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							:      List[Any]      							={
    '''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 A__  (			lowercase:     List[Any]					)				->		Dict:
       A						:     int         ={}
       state_dict.pop('pixel_mean',		lowercase					)
       state_dict.pop('pixel_std',		lowercase					)

       A						:     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:
                            A						:     Dict         =key.replace(lowercase,		lowercase					)

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

              A						:     Tuple         =value

       A						:     Dict         =model_state_dict[
           'prompt_encoder.shared_embedding.positional_embedding'
       ]

       return model_state_dict



def A__  (			lowercase:     Optional[Any],		lowercase:     List[str],		lowercase:     Any,		lowercase:     Tuple="ybelkada/segment-anything"					)				->		Union[str, Any]:
       A						:     Optional[int]         =hf_hub_download(lowercase,		F'checkpoints/{model_name}.pth'					)

       if "sam_vit_b" in model_name:
              A						:     Dict         =SamConfig()
       elif "sam_vit_l" in model_name:
              A						:     Optional[Any]         =SamVisionConfig(
                  hidden_size=1_024,		num_hidden_layers=24,		num_attention_heads=16,		global_attn_indexes=[5, 11, 17, 23],		)

              A						:     Any         =SamConfig(
                  vision_config=lowercase,		)
       elif "sam_vit_h" in model_name:
              A						:     Tuple         =SamVisionConfig(
                  hidden_size=1_280,		num_hidden_layers=32,		num_attention_heads=16,		global_attn_indexes=[7, 15, 23, 31],		)

              A						:     Any         =SamConfig(
                  vision_config=lowercase,		)

       A						:     int         =torch.load(lowercase,		map_location='cpu'					)
       A						:     List[Any]         =replace_keys(lowercase					)

       A						:     int         =SamImageProcessor()

       A						:     Optional[Any]         =SamProcessor(image_processor=lowercase					)
       A						:     Optional[int]         =SamModel(lowercase					)

       hf_model.load_state_dict(lowercase					)
       A						:     Tuple         =hf_model.to('cuda'					)

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

       A						:     List[Any]         =[[[400, 650]]]
       A						:     List[str]         =[[1]]

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

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

       if model_name == "sam_vit_h_4b8939":
              assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

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

              with torch.no_grad():
                     A						:     List[str]         =hf_model(**lowercase					)
              A						:     Tuple         =output.iou_scores.squeeze()

              assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

              A						:     List[Any]         =((75, 275, 1_725, 850),)

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

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

              assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

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

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

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

              assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
    _lowercase							:      str      							=argparse.ArgumentParser()
    _lowercase							:      Tuple      							=['''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							:      List[Any]      							=parser.parse_args()

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







def A__  (			lowercase:     int					)				->		int:
       if not isinstance(lowercase,		lowercase					) or number < 0:
              raise ValueError('Input must be a non-negative integer'					)

       A						:     Any         =0
       while number:
              # This way we arrive at next set bit (next 1) instead of looping
              # through each bit and checking for 1s hence the
              # loop won't run 32 times it will only run the number of `1` times
              number &= number - 1
              count += 1
       return count


if __name__ == "__main__":
    import doctest

    doctest.testmod()






