





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,
)


_A :   Union[str, 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						(   UpperCAmelCase					)    ->    Any:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							{}
   state_dict.pop('''pixel_mean'''      ,  UpperCAmelCase					)
   state_dict.pop('''pixel_std'''      ,  UpperCAmelCase					)

   lowerCamelCase__       :						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:
            lowerCamelCase__       :						Union[str, Any]        =							key.replace(UpperCAmelCase      ,  UpperCAmelCase					)

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

      lowerCamelCase__       :						str        =							value

   lowerCamelCase__       :						Optional[Any]        =							model_state_dict[
       '''prompt_encoder.shared_embedding.positional_embedding'''
   ]

   return model_state_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase="ybelkada/segment-anything"					)    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						str        =							hf_hub_download(UpperCAmelCase      ,  f"checkpoints/{model_name}.pth"					)

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

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

      lowerCamelCase__       :						List[str]        =							SamConfig(
          vision_config=UpperCAmelCase      ,  )

   lowerCamelCase__       :						Tuple        =							torch.load(UpperCAmelCase      ,  map_location='''cpu'''					)
   lowerCamelCase__       :						str        =							replace_keys(UpperCAmelCase					)

   lowerCamelCase__       :						Optional[Any]        =							SamImageProcessor()

   lowerCamelCase__       :						Dict        =							SamProcessor(image_processor=UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							SamModel(UpperCAmelCase					)

   hf_model.load_state_dict(UpperCAmelCase					)
   lowerCamelCase__       :						Optional[Any]        =							hf_model.to('''cuda'''					)

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

   lowerCamelCase__       :						Union[str, Any]        =							[[[400, 650]]]
   lowerCamelCase__       :						int        =							[[1]]

   lowerCamelCase__       :						Tuple        =							processor(images=np.array(UpperCAmelCase					)      ,  return_tensors='''pt'''					).to('''cuda'''					)

   with torch.no_grad():
      lowerCamelCase__       :						Tuple        =							hf_model(**UpperCAmelCase					)
   lowerCamelCase__       :						Union[str, Any]        =							output.iou_scores.squeeze()

   if model_name == "sam_vit_h_4b8939":
      assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

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

      with torch.no_grad():
         lowerCamelCase__       :						int        =							hf_model(**UpperCAmelCase					)
      lowerCamelCase__       :						Any        =							output.iou_scores.squeeze()

      assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

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

      lowerCamelCase__       :						Tuple        =							processor(images=np.array(UpperCAmelCase					)      ,  input_boxes=UpperCAmelCase      ,  return_tensors='''pt'''					).to('''cuda'''					)

      with torch.no_grad():
         lowerCamelCase__       :						int        =							hf_model(**UpperCAmelCase					)
      lowerCamelCase__       :						Union[str, Any]        =							output.iou_scores.squeeze()

      assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

      # Test with 2 points and 1 image.
      lowerCamelCase__       :						Optional[Any]        =							[[[400, 650], [800, 650]]]
      lowerCamelCase__       :						Union[str, Any]        =							[[1, 1]]

      lowerCamelCase__       :						str        =							processor(
          images=np.array(UpperCAmelCase					)      ,  input_points=UpperCAmelCase      ,  input_labels=UpperCAmelCase      ,  return_tensors='''pt'''					).to('''cuda'''					)

      with torch.no_grad():
         lowerCamelCase__       :						int        =							hf_model(**UpperCAmelCase					)
      lowerCamelCase__       :						Tuple        =							output.iou_scores.squeeze()

      assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
       _A :   Any  			=  argparse.ArgumentParser()
       _A :   Tuple  			=  ['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',
       )

       _A :   Optional[int]  			=  parser.parse_args()

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










import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def 	_a						(   *UpperCAmelCase      ,  UpperCAmelCase = None      ,  UpperCAmelCase=True      ,  UpperCAmelCase=2					)    ->    str:







   """simple docstring"""
   from .. import __version__

   lowerCamelCase__       :						Optional[Any]        =							take_from
   lowerCamelCase__       :						Any        =							()
   if not isinstance(args[0]      ,  UpperCAmelCase					):
      lowerCamelCase__       :						Optional[int]        =							(args,)

   for attribute, version_name, message in args:
      if version.parse(version.parse(UpperCAmelCase					).base_version					) >= version.parse(UpperCAmelCase					):
         raise ValueError(
             f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
             f" version {__version__} is >= {version_name}"					)

      lowerCamelCase__       :						List[Any]        =							None
      if isinstance(UpperCAmelCase      ,  UpperCAmelCase					) and attribute in deprecated_kwargs:
         values += (deprecated_kwargs.pop(UpperCAmelCase					),)
         lowerCamelCase__       :						Tuple        =							f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
      elif hasattr(UpperCAmelCase      ,  UpperCAmelCase					):
         values += (getattr(UpperCAmelCase      ,  UpperCAmelCase					),)
         lowerCamelCase__       :						Union[str, Any]        =							f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
      elif deprecated_kwargs is None:
         lowerCamelCase__       :						int        =							f"`{attribute}` is deprecated and will be removed in version {version_name}."

      if warning is not None:
         lowerCamelCase__       :						int        =							warning + ''' ''' if standard_warn else ''''''
         warnings.warn(warning + message      ,  UpperCAmelCase      ,  stacklevel=UpperCAmelCase					)

   if isinstance(UpperCAmelCase      ,  UpperCAmelCase					) and len(UpperCAmelCase					) > 0:
      lowerCamelCase__       :						Tuple        =							inspect.getouterframes(inspect.currentframe()					)[1]
      lowerCamelCase__       :						Optional[Any]        =							call_frame.filename
      lowerCamelCase__       :						List[Any]        =							call_frame.lineno
      lowerCamelCase__       :						Optional[Any]        =							call_frame.function
      lowerCamelCase__       ,							lowerCamelCase__       :						Union[str, Any]        =							next(iter(deprecated_kwargs.items()					)					)
      raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"					)

   if len(UpperCAmelCase					) == 0:
      return
   elif len(UpperCAmelCase					) == 1:
      return values[0]
   return values



