from __future__ import annotations

from collections.abc import Iterable




class 				__lowerCAmelCase					:



 '''simple docstring'''

 def __init__(     self    :						Dict    ,     lowerCamelCase    :						Union[str, Any]    ,     lowerCamelCase    :						Union[str, Any]					)	->							Tuple:
        _UpperCamelCase       :    List[Any]					  =					float(lowerCamelCase					), float(lowerCamelCase					)

 def __eq__(     self    :						List[str]    ,     lowerCamelCase    :						List[Any]					)	->							Any:
        return self.x == other.x and self.y == other.y

 def __ne__(     self    :						Tuple    ,     lowerCamelCase    :						List[str]					)	->							Dict:
        return not self == other

 def __gt__(     self    :						Tuple    ,     lowerCamelCase    :						Optional[int]					)	->							Dict:
        if self.x > other.x:
               return True
        elif self.x == other.x:
               return self.y > other.y
        return False

 def __lt__(     self    :						Union[str, Any]    ,     lowerCamelCase    :						Tuple					)	->							Optional[Any]:
        return not self > other

 def __ge__(     self    :						Dict    ,     lowerCamelCase    :						List[str]					)	->							Any:
        if self.x > other.x:
               return True
        elif self.x == other.x:
               return self.y >= other.y
        return False

 def __le__(     self    :						Tuple    ,     lowerCamelCase    :						str					)	->							Optional[int]:
        if self.x < other.x:
               return True
        elif self.x == other.x:
               return self.y <= other.y
        return False

 def __repr__(     self    :						List[str]					)	->							int:
        return f"({self.x}, {self.y})"

 def __hash__(     self    :						Any					)	->							List[str]:
        return hash(self.x					)







def SCREAMING_SNAKE_CASE			(       lowercase_:		list[Point] | list[list[float]] | Iterable[list[float]]						,  ):
       _UpperCamelCase       :    list[Point]					  =					[]
       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 SCREAMING_SNAKE_CASE			(       lowercase_:		list[Point] | list[list[float]] ):

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

       if not points:
              _UpperCamelCase       :    List[str]					  =					F"Expecting a list of points but got {points}"
              raise ValueError(lowercase_ )

       return _construct_points(lowercase_ )


def SCREAMING_SNAKE_CASE			(       lowercase_:		Point						,  lowercase_:		Point						,  lowercase_:		Point ):
       _UpperCamelCase       :    str					  =					(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 SCREAMING_SNAKE_CASE			(       lowercase_:		list[Point] ):
       _UpperCamelCase       :    Dict					  =					sorted(_validate_input(lowercase_ ) )
       _UpperCamelCase       :    str					  =					len(lowercase_ )
       _UpperCamelCase       :    Optional[int]					  =					set()

       for i in range(n - 1 ):
              for j in range(i + 1						,  lowercase_ ):
                     _UpperCamelCase       :    Union[str, Any]					  =					False
                     _UpperCamelCase       :    Tuple					  =					True
                     for k in range(lowercase_ ):
                            if k != i and k != j:
                                   _UpperCamelCase       :    Dict					  =					_det(points[i]						,  points[j]						,  points[k] )

                                   if det_k > 0:
                                          _UpperCamelCase       :    Dict					  =					True
                                   elif det_k < 0:
                                          _UpperCamelCase       :    Tuple					  =					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]:
                                                 _UpperCamelCase       :    Optional[int]					  =					False
                                                 break

                            if points_left_of_ij and points_right_of_ij:
                                   _UpperCamelCase       :    Optional[int]					  =					False
                                   break

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

       return sorted(lowercase_ )


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[Point] ):
       _UpperCamelCase       :    List[Any]					  =					sorted(_validate_input(lowercase_ ) )
       _UpperCamelCase       :    Tuple					  =					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

       _UpperCamelCase       :    Dict					  =					points[0]
       _UpperCamelCase       :    Union[str, Any]					  =					points[n - 1]

       _UpperCamelCase       :    List[Any]					  =					{left_most_point, right_most_point}
       _UpperCamelCase       :    Tuple					  =					[]
       _UpperCamelCase       :    Optional[Any]					  =					[]

       for i in range(1						,  n - 1 ):
              _UpperCamelCase       :    Union[str, Any]					  =					_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 SCREAMING_SNAKE_CASE			(       lowercase_:		list[Point]						,  lowercase_:		Point						,  lowercase_:		Point						,  lowercase_:		set[Point] ):
       if points:
              _UpperCamelCase       :    Optional[int]					  =					None
              _UpperCamelCase       :    Optional[int]					  =					float('-inf' )
              _UpperCamelCase       :    Union[str, Any]					  =					[]

              for p in points:
                     _UpperCamelCase       :    List[str]					  =					_det(lowercase_						,  lowercase_						,  lowercase_ )

                     if det > 0:
                            candidate_points.append(lowercase_ )

                            if det > extreme_point_distance:
                                   _UpperCamelCase       :    Union[str, Any]					  =					det
                                   _UpperCamelCase       :    Optional[int]					  =					p

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


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[Point] ):
       _UpperCamelCase       :    Union[str, Any]					  =					sorted(_validate_input(lowercase_ ) )
       _UpperCamelCase       :    Tuple					  =					len(lowercase_ )

       _UpperCamelCase       :    List[str]					  =					points[:2]
       for i in range(2						,  lowercase_ ):
              _UpperCamelCase       :    Optional[Any]					  =					_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:
                     _UpperCamelCase       :    Optional[int]					  =					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 SCREAMING_SNAKE_CASE			(       ):
       _UpperCamelCase       :    str					  =					[
           (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)]
       _UpperCamelCase       :    List[str]					  =					convex_hull_bf(lowercase_ )

       _UpperCamelCase       :    Tuple					  =					convex_hull_recursive(lowercase_ )
       assert results_bf == results_recursive

       _UpperCamelCase       :    Union[str, Any]					  =					convex_hull_melkman(lowercase_ )
       assert results_bf == results_melkman

       print(lowercase_ )


if __name__ == "__main__":
 main()




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase     						=			False




@nightly
@require_torch_gpu
class 				__lowerCAmelCase					(    unittest.TestCase  ):



 '''simple docstring'''

 def    __snake_case   (     self    :						Optional[Any]					)	->							str:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def    __snake_case   (     self    :						Dict					)	->							str:
        _UpperCamelCase       :    Dict					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Dict					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)

        _UpperCamelCase       :    Optional[int]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type='numpy'    ,     ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(lowerCamelCase					)
               _UpperCamelCase       :    int					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCamelCase					)

        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    str					  =					generator.manual_seed(0					)
        _UpperCamelCase       :    Tuple					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type='numpy'    ,     ).images

        assert np.abs(image - new_image					).sum() < 1e-5, "Models don't have the same forward pass"

 def    __snake_case   (     self    :						Tuple					)	->							Optional[int]:
        _UpperCamelCase       :    Optional[Any]					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Union[str, Any]					  =					'cyberpunk 2077'
        _UpperCamelCase       :    str					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
        _UpperCamelCase       :    List[Any]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt=lowerCamelCase    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=5_0    ,     output_type='numpy'    ,     ).images

        _UpperCamelCase       :    Any					  =					image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        _UpperCamelCase       :    Optional[int]					  =					np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2



