)



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		50
             _lowerCamelCase							:     Dict	=		[60, 100, 120]
             _lowerCamelCase							:     Dict	=		[10, 20, 30]
             _lowerCamelCase							:     Union[str, Any]	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							220)


if __name__ == "__main__":
       unittest.main()

