

"""simple docstring"""






import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


__lowerCamelCase										=		{
    '''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
    '''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
    '''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
    '''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
    '''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
    '''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
    '''mask_downscaling.0''': '''mask_embed.conv1''',
    '''mask_downscaling.1''': '''mask_embed.layer_norm1''',
    '''mask_downscaling.3''': '''mask_embed.conv2''',
    '''mask_downscaling.4''': '''mask_embed.layer_norm2''',
    '''mask_downscaling.6''': '''mask_embed.conv3''',
    '''point_embeddings''': '''point_embed''',
    '''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
    '''image_encoder''': '''vision_encoder''',
    '''neck.0''': '''neck.conv1''',
    '''neck.1''': '''neck.layer_norm1''',
    '''neck.2''': '''neck.conv2''',
    '''neck.3''': '''neck.layer_norm2''',
    '''patch_embed.proj''': '''patch_embed.projection''',
    '''.norm''': '''.layer_norm''',
    '''blocks''': '''layers''',
}





def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      {}
 state_dict.pop('''pixel_mean'''      ,   lowerCAmelCase__						)
 state_dict.pop('''pixel_std'''      ,   lowerCAmelCase__						)

 __magic_name__     =      r'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''

 for key, value in state_dict.items():
  for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
   if key_to_modify in key:
    __magic_name__     =      key.replace(lowerCAmelCase__      ,   lowerCAmelCase__						)

  if re.match(lowerCAmelCase__      ,   lowerCAmelCase__						):
   __magic_name__     =      int(re.match(lowerCAmelCase__      ,   lowerCAmelCase__						).group(2						)						)
   if layer_nb == 0:
    __magic_name__     =      key.replace('''layers.0'''      ,   '''proj_in'''						)
   elif layer_nb == 1:
    __magic_name__     =      key.replace('''layers.1'''      ,   '''layers.0'''						)
   elif layer_nb == 2:
    __magic_name__     =      key.replace('''layers.2'''      ,   '''proj_out'''						)

  __magic_name__     =      value

 __magic_name__     =      model_state_dict[
     '''prompt_encoder.shared_embedding.positional_embedding'''
 ]

 return model_state_dict





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase="ybelkada/segment-anything"						) ->      List[str]:
 __magic_name__     =      hf_hub_download(lowerCAmelCase__      ,   f'''checkpoints/{model_name}.pth'''						)

 if "sam_vit_b" in model_name:
  __magic_name__     =      SamConfig()
 elif "sam_vit_l" in model_name:
  __magic_name__     =      SamVisionConfig(
      hidden_size=1024      ,   num_hidden_layers=24      ,   num_attention_heads=16      ,   global_attn_indexes=[5, 11, 17, 23]      ,   )

  __magic_name__     =      SamConfig(
      vision_config=lowerCAmelCase__      ,   )
 elif "sam_vit_h" in model_name:
  __magic_name__     =      SamVisionConfig(
      hidden_size=1280      ,   num_hidden_layers=32      ,   num_attention_heads=16      ,   global_attn_indexes=[7, 15, 23, 31]      ,   )

  __magic_name__     =      SamConfig(
      vision_config=lowerCAmelCase__      ,   )

 __magic_name__     =      torch.load(lowerCAmelCase__      ,   map_location='''cpu'''						)
 __magic_name__     =      replace_keys(lowerCAmelCase__						)

 __magic_name__     =      SamImageProcessor()

 __magic_name__     =      SamProcessor(image_processor=lowerCAmelCase__						)
 __magic_name__     =      SamModel(lowerCAmelCase__						)

 hf_model.load_state_dict(lowerCAmelCase__						)
 __magic_name__     =      hf_model.to('''cuda'''						)

 __magic_name__     =      '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
 __magic_name__     =      Image.open(requests.get(lowerCAmelCase__      ,   stream=lowerCAmelCase__						).raw						).convert('''RGB'''						)

 __magic_name__     =      [[[400, 650]]]
 __magic_name__     =      [[1]]

 __magic_name__     =      processor(images=np.array(lowerCAmelCase__						)      ,   return_tensors='''pt'''						).to('''cuda'''						)

 with torch.no_grad():
  __magic_name__     =      hf_model(**lowerCAmelCase__						)
 __magic_name__     =      output.iou_scores.squeeze()

 if model_name == "sam_vit_h_4b8939":
  assert scores[-1].item() == 0.579_890_251_159_668

  __magic_name__     =      processor(
      images=np.array(lowerCAmelCase__						)      ,   input_points=lowerCAmelCase__      ,   input_labels=lowerCAmelCase__      ,   return_tensors='''pt'''						).to('''cuda'''						)

  with torch.no_grad():
   __magic_name__     =      hf_model(**lowerCAmelCase__						)
  __magic_name__     =      output.iou_scores.squeeze()

  assert scores[-1].item() == 0.9_712_603_092_193_604

  __magic_name__     =      ((75, 275, 1725, 850),)

  __magic_name__     =      processor(images=np.array(lowerCAmelCase__						)      ,   input_boxes=lowerCAmelCase__      ,   return_tensors='''pt'''						).to('''cuda'''						)

  with torch.no_grad():
   __magic_name__     =      hf_model(**lowerCAmelCase__						)
  __magic_name__     =      output.iou_scores.squeeze()

  assert scores[-1].item() == 0.8_686_015_605_926_514

  # Test with 2 points and 1 image.
  __magic_name__     =      [[[400, 650], [800, 650]]]
  __magic_name__     =      [[1, 1]]

  __magic_name__     =      processor(
      images=np.array(lowerCAmelCase__						)      ,   input_points=lowerCAmelCase__      ,   input_labels=lowerCAmelCase__      ,   return_tensors='''pt'''						).to('''cuda'''						)

  with torch.no_grad():
   __magic_name__     =      hf_model(**lowerCAmelCase__						)
  __magic_name__     =      output.iou_scores.squeeze()

  assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
    __lowerCamelCase										=		argparse.ArgumentParser()
    __lowerCamelCase										=		['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
    parser.add_argument(
        "--model_name",
        default="sam_vit_h_4b8939",
        choices=choices,
        type=str,
        help="Path to hf config.json of model to convert",
    )
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument(
        "--push_to_hub",
        action="store_true",
        help="Whether to push the model and processor to the hub after converting",
    )
    parser.add_argument(
        "--model_hub_id",
        default="ybelkada/segment-anything",
        choices=choices,
        type=str,
        help="Path to hf config.json of model to convert",
    )

    __lowerCamelCase										=		parser.parse_args()

    convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)




"""simple docstring"""






from __future__ import annotations

from random import choice





def 	lowercase      (   __UpperCamelCase						) ->      Any:
 return choice(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 __magic_name__     =      random_pivot(__UpperCamelCase						)

 # partition based on pivot
 # linear time
 __magic_name__     =      [e for e in lst if e < pivot]
 __magic_name__     =      [e for e in lst if e > pivot]

 # if we get lucky, pivot might be the element we want.
 # we can easily see this:
 # small (elements smaller than k)
 # + pivot (kth element)
 # + big (elements larger than k)
 if len(__UpperCamelCase						) == k - 1:
  return pivot
 # pivot is in elements bigger than k
 elif len(__UpperCamelCase						) < k - 1:
  return kth_number(__UpperCamelCase      ,   k - len(__UpperCamelCase						) - 1						)
 # pivot is in elements smaller than k
 else:
  return kth_number(__UpperCamelCase      ,   __UpperCamelCase						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

