




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						:   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 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						{}
 state_dict.pop("pixel_mean"						,							_lowerCAmelCase	)
 state_dict.pop("pixel_std"						,							_lowerCAmelCase	)

 UpperCAmelCase__							 =						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__							 =						key.replace(_lowerCAmelCase						,							_lowerCAmelCase	)

  if re.match(_lowerCAmelCase						,							_lowerCAmelCase	):
   UpperCAmelCase__							 =						int(re.match(_lowerCAmelCase						,							_lowerCAmelCase	).group(2	)	)
   if layer_nb == 0:
    UpperCAmelCase__							 =						key.replace("layers.0"						,							"proj_in"	)
   elif layer_nb == 1:
    UpperCAmelCase__							 =						key.replace("layers.1"						,							"layers.0"	)
   elif layer_nb == 2:
    UpperCAmelCase__							 =						key.replace("layers.2"						,							"proj_out"	)

  UpperCAmelCase__							 =						value

 UpperCAmelCase__							 =						model_state_dict[
     "prompt_encoder.shared_embedding.positional_embedding"
 ]

 return model_state_dict



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Optional[Any]						,							_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   str="ybelkada/segment-anything"	):




 """simple docstring"""







 UpperCAmelCase__							 =						hf_hub_download(_lowerCAmelCase						,							F'''checkpoints/{model_name}.pth'''	)

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

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

  UpperCAmelCase__							 =						SamConfig(
      vision_config=_lowerCAmelCase						,							)

 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase						,							map_location="cpu"	)
 UpperCAmelCase__							 =						replace_keys(_lowerCAmelCase	)

 UpperCAmelCase__							 =						SamImageProcessor()

 UpperCAmelCase__							 =						SamProcessor(image_processor=_lowerCAmelCase	)
 UpperCAmelCase__							 =						SamModel(_lowerCAmelCase	)

 hf_model.load_state_dict(_lowerCAmelCase	)
 UpperCAmelCase__							 =						hf_model.to("cuda"	)

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

 UpperCAmelCase__							 =						[[[400, 650]]]
 UpperCAmelCase__							 =						[[1]]

 UpperCAmelCase__							 =						processor(images=np.array(_lowerCAmelCase	)						,							return_tensors="pt"	).to("cuda"	)

 with torch.no_grad():
  UpperCAmelCase__							 =						hf_model(**_lowerCAmelCase	)
 UpperCAmelCase__							 =						output.iou_scores.squeeze()

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

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

  with torch.no_grad():
   UpperCAmelCase__							 =						hf_model(**_lowerCAmelCase	)
  UpperCAmelCase__							 =						output.iou_scores.squeeze()

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

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

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

  with torch.no_grad():
   UpperCAmelCase__							 =						hf_model(**_lowerCAmelCase	)
  UpperCAmelCase__							 =						output.iou_scores.squeeze()

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

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

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

  with torch.no_grad():
   UpperCAmelCase__							 =						hf_model(**_lowerCAmelCase	)
  UpperCAmelCase__							 =						output.iou_scores.squeeze()

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


if __name__ == "__main__":
  _lowerCAmelCase						:   Tuple		=   argparse.ArgumentParser()
  _lowerCAmelCase						:   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",
  )

  _lowerCAmelCase						:   str		=   parser.parse_args()

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











import torch

from transformers import AutoModel
class 			_UpperCamelCase      (				torch.nn.Module		):
      def __init__(			self   :str ,      lowerCamelCase   :Tuple="sayef/fsner-bert-base-uncased"      )     ->			int:
       super(lowerCamelCase ,      self      ).__init__()

       UpperCAmelCase__							 =						AutoModel.from_pretrained(lowerCamelCase ,      return_dict=lowerCamelCase      )
       UpperCAmelCase__							 =						torch.nn.CosineSimilarity(3 ,      1e-08      )
       UpperCAmelCase__							 =						torch.nn.Softmax(dim=1      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       return self.bert(**lowerCamelCase      ).last_hidden_state
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Union[str, Any]      )     ->			Union[str, Any]:
       return token_embeddings.sum(2 ,      keepdim=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any]=1      )     ->			Dict:
       return self.softmax(T * self.cos(lowerCamelCase ,      lowerCamelCase      )      )







      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Any      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						W_supports["sizes"].tolist()
       UpperCAmelCase__							 =						W_supports["start_token_id"].item()
       UpperCAmelCase__							 =						W_supports["end_token_id"].item()

       del W_supports["sizes"]
       del W_supports["start_token_id"]
       del W_supports["end_token_id"]

       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						None

       UpperCAmelCase__							 =						W_supports["input_ids"] == start_token_id
       UpperCAmelCase__							 =						W_supports["input_ids"] == end_token_id

       for i, size in enumerate(lowerCamelCase      ):
        if i == 0:
         UpperCAmelCase__							 =						0
        else:
         UpperCAmelCase__							 =						support_sizes[i - 1]

        UpperCAmelCase__							 =						S[s : s + size][start_token_masks[s : s + size]]
        UpperCAmelCase__							 =						S[s : s + size][end_token_masks[s : s + size]]

        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_start.T      ).sum(1      ).softmax(0      )
        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_end.T      ).sum(1      ).softmax(0      )

        if p_starts is not None:
         UpperCAmelCase__							 =						torch.vstack((p_starts, p_start)      )
         UpperCAmelCase__							 =						torch.vstack((p_ends, p_end)      )
        else:
         UpperCAmelCase__							 =						p_start
         UpperCAmelCase__							 =						p_end

       return p_starts, p_ends





