




'''simple docstring'''



from __future__ import annotations

from collections.abc import Iterable







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			Optional[int]						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   float(__lowerCamelCase					), float(__lowerCamelCase					)



   def __eq__(					self      :			Optional[int]						,   __lowerCamelCase      :			List[str]					):

      """simple docstring"""
      return self.x == other.x and self.y == other.y



   def __ne__(					self      :			Tuple						,   __lowerCamelCase      :			List[str]					):

      """simple docstring"""
      return not self == other



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

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



   def __lt__(					self      :			str						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      return not self > other



   def __ge__(					self      :			Any						,   __lowerCamelCase      :			List[Any]					):

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



   def __le__(					self      :			Union[str, Any]						,   __lowerCamelCase      :			int					):

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



   def __repr__(					self      :			Dict					):

      """simple docstring"""
      return F"""({self.x}, {self.y})"""



   def __hash__(					self      :			Optional[Any]					):

      """simple docstring"""
      return hash(self.x					)


def 				SCREAMING_SNAKE_CASE_				(							__A       :				list[Point] | list[list[float]] | Iterable[list[float]] ,  )				->				list[Point]:
   _SCREAMING_SNAKE_CASE =   []
   if list_of_tuples:
      for p in list_of_tuples:
         if isinstance(__A ,  __A						):
            points.append(__A						)
         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_				(							__A       :				list[Point] | list[list[float]]						)				->				list[Point]:

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

   if not points:
      _SCREAMING_SNAKE_CASE =   f"""Expecting a list of points but got {points}"""
      raise ValueError(__A						)

   return _construct_points(__A						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Point ,  __A       :				Point ,  __A       :				Point						)				->				float:
   _SCREAMING_SNAKE_CASE =   (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_				(							__A       :				list[Point]						)				->				list[Point]:
   _SCREAMING_SNAKE_CASE =   sorted(_validate_input(__A						)						)
   _SCREAMING_SNAKE_CASE =   len(__A						)
   _SCREAMING_SNAKE_CASE =   set()

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

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

            if points_left_of_ij and points_right_of_ij:
               _SCREAMING_SNAKE_CASE =   False
               break

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

   return sorted(__A						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				list[Point]						)				->				list[Point]:
   _SCREAMING_SNAKE_CASE =   sorted(_validate_input(__A						)						)
   _SCREAMING_SNAKE_CASE =   len(__A						)

   # 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

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

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

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

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

   _construct_hull(__A ,  __A ,  __A ,  __A						)
   _construct_hull(__A ,  __A ,  __A ,  __A						)

   return sorted(__A						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				list[Point] ,  __A       :				Point ,  __A       :				Point ,  __A       :				set[Point]						)				->				None:
   if points:
      _SCREAMING_SNAKE_CASE =   None
      _SCREAMING_SNAKE_CASE =   float("-inf"						)
      _SCREAMING_SNAKE_CASE =   []

      for p in points:
         _SCREAMING_SNAKE_CASE =   _det(__A ,  __A ,  __A						)

         if det > 0:
            candidate_points.append(__A						)

            if det > extreme_point_distance:
               _SCREAMING_SNAKE_CASE =   det
               _SCREAMING_SNAKE_CASE =   p

      if extreme_point:
         _construct_hull(__A ,  __A ,  __A ,  __A						)
         convex_set.add(__A						)
         _construct_hull(__A ,  __A ,  __A ,  __A						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				list[Point]						)				->				list[Point]:
   _SCREAMING_SNAKE_CASE =   sorted(_validate_input(__A						)						)
   _SCREAMING_SNAKE_CASE =   len(__A						)

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

   for j in range(__A ,  __A						):
      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(__A						) > 3 else convex_hull						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Dict:
   _SCREAMING_SNAKE_CASE =   [
       (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)]
   _SCREAMING_SNAKE_CASE =   convex_hull_bf(__A						)

   _SCREAMING_SNAKE_CASE =   convex_hull_recursive(__A						)
   assert results_bf == results_recursive

   _SCREAMING_SNAKE_CASE =   convex_hull_melkman(__A						)
   assert results_bf == results_melkman

   print(__A						)


if __name__ == "__main__":
 main()







'''simple docstring'''



import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:







 class 	lowercase_					:


    """simple docstring"""

    @staticmethod
    def 							lowerCAmelCase_    (					*__lowerCamelCase      :			Union[str, Any]						,   **__lowerCamelCase      :			str					):

       """simple docstring"""
       pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   lowerCamelCase_       						=		TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ImageClassificationPipeline(model=__lowerCamelCase						,   image_processor=__lowerCamelCase						,   top_k=2					)
      _SCREAMING_SNAKE_CASE =   [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
      ]
      return image_classifier, examples



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"					)

      self.assertEqual(
          __lowerCamelCase						,   [
              {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
          ]						,   )

      import datasets

      _SCREAMING_SNAKE_CASE =   datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,   "image"						,   split="test"					)

      # Accepts URL + PIL.Image + lists
      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              # RGBA
              dataset[0]["file"],
              # LA
              dataset[1]["file"],
              # L
              dataset[2]["file"],
          ]					)
      self.assertEqual(
          __lowerCamelCase						,   [
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
          ]						,   )



   @require_torch
   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]						,   )

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]						,   )



   @require_tf
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase						,   framework="tf"					)

      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]						,   )

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]						,   )



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   PreTrainedTokenizer()

      # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
      _SCREAMING_SNAKE_CASE =   pipeline(
          "image-classification"						,   model="hf-internal-testing/tiny-random-vit"						,   tokenizer=__lowerCamelCase					)

      self.assertIs(image_classifier.tokenizer						,   __lowerCamelCase					)



   @slow
   @require_torch
   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      # Perceiver is not tested by `run_pipeline_test` properly.
      # That is because the type of feature_extractor and model preprocessor need to be kept
      # in sync, which is not the case in the current design
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-conv"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
              {"score": 0.3_2_1, "label": "tiger cat"},
              {"score": 0.0_5_0_2, "label": "Egyptian cat"},
              {"score": 0.0_1_3_7, "label": "crib, cot"},
              {"score": 0.0_0_7, "label": "radiator"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-fourier"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
              {"score": 0.1_3_0_9, "label": "tiger cat"},
              {"score": 0.0_7_2_2, "label": "Egyptian cat"},
              {"score": 0.0_7_0_7, "label": "remote control, remote"},
              {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-learned"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
              {"score": 0.2_3_6_2, "label": "Egyptian cat"},
              {"score": 0.1_8_5_6, "label": "tiger cat"},
              {"score": 0.0_3_2_4, "label": "remote control, remote"},
              {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
          ]						,   )

