






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 DeiTImageProcessor


class 		lowerCAmelCase_							(     unittest.TestCase   ):





     def __init__(  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase=7       ,   _UpperCAmelCase=3       ,   _UpperCAmelCase=18       ,   _UpperCAmelCase=30       ,   _UpperCAmelCase=400       ,   _UpperCAmelCase=True       ,   _UpperCAmelCase=None       ,   _UpperCAmelCase=True       ,   _UpperCAmelCase=None       ,   _UpperCAmelCase=True       ,   _UpperCAmelCase=[0.5, 0.5, 0.5]       ,   _UpperCAmelCase=[0.5, 0.5, 0.5]       ,   )		->							Any:
        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





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




@require_torch
@require_vision
class 		lowerCAmelCase_							(     lowerCamelCase__							,    unittest.TestCase   ):
     __lowerCamelCase					:						List[Any]     =    DeiTImageProcessor if is_vision_available() else None
     __lowerCamelCase					:						Optional[int]     =    True





     def 	lowerCamelCase       (  self     )		->							List[Any]:
        lowerCamelCase											=DeiTImageProcessingTester(self     )





     @property
     def 	lowerCamelCase       (  self     )		->							Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 	lowerCamelCase       (  self     )		->							str:
        lowerCamelCase											=self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(lowercase_       ,   """do_resize"""     )     )
        self.assertTrue(hasattr(lowercase_       ,   """size"""     )     )
        self.assertTrue(hasattr(lowercase_       ,   """do_center_crop"""     )     )
        self.assertTrue(hasattr(lowercase_       ,   """center_crop"""     )     )
        self.assertTrue(hasattr(lowercase_       ,   """do_normalize"""     )     )
        self.assertTrue(hasattr(lowercase_       ,   """image_mean"""     )     )
        self.assertTrue(hasattr(lowercase_       ,   """image_std"""     )     )





     def 	lowerCamelCase       (  self     )		->							str:
        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}     )

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





     def 	lowerCamelCase       (  self     )		->							Tuple:
        pass





     def 	lowerCamelCase       (  self     )		->							Optional[Any]:
        lowerCamelCase											=self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        lowerCamelCase											=prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=lowercase_     )
        for image in image_inputs:
           self.assertIsInstance(lowercase_       ,   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(lowercase_       ,   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 	lowerCamelCase       (  self     )		->							Any:
        lowerCamelCase											=self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        lowerCamelCase											=prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=lowercase_       ,   numpify=lowercase_     )
        for image in image_inputs:
           self.assertIsInstance(lowercase_       ,   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(lowercase_       ,   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 	lowerCamelCase       (  self     )		->							Dict:
        lowerCamelCase											=self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        lowerCamelCase											=prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=lowercase_       ,   torchify=lowercase_     )
        for image in image_inputs:
           self.assertIsInstance(lowercase_       ,   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(lowercase_       ,   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"""],
            )       ,   )







import random






def    UpperCAmelCase__	(						lowerCamelCase  : Optional[Any]						):

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

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


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