



"""simple docstring"""







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 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      def __init__(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Tuple=7   ,      SCREAMING_SNAKE_CASE_    :				Dict=3   ,      SCREAMING_SNAKE_CASE_    :				List[str]=1_8   ,      SCREAMING_SNAKE_CASE_    :				Tuple=3_0   ,      SCREAMING_SNAKE_CASE_    :				Any=4_0_0   ,      SCREAMING_SNAKE_CASE_    :				List[str]=True   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=None   ,      SCREAMING_SNAKE_CASE_    :				str=True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=None   ,      SCREAMING_SNAKE_CASE_    :				Tuple=True   ,      ):
        lowerCAmelCase_       :    List[str]								=			size if size is not None else {'shortest_edge': 2_0}
        lowerCAmelCase_       :    Union[str, Any]								=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
        lowerCAmelCase_       :    Tuple								=			parent
        lowerCAmelCase_       :    Tuple								=			batch_size
        lowerCAmelCase_       :    Union[str, Any]								=			num_channels
        lowerCAmelCase_       :    List[str]								=			image_size
        lowerCAmelCase_       :    Optional[Any]								=			min_resolution
        lowerCAmelCase_       :    List[Any]								=			max_resolution
        lowerCAmelCase_       :    List[str]								=			do_resize
        lowerCAmelCase_       :    List[str]								=			size
        lowerCAmelCase_       :    Optional[int]								=			do_center_crop
        lowerCAmelCase_       :    Union[str, Any]								=			crop_size
        lowerCAmelCase_       :    Any								=			do_flip_channel_order





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        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 				UpperCamelCase__       (     lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					MobileViTImageProcessor if is_vision_available() else None



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    str								=			MobileViTImageProcessingTester(self	)



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        return self.image_processor_tester.prepare_image_processor_dict()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    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 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Dict								=			self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size   ,      {'shortest_edge': 2_0}	)
        self.assertEqual(image_processor.crop_size   ,      {'height': 1_8, 'width': 1_8}	)

        lowerCAmelCase_       :    str								=			self.image_processing_class.from_dict(self.image_processor_dict   ,      size=4_2   ,      crop_size=8_4	)
        self.assertEqual(image_processor.size   ,      {'shortest_edge': 4_2}	)
        self.assertEqual(image_processor.crop_size   ,      {'height': 8_4, 'width': 8_4}	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        pass



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        # Initialize image_processing
        lowerCAmelCase_       :    Optional[int]								=			self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        lowerCAmelCase_       :    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
        lowerCAmelCase_       :    Any								=			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_       :    int								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        # Initialize image_processing
        lowerCAmelCase_       :    Any								=			self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        lowerCAmelCase_       :    Optional[Any]								=			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
        lowerCAmelCase_       :    Dict								=			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_       :    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 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        # Initialize image_processing
        lowerCAmelCase_       :    int								=			self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        lowerCAmelCase_       :    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
        lowerCAmelCase_       :    Any								=			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_       :    Dict								=			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'],
            )   ,      )










"""simple docstring"""







def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    str						)						->       List[Any]:

  """simple docstring"""




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

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    str=0						)						->       Union[str, Any]:

  """simple docstring"""




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

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]=float('inf'						)						)						->       Optional[int]:

  """simple docstring"""




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

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Union[str, Any]=float('inf'						)						)						->       Dict:

  """simple docstring"""




  for i in range(min(6   ,       points_counts - 1						)   ,       lowerCAmelCase__						):
    for j in range(max(0   ,       i - 6						)   ,       lowerCAmelCase__						):
      lowerCAmelCase_       :    Any								=			euclidean_distance_sqr(points[i]   ,       points[j]						)
      if current_dis < min_dis:
        lowerCAmelCase_       :    Union[str, Any]								=			current_dis
  return min_dis

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    Optional[int]						)						->       Dict:

  """simple docstring"""




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

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

  lowerCAmelCase_       :    List[Any]								=			dis_between_closest_in_strip(
      lowerCAmelCase__   ,       len(lowerCAmelCase__						)   ,       lowerCAmelCase__						)
  return min(lowerCAmelCase__   ,       lowerCAmelCase__						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Tuple						)						->       List[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    List[str]								=			column_based_sort(lowerCAmelCase__   ,       column=0						)
  lowerCAmelCase_       :    Dict								=			column_based_sort(lowerCAmelCase__   ,       column=1						)
  return (
      closest_pair_of_points_sqr(
          lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
  ) ** 0.5


if __name__ == "__main__":
     lowercase__		:    List[str]				   =     [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
     print("""Distance:""", closest_pair_of_points(points, len(points)))





