



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   :					List[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    lowercase_	(	_A    :					Union[str, Any]		):

      """simple docstring"""




      lowerCamelCase__	:       str					=						{}
      state_dict.pop("pixel_mean"					,      UpperCamelCase__		)
      state_dict.pop("pixel_std"					,      UpperCamelCase__		)

      lowerCamelCase__	:       Optional[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__	:       str					=						key.replace(UpperCamelCase__					,      UpperCamelCase__		)

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

            lowerCamelCase__	:       Optional[int]					=						value

      lowerCamelCase__	:       List[str]					=						model_state_dict[
          "prompt_encoder.shared_embedding.positional_embedding"
      ]

      return model_state_dict

def    lowercase_	(	_A    :					Tuple					,      _A    :					Any					,      _A    :					Optional[int]					,      _A    :					str="ybelkada/segment-anything"		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						hf_hub_download(UpperCamelCase__					,      F"checkpoints/{model_name}.pth"		)

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

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

            lowerCamelCase__	:       Union[str, Any]					=						SamConfig(
                vision_config=UpperCamelCase__					,      )

      lowerCamelCase__	:       int					=						torch.load(UpperCamelCase__					,      map_location="cpu"		)
      lowerCamelCase__	:       Any					=						replace_keys(UpperCamelCase__		)

      lowerCamelCase__	:       Tuple					=						SamImageProcessor()

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

      hf_model.load_state_dict(UpperCamelCase__		)
      lowerCamelCase__	:       Union[str, Any]					=						hf_model.to("cuda"		)

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

      lowerCamelCase__	:       Dict					=						[[[400, 650]]]
      lowerCamelCase__	:       Tuple					=						[[1]]

      lowerCamelCase__	:       Optional[int]					=						processor(images=np.array(UpperCamelCase__		)					,      return_tensors="pt"		).to("cuda"		)

      with torch.no_grad():
            lowerCamelCase__	:       List[str]					=						hf_model(**UpperCamelCase__		)
      lowerCamelCase__	:       List[str]					=						output.iou_scores.squeeze()

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

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

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

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

            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__	:       Dict					=						hf_model(**UpperCamelCase__		)
            lowerCamelCase__	:       Optional[int]					=						output.iou_scores.squeeze()

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

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

            lowerCamelCase__	:       List[str]					=						processor(
                images=np.array(UpperCamelCase__		)					,      input_points=UpperCamelCase__					,      input_labels=UpperCamelCase__					,      return_tensors="pt"		).to("cuda"		)

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

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


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

    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 os
from pathlib import Path

def    lowercase_	(	):

      """simple docstring"""




      from torch.utils.cpp_extension import load

      lowerCamelCase__	:       Any					=						Path(_A		).resolve().parent.parent.parent / "kernels" / "deformable_detr"
      lowerCamelCase__	:       Optional[int]					=						[
          root / filename
          for filename in [
              "vision.cpp",
              os.path.join("cpu"					,      "ms_deform_attn_cpu.cpp"		),
              os.path.join("cuda"					,      "ms_deform_attn_cuda.cu"		),
          ]
      ]

      load(
          "MultiScaleDeformableAttention"					,      _A					,      with_cuda=_A					,      extra_include_paths=[str(_A		)]					,      extra_cflags=["-DWITH_CUDA=1"]					,      extra_cuda_cflags=[
              "-DCUDA_HAS_FP16=1",
              "-D__CUDA_NO_HALF_OPERATORS__",
              "-D__CUDA_NO_HALF_CONVERSIONS__",
              "-D__CUDA_NO_HALF2_OPERATORS__",
          ]					,      )

      import MultiScaleDeformableAttention as MSDA

      return MSDA


