




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 MobileViTImageProcessor

class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=7       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=18       ,_SCREAMING_SNAKE_CASE=30       ,_SCREAMING_SNAKE_CASE=400       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=True       ,)			->		Dict:
         UpperCAmelCase_      :							Union[str, Any]       =      size if size is not None else {'''shortest_edge''': 20}
         UpperCAmelCase_      :							Any       =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
         UpperCAmelCase_      :							Any       =      parent
         UpperCAmelCase_      :							Dict       =      batch_size
         UpperCAmelCase_      :							List[str]       =      num_channels
         UpperCAmelCase_      :							Union[str, Any]       =      image_size
         UpperCAmelCase_      :							Dict       =      min_resolution
         UpperCAmelCase_      :							str       =      max_resolution
         UpperCAmelCase_      :							List[Any]       =      do_resize
         UpperCAmelCase_      :							List[Any]       =      size
         UpperCAmelCase_      :							str       =      do_center_crop
         UpperCAmelCase_      :							str       =      crop_size
         UpperCAmelCase_      :							Any       =      do_flip_channel_order





    def 							a__				(				self  )			->		Tuple:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_flip_channel_order": self.do_flip_channel_order,
         }


@require_torch
@require_vision
class   __a(					_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				MobileViTImageProcessor if is_vision_available() else None





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							int       =      MobileViTImageProcessingTester(self  )





    @property
    def 							a__				(				self  )			->		Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Union[str, Any]       =      self.image_processing_class(**self.image_processor_dict  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,'''do_resize'''  )  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,'''size'''  )  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,'''do_center_crop'''  )  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,'''center_crop'''  )  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,'''do_flip_channel_order'''  )  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Union[str, Any]       =      self.image_processing_class.from_dict(self.image_processor_dict  )
         self.assertEqual(image_processor.size       ,{'''shortest_edge''': 20}  )
         self.assertEqual(image_processor.crop_size       ,{'''height''': 18, '''width''': 18}  )

         UpperCAmelCase_      :							Tuple       =      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 							a__				(				self  )			->		str:
         pass





    def 							a__				(				self  )			->		Optional[int]:
         # Initialize image_processing
         UpperCAmelCase_      :							int       =      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=_SCREAMING_SNAKE_CASE  )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,Image.Image  )

         # Test not batched input
         UpperCAmelCase_      :							str       =      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
         UpperCAmelCase_      :							Any       =      image_processing(_SCREAMING_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 							a__				(				self  )			->		Optional[Any]:
         # Initialize image_processing
         UpperCAmelCase_      :							int       =      self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         UpperCAmelCase_      :							str       =      prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_SCREAMING_SNAKE_CASE       ,numpify=_SCREAMING_SNAKE_CASE  )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,np.ndarray  )

         # Test not batched input
         UpperCAmelCase_      :							Optional[int]       =      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
         UpperCAmelCase_      :							List[str]       =      image_processing(_SCREAMING_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 							a__				(				self  )			->		Optional[Any]:
         # Initialize image_processing
         UpperCAmelCase_      :							Union[str, Any]       =      self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         UpperCAmelCase_      :							List[str]       =      prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_SCREAMING_SNAKE_CASE       ,torchify=_SCREAMING_SNAKE_CASE  )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,torch.Tensor  )

         # Test not batched input
         UpperCAmelCase_      :							List[str]       =      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
         UpperCAmelCase_      :							Tuple       =      image_processing(_SCREAMING_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 lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def lowerCamelCase__       (      _lowercase			,						_lowercase=0				):

     '''simple docstring'''


     return sorted(_lowercase			,						key=lambda _lowercase				: x[column]				)







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(points_counts - 1				):
          for j in range(i + 1			,						_lowercase				):
               UpperCAmelCase_      :							Optional[int]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[Any]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(min(6			,						points_counts - 1				)			,						_lowercase				):
          for j in range(max(0			,						i - 6				)			,						_lowercase				):
               UpperCAmelCase_      :							List[str]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[int]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     if points_counts <= 3:
          return dis_between_closest_pair(_lowercase			,						_lowercase				)

     # recursion
     UpperCAmelCase_      :							Optional[int]       =      points_counts // 2
     UpperCAmelCase_      :							List[Any]       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[:mid]			,						_lowercase				)
     UpperCAmelCase_      :							Dict       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[mid:]			,						points_counts - mid				)
     UpperCAmelCase_      :							Union[str, Any]       =      min(_lowercase			,						_lowercase				)
     UpperCAmelCase_      :							str       =      []
     for point in points_sorted_on_x:
          if abs(point[0] - points_sorted_on_x[mid][0]				) < closest_pair_dis:
               cross_strip.append(_lowercase				)

     UpperCAmelCase_      :							Optional[Any]       =      dis_between_closest_in_strip(
         _lowercase			,						len(_lowercase				)			,						_lowercase				)
     return min(_lowercase			,						_lowercase				)







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Tuple       =      column_based_sort(_lowercase			,						column=0				)
     UpperCAmelCase_      :							List[Any]       =      column_based_sort(_lowercase			,						column=1				)
     return (
         closest_pair_of_points_sqr(
             _lowercase			,						_lowercase			,						_lowercase				)
     ) ** 0.5


if __name__ == "__main__":
     __a          =			[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
     print('Distance:', closest_pair_of_points(points, len(points)))