





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       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]   ,    _lowercase     : Union[str, Any]="shi-labs/oneformer_demo"			)   ->							Dict:




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


class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def __init__(						self:						List[Any],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						List[str]=7,					lowerCamelCase_:						Union[str, Any]=3,					lowerCamelCase_:						Tuple=30,					lowerCamelCase_:						str=400,					lowerCamelCase_:						int=None,					lowerCamelCase_:						Union[str, Any]=True,					lowerCamelCase_:						int=True,					lowerCamelCase_:						Tuple=[0.5, 0.5, 0.5],					lowerCamelCase_:						Optional[int]=[0.5, 0.5, 0.5],					lowerCamelCase_:						Optional[Any]=10,					lowerCamelCase_:						List[str]=False,					lowerCamelCase_:						Optional[Any]=255,					lowerCamelCase_:						Union[str, Any]="shi-labs/oneformer_demo",					lowerCamelCase_:						Union[str, Any]="ade20k_panoptic.json",					lowerCamelCase_:						Tuple=10,					):
			lowercase__					:      Optional[Any]      =		parent
			lowercase__					:      int      =		batch_size
			lowercase__					:      List[Any]      =		num_channels
			lowercase__					:      Tuple      =		min_resolution
			lowercase__					:      Any      =		max_resolution
			lowercase__					:      str      =		do_resize
			lowercase__					:      List[str]      =		{'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
			lowercase__					:      List[Any]      =		do_normalize
			lowercase__					:      Dict      =		image_mean
			lowercase__					:      Optional[Any]      =		image_std
			lowercase__					:      Optional[int]      =		class_info_file
			lowercase__					:      Union[str, Any]      =		prepare_metadata(lowerCamelCase_,					lowerCamelCase_			)
			lowercase__					:      List[Any]      =		num_text
			lowercase__					:      int      =		repo_path

			# for the post_process_functions
			lowercase__					:      int      =		2
			lowercase__					:      int      =		10
			lowercase__					:      Dict      =		10
			lowercase__					:      int      =		3
			lowercase__					:      Dict      =		4
			lowercase__					:      Tuple      =		num_labels
			lowercase__					:      Dict      =		do_reduce_labels
			lowercase__					:      Tuple      =		ignore_index

	def    snake_case__(						self:						Optional[int]			):
			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:						int,					lowerCamelCase_:						int,					lowerCamelCase_:						Dict=False			):
			if not batched:
					lowercase__					:      List[str]      =		image_inputs[0]
					if isinstance(lowerCamelCase_,					Image.Image			):
							lowercase__   ,				lowercase__					:      Any      =		image.size
					else:
							lowercase__   ,				lowercase__					:      List[str]      =		image.shape[1], image.shape[2]
					if w < h:
							lowercase__					:      List[str]      =		int(self.size['shortest_edge'] * h / w			)
							lowercase__					:      int      =		self.size['shortest_edge']
					elif w > h:
							lowercase__					:      Optional[Any]      =		self.size['shortest_edge']
							lowercase__					:      Union[str, Any]      =		int(self.size['shortest_edge'] * w / h			)
					else:
							lowercase__					:      Optional[int]      =		self.size['shortest_edge']
							lowercase__					:      str      =		self.size['shortest_edge']

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

			return expected_height, expected_width





	def    snake_case__(						self:						Tuple			):
			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					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





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

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

	@property
	def    snake_case__(						self:						Dict			):
			return self.image_processing_tester.prepare_image_processor_dict()

	def    snake_case__(						self:						int			):
			lowercase__					:      Dict      =		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    snake_case__(						self:						Dict			):
			pass

	def    snake_case__(						self:						List[Any]			):
			# Initialize image_processor
			lowercase__					:      int      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			lowercase__					:      int      =		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__					:      str      =		image_processor(image_inputs[0],					['semantic'],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      List[Any]      =		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__					:      List[str]      =		self.image_processing_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			lowercase__					:      Dict      =		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    snake_case__(						self:						List[str]			):
			# Initialize image_processor
			lowercase__					:      List[str]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			lowercase__					:      List[Any]      =		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__					:      Tuple      =		image_processor(image_inputs[0],					['semantic'],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      int      =		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__					:      Any      =		self.image_processing_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			lowercase__					:      List[str]      =		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    snake_case__(						self:						Any			):
			# Initialize image_processor
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			lowercase__					:      List[Any]      =		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__					:      Optional[int]      =		image_processor(image_inputs[0],					['semantic'],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Dict      =		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__					:      int      =		self.image_processing_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			lowercase__					:      str      =		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    snake_case__(						self:						Dict,					lowerCamelCase_:						List[str]=False,					lowerCamelCase_:						Any=False,					lowerCamelCase_:						List[str]="np"			):
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# prepare image and target
			lowercase__					:      Any      =		self.image_processing_tester.num_labels
			lowercase__					:      Tuple      =		None
			lowercase__					:      Tuple      =		None
			lowercase__					:      str      =		prepare_image_inputs(self.image_processing_tester,					equal_resolution=lowerCamelCase_			)
			if with_segmentation_maps:
					lowercase__					:      int      =		num_labels
					if is_instance_map:
							lowercase__					:      Tuple      =		list(range(lowerCamelCase_			)			) * 2
							lowercase__					:      List[str]      =		dict(enumerate(lowerCamelCase_			)			)
					lowercase__					:      Dict      =		[
					    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__					:      Dict      =		[Image.fromarray(lowerCamelCase_			) for annotation in annotations]

			lowercase__					:      str      =		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    snake_case__(						self:						str			):
			pass

	def    snake_case__(						self:						List[str]			):
			def common(lowerCamelCase_:						str=False,					lowerCamelCase_:						Dict=None			):
					lowercase__					:      List[str]      =		self.comm_get_image_processor_inputs(
					    with_segmentation_maps=lowerCamelCase_,					is_instance_map=lowerCamelCase_,					segmentation_type=lowerCamelCase_			)

					lowercase__					:      Optional[Any]      =		inputs['mask_labels']
					lowercase__					:      Dict      =		inputs['class_labels']
					lowercase__					:      List[Any]      =		inputs['pixel_values']
					lowercase__					:      Tuple      =		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    snake_case__(						self:						Optional[Any]			):
			lowercase__					:      List[str]      =		np.zeros((20, 50)			)
			lowercase__					:      Optional[Any]      =		1
			lowercase__					:      Union[str, Any]      =		1
			lowercase__					:      List[str]      =		1

			lowercase__					:      Optional[Any]      =		binary_mask_to_rle(lowerCamelCase_			)
			self.assertEqual(len(lowerCamelCase_			),					4			)
			self.assertEqual(rle[0],					21			)
			self.assertEqual(rle[1],					45			)

	def    snake_case__(						self:						str			):
			lowercase__					:      List[str]      =		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__					:      List[Any]      =		self.image_processing_tester.get_fake_oneformer_outputs()

			lowercase__					:      int      =		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__					:      Tuple      =		[(1, 4) for i in range(self.image_processing_tester.batch_size			)]
			lowercase__					:      int      =		fature_extractor.post_process_semantic_segmentation(lowerCamelCase_,					target_sizes=lowerCamelCase_			)

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

	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      List[str]      =		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__					:      Any      =		self.image_processing_tester.get_fake_oneformer_outputs()
			lowercase__					:      List[str]      =		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    snake_case__(						self:						List[str]			):
			lowercase__					:      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',					)
			lowercase__					:      int      =		self.image_processing_tester.get_fake_oneformer_outputs()
			lowercase__					:      Union[str, Any]      =		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       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							Optional[int]:




		'''simple docstring'''
		if b == 0:
				return 1
		if (b % 2) == 0:
				return actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)
		else:
				return a * actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							float:




		'''simple docstring'''
		if b < 0:
				return 1 / actual_power(_lowercase   ,    _lowercase			)
		return actual_power(_lowercase   ,    _lowercase			)


if __name__ == "__main__":
			print(power(-2, -3))


