"""simple docstring"""







import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor






class     _SCREAMING_SNAKE_CASE       ( unittest.TestCase							):



   '''simple docstring'''



   def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=7   ,							_SCREAMING_SNAKE_CASE=3   ,							_SCREAMING_SNAKE_CASE=30   ,							_SCREAMING_SNAKE_CASE=400   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]   ,							_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]   ,							_SCREAMING_SNAKE_CASE=False   ,							):


         '''simple docstring'''

         __lowerCamelCase								=				size if size is not None else {'height': 30, 'width': 30}
         __lowerCamelCase								=				parent
         __lowerCamelCase								=				batch_size
         __lowerCamelCase								=				num_channels
         __lowerCamelCase								=				min_resolution
         __lowerCamelCase								=				max_resolution
         __lowerCamelCase								=				do_resize
         __lowerCamelCase								=				size
         __lowerCamelCase								=				do_normalize
         __lowerCamelCase								=				image_mean
         __lowerCamelCase								=				image_std
         __lowerCamelCase								=				do_reduce_labels







   def       _A							(  self  ):


         '''simple docstring'''

         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_reduce_labels": self.do_reduce_labels,
         }



def 			A__						(  )     ->       Tuple:
      __lowerCamelCase								=				load_dataset('hf-internal-testing/fixtures_ade20k'			,   split='test'	)

      __lowerCamelCase								=				Image.open(dataset[0]['file']	)
      __lowerCamelCase								=				Image.open(dataset[1]['file']	)

      return image, map



def 			A__						(  )     ->       int:
      __lowerCamelCase								=				load_dataset('hf-internal-testing/fixtures_ade20k'			,   split='test'	)

      __lowerCamelCase								=				Image.open(dataset[0]['file']	)
      __lowerCamelCase								=				Image.open(dataset[1]['file']	)
      __lowerCamelCase								=				Image.open(dataset[2]['file']	)
      __lowerCamelCase								=				Image.open(dataset[3]['file']	)

      return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class     _SCREAMING_SNAKE_CASE       ( _UpperCAmelCase						,							unittest.TestCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       Union[str, Any]						=						SegformerImageProcessor if is_vision_available() else None







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				SegformerImageProcessingTester(self  )







   @property
   def       _A							(  self  ):


         '''simple docstring'''

         return self.image_processor_tester.prepare_image_processor_dict()







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				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_normalize'  )  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,							'image_mean'  )  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,							'image_std'  )  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,							'do_reduce_labels'  )  )







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.image_processing_class.from_dict(self.image_processor_dict  )
         self.assertEqual(image_processor.size   ,							{'height': 30, 'width': 30}  )
         self.assertEqual(image_processor.do_reduce_labels   ,							_SCREAMING_SNAKE_CASE  )

         __lowerCamelCase								=				self.image_processing_class.from_dict(self.image_processor_dict   ,							size=42   ,							reduce_labels=_SCREAMING_SNAKE_CASE  )
         self.assertEqual(image_processor.size   ,							{'height': 42, 'width': 42}  )
         self.assertEqual(image_processor.do_reduce_labels   ,							_SCREAMING_SNAKE_CASE  )







   def       _A							(  self  ):


         '''simple docstring'''

         pass







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         __lowerCamelCase								=				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								=				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['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)

         # Test batched
         __lowerCamelCase								=				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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         __lowerCamelCase								=				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								=				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['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)

         # Test batched
         __lowerCamelCase								=				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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         __lowerCamelCase								=				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								=				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['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)

         # Test batched
         __lowerCamelCase								=				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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         __lowerCamelCase								=				prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=_SCREAMING_SNAKE_CASE   ,							torchify=_SCREAMING_SNAKE_CASE  )
         __lowerCamelCase								=				[]
         for image in image_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,							torch.Tensor  )
               maps.append(torch.zeros(image.shape[-2:]  ).long()  )

         # Test not batched input
         __lowerCamelCase								=				image_processing(image_inputs[0]   ,							maps[0]   ,							return_tensors='pt'  )
         self.assertEqual(
             encoding['pixel_values'].shape   ,							(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)
         self.assertEqual(
             encoding['labels'].shape   ,							(
                 1,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)
         self.assertEqual(encoding['labels'].dtype   ,							torch.long  )
         self.assertTrue(encoding['labels'].min().item() >= 0  )
         self.assertTrue(encoding['labels'].max().item() <= 255  )

         # Test batched
         __lowerCamelCase								=				image_processing(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							return_tensors='pt'  )
         self.assertEqual(
             encoding['pixel_values'].shape   ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)
         self.assertEqual(
             encoding['labels'].shape   ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)
         self.assertEqual(encoding['labels'].dtype   ,							torch.long  )
         self.assertTrue(encoding['labels'].min().item() >= 0  )
         self.assertTrue(encoding['labels'].max().item() <= 255  )

         # Test not batched input (PIL images)
         __lowerCamelCase		,__lowerCamelCase								=				prepare_semantic_single_inputs()

         __lowerCamelCase								=				image_processing(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							return_tensors='pt'  )
         self.assertEqual(
             encoding['pixel_values'].shape   ,							(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)
         self.assertEqual(
             encoding['labels'].shape   ,							(
                 1,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)
         self.assertEqual(encoding['labels'].dtype   ,							torch.long  )
         self.assertTrue(encoding['labels'].min().item() >= 0  )
         self.assertTrue(encoding['labels'].max().item() <= 255  )

         # Test batched input (PIL images)
         __lowerCamelCase		,__lowerCamelCase								=				prepare_semantic_batch_inputs()

         __lowerCamelCase								=				image_processing(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							return_tensors='pt'  )
         self.assertEqual(
             encoding['pixel_values'].shape   ,							(
                 2,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)
         self.assertEqual(
             encoding['labels'].shape   ,							(
                 2,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,							)
         self.assertEqual(encoding['labels'].dtype   ,							torch.long  )
         self.assertTrue(encoding['labels'].min().item() >= 0  )
         self.assertTrue(encoding['labels'].max().item() <= 255  )







   def       _A							(  self  ):


         '''simple docstring'''

         __lowerCamelCase								=				self.image_processing_class(**self.image_processor_dict  )

         # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
         __lowerCamelCase		,__lowerCamelCase								=				prepare_semantic_single_inputs()
         __lowerCamelCase								=				image_processing(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							return_tensors='pt'  )
         self.assertTrue(encoding['labels'].min().item() >= 0  )
         self.assertTrue(encoding['labels'].max().item() <= 150  )

         __lowerCamelCase								=				True
         __lowerCamelCase								=				image_processing(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							return_tensors='pt'  )
         self.assertTrue(encoding['labels'].min().item() >= 0  )
         self.assertTrue(encoding['labels'].max().item() <= 255  )







"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterable






class     _SCREAMING_SNAKE_CASE       :



   '''simple docstring'''



   def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase								=				float(_SCREAMING_SNAKE_CASE  ), float(_SCREAMING_SNAKE_CASE  )







   def __eq__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      return self.x == other.x and self.y == other.y







   def __ne__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      return not self == other







   def __gt__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      if self.x > other.x:
         return True
      elif self.x == other.x:
         return self.y > other.y
      return False







   def __lt__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      return not self > other







   def __ge__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      if self.x > other.x:
         return True
      elif self.x == other.x:
         return self.y >= other.y
      return False







   def __le__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      if self.x < other.x:
         return True
      elif self.x == other.x:
         return self.y <= other.y
      return False







   def __repr__(  self  ):


      '''simple docstring'''

      return F'''({self.x}, {self.y})'''







   def __hash__(  self  ):


      '''simple docstring'''

      return hash(self.x  )



def 			A__						(  lowercase_    :  list[Point] | list[list[float]] | Iterable[list[float]]			,   )     ->       list[Point]:
   __lowerCamelCase								=				[]
   if list_of_tuples:
      for p in list_of_tuples:
         if isinstance(lowercase_			,   lowercase_	):
            points.append(lowercase_	)
         else:
            try:
               points.append(Point(p[0]			,   p[1]	)	)
            except (IndexError, TypeError):
               print(
                   F'''Ignoring deformed point {p}. All points'''
                   ' must have at least 2 coordinates.'	)
   return points



def 			A__						(  lowercase_    :  list[Point] | list[list[float]]	)     ->       list[Point]:

   if not hasattr(lowercase_			,   '__iter__'	):
      __lowerCamelCase								=				F'''Expecting an iterable object but got an non-iterable type {points}'''
      raise ValueError(lowercase_	)

   if not points:
      __lowerCamelCase								=				F'''Expecting a list of points but got {points}'''
      raise ValueError(lowercase_	)

   return _construct_points(lowercase_	)



def 			A__						(  lowercase_    :  Point			,   lowercase_    :  Point			,   lowercase_    :  Point	)     ->       float:
   __lowerCamelCase								=				(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
   return det



def 			A__						(  lowercase_    :  list[Point]	)     ->       list[Point]:
   __lowerCamelCase								=				sorted(_validate_input(lowercase_	)	)
   __lowerCamelCase								=				len(lowercase_	)
   __lowerCamelCase								=				set()

   for i in range(n - 1	):
      for j in range(i + 1			,   lowercase_	):
         __lowerCamelCase								=				__lowerCamelCase								=				False
         __lowerCamelCase								=				True
         for k in range(lowercase_	):
            if k != i and k != j:
               __lowerCamelCase								=				_det(points[i]			,   points[j]			,   points[k]	)

               if det_k > 0:
                  __lowerCamelCase								=				True
               elif det_k < 0:
                  __lowerCamelCase								=				True
               else:
                  # point[i], point[j], point[k] all lie on a straight line
                  # if point[k] is to the left of point[i] or it's to the
                  # right of point[j], then point[i], point[j] cannot be
                  # part of the convex hull of A
                  if points[k] < points[i] or points[k] > points[j]:
                     __lowerCamelCase								=				False
                     break

            if points_left_of_ij and points_right_of_ij:
               __lowerCamelCase								=				False
               break

         if ij_part_of_convex_hull:
            convex_set.update([points[i], points[j]]	)

   return sorted(lowercase_	)



def 			A__						(  lowercase_    :  list[Point]	)     ->       list[Point]:
   __lowerCamelCase								=				sorted(_validate_input(lowercase_	)	)
   __lowerCamelCase								=				len(lowercase_	)

   # divide all the points into an upper hull and a lower hull
   # the left most point and the right most point are definitely
   # members of the convex hull by definition.
   # use these two anchors to divide all the points into two hulls,
   # an upper hull and a lower hull.

   # all points to the left (above) the line joining the extreme points belong to the
   # upper hull
   # all points to the right (below) the line joining the extreme points below to the
   # lower hull
   # ignore all points on the line joining the extreme points since they cannot be
   # part of the convex hull

   __lowerCamelCase								=				points[0]
   __lowerCamelCase								=				points[n - 1]

   __lowerCamelCase								=				{left_most_point, right_most_point}
   __lowerCamelCase								=				[]
   __lowerCamelCase								=				[]

   for i in range(1			,   n - 1	):
      __lowerCamelCase								=				_det(lowercase_			,   lowercase_			,   points[i]	)

      if det > 0:
         upper_hull.append(points[i]	)
      elif det < 0:
         lower_hull.append(points[i]	)

   _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)
   _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)

   return sorted(lowercase_	)



def 			A__						(  lowercase_    :  list[Point]			,   lowercase_    :  Point			,   lowercase_    :  Point			,   lowercase_    :  set[Point]	)     ->       None:
   if points:
      __lowerCamelCase								=				None
      __lowerCamelCase								=				float('-inf'	)
      __lowerCamelCase								=				[]

      for p in points:
         __lowerCamelCase								=				_det(lowercase_			,   lowercase_			,   lowercase_	)

         if det > 0:
            candidate_points.append(lowercase_	)

            if det > extreme_point_distance:
               __lowerCamelCase								=				det
               __lowerCamelCase								=				p

      if extreme_point:
         _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)
         convex_set.add(lowercase_	)
         _construct_hull(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)



def 			A__						(  lowercase_    :  list[Point]	)     ->       list[Point]:
   __lowerCamelCase								=				sorted(_validate_input(lowercase_	)	)
   __lowerCamelCase								=				len(lowercase_	)

   __lowerCamelCase								=				points[:2]
   for i in range(2			,   lowercase_	):
      __lowerCamelCase								=				_det(convex_hull[1]			,   convex_hull[0]			,   points[i]	)
      if det > 0:
         convex_hull.insert(0			,   points[i]	)
         break
      elif det < 0:
         convex_hull.append(points[i]	)
         break
      else:
         __lowerCamelCase								=				points[i]
   i += 1

   for j in range(lowercase_			,   lowercase_	):
      if (
          _det(convex_hull[0]			,   convex_hull[-1]			,   points[j]	) > 0
          and _det(convex_hull[-1]			,   convex_hull[0]			,   points[1]	) < 0
      ):
         # The point lies within the convex hull
         continue

      convex_hull.insert(0			,   points[j]	)
      convex_hull.append(points[j]	)
      while _det(convex_hull[0]			,   convex_hull[1]			,   convex_hull[2]	) >= 0:
         del convex_hull[1]
      while _det(convex_hull[-1]			,   convex_hull[-2]			,   convex_hull[-3]	) <= 0:
         del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
   return sorted(convex_hull[1:] if len(lowercase_	) > 3 else convex_hull	)



def 			A__						(  )     ->       List[Any]:
   __lowerCamelCase								=				[
       (0, 3),
       (2, 2),
       (1, 1),
       (2, 1),
       (3, 0),
       (0, 0),
       (3, 3),
       (2, -1),
       (2, -4),
       (1, -3),
   ]
   # the convex set of points is
   # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
   __lowerCamelCase								=				convex_hull_bf(lowercase_	)

   __lowerCamelCase								=				convex_hull_recursive(lowercase_	)
   assert results_bf == results_recursive

   __lowerCamelCase								=				convex_hull_melkman(lowercase_	)
   assert results_bf == results_melkman

   print(lowercase_	)


if __name__ == "__main__":
     main()






