




'''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							,				a__							,				a__ = True							,				a__ = None							,				a__ = 32							,				a__ = True							,				a__ = 1 / 255							,				a__ = True							,				a__ = True							,				a__ = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							,				a__ = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							,				a__ = True							,				a__=7							,				a__=30							,				a__=400							,				a__=3							,				)							->							Tuple:

										'''simple docstring'''




										snake_case_         = parent
										snake_case_         = do_resize
										snake_case_         = size if size is not None else {"shortest_edge": 288}
										snake_case_         = size_divisor
										snake_case_         = do_rescale
										snake_case_         = rescale_factor
										snake_case_         = do_normalize
										snake_case_         = do_center_crop
										snake_case_         = image_mean
										snake_case_         = image_std
										snake_case_         = do_pad
										snake_case_         = batch_size
										snake_case_         = num_channels
										snake_case_         = min_resolution
										snake_case_         = max_resolution




			def   lowerCAmelCase__							(   self				)							->							Union[str, Any]:

										'''simple docstring'''




										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   lowerCAmelCase__							(   self							,				a__							,				a__=False				)							->							List[str]:

										'''simple docstring'''




										if not batched:
																	snake_case_         = self.size["shortest_edge"]
																	snake_case_         = image_inputs[0]
																	if isinstance(a__							,				Image.Image				):
																								snake_case_   , snake_case_         = image.size
																	else:
																								snake_case_   , snake_case_         = image.shape[1], image.shape[2]
																	snake_case_         = size / min(a__							,				a__				)
																	if h < w:
																								snake_case_   , snake_case_         = size, scale * w
																	else:
																								snake_case_   , snake_case_         = scale * h, size

																	snake_case_         = int((1_333 / 800) * size				)
																	if max(a__							,				a__				) > max_size:
																								snake_case_         = max_size / max(a__							,				a__				)
																								snake_case_         = newh * scale
																								snake_case_         = neww * scale

																	snake_case_   , snake_case_         = int(newh + 0.5				), int(neww + 0.5				)
																	snake_case_   , snake_case_         = (
																	    newh // self.size_divisor * self.size_divisor,
																	    neww // self.size_divisor * self.size_divisor,
																	)

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

										return expected_height, expected_width




@require_torch
@require_vision
class 					_snake_case						(      SCREAMING_SNAKE_CASE__		,		unittest.TestCase				):
			lowerCAmelCase_	: Optional[Any]					=       BridgeTowerImageProcessor if is_vision_available() else None




			def   lowerCAmelCase__							(   self				)							->							Tuple:

										'''simple docstring'''




										snake_case_         = BridgeTowerImageProcessingTester(self				)




			@property
			def   lowerCAmelCase__							(   self				)							->							Optional[int]:

										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()




			def   lowerCAmelCase__							(   self				)							->							Optional[int]:

										'''simple docstring'''




										snake_case_         = 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   lowerCAmelCase__							(   self				)							->							Tuple:

										'''simple docstring'''




										pass




			def   lowerCAmelCase__							(   self				)							->							List[Any]:

										'''simple docstring'''




										snake_case_         = self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										snake_case_         = prepare_image_inputs(self.image_processor_tester							,				equal_resolution=a__				)
										for image in image_inputs:
																	self.assertIsInstance(a__							,				Image.Image				)

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

										snake_case_   , snake_case_         = 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
										snake_case_         = image_processing(a__							,				return_tensors="pt"				).pixel_values

										snake_case_   , snake_case_         = 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   lowerCAmelCase__							(   self				)							->							List[str]:

										'''simple docstring'''




										snake_case_         = self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										snake_case_         = 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
										snake_case_         = image_processing(image_inputs[0]							,				return_tensors="pt"				).pixel_values

										snake_case_   , snake_case_         = 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
										snake_case_         = image_processing(a__							,				return_tensors="pt"				).pixel_values

										snake_case_   , snake_case_         = 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   lowerCAmelCase__							(   self				)							->							Optional[Any]:

										'''simple docstring'''




										snake_case_         = self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										snake_case_         = 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
										snake_case_         = image_processing(image_inputs[0]							,				return_tensors="pt"				).pixel_values

										snake_case_   , snake_case_         = 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
										snake_case_         = image_processing(a__							,				return_tensors="pt"				).pixel_values

										snake_case_   , snake_case_         = 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 lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE )
			while cur > 1:
						# Find the maximum number in arr
						SCREAMING_SNAKE_CASE_								=						arr.index(max(arr[0:cur] ) )
						# Reverse from 0 to mi
						SCREAMING_SNAKE_CASE_								=						arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE )]
						# Reverse whole list
						SCREAMING_SNAKE_CASE_								=						arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE )]
						cur -= 1
			return arr


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