

"""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,
)


SCREAMING_SNAKE_CASE_     :			str	  =			{
    '''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 	UpperCAmelCase__   (				A__   )      ->      List[Any]:




   """simple docstring"""
   lowerCamelCase__						=			{}
   state_dict.pop("pixel_mean"	,    A__   )
   state_dict.pop("pixel_std"	,    A__   )

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

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

      lowerCamelCase__						=			value

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

   return model_state_dict



def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__="ybelkada/segment-anything"   )      ->      Optional[Any]:




   """simple docstring"""
   lowerCamelCase__						=			hf_hub_download(A__	,    f'checkpoints/{model_name}.pth'   )

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

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

      lowerCamelCase__						=			SamConfig(
          vision_config=A__	,    )

   lowerCamelCase__						=			torch.load(A__	,    map_location="cpu"   )
   lowerCamelCase__						=			replace_keys(A__   )

   lowerCamelCase__						=			SamImageProcessor()

   lowerCamelCase__						=			SamProcessor(image_processor=A__   )
   lowerCamelCase__						=			SamModel(A__   )

   hf_model.load_state_dict(A__   )
   lowerCamelCase__						=			hf_model.to("cuda"   )

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

   lowerCamelCase__						=			[[[400, 650]]]
   lowerCamelCase__						=			[[1]]

   lowerCamelCase__						=			processor(images=np.array(A__   )	,    return_tensors="pt"   ).to("cuda"   )

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

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

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

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

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

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

      lowerCamelCase__						=			processor(images=np.array(A__   )	,    input_boxes=A__	,    return_tensors="pt"   ).to("cuda"   )

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

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

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

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

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

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


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			List[str]	  =			argparse.ArgumentParser()
     SCREAMING_SNAKE_CASE_     :			int	  =			['''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''',
     )

     SCREAMING_SNAKE_CASE_     :			Tuple	  =			parser.parse_args()

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







"""simple docstring"""




def 	UpperCAmelCase__   (				)      ->      int:




   """simple docstring"""
   return 1



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else two_pence(x - 2   ) + one_pence()



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else five_pence(x - 5   ) + two_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else ten_pence(x - 10   ) + five_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else twenty_pence(x - 20   ) + ten_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else fifty_pence(x - 50   ) + twenty_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else one_pound(x - 100   ) + fifty_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else two_pound(x - 200   ) + one_pound(A__   )



def 	UpperCAmelCase__   (				A__ = 200   )      ->      int:




   """simple docstring"""
   return two_pound(A__   )


if __name__ == "__main__":
     print(solution(int(input().strip())))




