






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     : Dict   	=      {
    """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 					snake_case__				(				UpperCAmelCase				:	Any							):
    lowerCAmelCase__ :str   				=					{}
    state_dict.pop("pixel_mean"     ,      UpperCamelCase_							)
    state_dict.pop("pixel_std"     ,      UpperCamelCase_							)

    lowerCAmelCase__ :str   				=					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__ :Union[str, Any]   				=					key.replace(UpperCamelCase_     ,      UpperCamelCase_							)

        if re.match(UpperCamelCase_     ,      UpperCamelCase_							):
            lowerCAmelCase__ :List[Any]   				=					int(re.match(UpperCamelCase_     ,      UpperCamelCase_							).group(2							)							)
            if layer_nb == 0:
                lowerCAmelCase__ :Dict   				=					key.replace("layers.0"     ,      "proj_in"							)
            elif layer_nb == 1:
                lowerCAmelCase__ :Tuple   				=					key.replace("layers.1"     ,      "layers.0"							)
            elif layer_nb == 2:
                lowerCAmelCase__ :Union[str, Any]   				=					key.replace("layers.2"     ,      "proj_out"							)

        lowerCAmelCase__ :List[Any]   				=					value

    lowerCAmelCase__ :List[Any]   				=					model_state_dict[
        "prompt_encoder.shared_embedding.positional_embedding"
    ]

    return model_state_dict


def 					snake_case__				(				UpperCAmelCase				:	Union[str, Any]     ,      UpperCAmelCase				:	List[str]     ,      UpperCAmelCase				:	Any     ,      UpperCAmelCase				:	Union[str, Any]="ybelkada/segment-anything"							):
    lowerCAmelCase__ :List[str]   				=					hf_hub_download(UpperCamelCase_     ,      F'''checkpoints/{model_name}.pth'''							)

    if "sam_vit_b" in model_name:
        lowerCAmelCase__ :int   				=					SamConfig()
    elif "sam_vit_l" in model_name:
        lowerCAmelCase__ :Optional[int]   				=					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]     ,      )

        lowerCAmelCase__ :Union[str, Any]   				=					SamConfig(
            vision_config=UpperCamelCase_     ,      )
    elif "sam_vit_h" in model_name:
        lowerCAmelCase__ :List[Any]   				=					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]     ,      )

        lowerCAmelCase__ :Dict   				=					SamConfig(
            vision_config=UpperCamelCase_     ,      )

    lowerCAmelCase__ :Union[str, Any]   				=					torch.load(UpperCamelCase_     ,      map_location="cpu"							)
    lowerCAmelCase__ :Optional[Any]   				=					replace_keys(UpperCamelCase_							)

    lowerCAmelCase__ :List[str]   				=					SamImageProcessor()

    lowerCAmelCase__ :Union[str, Any]   				=					SamProcessor(image_processor=UpperCamelCase_							)
    lowerCAmelCase__ :int   				=					SamModel(UpperCamelCase_							)

    hf_model.load_state_dict(UpperCamelCase_							)
    lowerCAmelCase__ :List[Any]   				=					hf_model.to("cuda"							)

    lowerCAmelCase__ :Any   				=					"https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
    lowerCAmelCase__ :Optional[Any]   				=					Image.open(requests.get(UpperCamelCase_     ,      stream=UpperCamelCase_							).raw							).convert("RGB"							)

    lowerCAmelCase__ :List[Any]   				=					[[[4_0_0, 6_5_0]]]
    lowerCAmelCase__ :int   				=					[[1]]

    lowerCAmelCase__ :Optional[int]   				=					processor(images=np.array(UpperCamelCase_							)     ,      return_tensors="pt"							).to("cuda"							)

    with torch.no_grad():
        lowerCAmelCase__ :List[Any]   				=					hf_model(**UpperCamelCase_							)
    lowerCAmelCase__ :List[Any]   				=					output.iou_scores.squeeze()

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

        lowerCAmelCase__ :Union[str, Any]   				=					processor(
            images=np.array(UpperCamelCase_							)     ,      input_points=UpperCamelCase_     ,      input_labels=UpperCamelCase_     ,      return_tensors="pt"							).to("cuda"							)

        with torch.no_grad():
            lowerCAmelCase__ :Dict   				=					hf_model(**UpperCamelCase_							)
        lowerCAmelCase__ :str   				=					output.iou_scores.squeeze()

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

        lowerCAmelCase__ :Optional[int]   				=					((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

        lowerCAmelCase__ :Optional[int]   				=					processor(images=np.array(UpperCamelCase_							)     ,      input_boxes=UpperCamelCase_     ,      return_tensors="pt"							).to("cuda"							)

        with torch.no_grad():
            lowerCAmelCase__ :str   				=					hf_model(**UpperCamelCase_							)
        lowerCAmelCase__ :Tuple   				=					output.iou_scores.squeeze()

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

        # Test with 2 points and 1 image.
        lowerCAmelCase__ :Tuple   				=					[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
        lowerCAmelCase__ :Any   				=					[[1, 1]]

        lowerCAmelCase__ :Union[str, Any]   				=					processor(
            images=np.array(UpperCamelCase_							)     ,      input_points=UpperCamelCase_     ,      input_labels=UpperCamelCase_     ,      return_tensors="pt"							).to("cuda"							)

        with torch.no_grad():
            lowerCAmelCase__ :List[str]   				=					hf_model(**UpperCamelCase_							)
        lowerCAmelCase__ :Union[str, Any]   				=					output.iou_scores.squeeze()

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


if __name__ == "__main__":
 _a     : Union[str, Any]   	=      argparse.ArgumentParser()
 _a     : 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""",
 )

 _a     : int   	=      parser.parse_args()

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








import re



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	str:

    if len(re.findall('''[ATCG]'''			,							UpperCamelCase_						)						) != len(UpperCamelCase_						):
        raise ValueError('''Invalid Strand'''						)

    return dna.translate(dna.maketrans('''ATCG'''			,							'''TAGC'''						)						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
