



'''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__          =  {
    "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 							lowerCamelCase	(					_snake_case	:    Optional[Any]							):





    '''simple docstring'''


    lowercase__									=    {}
    state_dict.pop("pixel_mean"  ,_snake_case							)
    state_dict.pop("pixel_std"  ,_snake_case							)

    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(_snake_case  ,_snake_case							)

        if re.match(_snake_case  ,_snake_case							):
            lowercase__									=    int(re.match(_snake_case  ,_snake_case							).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 							lowerCamelCase	(					_snake_case	:    Any  ,_snake_case	:    Tuple  ,_snake_case	:    Optional[int]  ,_snake_case	:    int="ybelkada/segment-anything"							):





    '''simple docstring'''


    lowercase__									=    hf_hub_download(_snake_case  ,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_024  ,num_hidden_layers=24  ,num_attention_heads=16  ,global_attn_indexes=[5, 11, 17, 23]  ,)

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

        lowercase__									=    SamConfig(
            vision_config=_snake_case  ,)

    lowercase__									=    torch.load(_snake_case  ,map_location="cpu"							)
    lowercase__									=    replace_keys(_snake_case							)

    lowercase__									=    SamImageProcessor()

    lowercase__									=    SamProcessor(image_processor=_snake_case							)
    lowercase__									=    SamModel(_snake_case							)

    hf_model.load_state_dict(_snake_case							)
    lowercase__									=    hf_model.to("cuda"							)

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

    lowercase__									=    [[[400, 650]]]
    lowercase__									=    [[1]]

    lowercase__									=    processor(images=np.array(_snake_case							)  ,return_tensors="pt"							).to("cuda"							)

    with torch.no_grad():
        lowercase__									=    hf_model(**_snake_case							)
    lowercase__									=    output.iou_scores.squeeze()

    if model_name == "sam_vit_h_4b8939":
        assert scores[-1].item() == 0.579_890_251_159_668

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

        with torch.no_grad():
            lowercase__									=    hf_model(**_snake_case							)
        lowercase__									=    output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9_712_603_092_193_604

        lowercase__									=    ((75, 275, 1_725, 850),)

        lowercase__									=    processor(images=np.array(_snake_case							)  ,input_boxes=_snake_case  ,return_tensors="pt"							).to("cuda"							)

        with torch.no_grad():
            lowercase__									=    hf_model(**_snake_case							)
        lowercase__									=    output.iou_scores.squeeze()

        assert scores[-1].item() == 0.8_686_015_605_926_514

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

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

        with torch.no_grad():
            lowercase__									=    hf_model(**_snake_case							)
        lowercase__									=    output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__          =  argparse.ArgumentParser()
 SCREAMING_SNAKE_CASE__          =  ["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__          =  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 							lowerCamelCase	(					_snake_case	:    str							):





    '''simple docstring'''


    return " ".join(
        "".join(word[::-1]							) if len(_snake_case							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(reverse_long_words("Hey wollef sroirraw"))




