






'''simple docstring'''






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    	=   {
    "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        _SCREAMING_SNAKE_CASE  (				A  :       Dict  )    -> str:







   """simple docstring"""
   __snake_case				:      str     							= {}
   state_dict.pop('pixel_mean'  ,		A  )
   state_dict.pop('pixel_std'  ,		A  )

   __snake_case				:      List[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:
            __snake_case				:      List[Any]     							= key.replace(A  ,		A  )

      if re.match(A  ,		A  ):
         __snake_case				:      List[str]     							= int(re.match(A  ,		A  ).group(2  )  )
         if layer_nb == 0:
            __snake_case				:      List[Any]     							= key.replace('layers.0'  ,		'proj_in'  )
         elif layer_nb == 1:
            __snake_case				:      Tuple     							= key.replace('layers.1'  ,		'layers.0'  )
         elif layer_nb == 2:
            __snake_case				:      Any     							= key.replace('layers.2'  ,		'proj_out'  )

      __snake_case				:      int     							= value

   __snake_case				:      Union[str, Any]     							= model_state_dict[
       '''prompt_encoder.shared_embedding.positional_embedding'''
   ]

   return model_state_dict




def        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  ,		A  :       Optional[Any]  ,		A  :       List[str]  ,		A  :       Optional[int]="ybelkada/segment-anything"  )    -> List[Any]:







   """simple docstring"""
   __snake_case				:      Tuple     							= hf_hub_download(A  ,		F"""checkpoints/{model_name}.pth"""  )

   if "sam_vit_b" in model_name:
      __snake_case				:      Optional[int]     							= SamConfig()
   elif "sam_vit_l" in model_name:
      __snake_case				:      str     							= SamVisionConfig(
          hidden_size=10_24  ,		num_hidden_layers=24  ,		num_attention_heads=16  ,		global_attn_indexes=[5, 11, 17, 23]  ,		)

      __snake_case				:      Tuple     							= SamConfig(
          vision_config=A  ,		)
   elif "sam_vit_h" in model_name:
      __snake_case				:      Optional[Any]     							= SamVisionConfig(
          hidden_size=12_80  ,		num_hidden_layers=32  ,		num_attention_heads=16  ,		global_attn_indexes=[7, 15, 23, 31]  ,		)

      __snake_case				:      str     							= SamConfig(
          vision_config=A  ,		)

   __snake_case				:      Any     							= torch.load(A  ,		map_location='cpu'  )
   __snake_case				:      Tuple     							= replace_keys(A  )

   __snake_case				:      int     							= SamImageProcessor()

   __snake_case				:      str     							= SamProcessor(image_processor=A  )
   __snake_case				:      List[str]     							= SamModel(A  )

   hf_model.load_state_dict(A  )
   __snake_case				:      int     							= hf_model.to('cuda'  )

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

   __snake_case				:      str     							= [[[4_00, 6_50]]]
   __snake_case				:      Tuple     							= [[1]]

   __snake_case				:      Any     							= processor(images=np.array(A  )  ,		return_tensors='pt'  ).to('cuda'  )

   with torch.no_grad():
      __snake_case				:      Optional[int]     							= hf_model(**A  )
   __snake_case				:      List[Any]     							= output.iou_scores.squeeze()

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

      __snake_case				:      Any     							= processor(
          images=np.array(A  )  ,		input_points=A  ,		input_labels=A  ,		return_tensors='pt'  ).to('cuda'  )

      with torch.no_grad():
         __snake_case				:      Any     							= hf_model(**A  )
      __snake_case				:      str     							= output.iou_scores.squeeze()

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

      __snake_case				:      str     							= ((75, 2_75, 17_25, 8_50),)

      __snake_case				:      Any     							= processor(images=np.array(A  )  ,		input_boxes=A  ,		return_tensors='pt'  ).to('cuda'  )

      with torch.no_grad():
         __snake_case				:      Tuple     							= hf_model(**A  )
      __snake_case				:      str     							= output.iou_scores.squeeze()

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

      # Test with 2 points and 1 image.
      __snake_case				:      Optional[int]     							= [[[4_00, 6_50], [8_00, 6_50]]]
      __snake_case				:      List[str]     							= [[1, 1]]

      __snake_case				:      Union[str, Any]     							= processor(
          images=np.array(A  )  ,		input_points=A  ,		input_labels=A  ,		return_tensors='pt'  ).to('cuda'  )

      with torch.no_grad():
         __snake_case				:      Any     							= hf_model(**A  )
      __snake_case				:      Any     							= output.iou_scores.squeeze()

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


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

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







'''simple docstring'''






from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any




@dataclass
class a_    :
   _snake_case        =							42
   _snake_case        =							None
   _snake_case        =							None




def        _SCREAMING_SNAKE_CASE  (				)    -> Node | None:







   """simple docstring"""
   __snake_case				:      str     							= Node(1  )
   __snake_case				:      Tuple     							= Node(2  )
   __snake_case				:      Optional[int]     							= Node(3  )
   __snake_case				:      List[str]     							= Node(4  )
   __snake_case				:      List[str]     							= Node(5  )
   return tree




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return [root.data, *preorder(root.left  ), *preorder(root.right  )] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return postorder(root.left  ) + postorder(root.right  ) + [root.data] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return [*inorder(root.left  ), root.data, *inorder(root.right  )] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> int:







   """simple docstring"""
   return (max(height(root.left  )  ,		height(root.right  )  ) + 1) if root else 0




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   if root is None:
      return output

   __snake_case				:      Optional[int]     							= deque([root]  )

   while process_queue:
      __snake_case				:      List[str]     							= process_queue.popleft()
      output.append(node.data  )

      if node.left:
         process_queue.append(node.left  )
      if node.right:
         process_queue.append(node.right  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       int  ) -> None:
      if not root:
         return
      if level == 1:
         output.append(root.data  )
      elif level > 1:
         populate_output(root.left  ,		level - 1  )
         populate_output(root.right  ,		level - 1  )

   populate_output(A  ,		A  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       int  ) -> None:
      if root is None:
         return
      if level == 1:
         output.append(root.data  )
      elif level > 1:
         populate_output(root.right  ,		level - 1  )
         populate_output(root.left  ,		level - 1  )

   populate_output(A  ,		A  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None] | list[Any]:







   """simple docstring"""
   if root is None:
      return []

   __snake_case				:      list[Sequence[Node | None]]     							= []

   __snake_case				:      List[Any]     							= 0
   __snake_case				:      int     							= height(A  )

   for h in range(1  ,		height_tree + 1  ):
      if not flag:
         output.append(get_nodes_from_left_to_right(A  ,		A  )  )
         __snake_case				:      int     							= 1
      else:
         output.append(get_nodes_from_right_to_left(A  ,		A  )  )
         __snake_case				:      Tuple     							= 0

   return output




def        _SCREAMING_SNAKE_CASE  (				)    -> None:  # Main function for testing.







   """simple docstring"""
   __snake_case				:      Optional[int]     							= make_tree()
   print(F"""In-order Traversal: {inorder(A  )}"""  )
   print(F"""Pre-order Traversal: {preorder(A  )}"""  )
   print(F"""Post-order Traversal: {postorder(A  )}"""  ,		'\n'  )

   print(F"""Height of Tree: {height(A  )}"""  ,		'\n'  )

   print('Complete Level Order Traversal: '  )
   print(level_order(A  )  ,		'\n'  )

   print('Level-wise order Traversal: '  )

   for level in range(1  ,		height(A  ) + 1  ):
      print(F"""Level {level}:"""  ,		get_nodes_from_left_to_right(A  ,		level=A  )  )

   print('\nZigZag order Traversal: '  )
   print(zigzag(A  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()