



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						    =  {
    """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   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    int       =	{}
 state_dict.pop("pixel_mean"		,	lowerCAmelCase__     )
 state_dict.pop("pixel_std"		,	lowerCAmelCase__     )

 __SCREAMING_SNAKE_CASE       :    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:
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	key.replace(lowerCAmelCase__		,	lowerCAmelCase__     )

  if re.match(lowerCAmelCase__		,	lowerCAmelCase__     ):
   __SCREAMING_SNAKE_CASE       :    str       =	int(re.match(lowerCAmelCase__		,	lowerCAmelCase__     ).group(2     )     )
   if layer_nb == 0:
    __SCREAMING_SNAKE_CASE       :    Tuple       =	key.replace("layers.0"		,	"proj_in"     )
   elif layer_nb == 1:
    __SCREAMING_SNAKE_CASE       :    Dict       =	key.replace("layers.1"		,	"layers.0"     )
   elif layer_nb == 2:
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	key.replace("layers.2"		,	"proj_out"     )

  __SCREAMING_SNAKE_CASE       :    List[Any]       =	value

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

 return model_state_dict






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     Optional[Any]="ybelkada/segment-anything"     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Dict       =	hf_hub_download(lowerCAmelCase__		,	F"""checkpoints/{model_name}.pth"""     )

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

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

  __SCREAMING_SNAKE_CASE       :    Dict       =	SamConfig(
      vision_config=lowerCAmelCase__		,	)

 __SCREAMING_SNAKE_CASE       :    List[str]       =	torch.load(lowerCAmelCase__		,	map_location="cpu"     )
 __SCREAMING_SNAKE_CASE       :    Any       =	replace_keys(lowerCAmelCase__     )

 __SCREAMING_SNAKE_CASE       :    Any       =	SamImageProcessor()

 __SCREAMING_SNAKE_CASE       :    str       =	SamProcessor(image_processor=lowerCAmelCase__     )
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	SamModel(lowerCAmelCase__     )

 hf_model.load_state_dict(lowerCAmelCase__     )
 __SCREAMING_SNAKE_CASE       :    Any       =	hf_model.to("cuda"     )

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

 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[[[400, 650]]]
 __SCREAMING_SNAKE_CASE       :    Any       =	[[1]]

 __SCREAMING_SNAKE_CASE       :    Tuple       =	processor(images=np.array(lowerCAmelCase__     )		,	return_tensors="pt"     ).to("cuda"     )

 with torch.no_grad():
  __SCREAMING_SNAKE_CASE       :    Any       =	hf_model(**lowerCAmelCase__     )
 __SCREAMING_SNAKE_CASE       :    int       =	output.iou_scores.squeeze()

 if model_name == "sam_vit_h_4b8939":
  assert scores[-1].item() == 0.579890251159668

  __SCREAMING_SNAKE_CASE       :    str       =	processor(
      images=np.array(lowerCAmelCase__     )		,	input_points=lowerCAmelCase__		,	input_labels=lowerCAmelCase__		,	return_tensors="pt"     ).to("cuda"     )

  with torch.no_grad():
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	hf_model(**lowerCAmelCase__     )
  __SCREAMING_SNAKE_CASE       :    Any       =	output.iou_scores.squeeze()

  assert scores[-1].item() == 0.9712603092193604

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

  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	processor(images=np.array(lowerCAmelCase__     )		,	input_boxes=lowerCAmelCase__		,	return_tensors="pt"     ).to("cuda"     )

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

  assert scores[-1].item() == 0.8686015605926514

  # Test with 2 points and 1 image.
  __SCREAMING_SNAKE_CASE       :    int       =	[[[400, 650], [800, 650]]]
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[[1, 1]]

  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	processor(
      images=np.array(lowerCAmelCase__     )		,	input_points=lowerCAmelCase__		,	input_labels=lowerCAmelCase__		,	return_tensors="pt"     ).to("cuda"     )

  with torch.no_grad():
   __SCREAMING_SNAKE_CASE       :    Any       =	hf_model(**lowerCAmelCase__     )
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	output.iou_scores.squeeze()

  assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
      _lowerCamelCase						    =  argparse.ArgumentParser()
      _lowerCamelCase						    =  ["""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						    =  parser.parse_args()

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










import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_lowerCamelCase						    =  importlib.util.find_spec("""s3fs""") is not None

if _has_safs:
      from .safilesystem import SaFileSystem  # noqa: F401

_lowerCamelCase						    =  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
      if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
            warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
      fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 if "://" in dataset_path:
  __SCREAMING_SNAKE_CASE       :    Any       =	dataset_path.split("://"     )[1]
 return dataset_path






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem     ):



 """simple docstring"""





 if fs is not None and fs.protocol != "file":
  return True
 else:
  return False






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	not is_remote_filesystem(__lowerCamelCase     )
 if is_local:
  # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
  shutil.move(fs._strip_protocol(__lowerCamelCase     )		,	fs._strip_protocol(__lowerCamelCase     )     )
 else:
  fs.mv(__lowerCamelCase		,	__lowerCamelCase		,	recursive=__lowerCamelCase     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 if hasattr(fsspec.asyn		,	"reset_lock"     ):
  # for future fsspec>2022.05.0
  fsspec.asyn.reset_lock()
 else:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	None
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	None
  __SCREAMING_SNAKE_CASE       :    Dict       =	threading.Lock()






