


'''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,
)


__UpperCamelCase       :       Optional[Any]						       =				{
    "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 			_a  (				SCREAMING_SNAKE_CASE	:				Dict     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   {}
 state_dict.pop('''pixel_mean'''    , SCREAMING_SNAKE_CASE     )
 state_dict.pop('''pixel_std'''    , SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    List[Any]          =   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:
    UpperCamelCase__					:    List[Any]          =   key.replace(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

  if re.match(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ):
   UpperCamelCase__					:    List[str]          =   int(re.match(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ).group(2     )     )
   if layer_nb == 0:
    UpperCamelCase__					:    List[Any]          =   key.replace('''layers.0'''    , '''proj_in'''     )
   elif layer_nb == 1:
    UpperCamelCase__					:    Tuple          =   key.replace('''layers.1'''    , '''layers.0'''     )
   elif layer_nb == 2:
    UpperCamelCase__					:    Any          =   key.replace('''layers.2'''    , '''proj_out'''     )

  UpperCamelCase__					:    int          =   value

 UpperCamelCase__					:    Union[str, Any]          =   model_state_dict[
     '''prompt_encoder.shared_embedding.positional_embedding'''
 ]

 return model_state_dict

def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				Optional[int]="ybelkada/segment-anything"     ):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   hf_hub_download(SCREAMING_SNAKE_CASE    , F"checkpoints/{model_name}.pth"     )

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

  UpperCamelCase__					:    Tuple          =   SamConfig(
      vision_config=SCREAMING_SNAKE_CASE    , )
 elif "sam_vit_h" in model_name:
  UpperCamelCase__					:    Optional[Any]          =   SamVisionConfig(
      hidden_size=1280    , num_hidden_layers=32    , num_attention_heads=16    , global_attn_indexes=[7, 15, 23, 31]    , )

  UpperCamelCase__					:    str          =   SamConfig(
      vision_config=SCREAMING_SNAKE_CASE    , )

 UpperCamelCase__					:    Any          =   torch.load(SCREAMING_SNAKE_CASE    , map_location='''cpu'''     )
 UpperCamelCase__					:    Tuple          =   replace_keys(SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    int          =   SamImageProcessor()

 UpperCamelCase__					:    str          =   SamProcessor(image_processor=SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[str]          =   SamModel(SCREAMING_SNAKE_CASE     )

 hf_model.load_state_dict(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    int          =   hf_model.to('''cuda'''     )

 UpperCamelCase__					:    Union[str, Any]          =   '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
 UpperCamelCase__					:    Union[str, Any]          =   Image.open(requests.get(SCREAMING_SNAKE_CASE    , stream=SCREAMING_SNAKE_CASE     ).raw     ).convert('''RGB'''     )

 UpperCamelCase__					:    str          =   [[[400, 650]]]
 UpperCamelCase__					:    Tuple          =   [[1]]

 UpperCamelCase__					:    Any          =   processor(images=np.array(SCREAMING_SNAKE_CASE     )    , return_tensors='''pt'''     ).to('''cuda'''     )

 with torch.no_grad():
  UpperCamelCase__					:    Optional[int]          =   hf_model(**SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[Any]          =   output.iou_scores.squeeze()

 if model_name == "sam_vit_h_4b8939":
  assert scores[-1].item() == 0.579_8902_5115_9668

  UpperCamelCase__					:    Any          =   processor(
      images=np.array(SCREAMING_SNAKE_CASE     )    , input_points=SCREAMING_SNAKE_CASE    , input_labels=SCREAMING_SNAKE_CASE    , return_tensors='''pt'''     ).to('''cuda'''     )

  with torch.no_grad():
   UpperCamelCase__					:    Any          =   hf_model(**SCREAMING_SNAKE_CASE     )
  UpperCamelCase__					:    str          =   output.iou_scores.squeeze()

  assert scores[-1].item() == 0.9712_6030_9219_3604

  UpperCamelCase__					:    str          =   ((75, 275, 1725, 850),)

  UpperCamelCase__					:    Any          =   processor(images=np.array(SCREAMING_SNAKE_CASE     )    , input_boxes=SCREAMING_SNAKE_CASE    , return_tensors='''pt'''     ).to('''cuda'''     )

  with torch.no_grad():
   UpperCamelCase__					:    Tuple          =   hf_model(**SCREAMING_SNAKE_CASE     )
  UpperCamelCase__					:    str          =   output.iou_scores.squeeze()

  assert scores[-1].item() == 0.8686_0156_0592_6514

  # Test with 2 points and 1 image.
  UpperCamelCase__					:    Optional[int]          =   [[[400, 650], [800, 650]]]
  UpperCamelCase__					:    List[str]          =   [[1, 1]]

  UpperCamelCase__					:    Union[str, Any]          =   processor(
      images=np.array(SCREAMING_SNAKE_CASE     )    , input_points=SCREAMING_SNAKE_CASE    , input_labels=SCREAMING_SNAKE_CASE    , return_tensors='''pt'''     ).to('''cuda'''     )

  with torch.no_grad():
   UpperCamelCase__					:    Any          =   hf_model(**SCREAMING_SNAKE_CASE     )
  UpperCamelCase__					:    Any          =   output.iou_scores.squeeze()

  assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
       __UpperCamelCase       :       List[Any]						       =				argparse.ArgumentParser()
       __UpperCamelCase       :       int						       =				["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",
       )

       __UpperCamelCase       :       List[Any]						       =				parser.parse_args()

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






import functools

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   len(SCREAMING_SNAKE_CASE     )

 @functools.cache
 def min_distance(SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase__					:    Tuple          =   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1    , SCREAMING_SNAKE_CASE     )    , 1 + min_distance(SCREAMING_SNAKE_CASE    , indexa + 1     )    , diff + min_distance(indexa + 1    , indexa + 1     )    , )

 return min_distance(0    , 0     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


