
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, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ConvNextImageProcessor





class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def __init__(     self					:			Tuple  ,      _lowerCAmelCase					:			List[Any]  ,      _lowerCAmelCase					:			Tuple=7  ,      _lowerCAmelCase					:			List[Any]=3  ,      _lowerCAmelCase					:			int=18  ,      _lowerCAmelCase					:			List[Any]=30  ,      _lowerCAmelCase					:			Optional[int]=400  ,      _lowerCAmelCase					:			int=True  ,      _lowerCAmelCase					:			str=None  ,      _lowerCAmelCase					:			str=0.875  ,      _lowerCAmelCase					:			int=True  ,      _lowerCAmelCase					:			List[Any]=[0.5, 0.5, 0.5]  ,      _lowerCAmelCase					:			str=[0.5, 0.5, 0.5]  ,      )					->   int:
         _lowerCAmelCase						=		size if size is not None else {'shortest_edge': 20}
         _lowerCAmelCase						=		parent
         _lowerCAmelCase						=		batch_size
         _lowerCAmelCase						=		num_channels
         _lowerCAmelCase						=		image_size
         _lowerCAmelCase						=		min_resolution
         _lowerCAmelCase						=		max_resolution
         _lowerCAmelCase						=		do_resize
         _lowerCAmelCase						=		size
         _lowerCAmelCase						=		crop_pct
         _lowerCAmelCase						=		do_normalize
         _lowerCAmelCase						=		image_mean
         _lowerCAmelCase						=		image_std






       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   Dict:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }





@require_torch
@require_vision
class 						__lowercase(     SCREAMING_SNAKE_CASE		,  unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							ConvNextImageProcessor if is_vision_available() else None



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   List[Any]:
         _lowerCAmelCase						=		ConvNextImageProcessingTester(self       )



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   Optional[int]:
         return self.image_processor_tester.prepare_image_processor_dict()



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Tuple:
         _lowerCAmelCase						=		self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'do_resize'       )       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'size'       )       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'crop_pct'       )       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'do_normalize'       )       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'image_mean'       )       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'image_std'       )       )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   List[Any]:
         _lowerCAmelCase						=		self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size  ,      {'shortest_edge': 20}       )

         _lowerCAmelCase						=		self.image_processing_class.from_dict(self.image_processor_dict  ,      size=42       )
         self.assertEqual(image_processor.size  ,      {'shortest_edge': 42}       )



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



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   List[Any]:
         # 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.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )  ,      )

         # 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.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )  ,      )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   Tuple:
         # 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.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )  ,      )

         # 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.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )  ,      )






       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Optional[Any]:
         # 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.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )  ,      )

         # 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.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )  ,      )


import random



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




  def is_sorted(__SCREAMING_SNAKE_CASE					:  str    ):
    for i in range(len(__SCREAMING_SNAKE_CASE    ) - 1    ):
      if collection[i] > collection[i + 1]:
        return False
    return True

  while not is_sorted(__SCREAMING_SNAKE_CASE    ):
    random.shuffle(__SCREAMING_SNAKE_CASE    )
  return collection


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