






'''simple docstring'''







import unittest

import numpy as np
from datasets import load_dataset

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 PIL import Image

							from transformers import BeitImageProcessor




class 		UpperCamelCase__     (unittest.TestCase      ):




		'''simple docstring'''





		def __init__(   self ,_lowerCAmelCase ,_lowerCAmelCase=7 ,_lowerCAmelCase=3 ,_lowerCAmelCase=18 ,_lowerCAmelCase=30 ,_lowerCAmelCase=4_00 ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase=[0.5, 0.5, 0.5] ,_lowerCAmelCase=[0.5, 0.5, 0.5] ,_lowerCAmelCase=False ,):
								lowerCamelCase__								=	size if size is not None else {"""height""": 20, """width""": 20}
								lowerCamelCase__								=	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								lowerCamelCase__								=	parent
								lowerCamelCase__								=	batch_size
								lowerCamelCase__								=	num_channels
								lowerCamelCase__								=	image_size
								lowerCamelCase__								=	min_resolution
								lowerCamelCase__								=	max_resolution
								lowerCamelCase__								=	do_resize
								lowerCamelCase__								=	size
								lowerCamelCase__								=	do_center_crop
								lowerCamelCase__								=	crop_size
								lowerCamelCase__								=	do_normalize
								lowerCamelCase__								=	image_mean
								lowerCamelCase__								=	image_std
								lowerCamelCase__								=	do_reduce_labels






		def       UpperCamelCase_       (   self							):
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_reduce_labels": self.do_reduce_labels,
								}



def     A__      (					):
						lowerCamelCase__								=	load_dataset("""hf-internal-testing/fixtures_ade20k""" ,     split="""test"""    )

						lowerCamelCase__								=	Image.open(dataset[0]["""file"""]    )
						lowerCamelCase__								=	Image.open(dataset[1]["""file"""]    )

						return image, map
def     A__      (					):
						lowerCamelCase__								=	load_dataset("""hf-internal-testing/fixtures_ade20k""" ,     split="""test"""    )

						lowerCamelCase__								=	Image.open(ds[0]["""file"""]    )
						lowerCamelCase__								=	Image.open(ds[1]["""file"""]    )
						lowerCamelCase__								=	Image.open(ds[2]["""file"""]    )
						lowerCamelCase__								=	Image.open(ds[3]["""file"""]    )

						return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class 		UpperCamelCase__     (a ,unittest.TestCase      ):




		'''simple docstring'''





		_UpperCamelCase						=       BeitImageProcessor if is_vision_available() else None






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	BeitImageProcessingTester(self							)






		@property
		def       UpperCamelCase_       (   self							):
								return self.image_processor_tester.prepare_image_processor_dict()






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.image_processing_class(**self.image_processor_dict							)
								self.assertTrue(hasattr(_lowerCAmelCase ,"""do_resize"""							)							)
								self.assertTrue(hasattr(_lowerCAmelCase ,"""size"""							)							)
								self.assertTrue(hasattr(_lowerCAmelCase ,"""do_center_crop"""							)							)
								self.assertTrue(hasattr(_lowerCAmelCase ,"""center_crop"""							)							)
								self.assertTrue(hasattr(_lowerCAmelCase ,"""do_normalize"""							)							)
								self.assertTrue(hasattr(_lowerCAmelCase ,"""image_mean"""							)							)
								self.assertTrue(hasattr(_lowerCAmelCase ,"""image_std"""							)							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.image_processing_class.from_dict(self.image_processor_dict							)
								self.assertEqual(image_processor.size ,{"""height""": 20, """width""": 20}							)
								self.assertEqual(image_processor.crop_size ,{"""height""": 18, """width""": 18}							)
								self.assertEqual(image_processor.do_reduce_labels ,_lowerCAmelCase							)

								lowerCamelCase__								=	self.image_processing_class.from_dict(
								    self.image_processor_dict ,size=42 ,crop_size=84 ,reduce_labels=_lowerCAmelCase							)
								self.assertEqual(image_processor.size ,{"""height""": 42, """width""": 42}							)
								self.assertEqual(image_processor.crop_size ,{"""height""": 84, """width""": 84}							)
								self.assertEqual(image_processor.do_reduce_labels ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								pass






		def       UpperCamelCase_       (   self							):
								# Initialize image_processing
								lowerCamelCase__								=	self.image_processing_class(**self.image_processor_dict							)
								# create random PIL images
								lowerCamelCase__								=	prepare_image_inputs(self.image_processor_tester ,equal_resolution=_lowerCAmelCase							)
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase ,Image.Image							)

								# Test not batched input
								lowerCamelCase__								=	image_processing(image_inputs[0] ,return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_images.shape ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)

								# Test batched
								lowerCamelCase__								=	image_processing(_lowerCAmelCase ,return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_images.shape ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)






		def       UpperCamelCase_       (   self							):
								# Initialize image_processing
								lowerCamelCase__								=	self.image_processing_class(**self.image_processor_dict							)
								# create random numpy tensors
								lowerCamelCase__								=	prepare_image_inputs(self.image_processor_tester ,equal_resolution=_lowerCAmelCase ,numpify=_lowerCAmelCase							)
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase ,np.ndarray							)

								# Test not batched input
								lowerCamelCase__								=	image_processing(image_inputs[0] ,return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_images.shape ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)

								# Test batched
								lowerCamelCase__								=	image_processing(_lowerCAmelCase ,return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_images.shape ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)






		def       UpperCamelCase_       (   self							):
								# Initialize image_processing
								lowerCamelCase__								=	self.image_processing_class(**self.image_processor_dict							)
								# create random PyTorch tensors
								lowerCamelCase__								=	prepare_image_inputs(self.image_processor_tester ,equal_resolution=_lowerCAmelCase ,torchify=_lowerCAmelCase							)
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase ,torch.Tensor							)

								# Test not batched input
								lowerCamelCase__								=	image_processing(image_inputs[0] ,return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_images.shape ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)

								# Test batched
								lowerCamelCase__								=	image_processing(_lowerCAmelCase ,return_tensors="""pt"""							).pixel_values
								self.assertEqual(
								    encoded_images.shape ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)






		def       UpperCamelCase_       (   self							):
								# Initialize image_processing
								lowerCamelCase__								=	self.image_processing_class(**self.image_processor_dict							)
								# create random PyTorch tensors
								lowerCamelCase__								=	prepare_image_inputs(self.image_processor_tester ,equal_resolution=_lowerCAmelCase ,torchify=_lowerCAmelCase							)
								lowerCamelCase__								=	[]
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase ,torch.Tensor							)
														maps.append(torch.zeros(image.shape[-2:]							).long()							)

								# Test not batched input
								lowerCamelCase__								=	image_processing(image_inputs[0] ,maps[0] ,return_tensors="""pt"""							)
								self.assertEqual(
								    encoding["""pixel_values"""].shape ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)
								self.assertEqual(
								    encoding["""labels"""].shape ,(
								        1,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)
								self.assertEqual(encoding["""labels"""].dtype ,torch.long							)
								self.assertTrue(encoding["""labels"""].min().item() >= 0							)
								self.assertTrue(encoding["""labels"""].max().item() <= 2_55							)

								# Test batched
								lowerCamelCase__								=	image_processing(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""pt"""							)
								self.assertEqual(
								    encoding["""pixel_values"""].shape ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)
								self.assertEqual(
								    encoding["""labels"""].shape ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)
								self.assertEqual(encoding["""labels"""].dtype ,torch.long							)
								self.assertTrue(encoding["""labels"""].min().item() >= 0							)
								self.assertTrue(encoding["""labels"""].max().item() <= 2_55							)

								# Test not batched input (PIL images)
								lowerCamelCase__							,      lowerCamelCase__								=	prepare_semantic_single_inputs()

								lowerCamelCase__								=	image_processing(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""pt"""							)
								self.assertEqual(
								    encoding["""pixel_values"""].shape ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)
								self.assertEqual(
								    encoding["""labels"""].shape ,(
								        1,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)
								self.assertEqual(encoding["""labels"""].dtype ,torch.long							)
								self.assertTrue(encoding["""labels"""].min().item() >= 0							)
								self.assertTrue(encoding["""labels"""].max().item() <= 2_55							)

								# Test batched input (PIL images)
								lowerCamelCase__							,      lowerCamelCase__								=	prepare_semantic_batch_inputs()

								lowerCamelCase__								=	image_processing(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""pt"""							)
								self.assertEqual(
								    encoding["""pixel_values"""].shape ,(
								        2,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)
								self.assertEqual(
								    encoding["""labels"""].shape ,(
								        2,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    ) ,)
								self.assertEqual(encoding["""labels"""].dtype ,torch.long							)
								self.assertTrue(encoding["""labels"""].min().item() >= 0							)
								self.assertTrue(encoding["""labels"""].max().item() <= 2_55							)






		def       UpperCamelCase_       (   self							):
								# Initialize image_processing
								lowerCamelCase__								=	self.image_processing_class(**self.image_processor_dict							)

								# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
								lowerCamelCase__							,      lowerCamelCase__								=	prepare_semantic_single_inputs()
								lowerCamelCase__								=	image_processing(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""pt"""							)
								self.assertTrue(encoding["""labels"""].min().item() >= 0							)
								self.assertTrue(encoding["""labels"""].max().item() <= 1_50							)

								lowerCamelCase__								=	True
								lowerCamelCase__								=	image_processing(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""pt"""							)
								self.assertTrue(encoding["""labels"""].min().item() >= 0							)
								self.assertTrue(encoding["""labels"""].max().item() <= 2_55							)






'''simple docstring'''

from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class 						a_		(     snake_case    ):


							def __lt__(				self   :						List[Any]      ,						a_   :						Optional[int]			)			->					List[str]:
										return self[-1] < other[-1]




							def __eq__(				self   :						int      ,						a_   :						Union[str, Any]			)			->					List[str]:
										return self[-1] == other[-1]







def a_     (    __UpperCAmelCase					)				->					list:





			"""simple docstring"""







			snake_case:    list[Stack]      							=[]
			# sort into stacks
			for element in collection:
						snake_case:    int      							=Stack([element]					)
						snake_case:    Union[str, Any]      							=bisect_left(__UpperCAmelCase				,							__UpperCAmelCase					)
						if i != len(__UpperCAmelCase					):
									stacks[i].append(__UpperCAmelCase					)
						else:
									stacks.append(__UpperCAmelCase					)

    # use a heap-based merge to merge stack efficiently
			snake_case:    int      							=merge(*(reversed(__UpperCAmelCase					) for stack in stacks)					)
			return collection


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




