
import unittest

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


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor







class 			__A( unittest.TestCase			):




				def __init__(     self		,     _snake_case		,     _snake_case=7		,     _snake_case=3		,     _snake_case=18		,     _snake_case=30		,     _snake_case=400		,     _snake_case=True		,     _snake_case=None		,     _snake_case=True		,     _snake_case=None		,     _snake_case=True		,     _snake_case=[0.4814_5466, 0.457_8275, 0.4082_1073]		,     _snake_case=[0.2686_2954, 0.2613_0258, 0.2757_7711]		,     _snake_case=True		,     )    ->      Any:


								'''simple docstring'''




								__a										=    size if size is not None else {'''height''': 224, '''width''': 224}
								__a										=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								__a										=    parent
								__a										=    batch_size
								__a										=    num_channels
								__a										=    image_size
								__a										=    min_resolution
								__a										=    max_resolution
								__a										=    do_resize
								__a										=    size
								__a										=    do_center_crop
								__a										=    crop_size
								__a										=    do_normalize
								__a										=    image_mean
								__a										=    image_std
								__a										=    do_convert_rgb




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								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_convert_rgb": self.do_convert_rgb,
								}




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=False		,     _snake_case=False		,     _snake_case=False				)    ->      Union[str, Any]:


								'''simple docstring'''




								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
												__a										=    []
												for i in range(self.batch_size				):
																image_inputs.append(
																    np.random.randint(
																        255		,     size=(self.num_channels, self.max_resolution, self.max_resolution)		,     dtype=np.uinta				)				)
								else:
												__a										=    []
												for i in range(self.batch_size				):
																__a							,     __a										=    np.random.choice(np.arange(self.min_resolution		,     self.max_resolution				)		,     2				)
																image_inputs.append(np.random.randint(255		,     size=(self.num_channels, width, height)		,     dtype=np.uinta				)				)

								if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												__a										=    [Image.fromarray(np.moveaxis(_snake_case		,     0		,     -1				)				) for x in image_inputs]

								if torchify:
												__a										=    [torch.from_numpy(_snake_case				) for x in image_inputs]

								return image_inputs







@require_torch
@require_vision
class 			__A( a  ,   unittest.TestCase			):
				snake_case_						   =    ChineseCLIPImageProcessor if is_vision_available() else None




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    ChineseCLIPImageProcessingTester(self		,     do_center_crop=_snake_case				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




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


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(_snake_case		,     '''do_resize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''size'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_center_crop'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''center_crop'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_normalize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''image_mean'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''image_std'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_convert_rgb'''				)				)




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


								'''simple docstring'''




								__a										=    self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size		,     {'''height''': 224, '''width''': 224}				)
								self.assertEqual(image_processor.crop_size		,     {'''height''': 18, '''width''': 18}				)

								__a										=    self.image_processing_class.from_dict(self.image_processor_dict		,     size=42		,     crop_size=84				)
								self.assertEqual(image_processor.size		,     {'''shortest_edge''': 42}				)
								self.assertEqual(image_processor.crop_size		,     {'''height''': 84, '''width''': 84}				)




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


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								__a										=    self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case				)
								for image in image_inputs:
												self.assertIsInstance(_snake_case		,     Image.Image				)

								# Test not batched input
								__a										=    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
								__a										=    image_processing(_snake_case		,     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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random numpy tensors
								__a										=    self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case		,     numpify=_snake_case				)
								for image in image_inputs:
												self.assertIsInstance(_snake_case		,     np.ndarray				)

								# Test not batched input
								__a										=    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
								__a										=    image_processing(_snake_case		,     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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random PyTorch tensors
								__a										=    self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case		,     torchify=_snake_case				)
								for image in image_inputs:
												self.assertIsInstance(_snake_case		,     torch.Tensor				)

								# Test not batched input
								__a										=    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
								__a										=    image_processing(_snake_case		,     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'''],
								    )		,     )







@require_torch
@require_vision
class 			__A( a  ,   unittest.TestCase			):
				snake_case_						   =    ChineseCLIPImageProcessor if is_vision_available() else None




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


								'''simple docstring'''




								__a										=    ChineseCLIPImageProcessingTester(self		,     num_channels=4		,     do_center_crop=_snake_case				)
								__a										=    3




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


								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(_snake_case		,     '''do_resize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''size'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_center_crop'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''center_crop'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_normalize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''image_mean'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''image_std'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_convert_rgb'''				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								__a										=    self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case				)
								for image in image_inputs:
												self.assertIsInstance(_snake_case		,     Image.Image				)

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

								# Test batched
								__a										=    image_processing(_snake_case		,     return_tensors='''pt'''				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,     (
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''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__						(      a_						):
   def __lt__(			self	,    _a				):
          return self[-1] < other[-1]

   def __eq__(			self	,    _a				):
          return self[-1] == other[-1]


def   __magic_name__	(      __snake_case				:       list  ) ->				list:
       lowercase    :				list[Stack]       		=							[]
       # sort into stacks
       for element in collection:
              lowercase    :				Union[str, Any]       		=							Stack([element]  )
              lowercase    :				Dict       		=							bisect_left(__snake_case     , __snake_case  )
              if i != len(__snake_case  ):
                     stacks[i].append(__snake_case  )
              else:
                     stacks.append(__snake_case  )

    # use a heap-based merge to merge stack efficiently
       lowercase    :				Dict       		=							merge(*(reversed(__snake_case  ) for stack in stacks)  )
       return collection


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


