"""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,
)


_a				:		Tuple	   =       {
    '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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Tuple     )	->				Union[str, Any]:
  _lowerCAmelCase						:	str        =					{}
  state_dict.pop("""pixel_mean"""      ,_lowerCamelCase     )
  state_dict.pop("""pixel_std"""      ,_lowerCamelCase     )

  _lowerCAmelCase						:	str        =					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(_lowerCamelCase      ,_lowerCamelCase     )

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

    _lowerCAmelCase						:	Optional[int]        =					value

  _lowerCAmelCase						:	int        =					model_state_dict[
      """prompt_encoder.shared_embedding.positional_embedding"""
  ]

  return model_state_dict





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	Union[str, Any]="ybelkada/segment-anything"     )	->				List[Any]:
  _lowerCAmelCase						:	Optional[Any]        =					hf_hub_download(_lowerCamelCase      ,f"checkpoints/{model_name}.pth"     )

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

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

    _lowerCAmelCase						:	Optional[Any]        =					SamConfig(
        vision_config=_lowerCamelCase      ,)

  _lowerCAmelCase						:	Optional[int]        =					torch.load(_lowerCamelCase      ,map_location="""cpu"""     )
  _lowerCAmelCase						:	int        =					replace_keys(_lowerCamelCase     )

  _lowerCAmelCase						:	Tuple        =					SamImageProcessor()

  _lowerCAmelCase						:	List[str]        =					SamProcessor(image_processor=_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					SamModel(_lowerCamelCase     )

  hf_model.load_state_dict(_lowerCamelCase     )
  _lowerCAmelCase						:	List[Any]        =					hf_model.to("""cuda"""     )

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

  _lowerCAmelCase						:	Optional[int]        =					[[[400, 650]]]
  _lowerCAmelCase						:	List[Any]        =					[[1]]

  _lowerCAmelCase						:	str        =					processor(images=np.array(_lowerCamelCase     )      ,return_tensors="""pt"""     ).to("""cuda"""     )

  with torch.no_grad():
    _lowerCAmelCase						:	int        =					hf_model(**_lowerCamelCase     )
  _lowerCAmelCase						:	Optional[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						:	List[str]        =					processor(
        images=np.array(_lowerCamelCase     )      ,input_points=_lowerCamelCase      ,input_labels=_lowerCamelCase      ,return_tensors="""pt"""     ).to("""cuda"""     )

    with torch.no_grad():
      _lowerCAmelCase						:	int        =					hf_model(**_lowerCamelCase     )
    _lowerCAmelCase						:	List[Any]        =					output.iou_scores.squeeze()

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

    _lowerCAmelCase						:	int        =					((75, 275, 1725, 850),)

    _lowerCAmelCase						:	Any        =					processor(images=np.array(_lowerCamelCase     )      ,input_boxes=_lowerCamelCase      ,return_tensors="""pt"""     ).to("""cuda"""     )

    with torch.no_grad():
      _lowerCAmelCase						:	Optional[Any]        =					hf_model(**_lowerCamelCase     )
    _lowerCAmelCase						:	Dict        =					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						:	int        =					[[[400, 650], [800, 650]]]
    _lowerCAmelCase						:	Any        =					[[1, 1]]

    _lowerCAmelCase						:	int        =					processor(
        images=np.array(_lowerCamelCase     )      ,input_points=_lowerCamelCase      ,input_labels=_lowerCamelCase      ,return_tensors="""pt"""     ).to("""cuda"""     )

    with torch.no_grad():
      _lowerCAmelCase						:	str        =					hf_model(**_lowerCamelCase     )
    _lowerCAmelCase						:	Dict        =					output.iou_scores.squeeze()

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


if __name__ == "__main__":
     _a				:		Optional[int]	   =       argparse.ArgumentParser()
     _a				:		List[str]	   =       ['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				:		Any	   =       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





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	list     )	->				float:
  if not nums:
    raise ValueError("""List is empty"""     )
  return sum(_lowerCamelCase     ) / len(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




