


'''simple docstring'''



import unittest
from typing import Dict, List, Optional, Union

import numpy as np

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 BridgeTowerImageProcessor


class 		_snake_case       (  unittest.TestCase						):




					def __init__(   self				,				_lowerCamelCase				,				_lowerCamelCase = True				,				_lowerCamelCase = None				,				_lowerCamelCase = 32				,				_lowerCamelCase = True				,				_lowerCamelCase = 1 / 255				,				_lowerCamelCase = True				,				_lowerCamelCase = True				,				_lowerCamelCase = [0.48145466, 0.4578275, 0.40821073]				,				_lowerCamelCase = [0.26862954, 0.26130258, 0.27577711]				,				_lowerCamelCase = True				,				_lowerCamelCase=7				,				_lowerCamelCase=30				,				_lowerCamelCase=400				,				_lowerCamelCase=3				,				):
								UpperCAmelCase__	:       int   =  parent
								UpperCAmelCase__	:       Union[str, Any]   =  do_resize
								UpperCAmelCase__	:       Union[str, Any]   =  size if size is not None else {'shortest_edge': 288}
								UpperCAmelCase__	:       Union[str, Any]   =  size_divisor
								UpperCAmelCase__	:       Dict   =  do_rescale
								UpperCAmelCase__	:       List[Any]   =  rescale_factor
								UpperCAmelCase__	:       Tuple   =  do_normalize
								UpperCAmelCase__	:       List[Any]   =  do_center_crop
								UpperCAmelCase__	:       Union[str, Any]   =  image_mean
								UpperCAmelCase__	:       Optional[Any]   =  image_std
								UpperCAmelCase__	:       Union[str, Any]   =  do_pad
								UpperCAmelCase__	:       int   =  batch_size
								UpperCAmelCase__	:       Optional[Any]   =  num_channels
								UpperCAmelCase__	:       str   =  min_resolution
								UpperCAmelCase__	:       List[str]   =  max_resolution




					def 					snake_case__    (   self):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "size_divisor": self.size_divisor,
								}




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase=False):
								if not batched:
											UpperCAmelCase__	:       str   =  self.size['shortest_edge']
											UpperCAmelCase__	:       Union[str, Any]   =  image_inputs[0]
											if isinstance(a_				,				Image.Image):
														UpperCAmelCase__	:       Tuple   =  image.size
											else:
														UpperCAmelCase__	:       Dict   =  image.shape[1], image.shape[2]
											UpperCAmelCase__	:       Tuple   =  size / min(a_				,				a_)
											if h < w:
														UpperCAmelCase__	:       List[Any]   =  size, scale * w
											else:
														UpperCAmelCase__	:       List[str]   =  scale * h, size

											UpperCAmelCase__	:       List[Any]   =  int((1333 / 800) * size)
											if max(a_				,				a_) > max_size:
														UpperCAmelCase__	:       List[Any]   =  max_size / max(a_				,				a_)
														UpperCAmelCase__	:       List[str]   =  newh * scale
														UpperCAmelCase__	:       Optional[int]   =  neww * scale

											UpperCAmelCase__	:       Dict   =  int(newh + 0.5), int(neww + 0.5)
											UpperCAmelCase__	:       List[str]   =  (
											    newh // self.size_divisor * self.size_divisor,
											    neww // self.size_divisor * self.size_divisor,
											)

								else:
											UpperCAmelCase__	:       Any   =  []
											for image in image_inputs:
														UpperCAmelCase__	:       Union[str, Any]   =  self.get_expected_values([image])
														expected_values.append((expected_height, expected_width))
											UpperCAmelCase__	:       int   =  max(a_				,				key=lambda _lowerCamelCase: item[0])[0]
											UpperCAmelCase__	:       Any   =  max(a_				,				key=lambda _lowerCamelCase: item[1])[1]

								return expected_height, expected_width


@require_torch
@require_vision
class 		_snake_case       (  a__							, unittest.TestCase						):
					lowerCAmelCase   :Any				=       BridgeTowerImageProcessor if is_vision_available() else None




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       Tuple   =  BridgeTowerImageProcessingTester(self)




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




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       Optional[Any]   =  self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(a_				,				"""image_mean"""))
								self.assertTrue(hasattr(a_				,				"""image_std"""))
								self.assertTrue(hasattr(a_				,				"""do_normalize"""))
								self.assertTrue(hasattr(a_				,				"""do_resize"""))
								self.assertTrue(hasattr(a_				,				"""size"""))
								self.assertTrue(hasattr(a_				,				"""size_divisor"""))




					def 					snake_case__    (   self):
								pass




					def 					snake_case__    (   self):
								# Initialize image processor
								UpperCAmelCase__	:       Dict   =  self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								UpperCAmelCase__	:       Union[str, Any]   =  prepare_image_inputs(self.image_processor_tester				,				equal_resolution=a_)
								for image in image_inputs:
											self.assertIsInstance(a_				,				Image.Image)

								# Test not batched input
								UpperCAmelCase__	:       List[str]   =  image_processing(image_inputs[0]				,				return_tensors="""pt""").pixel_values

								UpperCAmelCase__	:       Optional[int]   =  self.image_processor_tester.get_expected_values(a_)
								self.assertEqual(
								    encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

								# Test batched
								UpperCAmelCase__	:       str   =  image_processing(a_				,				return_tensors="""pt""").pixel_values

								UpperCAmelCase__	:       Optional[Any]   =  self.image_processor_tester.get_expected_values(a_				,				batched=a_)
								self.assertEqual(
								    encoded_images.shape				,				(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,				)




					def 					snake_case__    (   self):
								# Initialize image processor
								UpperCAmelCase__	:       Optional[Any]   =  self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								UpperCAmelCase__	:       str   =  prepare_image_inputs(self.image_processor_tester				,				equal_resolution=a_				,				numpify=a_)
								for image in image_inputs:
											self.assertIsInstance(a_				,				np.ndarray)

								# Test not batched input
								UpperCAmelCase__	:       Optional[Any]   =  image_processing(image_inputs[0]				,				return_tensors="""pt""").pixel_values

								UpperCAmelCase__	:       Optional[int]   =  self.image_processor_tester.get_expected_values(a_)
								self.assertEqual(
								    encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

								# Test batched
								UpperCAmelCase__	:       int   =  image_processing(a_				,				return_tensors="""pt""").pixel_values

								UpperCAmelCase__	:       str   =  self.image_processor_tester.get_expected_values(a_				,				batched=a_)
								self.assertEqual(
								    encoded_images.shape				,				(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,				)




					def 					snake_case__    (   self):
								# Initialize image processor
								UpperCAmelCase__	:       int   =  self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								UpperCAmelCase__	:       Dict   =  prepare_image_inputs(self.image_processor_tester				,				equal_resolution=a_				,				torchify=a_)
								for image in image_inputs:
											self.assertIsInstance(a_				,				torch.Tensor)

								# Test not batched input
								UpperCAmelCase__	:       int   =  image_processing(image_inputs[0]				,				return_tensors="""pt""").pixel_values

								UpperCAmelCase__	:       List[str]   =  self.image_processor_tester.get_expected_values(a_)
								self.assertEqual(
								    encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

								# Test batched
								UpperCAmelCase__	:       Union[str, Any]   =  image_processing(a_				,				return_tensors="""pt""").pixel_values

								UpperCAmelCase__	:       Optional[int]   =  self.image_processor_tester.get_expected_values(a_				,				batched=a_)
								self.assertEqual(
								    encoded_images.shape				,				(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,				)




'''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))




