






from collections.abc import Sequence
from queue import Queue




class 							_lowerCAmelCase:





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None				):
         UpperCamelCase_:   Tuple        =							start
         UpperCamelCase_:   Tuple        =							end
         UpperCamelCase_:   Optional[Any]        =							val
         UpperCamelCase_:   List[str]        =							(start + end) // 2
         UpperCamelCase_:   List[str]        =							left
         UpperCamelCase_:   List[Any]        =							right





       def __repr__(   self				):
         return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''







class 							_lowerCAmelCase:





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   str        =							collection
         UpperCamelCase_:   Optional[Any]        =							function
         if self.collection:
           UpperCamelCase_:   str        =							self._build_tree(0    ,     len(_lowerCamelCase				) - 1				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         self._update_tree(self.root    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         return self._query_range(self.root    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         if start == end:
           return SegmentTreeNode(_lowerCamelCase    ,     _lowerCamelCase    ,     self.collection[start]				)
         UpperCamelCase_:   int        =							(start + end) // 2
         UpperCamelCase_:   List[str]        =							self._build_tree(_lowerCamelCase    ,     _lowerCamelCase				)
         UpperCamelCase_:   Any        =							self._build_tree(mid + 1    ,     _lowerCamelCase				)
         return SegmentTreeNode(_lowerCamelCase    ,     _lowerCamelCase    ,     self.fn(left.val    ,     right.val				)    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         if node.start == i and node.end == i:
           UpperCamelCase_:   List[Any]        =							val
           return
         if i <= node.mid:
           self._update_tree(node.left    ,     _lowerCamelCase    ,     _lowerCamelCase				)
         else:
           self._update_tree(node.right    ,     _lowerCamelCase    ,     _lowerCamelCase				)
         UpperCamelCase_:   Any        =							self.fn(node.left.val    ,     node.right.val				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         if node.start == i and node.end == j:
           return node.val

         if i <= node.mid:
           if j <= node.mid:
             # range in left child tree
             return self._query_range(node.left    ,     _lowerCamelCase    ,     _lowerCamelCase				)
           else:
             # range in left child tree and right child tree
             return self.fn(
                 self._query_range(node.left    ,     _lowerCamelCase    ,     node.mid				)    ,     self._query_range(node.right    ,     node.mid + 1    ,     _lowerCamelCase				)    ,     )
         else:
           # range in right child tree
           return self._query_range(node.right    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self				):
         if self.root is not None:
           UpperCamelCase_:   Union[str, Any]        =							Queue()
           queue.put(self.root				)
           while not queue.empty():
             UpperCamelCase_:   List[str]        =							queue.get()
             yield node

             if node.left is not None:
               queue.put(node.left				)

             if node.right is not None:
               queue.put(node.right				)


if __name__ == "__main__":
   import operator

   for fn in [operator.add, max, min]:
      print('*' * 50)
      A_						:						List[str]						      =					SegmentTree([2, 1, 5, 3, 4], fn)
      for node in arr.traverse():
         print(node)
      print()

      arr.update(1, 5)
      for node in arr.traverse():
         print(node)
      print()

      print(arr.query_range(3, 4))  # 7
      print(arr.query_range(2, 2))  # 5
      print(arr.query_range(1, 3))  # 13
      print()




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,
)


_lowercase    	=		{
    "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__     (       UpperCamelCase_					:							Any						)->	int:
    A__					=       {}
    state_dict.pop('''pixel_mean'''			,							UpperCamelCase_						)
    state_dict.pop('''pixel_std'''			,							UpperCamelCase_						)

    A__					=       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:
                A__					=       key.replace(UpperCamelCase_			,							UpperCamelCase_						)

        if re.match(UpperCamelCase_			,							UpperCamelCase_						):
            A__					=       int(re.match(UpperCamelCase_			,							UpperCamelCase_						).group(2						)						)
            if layer_nb == 0:
                A__					=       key.replace('''layers.0'''			,							'''proj_in'''						)
            elif layer_nb == 1:
                A__					=       key.replace('''layers.1'''			,							'''layers.0'''						)
            elif layer_nb == 2:
                A__					=       key.replace('''layers.2'''			,							'''proj_out'''						)

        A__					=       value

    A__					=       model_state_dict[
        '''prompt_encoder.shared_embedding.positional_embedding'''
    ]

    return model_state_dict



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

    if "sam_vit_b" in model_name:
        A__					=       SamConfig()
    elif "sam_vit_l" in model_name:
        A__					=       SamVisionConfig(
            hidden_size=1_0_2_4			,							num_hidden_layers=2_4			,							num_attention_heads=1_6			,							global_attn_indexes=[5, 1_1, 1_7, 2_3]			,							)

        A__					=       SamConfig(
            vision_config=UpperCamelCase_			,							)
    elif "sam_vit_h" in model_name:
        A__					=       SamVisionConfig(
            hidden_size=1_2_8_0			,							num_hidden_layers=3_2			,							num_attention_heads=1_6			,							global_attn_indexes=[7, 1_5, 2_3, 3_1]			,							)

        A__					=       SamConfig(
            vision_config=UpperCamelCase_			,							)

    A__					=       torch.load(UpperCamelCase_			,							map_location='''cpu'''						)
    A__					=       replace_keys(UpperCamelCase_						)

    A__					=       SamImageProcessor()

    A__					=       SamProcessor(image_processor=UpperCamelCase_						)
    A__					=       SamModel(UpperCamelCase_						)

    hf_model.load_state_dict(UpperCamelCase_						)
    A__					=       hf_model.to('''cuda'''						)

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

    A__					=       [[[4_0_0, 6_5_0]]]
    A__					=       [[1]]

    A__					=       processor(images=np.array(UpperCamelCase_						)			,							return_tensors='''pt'''						).to('''cuda'''						)

    with torch.no_grad():
        A__					=       hf_model(**UpperCamelCase_						)
    A__					=       output.iou_scores.squeeze()

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

        A__					=       processor(
            images=np.array(UpperCamelCase_						)			,							input_points=UpperCamelCase_			,							input_labels=UpperCamelCase_			,							return_tensors='''pt'''						).to('''cuda'''						)

        with torch.no_grad():
            A__					=       hf_model(**UpperCamelCase_						)
        A__					=       output.iou_scores.squeeze()

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

        A__					=       ((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

        A__					=       processor(images=np.array(UpperCamelCase_						)			,							input_boxes=UpperCamelCase_			,							return_tensors='''pt'''						).to('''cuda'''						)

        with torch.no_grad():
            A__					=       hf_model(**UpperCamelCase_						)
        A__					=       output.iou_scores.squeeze()

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

        # Test with 2 points and 1 image.
        A__					=       [[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
        A__					=       [[1, 1]]

        A__					=       processor(
            images=np.array(UpperCamelCase_						)			,							input_points=UpperCamelCase_			,							input_labels=UpperCamelCase_			,							return_tensors='''pt'''						).to('''cuda'''						)

        with torch.no_grad():
            A__					=       hf_model(**UpperCamelCase_						)
        A__					=       output.iou_scores.squeeze()

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


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser()
   _lowercase    	=		["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",
   )

   _lowercase    	=		parser.parse_args()

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