

"""simple docstring"""




from __future__ import annotations

from collections.abc import Iterable





class 			__UpperCamelCase :


		def __init__(self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		Optional[int]):
							A			,    A			=					float(__A), float(__A)


		def __eq__(self :		Tuple     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]):
							return self.x == other.x and self.y == other.y


		def __ne__(self :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]):
							return not self == other


		def __gt__(self :		int     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]):



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


		def __lt__(self :		int     ,     __SCREAMING_SNAKE_CASE :		List[Any]):
							return not self > other


		def __ge__(self :		str     ,     __SCREAMING_SNAKE_CASE :		Dict):



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


		def __le__(self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Tuple):



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


		def __repr__(self :		List[str]):
							return F"""({self.x}, {self.y})"""


		def __hash__(self :		Any):
							return hash(self.x)

def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							):
					"""simple docstring"""
					A			=					[]
					if list_of_tuples:
										for p in list_of_tuples:
															if isinstance(UpperCamelCase__			,							UpperCamelCase__      ):
																				points.append(UpperCamelCase__      )
															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__      ):
					"""simple docstring"""

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

					if not points:
										A			=					F"""Expecting a list of points but got {points}"""
										raise ValueError(UpperCamelCase__      )

					return _construct_points(UpperCamelCase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					(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__      ):
					"""simple docstring"""
					A			=					sorted(_validate_input(UpperCamelCase__      )      )
					A			=					len(UpperCamelCase__      )
					A			=					set()

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

																									if det_k > 0:
																														A			=					True
																									elif det_k < 0:
																														A			=					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]:
																																			A			=					False
																																			break

																				if points_left_of_ij and points_right_of_ij:
																									A			=					False
																									break

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

					return sorted(UpperCamelCase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					sorted(_validate_input(UpperCamelCase__      )      )
					A			=					len(UpperCamelCase__      )

					# 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

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

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

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

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

					_construct_hull(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__      )
					_construct_hull(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__      )

					return sorted(UpperCamelCase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					if points:
										A			=					None
										A			=					float("-inf"      )
										A			=					[]

										for p in points:
															A			=					_det(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__      )

															if det > 0:
																				candidate_points.append(UpperCamelCase__      )

																				if det > extreme_point_distance:
																									A			=					det
																									A			=					p

										if extreme_point:
															_construct_hull(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__      )
															convex_set.add(UpperCamelCase__      )
															_construct_hull(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					sorted(_validate_input(UpperCamelCase__      )      )
					A			=					len(UpperCamelCase__      )

					A			=					points[:2]
					for i in range(2			,							UpperCamelCase__      ):
										A			=					_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:
															A			=					points[i]
					i += 1

					for j in range(UpperCamelCase__			,							UpperCamelCase__      ):
										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(UpperCamelCase__      ) > 3 else convex_hull      )
def    __SCREAMING_SNAKE_CASE						(						):
					"""simple docstring"""
					A			=					[
					    (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)]
					A			=					convex_hull_bf(UpperCamelCase__      )

					A			=					convex_hull_recursive(UpperCamelCase__      )
					assert results_bf == results_recursive

					A			=					convex_hull_melkman(UpperCamelCase__      )
					assert results_bf == results_melkman

					print(UpperCamelCase__      )


if __name__ == "__main__":
	main()












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    ["image_processor", "tokenizer"]
							lowerCamelCase_							=    "AutoImageProcessor"
							lowerCamelCase_							=    "AutoTokenizer"


							def __init__(							self			:Any							, __A			:List[str]=None							, __A			:Dict=None							, **__A			:List[str]						)      ->      List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""							, __A							, )
											SCREAMING_SNAKE_CASE__								=					kwargs.pop("""feature_extractor"""						)

									SCREAMING_SNAKE_CASE__								=					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""						)
									if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""						)

									super().__init__(__A							, __A						)
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							def __call__(							self			:List[str]							, *__A			:int							, **__A			:Optional[int]						)      ->      Optional[Any]:



									"""simple docstring"""



									if self._in_target_context_manager:
											return self.current_processor(*__A							, **__A						)

									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""images"""							, __A						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""text"""							, __A						)
									if len(__A						) > 0:
											SCREAMING_SNAKE_CASE__								=					args[0]
											SCREAMING_SNAKE_CASE__								=					args[1:]

									if images is None and text is None:
											raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=					self.image_processor(__A							, *__A							, **__A						)
									if text is not None:
											SCREAMING_SNAKE_CASE__								=					self.tokenizer(__A							, **__A						)

									if text is None:
											return inputs
									elif images is None:
											return encodings
									else:
											SCREAMING_SNAKE_CASE__								=					encodings["""input_ids"""]
											return inputs


							def    _snake_case     (							self			:Any							, *__A			:Optional[Any]							, **__A			:int						)      ->      List[Any]:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A							, **__A						)


							def    _snake_case     (							self			:str							, *__A			:Optional[int]							, **__A			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									return self.tokenizer.decode(*__A							, **__A						)


							@contextmanager
							def    _snake_case     (							self			:Dict						)      ->      List[str]:



									"""simple docstring"""



									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your images inputs, or in a separate call."""						)
									SCREAMING_SNAKE_CASE__								=					True
									SCREAMING_SNAKE_CASE__								=					self.tokenizer
									yield
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							@property
							def    _snake_case     (							self			:List[str]						)      ->      int:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							, __A							, )
									return self.image_processor_class


							@property
							def    _snake_case     (							self			:str						)      ->      List[Any]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							, __A							, )
									return self.image_processor