import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__      		=					logging.get_logger(__name__)






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       lowerCAmelCase__	      =   ConvNextVaConfig()

       if "atto" in checkpoint_url:
              lowerCAmelCase__	      =   [2, 2, 6, 2]
              lowerCAmelCase__	      =   [4_0, 8_0, 1_6_0, 3_2_0]
       if "femto" in checkpoint_url:
              lowerCAmelCase__	      =   [2, 2, 6, 2]
              lowerCAmelCase__	      =   [4_8, 9_6, 1_9_2, 3_8_4]
       if "pico" in checkpoint_url:
              lowerCAmelCase__	      =   [2, 2, 6, 2]
              lowerCAmelCase__	      =   [6_4, 1_2_8, 2_5_6, 5_1_2]
       if "nano" in checkpoint_url:
              lowerCAmelCase__	      =   [2, 2, 8, 2]
              lowerCAmelCase__	      =   [8_0, 1_6_0, 3_2_0, 6_4_0]
       if "tiny" in checkpoint_url:
              lowerCAmelCase__	      =   [3, 3, 9, 3]
              lowerCAmelCase__	      =   [9_6, 1_9_2, 3_8_4, 7_6_8]
       if "base" in checkpoint_url:
              lowerCAmelCase__	      =   [3, 3, 2_7, 3]
              lowerCAmelCase__	      =   [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
       if "large" in checkpoint_url:
              lowerCAmelCase__	      =   [3, 3, 2_7, 3]
              lowerCAmelCase__	      =   [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
       if "huge" in checkpoint_url:
              lowerCAmelCase__	      =   [3, 3, 2_7, 3]
              lowerCAmelCase__	      =   [3_5_2, 7_0_4, 1_4_0_8, 2_8_1_6]

       lowerCAmelCase__	      =   1_0_0_0
       lowerCAmelCase__	      =   'imagenet-1k-id2label.json'
       lowerCAmelCase__	      =   (1, 1_0_0_0)

       lowerCAmelCase__	      =   'huggingface/label-files'
       lowerCAmelCase__	      =   num_labels
       lowerCAmelCase__	      =   json.load(open(hf_hub_download(lowerCAmelCase__      ,    lowerCAmelCase__      ,    repo_type='dataset'      )      ,    'r'      )      )
       lowerCAmelCase__	      =   {int(lowerCAmelCase__      ): v for k, v in idalabel.items()}

       lowerCAmelCase__	      =   idalabel
       lowerCAmelCase__	      =   {v: k for k, v in idalabel.items()}
       lowerCAmelCase__	      =   hidden_sizes
       lowerCAmelCase__	      =   depths

       return config, expected_shape






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       if "downsample_layers.0.0" in name:
              lowerCAmelCase__	      =   name.replace('downsample_layers.0.0'      ,    'embeddings.patch_embeddings'      )
       if "downsample_layers.0.1" in name:
              lowerCAmelCase__	      =   name.replace('downsample_layers.0.1'      ,    'embeddings.norm'      )  # we rename to layernorm later on
       if "downsample_layers.1.0" in name:
              lowerCAmelCase__	      =   name.replace('downsample_layers.1.0'      ,    'stages.1.downsampling_layer.0'      )
       if "downsample_layers.1.1" in name:
              lowerCAmelCase__	      =   name.replace('downsample_layers.1.1'      ,    'stages.1.downsampling_layer.1'      )
       if "downsample_layers.2.0" in name:
              lowerCAmelCase__	      =   name.replace('downsample_layers.2.0'      ,    'stages.2.downsampling_layer.0'      )
       if "downsample_layers.2.1" in name:
              lowerCAmelCase__	      =   name.replace('downsample_layers.2.1'      ,    'stages.2.downsampling_layer.1'      )
       if "downsample_layers.3.0" in name:
              lowerCAmelCase__	      =   name.replace('downsample_layers.3.0'      ,    'stages.3.downsampling_layer.0'      )
       if "downsample_layers.3.1" in name:
              lowerCAmelCase__	      =   name.replace('downsample_layers.3.1'      ,    'stages.3.downsampling_layer.1'      )
       if "stages" in name and "downsampling_layer" not in name:
              # stages.0.0. for instance should be renamed to stages.0.layers.0.
              lowerCAmelCase__	      =   name[: len('stages.0'      )] + '.layers' + name[len('stages.0'      ) :]
       if "gamma" in name:
              lowerCAmelCase__	      =   name.replace('gamma'      ,    'weight'      )
       if "beta" in name:
              lowerCAmelCase__	      =   name.replace('beta'      ,    'bias'      )
       if "stages" in name:
              lowerCAmelCase__	      =   name.replace('stages'      ,    'encoder.stages'      )
       if "norm" in name:
              lowerCAmelCase__	      =   name.replace('norm'      ,    'layernorm'      )
       if "head" in name:
              lowerCAmelCase__	      =   name.replace('head'      ,    'classifier'      )

       return name






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowerCAmelCase__	      =   Image.open(requests.get(lowerCAmelCase__      ,    stream=lowerCAmelCase__      ).raw      )
       return im






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       if "224" in checkpoint_url:
              lowerCAmelCase__	      =   2_2_4
              lowerCAmelCase__	      =   2_2_4 / 2_5_6
       elif "384" in checkpoint_url:
              lowerCAmelCase__	      =   3_8_4
              lowerCAmelCase__	      =   None
       else:
              lowerCAmelCase__	      =   5_1_2
              lowerCAmelCase__	      =   None

       return ConvNextImageProcessor(
           size=lowerCAmelCase__      ,    crop_pct=lowerCAmelCase__      ,    image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]      ,    image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]      ,    resample=PILImageResampling.BICUBIC      ,    )






@torch.no_grad()
def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       print('Downloading original model from checkpoint...'      )
       # define ConvNeXTV2 configuration based on URL
       lowerCAmelCase__      ,   lowerCAmelCase__	      =   get_convnextva_config(lowerCAmelCase__      )
       # load original state_dict from URL
       lowerCAmelCase__	      =   torch.hub.load_state_dict_from_url(lowerCAmelCase__      )['model']

       print('Converting model parameters...'      )
       # rename keys
       for key in state_dict.copy().keys():
              lowerCAmelCase__	      =   state_dict.pop(lowerCAmelCase__      )
              lowerCAmelCase__	      =   val
       # add prefix to all keys expect classifier head
       for key in state_dict.copy().keys():
              lowerCAmelCase__	      =   state_dict.pop(lowerCAmelCase__      )
              if not key.startswith('classifier'      ):
                     lowerCAmelCase__	      =   'convnextv2.' + key
              lowerCAmelCase__	      =   val

       # load HuggingFace model
       lowerCAmelCase__	      =   ConvNextVaForImageClassification(lowerCAmelCase__      )
       model.load_state_dict(lowerCAmelCase__      )
       model.eval()

       # Check outputs on an image, prepared by ConvNextImageProcessor
       lowerCAmelCase__	      =   convert_preprocessor(lowerCAmelCase__      )
       lowerCAmelCase__	      =   preprocessor(images=prepare_img()      ,    return_tensors='pt'      )
       lowerCAmelCase__	      =   model(**lowerCAmelCase__      ).logits

       # note: the logits below were obtained without center cropping
       if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]      )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
              lowerCAmelCase__	      =   torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]      )
       else:
              raise ValueError(F"""Unknown URL: {checkpoint_url}"""      )

       assert torch.allclose(logits[0, :5]      ,    lowerCAmelCase__      ,    atol=1e-3      )
       assert logits.shape == expected_shape
       print('Model outputs match the original results!'      )

       if save_model:
              print('Saving model to local...'      )
              # Create folder to save model
              if not os.path.isdir(lowerCAmelCase__      ):
                     os.mkdir(lowerCAmelCase__      )

              model.save_pretrained(lowerCAmelCase__      )
              preprocessor.save_pretrained(lowerCAmelCase__      )

       lowerCAmelCase__	      =   'convnextv2'
       if "atto" in checkpoint_url:
              model_name += "-atto"
       if "femto" in checkpoint_url:
              model_name += "-femto"
       if "pico" in checkpoint_url:
              model_name += "-pico"
       if "nano" in checkpoint_url:
              model_name += "-nano"
       elif "tiny" in checkpoint_url:
              model_name += "-tiny"
       elif "base" in checkpoint_url:
              model_name += "-base"
       elif "large" in checkpoint_url:
              model_name += "-large"
       elif "huge" in checkpoint_url:
              model_name += "-huge"
       if "22k" in checkpoint_url and "1k" not in checkpoint_url:
              model_name += "-22k"
       elif "22k" in checkpoint_url and "1k" in checkpoint_url:
              model_name += "-22k-1k"
       elif "1k" in checkpoint_url:
              model_name += "-1k"
       if "224" in checkpoint_url:
              model_name += "-224"
       elif "384" in checkpoint_url:
              model_name += "-384"
       elif "512" in checkpoint_url:
              model_name += "-512"

       if push_to_hub:
              print(F"""Pushing {model_name} to the hub..."""      )
              model.push_to_hub(lowerCAmelCase__      )
              preprocessor.push_to_hub(lowerCAmelCase__      )


if __name__ == "__main__":
    lowerCAmelCase__      		=					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--checkpoint_url',
        default='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
        type=str,
        help='URL of the original ConvNeXTV2 checkpoint you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default='model',
        type=str,
        help='Path to the output PyTorch model directory.',
    )
    parser.add_argument('--save_model', action='store_true', help='Save model to local')
    parser.add_argument('--push_to_hub', action='store_true', help='Push model and image preprocessor to the hub')

    lowerCAmelCase__      		=					parser.parse_args()
    convert_convnextva_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
    )

from __future__ import annotations

from collections.abc import Iterable
class 							a_					:






 '''simple docstring'''






 def __init__(     self   :    int     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   float(lowercase__), float(lowercase__)




 def __eq__(     self   :    int     ,       lowercase__   :    int):







        '''simple docstring'''



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




 def __ne__(     self   :    Tuple     ,       lowercase__   :    List[Any]):







        '''simple docstring'''



        return not self == other




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







        '''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     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        return not self > other




 def __ge__(     self   :    List[Any]     ,       lowercase__   :    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   :    Dict     ,       lowercase__   :    str):







        '''simple docstring'''



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




 def __repr__(     self   :    List[str]):







        '''simple docstring'''



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




 def __hash__(     self   :    Dict):







        '''simple docstring'''



        return hash(self.x)






def  __lowerCamelCase						(		lowerCAmelCase__      ,    ):
       lowerCAmelCase__	      =   []
       if list_of_tuples:
              for p in list_of_tuples:
                     if isinstance(lowerCAmelCase__      ,    lowerCAmelCase__      ):
                            points.append(lowerCAmelCase__      )
                     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  __lowerCamelCase						(		lowerCAmelCase__      ):

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

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

       return _construct_points(lowerCAmelCase__      )






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       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  __lowerCamelCase						(		lowerCAmelCase__      ):
       lowerCAmelCase__	      =   sorted(_validate_input(lowerCAmelCase__      )      )
       lowerCAmelCase__	      =   len(lowerCAmelCase__      )
       lowerCAmelCase__	      =   set()

       for i in range(n - 1      ):
              for j in range(i + 1      ,    lowerCAmelCase__      ):
                     lowerCAmelCase__	      =   lowerCAmelCase__	      =   False
                     lowerCAmelCase__	      =   True
                     for k in range(lowerCAmelCase__      ):
                            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(lowerCAmelCase__      )






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       lowerCAmelCase__	      =   sorted(_validate_input(lowerCAmelCase__      )      )
       lowerCAmelCase__	      =   len(lowerCAmelCase__      )

       # 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(lowerCAmelCase__      ,    lowerCAmelCase__      ,    points[i]      )

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

       _construct_hull(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      )
       _construct_hull(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      )

       return sorted(lowerCAmelCase__      )






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if points:
              lowerCAmelCase__	      =   None
              lowerCAmelCase__	      =   float('-inf'      )
              lowerCAmelCase__	      =   []

              for p in points:
                     lowerCAmelCase__	      =   _det(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      )

                     if det > 0:
                            candidate_points.append(lowerCAmelCase__      )

                            if det > extreme_point_distance:
                                   lowerCAmelCase__	      =   det
                                   lowerCAmelCase__	      =   p

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






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       lowerCAmelCase__	      =   sorted(_validate_input(lowerCAmelCase__      )      )
       lowerCAmelCase__	      =   len(lowerCAmelCase__      )

       lowerCAmelCase__	      =   points[:2]
       for i in range(2      ,    lowerCAmelCase__      ):
              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(lowerCAmelCase__      ,    lowerCAmelCase__      ):
              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(lowerCAmelCase__      ) > 3 else convex_hull      )






def  __lowerCamelCase						(		):
       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(lowerCAmelCase__      )

       lowerCAmelCase__	      =   convex_hull_recursive(lowerCAmelCase__      )
       assert results_bf == results_recursive

       lowerCAmelCase__	      =   convex_hull_melkman(lowerCAmelCase__      )
       assert results_bf == results_melkman

       print(lowerCAmelCase__      )


if __name__ == "__main__":
    main()
