


'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a_   :      List[Any]				    =      logging.get_logger(__name__)






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Tuple=False						)				->					Dict:




     '''simple docstring'''



     _a      =							OrderedDict()
     for key, value in state_dict.items():
          if encoder_only and not key.startswith('head'						):
               _a      =							'segformer.encoder.' + key
          if key.startswith('backbone'						):
               _a      =							key.replace('backbone'			,  'segformer.encoder'						)
          if "patch_embed" in key:
               # replace for example patch_embed1 by patch_embeddings.0
               _a      =							key[key.find('patch_embed'						) + len('patch_embed'						)]
               _a      =							key.replace(f'patch_embed{idx}'			,  f'patch_embeddings.{int(lowerCAmelCase__						)-1}'						)
          if "norm" in key:
               _a      =							key.replace('norm'			,  'layer_norm'						)
          if "segformer.encoder.layer_norm" in key:
               # replace for example layer_norm1 by layer_norm.0
               _a      =							key[key.find('segformer.encoder.layer_norm'						) + len('segformer.encoder.layer_norm'						)]
               _a      =							key.replace(f'layer_norm{idx}'			,  f'layer_norm.{int(lowerCAmelCase__						)-1}'						)
          if "layer_norm1" in key:
               _a      =							key.replace('layer_norm1'			,  'layer_norm_1'						)
          if "layer_norm2" in key:
               _a      =							key.replace('layer_norm2'			,  'layer_norm_2'						)
          if "block" in key:
               # replace for example block1 by block.0
               _a      =							key[key.find('block'						) + len('block'						)]
               _a      =							key.replace(f'block{idx}'			,  f'block.{int(lowerCAmelCase__						)-1}'						)
          if "attn.q" in key:
               _a      =							key.replace('attn.q'			,  'attention.self.query'						)
          if "attn.proj" in key:
               _a      =							key.replace('attn.proj'			,  'attention.output.dense'						)
          if "attn" in key:
               _a      =							key.replace('attn'			,  'attention.self'						)
          if "fc1" in key:
               _a      =							key.replace('fc1'			,  'dense1'						)
          if "fc2" in key:
               _a      =							key.replace('fc2'			,  'dense2'						)
          if "linear_pred" in key:
               _a      =							key.replace('linear_pred'			,  'classifier'						)
          if "linear_fuse" in key:
               _a      =							key.replace('linear_fuse.conv'			,  'linear_fuse'						)
               _a      =							key.replace('linear_fuse.bn'			,  'batch_norm'						)
          if "linear_c" in key:
               # replace for example linear_c4 by linear_c.3
               _a      =							key[key.find('linear_c'						) + len('linear_c'						)]
               _a      =							key.replace(f'linear_c{idx}'			,  f'linear_c.{int(lowerCAmelCase__						)-1}'						)
          if key.startswith('head'						):
               _a      =							key.replace('head'			,  'classifier'						)
          _a      =							value

     return new_state_dict






def 		_A			(lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Optional[int]						)				->					List[Any]:




     '''simple docstring'''



     for i in range(config.num_encoder_blocks						):
          for j in range(config.depths[i]						):
               # read in weights + bias of keys and values (which is a single matrix in the original implementation)
               _a      =							state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'						)
               _a      =							state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.bias'						)
               # next, add keys and values (in that order) to the state dict
               _a      =							kv_weight[
                   : config.hidden_sizes[i], :
               ]
               _a      =							kv_bias[: config.hidden_sizes[i]]
               _a      =							kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               _a      =							kv_bias[
                   config.hidden_sizes[i] :
               ]






def 		_A			()				->					Any:




     '''simple docstring'''



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

     return image






@torch.no_grad()
def 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Optional[Any]						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							SegformerConfig()
     _a      =							False

     # set attributes based on model_name
     _a      =							'huggingface/label-files'
     if "segformer" in model_name:
          _a      =							model_name[len('segformer.'						) : len('segformer.'						) + 2]
          if "ade" in model_name:
               _a      =							1_50
               _a      =							'ade20k-id2label.json'
               _a      =							(1, 1_50, 1_28, 1_28)
          elif "city" in model_name:
               _a      =							19
               _a      =							'cityscapes-id2label.json'
               _a      =							(1, 19, 1_28, 1_28)
          else:
               raise ValueError(f'Model {model_name} not supported'						)
     elif "mit" in model_name:
          _a      =							True
          _a      =							model_name[4:6]
          _a      =							10_00
          _a      =							'imagenet-1k-id2label.json'
          _a      =							(1, 10_00)
     else:
          raise ValueError(f'Model {model_name} not supported'						)

     # set config attributes
     _a      =							json.load(open(hf_hub_download(lowerCAmelCase__			,  lowerCAmelCase__			,  repo_type='dataset'						)			,  'r'						)						)
     _a      =							{int(lowerCAmelCase__						): v for k, v in idalabel.items()}
     _a      =							idalabel
     _a      =							{v: k for k, v in idalabel.items()}
     if size == "b0":
          pass
     elif size == "b1":
          _a      =							[64, 1_28, 3_20, 5_12]
          _a      =							2_56
     elif size == "b2":
          _a      =							[64, 1_28, 3_20, 5_12]
          _a      =							7_68
          _a      =							[3, 4, 6, 3]
     elif size == "b3":
          _a      =							[64, 1_28, 3_20, 5_12]
          _a      =							7_68
          _a      =							[3, 4, 18, 3]
     elif size == "b4":
          _a      =							[64, 1_28, 3_20, 5_12]
          _a      =							7_68
          _a      =							[3, 8, 27, 3]
     elif size == "b5":
          _a      =							[64, 1_28, 3_20, 5_12]
          _a      =							7_68
          _a      =							[3, 6, 40, 3]
     else:
          raise ValueError(f'Size {size} not supported'						)

     # load image processor (only resize + normalize)
     _a      =							SegformerImageProcessor(
         image_scale=(5_12, 5_12)			,  keep_ratio=lowerCAmelCase__			,  align=lowerCAmelCase__			,  do_random_crop=lowerCAmelCase__						)

     # prepare image
     _a      =							prepare_img()
     _a      =							image_processor(images=lowerCAmelCase__			,  return_tensors='pt'						).pixel_values

     logger.info(f'Converting model {model_name}...'						)

     # load original state dict
     if encoder_only:
          _a      =							torch.load(lowerCAmelCase__			,  map_location=torch.device('cpu'						)						)
     else:
          _a      =							torch.load(lowerCAmelCase__			,  map_location=torch.device('cpu'						)						)['state_dict']

     # rename keys
     _a      =							rename_keys(lowerCAmelCase__			,  encoder_only=lowerCAmelCase__						)
     if not encoder_only:
          del state_dict["decode_head.conv_seg.weight"]
          del state_dict["decode_head.conv_seg.bias"]

     # key and value matrices need special treatment
     read_in_k_v(lowerCAmelCase__			,  lowerCAmelCase__						)

     # create HuggingFace model and load state dict
     if encoder_only:
          _a      =							False
          _a      =							SegformerForImageClassification(lowerCAmelCase__						)
     else:
          _a      =							SegformerForSemanticSegmentation(lowerCAmelCase__						)
     model.load_state_dict(lowerCAmelCase__						)
     model.eval()

     # forward pass
     _a      =							model(lowerCAmelCase__						)
     _a      =							outputs.logits

     # set expected_slice based on model name
     # ADE20k checkpoints
     if model_name == "segformer.b0.512x512.ade.160k":
          _a      =							torch.tensor(
              [
                  [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
                  [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
                  [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
              ]						)
     elif model_name == "segformer.b1.512x512.ade.160k":
          _a      =							torch.tensor(
              [
                  [[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
                  [[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
                  [[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
              ]						)
     elif model_name == "segformer.b2.512x512.ade.160k":
          _a      =							torch.tensor(
              [
                  [[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
                  [[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
                  [[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
              ]						)
     elif model_name == "segformer.b3.512x512.ade.160k":
          _a      =							torch.tensor(
              [
                  [[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -1_0.3_8_5_5, -1_0.5_7_0_4]],
                  [[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
                  [[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
              ]						)
     elif model_name == "segformer.b4.512x512.ade.160k":
          _a      =							torch.tensor(
              [
                  [[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
                  [[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
                  [[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
              ]						)
     elif model_name == "segformer.b5.640x640.ade.160k":
          _a      =							torch.tensor(
              [
                  [[-9.5_5_2_4, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.5_8_4_2, -1_2.8_8_5_1, -1_3.9_4_1_4]],
                  [[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
                  [[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
              ]						)
     # Cityscapes checkpoints
     elif model_name == "segformer.b0.1024x1024.city.160k":
          _a      =							torch.tensor(
              [
                  [[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
                  [[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
                  [[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
              ]						)
     elif model_name == "segformer.b0.512x1024.city.160k":
          _a      =							torch.tensor(
              [
                  [[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
                  [[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
                  [[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
              ]						)
     elif model_name == "segformer.b0.640x1280.city.160k":
          _a      =							torch.tensor(
              [
                  [
                      [-1.1372E01, -1.2787E01, -1.3477E01],
                      [-1.2536E01, -1.4194E01, -1.4409E01],
                      [-1.3217E01, -1.4888E01, -1.5327E01],
                  ],
                  [
                      [-1.4791E01, -1.7122E01, -1.8277E01],
                      [-1.7163E01, -1.9192E01, -1.9533E01],
                      [-1.7897E01, -1.9991E01, -2.0315E01],
                  ],
                  [
                      [7.6723E-01, 4.1921E-01, -7.7878E-02],
                      [4.7772E-01, 9.5557E-03, -2.8082E-01],
                      [3.6032E-01, -2.4826E-01, -5.1168E-01],
                  ],
              ]						)
     elif model_name == "segformer.b0.768x768.city.160k":
          _a      =							torch.tensor(
              [
                  [[-9.4_9_5_9, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
                  [[-9.8_9_0_5, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
                  [[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
              ]						)
     elif model_name == "segformer.b1.1024x1024.city.160k":
          _a      =							torch.tensor(
              [
                  [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
                  [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
                  [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
              ]						)
     elif model_name == "segformer.b2.1024x1024.city.160k":
          _a      =							torch.tensor(
              [
                  [[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
                  [[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
                  [[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
              ]						)
     elif model_name == "segformer.b3.1024x1024.city.160k":
          _a      =							torch.tensor(
              [
                  [[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
                  [[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
                  [[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
              ]						)
     elif model_name == "segformer.b4.1024x1024.city.160k":
          _a      =							torch.tensor(
              [
                  [[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
                  [[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
                  [[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
              ]						)
     elif model_name == "segformer.b5.1024x1024.city.160k":
          _a      =							torch.tensor(
              [
                  [[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
                  [[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
                  [[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
              ]						)
     else:
          _a      =							logits.argmax(-1						).item()
          print('Predicted class:'			,  model.config.idalabel[predicted_class_idx]						)

     # verify logits
     if not encoder_only:
          assert logits.shape == expected_shape
          assert torch.allclose(logits[0, :3, :3, :3]			,  lowerCAmelCase__			,  atol=1E-2						)

     # finally, save model and image processor
     logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'						)
     Path(lowerCAmelCase__						).mkdir(exist_ok=lowerCAmelCase__						)
     model.save_pretrained(lowerCAmelCase__						)
     image_processor.save_pretrained(lowerCAmelCase__						)


if __name__ == "__main__":
 a_   :      str				    =      argparse.ArgumentParser()

 parser.add_argument(
     "--model_name",
     default="segformer.b0.512x512.ade.160k",
     type=str,
     help="Name of the model you'd like to convert.",
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 a_   :      Optional[Any]				    =      parser.parse_args()
 convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''






from timeit import timeit






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     if number < 0:
          raise ValueError('the value of input must not be negative'						)
     _a      =							0
     while number:
          number &= number - 1
          result += 1
     return result






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     if number < 0:
          raise ValueError('the value of input must not be negative'						)
     _a      =							0
     while number:
          if number % 2 == 1:
               result += 1
          number >>= 1
     return result






def 		_A			()				->					None:




     '''simple docstring'''



     def do_benchmark(lowerCAmelCase__					:int						) -> None:
          _a      =							'import __main__ as z'
          print(f'Benchmark when {number = }:'						)
          print(f'{get_set_bits_count_using_modulo_operator(lowerCAmelCase__						) = }'						)
          _a      =							timeit('z.get_set_bits_count_using_modulo_operator(25)'			,  setup=lowerCAmelCase__						)
          print(f'timeit() runs in {timing} seconds'						)
          print(f'{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase__						) = }'						)
          _a      =							timeit(
              'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'			,  setup=lowerCAmelCase__			,  )
          print(f'timeit() runs in {timing} seconds'						)

     for number in (25, 37, 58, 0):
          do_benchmark(lowerCAmelCase__						)
          print()


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()

