


"""simple docstring"""






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


class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







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

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



				def   lowerCAmelCase__		(						self     ):
								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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=False     ):
								if not batched:
												SCREAMING_SNAKE_CASE__     							=				image_inputs[0]
												if isinstance(UpperCAmelCase__       ,		Image.Image     ):
																SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				image.size
												else:
																SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				image.shape[1], image.shape[2]
												if w < h:
																SCREAMING_SNAKE_CASE__     							=				int(self.size["shortest_edge"] * h / w     )
																SCREAMING_SNAKE_CASE__     							=				self.size["shortest_edge"]
												elif w > h:
																SCREAMING_SNAKE_CASE__     							=				self.size["shortest_edge"]
																SCREAMING_SNAKE_CASE__     							=				int(self.size["shortest_edge"] * w / h     )
												else:
																SCREAMING_SNAKE_CASE__     							=				self.size["shortest_edge"]
																SCREAMING_SNAKE_CASE__     							=				self.size["shortest_edge"]

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

								return expected_height, expected_width






				def   lowerCAmelCase__		(						self     ):
								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_		(			lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      int      							=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
				# only for test_image_processing_common.test_image_proc_to_json_string
				_lowerCAmelCase      :      Dict      							=					image_processing_class



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				OneFormerImageProcessorTester(self     )



				@property
				def   lowerCAmelCase__		(						self     ):
								return self.image_processing_tester.prepare_image_processor_dict()



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				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"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"ignore_index"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"class_info_file"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"num_text"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"repo_path"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"metadata"     )     )
								self.assertTrue(hasattr(UpperCAmelCase__       ,		"do_reduce_labels"     )     )



				def   lowerCAmelCase__		(						self     ):
								pass



				def   lowerCAmelCase__		(						self     ):
								# Initialize image_processor
								SCREAMING_SNAKE_CASE__     							=				self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								SCREAMING_SNAKE_CASE__     							=				prepare_image_inputs(self.image_processing_tester       ,		equal_resolution=UpperCAmelCase__     )
								for image in image_inputs:
												self.assertIsInstance(UpperCAmelCase__       ,		Image.Image     )

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

								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processing_tester.get_expected_values(UpperCAmelCase__     )

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

								# Test batched
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processing_tester.get_expected_values(UpperCAmelCase__       ,		batched=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				image_processor(
								    UpperCAmelCase__       ,		["semantic"] * len(UpperCAmelCase__     )       ,		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   lowerCAmelCase__		(						self     ):
								# Initialize image_processor
								SCREAMING_SNAKE_CASE__     							=				self.image_processing_class(**self.image_processor_dict     )
								# create random numpy tensors
								SCREAMING_SNAKE_CASE__     							=				prepare_image_inputs(self.image_processing_tester       ,		equal_resolution=UpperCAmelCase__       ,		numpify=UpperCAmelCase__     )
								for image in image_inputs:
												self.assertIsInstance(UpperCAmelCase__       ,		np.ndarray     )

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

								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processing_tester.get_expected_values(UpperCAmelCase__     )

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

								# Test batched
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processing_tester.get_expected_values(UpperCAmelCase__       ,		batched=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				image_processor(
								    UpperCAmelCase__       ,		["semantic"] * len(UpperCAmelCase__     )       ,		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   lowerCAmelCase__		(						self     ):
								# Initialize image_processor
								SCREAMING_SNAKE_CASE__     							=				self.image_processing_class(**self.image_processor_dict     )
								# create random PyTorch tensors
								SCREAMING_SNAKE_CASE__     							=				prepare_image_inputs(self.image_processing_tester       ,		equal_resolution=UpperCAmelCase__       ,		torchify=UpperCAmelCase__     )
								for image in image_inputs:
												self.assertIsInstance(UpperCAmelCase__       ,		torch.Tensor     )

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

								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processing_tester.get_expected_values(UpperCAmelCase__     )

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

								# Test batched
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.image_processing_tester.get_expected_values(UpperCAmelCase__       ,		batched=UpperCAmelCase__     )

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

								SCREAMING_SNAKE_CASE__     							=				image_processor(
								    UpperCAmelCase__       ,		["semantic"] * len(UpperCAmelCase__     )       ,		UpperCAmelCase__       ,		return_tensors="pt"       ,		instance_id_to_semantic_id=UpperCAmelCase__       ,		pad_and_return_pixel_mask=UpperCAmelCase__       ,		)

								return inputs



				def   lowerCAmelCase__		(						self     ):
								pass



				def   lowerCAmelCase__		(						self     ):
								def common(UpperCAmelCase__=False       ,		UpperCAmelCase__=None     ):
												SCREAMING_SNAKE_CASE__     							=				self.comm_get_image_processor_inputs(
												    with_segmentation_maps=UpperCAmelCase__       ,		is_instance_map=UpperCAmelCase__       ,		segmentation_type=UpperCAmelCase__     )

												SCREAMING_SNAKE_CASE__     							=				inputs["mask_labels"]
												SCREAMING_SNAKE_CASE__     							=				inputs["class_labels"]
												SCREAMING_SNAKE_CASE__     							=				inputs["pixel_values"]
												SCREAMING_SNAKE_CASE__     							=				inputs["text_inputs"]

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

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



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				np.zeros((20, 50)     )
								SCREAMING_SNAKE_CASE__     							=				1
								SCREAMING_SNAKE_CASE__     							=				1
								SCREAMING_SNAKE_CASE__     							=				1

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



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				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"       ,		)
								SCREAMING_SNAKE_CASE__     							=				self.image_processing_tester.get_fake_oneformer_outputs()

								SCREAMING_SNAKE_CASE__     							=				fature_extractor.post_process_semantic_segmentation(UpperCAmelCase__     )

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

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

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



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				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"       ,		)
								SCREAMING_SNAKE_CASE__     							=				self.image_processing_tester.get_fake_oneformer_outputs()
								SCREAMING_SNAKE_CASE__     							=				image_processor.post_process_instance_segmentation(UpperCAmelCase__       ,		threshold=0     )

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






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				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"       ,		)
								SCREAMING_SNAKE_CASE__     							=				self.image_processing_tester.get_fake_oneformer_outputs()
								SCREAMING_SNAKE_CASE__     							=				image_processor.post_process_panoptic_segmentation(UpperCAmelCase__       ,		threshold=0     )

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




"""simple docstring"""






from __future__ import annotations


def __lowercase     (      lowerCamelCase_  :      list						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				input_list[low:mid], input_list[mid : high + 1]
				while left and right:
								result.append((left if left[0] <= right[0] else right).pop(0  )  )
				SCREAMING_SNAKE_CASE__     							=				result + left + right
				return input_list


def __lowercase     (      lowerCamelCase_  :      list  ):
				if len(lowerCamelCase_  ) <= 1:
								return input_list
				SCREAMING_SNAKE_CASE__     							=				list(lowerCamelCase_  )

				# iteration for two-way merging
				SCREAMING_SNAKE_CASE__     							=				2
				while p <= len(lowerCamelCase_  ):
								# getting low, high and middle value for merge-sort of single list
								for i in range(0						,					len(lowerCamelCase_  )						,					lowerCamelCase_  ):
												SCREAMING_SNAKE_CASE__     							=				i
												SCREAMING_SNAKE_CASE__     							=				i + p - 1
												SCREAMING_SNAKE_CASE__     							=				(low + high + 1) // 2
												SCREAMING_SNAKE_CASE__     							=				merge(lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_  )
								# final merge of last two parts
								if p * 2 >= len(lowerCamelCase_  ):
												SCREAMING_SNAKE_CASE__     							=				i
												SCREAMING_SNAKE_CASE__     							=				merge(lowerCamelCase_						,					0						,					lowerCamelCase_						,					len(lowerCamelCase_  ) - 1  )
												break
								p *= 2

				return input_list


if __name__ == "__main__":
	_lowerCamelCase     =  input('Enter numbers separated by a comma:\n').strip()
	if user_input == "":
		_lowerCamelCase     =  []
	else:
		_lowerCamelCase     =  [int(item.strip()) for item in user_input.split(',')]
	print(iter_merge_sort(unsorted))
