





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 :  int										=  {
    """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_       (  _lowerCAmelCase  )		->				List[str]:
 UpperCamelCase			:      Optional[int] 			=     {}
 state_dict.pop("pixel_mean"							,     _lowerCAmelCase  )
 state_dict.pop("pixel_std"							,     _lowerCAmelCase  )

 UpperCamelCase			:      int 			=     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			:      Optional[Any] 			=     key.replace(_lowerCAmelCase							,     _lowerCAmelCase  )

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

  UpperCamelCase			:      Dict 			=     value

 UpperCamelCase			:      Optional[Any] 			=     model_state_dict[
     "prompt_encoder.shared_embedding.positional_embedding"
 ]

 return model_state_dict



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase="ybelkada/segment-anything"  )		->				int:
 UpperCamelCase			:      List[Any] 			=     hf_hub_download(_lowerCAmelCase							,     F"""checkpoints/{model_name}.pth"""  )

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

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

  UpperCamelCase			:      Union[str, Any] 			=     SamConfig(
      vision_config=_lowerCAmelCase							,     )

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

 UpperCamelCase			:      List[str] 			=     SamImageProcessor()

 UpperCamelCase			:      List[Any] 			=     SamProcessor(image_processor=_lowerCAmelCase  )
 UpperCamelCase			:      str 			=     SamModel(_lowerCAmelCase  )

 hf_model.load_state_dict(_lowerCAmelCase  )
 UpperCamelCase			:      Tuple 			=     hf_model.to("cuda"  )

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

 UpperCamelCase			:      Union[str, Any] 			=     [[[400, 650]]]
 UpperCamelCase			:      str 			=     [[1]]

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

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

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

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

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

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

  UpperCamelCase			:      Union[str, Any] 			=     ((75, 275, 1725, 850),)

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

  with torch.no_grad():
   UpperCamelCase			:      Union[str, Any] 			=     hf_model(**_lowerCAmelCase  )
  UpperCamelCase			:      List[Any] 			=     output.iou_scores.squeeze()

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

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

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

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

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


if __name__ == "__main__":
    __lowerCamelCase :  Optional[Any]										=  argparse.ArgumentParser()
    __lowerCamelCase :  Dict										=  ["""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 :  List[str]										=  parser.parse_args()

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







from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_ ,							A_ = None ,							A_ = None ,							A_ = False ,							A_ = False ,							A_ = None ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           features=A_ ,							cache_dir=A_ ,							keep_in_memory=A_ ,							streaming=A_ ,							num_proc=A_ ,							**A_ ,							)
       UpperCamelCase			:      Optional[int] 			=     Generator(
           cache_dir=A_ ,							features=A_ ,							generator=A_ ,							gen_kwargs=A_ ,							**A_ ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.streaming:
        UpperCamelCase			:      Optional[Any] 			=     self.builder.as_streaming_dataset(split="train"     )
       # Build regular (map-style) dataset
       else:
        UpperCamelCase			:      Union[str, Any] 			=     None
        UpperCamelCase			:      Union[str, Any] 			=     None
        UpperCamelCase			:      List[Any] 			=     None
        UpperCamelCase			:      List[str] 			=     None

        self.builder.download_and_prepare(
            download_config=A_ ,							download_mode=A_ ,							verification_mode=A_ ,							base_path=A_ ,							num_proc=self.num_proc ,							)
        UpperCamelCase			:      int 			=     self.builder.as_dataset(
            split="train" ,							verification_mode=A_ ,							in_memory=self.keep_in_memory     )
       return dataset
