

'''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,
)


_UpperCAmelCase  :		Optional[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 	UpperCamelCase   (       lowercase_						:    List[Any]	)		->	Union[str, Any]:




  '''simple docstring'''



  lowercase									={}
  state_dict.pop('''pixel_mean'''     ,					lowerCAmelCase_	)
  state_dict.pop('''pixel_std'''     ,					lowerCAmelCase_	)

  lowercase									=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:
        lowercase									=key.replace(lowerCAmelCase_     ,					lowerCAmelCase_	)

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

    lowercase									=value

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

  return model_state_dict




def 	UpperCamelCase   (       lowercase_						:    List[Any]     ,					lowercase_						:    Dict     ,					lowercase_						:    List[str]     ,					lowercase_						:    int="ybelkada/segment-anything"	)		->	Any:




  '''simple docstring'''



  lowercase									=hf_hub_download(lowerCAmelCase_     ,					f'checkpoints/{model_name}.pth'	)

  if "sam_vit_b" in model_name:
    lowercase									=SamConfig()
  elif "sam_vit_l" in model_name:
    lowercase									=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]     ,					)

    lowercase									=SamConfig(
        vision_config=lowerCAmelCase_     ,					)
  elif "sam_vit_h" in model_name:
    lowercase									=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]     ,					)

    lowercase									=SamConfig(
        vision_config=lowerCAmelCase_     ,					)

  lowercase									=torch.load(lowerCAmelCase_     ,					map_location='''cpu'''	)
  lowercase									=replace_keys(lowerCAmelCase_	)

  lowercase									=SamImageProcessor()

  lowercase									=SamProcessor(image_processor=lowerCAmelCase_	)
  lowercase									=SamModel(lowerCAmelCase_	)

  hf_model.load_state_dict(lowerCAmelCase_	)
  lowercase									=hf_model.to('''cuda'''	)

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

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

  lowercase									=processor(images=np.array(lowerCAmelCase_	)     ,					return_tensors='''pt'''	).to('''cuda'''	)

  with torch.no_grad():
    lowercase									=hf_model(**lowerCAmelCase_	)
  lowercase									=output.iou_scores.squeeze()

  if model_name == "sam_vit_h_4b8939":
    assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

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

    with torch.no_grad():
      lowercase									=hf_model(**lowerCAmelCase_	)
    lowercase									=output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

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

    lowercase									=processor(images=np.array(lowerCAmelCase_	)     ,					input_boxes=lowerCAmelCase_     ,					return_tensors='''pt'''	).to('''cuda'''	)

    with torch.no_grad():
      lowercase									=hf_model(**lowerCAmelCase_	)
    lowercase									=output.iou_scores.squeeze()

    assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

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

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

    with torch.no_grad():
      lowercase									=hf_model(**lowerCAmelCase_	)
    lowercase									=output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
     _UpperCAmelCase  :		Optional[Any]  =		argparse.ArgumentParser()
     _UpperCAmelCase  :		Any  =		['''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''',
     )

     _UpperCAmelCase  :		str  =		parser.parse_args()

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




from __future__ import annotations
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[int]:
   # Checks if the entire collection has been sorted
   if len(lowerCAmelCase_ ) <= 1 or n <= 1:
      return

   insert_next(lowerCAmelCase_	,    n - 1 )
   rec_insertion_sort(lowerCAmelCase_	,    n - 1 )
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   # Checks order between adjacent elements
   if index >= len(lowerCAmelCase_ ) or collection[index - 1] <= collection[index]:
      return

   # Swaps adjacent elements since they are not in ascending order
   UpperCAmelCase					,	UpperCAmelCase      					=  (
       collection[index],
       collection[index - 1],
   )

   insert_next(lowerCAmelCase_	,    index + 1 )


if __name__ == "__main__":
 __a	 =      input("""Enter integers separated by spaces: """)
 __a	 =      [int(num) for num in numbers.split()]
 rec_insertion_sort(number_list, len(number_list))
 print(number_list)



