



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"   ) ->      List[str]:


						with open(hf_hub_download(__snake_case						,       __snake_case						,       repo_type="""dataset"""   )						,       """r"""   ) as f:
												__lowercase					=				json.load(__snake_case   )
						__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(__snake_case   )   )
						__lowercase					=				thing_ids
						__lowercase					=				class_names
						return metadata
class 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





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







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

									return expected_height, expected_width







			def 		snake_case__			(				self	:       Any   )						->					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 			_lowerCAmelCase   ( _A    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  List[str]      =			OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
			# only for test_image_processing_common.test_image_proc_to_json_string
			lowercase__      :  Optional[int]      =			image_processing_class





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					str:
									"""simple docstring"""







									__lowercase					=				OneFormerImageProcessorTester(self   )





			@property
			def 		snake_case__			(				self	:       List[str]   )						->					Tuple:
									"""simple docstring"""







									return self.image_processing_tester.prepare_image_processor_dict()





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(lowercase			,     """image_mean"""   )   )
									self.assertTrue(hasattr(lowercase			,     """image_std"""   )   )
									self.assertTrue(hasattr(lowercase			,     """do_normalize"""   )   )
									self.assertTrue(hasattr(lowercase			,     """do_resize"""   )   )
									self.assertTrue(hasattr(lowercase			,     """size"""   )   )
									self.assertTrue(hasattr(lowercase			,     """ignore_index"""   )   )
									self.assertTrue(hasattr(lowercase			,     """class_info_file"""   )   )
									self.assertTrue(hasattr(lowercase			,     """num_text"""   )   )
									self.assertTrue(hasattr(lowercase			,     """repo_path"""   )   )
									self.assertTrue(hasattr(lowercase			,     """metadata"""   )   )
									self.assertTrue(hasattr(lowercase			,     """do_reduce_labels"""   )   )





			def 		snake_case__			(				self	:       int   )						->					str:
									"""simple docstring"""







									pass





			def 		snake_case__			(				self	:       List[Any]   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				self.image_processing_class(**self.image_processor_dict   )
									# create random PIL images
									__lowercase					=				prepare_image_inputs(self.image_processing_tester			,     equal_resolution=lowercase   )
									for image in image_inputs:
															self.assertIsInstance(lowercase			,     Image.Image   )

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

									__lowercase					=				self.image_processing_tester.get_expected_values(lowercase   )

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

									# Test batched
									__lowercase					=				self.image_processing_tester.get_expected_values(lowercase			,     batched=lowercase   )

									__lowercase					=				image_processor(
									    lowercase			,     ["""semantic"""] * len(lowercase   )			,     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	:       List[Any]   )						->					List[str]:
									"""simple docstring"""







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

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

									__lowercase					=				self.image_processing_tester.get_expected_values(lowercase   )

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

									# Test batched
									__lowercase					=				self.image_processing_tester.get_expected_values(lowercase			,     batched=lowercase   )

									__lowercase					=				image_processor(
									    lowercase			,     ["""semantic"""] * len(lowercase   )			,     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	:       int   )						->					Tuple:
									"""simple docstring"""







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

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

									__lowercase					=				self.image_processing_tester.get_expected_values(lowercase   )

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

									# Test batched
									__lowercase					=				self.image_processing_tester.get_expected_values(lowercase			,     batched=lowercase   )

									__lowercase					=				image_processor(
									    lowercase			,     ["""semantic"""] * len(lowercase   )			,     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	:       str			,     lowercase	:       Union[str, Any]=False			,     lowercase	:       Tuple=False			,     lowercase	:       Union[str, Any]="np"   )						->					List[Any]:
									"""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=lowercase   )
									if with_segmentation_maps:
															__lowercase					=				num_labels
															if is_instance_map:
																					__lowercase					=				list(range(lowercase   )   ) * 2
																					__lowercase					=				dict(enumerate(lowercase   )   )
															__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(lowercase   ) for annotation in annotations]

									__lowercase					=				image_processor(
									    lowercase			,     ["""semantic"""] * len(lowercase   )			,     lowercase			,     return_tensors="""pt"""			,     instance_id_to_semantic_id=lowercase			,     pad_and_return_pixel_mask=lowercase			,     )

									return inputs





			def 		snake_case__			(				self	:       int   )						->					int:
									"""simple docstring"""







									pass





			def 		snake_case__			(				self	:       Optional[Any]   )						->					int:
									"""simple docstring"""







									def common(lowercase	:       Optional[int]=False			,     lowercase	:       Any=None   ):
															__lowercase					=				self.comm_get_image_processor_inputs(
															    with_segmentation_maps=lowercase			,     is_instance_map=lowercase			,     segmentation_type=lowercase   )

															__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(lowercase			,     lowercase			,     lowercase   ):
																					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(lowercase   )			,     self.image_processing_tester.num_text   )

									common()
									common(is_instance_map=lowercase   )
									common(is_instance_map=lowercase			,     segmentation_type="""pil"""   )
									common(is_instance_map=lowercase			,     segmentation_type="""pil"""   )





			def 		snake_case__			(				self	:       Tuple   )						->					Dict:
									"""simple docstring"""







									__lowercase					=				np.zeros((20, 50)   )
									__lowercase					=				1
									__lowercase					=				1
									__lowercase					=				1

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





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Optional[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(lowercase   )

									self.assertEqual(len(lowercase   )			,     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(lowercase			,     target_sizes=lowercase   )

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





			def 		snake_case__			(				self	:       List[Any]   )						->					Tuple:
									"""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(lowercase			,     threshold=0   )

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







			def 		snake_case__			(				self	:       Dict   )						->					Tuple:
									"""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(lowercase			,     threshold=0   )

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










def  UpperCAmelCase__    (    lowercase__ = 100   ) ->      int:
						__lowercase					=				n * (n + 1) * (2 * n + 1) / 6
						__lowercase					=				(n * (n + 1) / 2) ** 2
						return int(square_of_sum - sum_of_squares   )


if __name__ == "__main__":
			print(F"""{solution() = }""")





