






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     a     (		lowerCamelCase_						,  lowerCamelCase_="shi-labs/oneformer_demo"							):






				'''simple docstring'''
				with open(hf_hub_download(lowerCamelCase_						,  lowerCamelCase_						,  repo_type='''dataset'''							)						,  '''r'''							) as f:
								lowercase__  					=     json.load(lowerCamelCase_							)
				lowercase__  					=     {}
				lowercase__  					=     []
				lowercase__  					=     []
				for key, info in class_info.items():
								lowercase__  					=     info['''name''']
								class_names.append(info['''name''']							)
								if info["isthing"]:
												thing_ids.append(int(lowerCamelCase_							)							)
				lowercase__  					=     thing_ids
				lowercase__  					=     class_names
				return metadata
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def __init__(	self		:       Dict,    lowerCamelCase		:       Any,    lowerCamelCase		:       Dict=7,    lowerCamelCase		:       int=3,    lowerCamelCase		:       List[str]=30,    lowerCamelCase		:       List[Any]=400,    lowerCamelCase		:       Tuple=None,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       Dict=True,    lowerCamelCase		:       Optional[Any]=[0.5, 0.5, 0.5],    lowerCamelCase		:       List[Any]=[0.5, 0.5, 0.5],    lowerCamelCase		:       Tuple=10,    lowerCamelCase		:       Dict=False,    lowerCamelCase		:       List[Any]=255,    lowerCamelCase		:       Optional[Any]="shi-labs/oneformer_demo",    lowerCamelCase		:       List[Any]="ade20k_panoptic.json",    lowerCamelCase		:       Dict=10,    ):

								'''simple docstring'''
								lowercase__  					=     parent
								lowercase__  					=     batch_size
								lowercase__  					=     num_channels
								lowercase__  					=     min_resolution
								lowercase__  					=     max_resolution
								lowercase__  					=     do_resize
								lowercase__  					=     {'''shortest_edge''': 32, '''longest_edge''': 1_333} if size is None else size
								lowercase__  					=     do_normalize
								lowercase__  					=     image_mean
								lowercase__  					=     image_std
								lowercase__  					=     class_info_file
								lowercase__  					=     prepare_metadata(lowerCamelCase,    lowerCamelCase    )
								lowercase__  					=     num_text
								lowercase__  					=     repo_path

								# for the post_process_functions
								lowercase__  					=     2
								lowercase__  					=     10
								lowercase__  					=     10
								lowercase__  					=     3
								lowercase__  					=     4
								lowercase__  					=     num_labels
								lowercase__  					=     do_reduce_labels
								lowercase__  					=     ignore_index




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''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 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       str,    lowerCamelCase		:       Dict=False    ):

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

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

								return expected_height, expected_width




				def 							lowercase__  (	self		:       str    ):

								'''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  _UpperCAmelCase   (			A__						,unittest.TestCase      ):

				"""simple docstring"""




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




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     OneFormerImageProcessorTester(self    )




				@property
				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								return self.image_processing_tester.prepare_image_processor_dict()




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(lowerCamelCase,    '''image_mean'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''image_std'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''do_normalize'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''do_resize'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''size'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''ignore_index'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''class_info_file'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''num_text'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''repo_path'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''metadata'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''do_reduce_labels'''    )    )




				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								# Initialize image_processor
								lowercase__  					=     self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								lowercase__  					=     prepare_image_inputs(self.image_processing_tester,    equal_resolution=lowerCamelCase    )
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,    Image.Image    )

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

								lowercase__					,		lowercase__  					=     self.image_processing_tester.get_expected_values(lowerCamelCase    )

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

								# Test batched
								lowercase__					,		lowercase__  					=     self.image_processing_tester.get_expected_values(lowerCamelCase,    batched=lowerCamelCase    )

								lowercase__  					=     image_processor(
								    lowerCamelCase,    ['''semantic'''] * len(lowerCamelCase    ),    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 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								# Initialize image_processor
								lowercase__  					=     self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								lowercase__  					=     prepare_image_inputs(self.image_processing_tester,    equal_resolution=lowerCamelCase,    numpify=lowerCamelCase    )
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,    np.ndarray    )

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

								lowercase__					,		lowercase__  					=     self.image_processing_tester.get_expected_values(lowerCamelCase    )

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

								# Test batched
								lowercase__					,		lowercase__  					=     self.image_processing_tester.get_expected_values(lowerCamelCase,    batched=lowerCamelCase    )

								lowercase__  					=     image_processor(
								    lowerCamelCase,    ['''semantic'''] * len(lowerCamelCase    ),    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 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								# Initialize image_processor
								lowercase__  					=     self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								lowercase__  					=     prepare_image_inputs(self.image_processing_tester,    equal_resolution=lowerCamelCase,    torchify=lowerCamelCase    )
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,    torch.Tensor    )

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

								lowercase__					,		lowercase__  					=     self.image_processing_tester.get_expected_values(lowerCamelCase    )

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

								# Test batched
								lowercase__					,		lowercase__  					=     self.image_processing_tester.get_expected_values(lowerCamelCase,    batched=lowerCamelCase    )

								lowercase__  					=     image_processor(
								    lowerCamelCase,    ['''semantic'''] * len(lowerCamelCase    ),    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 							lowercase__  (	self		:       List[str],    lowerCamelCase		:       List[str]=False,    lowerCamelCase		:       Union[str, Any]=False,    lowerCamelCase		:       str="np"    ):

								'''simple docstring'''
								lowercase__  					=     self.image_processing_class(**self.image_processor_dict    )
								# prepare image and target
								lowercase__  					=     self.image_processing_tester.num_labels
								lowercase__  					=     None
								lowercase__  					=     None
								lowercase__  					=     prepare_image_inputs(self.image_processing_tester,    equal_resolution=lowerCamelCase    )
								if with_segmentation_maps:
												lowercase__  					=     num_labels
												if is_instance_map:
																lowercase__  					=     list(range(lowerCamelCase    )    ) * 2
																lowercase__  					=     dict(enumerate(lowerCamelCase    )    )
												lowercase__  					=     [
												    np.random.randint(0,    high * 2,    (img.size[1], img.size[0])    ).astype(np.uinta    ) for img in image_inputs
												]
												if segmentation_type == "pil":
																lowercase__  					=     [Image.fromarray(lowerCamelCase    ) for annotation in annotations]

								lowercase__  					=     image_processor(
								    lowerCamelCase,    ['''semantic'''] * len(lowerCamelCase    ),    lowerCamelCase,    return_tensors='''pt''',    instance_id_to_semantic_id=lowerCamelCase,    pad_and_return_pixel_mask=lowerCamelCase,    )

								return inputs




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								def common(lowerCamelCase		:       int=False,    lowerCamelCase		:       List[str]=None    ):
												lowercase__  					=     self.comm_get_image_processor_inputs(
												    with_segmentation_maps=lowerCamelCase,    is_instance_map=lowerCamelCase,    segmentation_type=lowerCamelCase    )

												lowercase__  					=     inputs['''mask_labels''']
												lowercase__  					=     inputs['''class_labels''']
												lowercase__  					=     inputs['''pixel_values''']
												lowercase__  					=     inputs['''text_inputs''']

												# check the batch_size
												for mask_label, class_label, text_input in zip(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ):
																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(lowerCamelCase    ),    self.image_processing_tester.num_text    )

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




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     np.zeros((20, 50)    )
								lowercase__  					=     1
								lowercase__  					=     1
								lowercase__  					=     1

								lowercase__  					=     binary_mask_to_rle(lowerCamelCase    )
								self.assertEqual(len(lowerCamelCase    ),    4    )
								self.assertEqual(rle[0],    21    )
								self.assertEqual(rle[1],    45    )




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     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''',    )
								lowercase__  					=     self.image_processing_tester.get_fake_oneformer_outputs()

								lowercase__  					=     fature_extractor.post_process_semantic_segmentation(lowerCamelCase    )

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

								lowercase__  					=     [(1, 4) for i in range(self.image_processing_tester.batch_size    )]
								lowercase__  					=     fature_extractor.post_process_semantic_segmentation(lowerCamelCase,    target_sizes=lowerCamelCase    )

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




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     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''',    )
								lowercase__  					=     self.image_processing_tester.get_fake_oneformer_outputs()
								lowercase__  					=     image_processor.post_process_instance_segmentation(lowerCamelCase,    threshold=0    )

								self.assertTrue(len(lowerCamelCase    ) == 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''']    ),    lowerCamelCase    )
												self.assertEqual(
												    el['''segmentation'''].shape,    (self.image_processing_tester.height, self.image_processing_tester.width)    )




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     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''',    )
								lowercase__  					=     self.image_processing_tester.get_fake_oneformer_outputs()
								lowercase__  					=     image_processor.post_process_panoptic_segmentation(lowerCamelCase,    threshold=0    )

								self.assertTrue(len(lowerCamelCase    ) == 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''']    ),    lowerCamelCase    )
												self.assertEqual(
												    el['''segmentation'''].shape,    (self.image_processing_tester.height, self.image_processing_tester.width)    )












def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     len(lowerCamelCase_							)
				lowercase__  					=     [[False] * (required_sum + 1) for _ in range(arr_len + 1							)]

				# for each arr value, a sum of zero(0) can be formed by not taking any element
				# hence True/1
				for i in range(arr_len + 1							):
								lowercase__  					=     True

				# sum is not zero and set is empty then false
				for i in range(1						,  required_sum + 1							):
								lowercase__  					=     False

				for i in range(1						,  arr_len + 1							):
								for j in range(1						,  required_sum + 1							):
												if arr[i - 1] > j:
																lowercase__  					=     subset[i - 1][j]
												if arr[i - 1] <= j:
																lowercase__  					=     subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

				return subset[arr_len][required_sum]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




