

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			:					Optional[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     (      A__			:  List[str]       ):
    SCREAMING_SNAKE_CASE										=   {}
    state_dict.pop("pixel_mean"			,  A__       )
    state_dict.pop("pixel_std"			,  A__       )

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

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

        SCREAMING_SNAKE_CASE										=   value

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

    return model_state_dict





def 	__a     (      A__			:  Union[str, Any]			,  A__			:  Tuple			,  A__			:  int			,  A__			:  str="ybelkada/segment-anything"       ):
    SCREAMING_SNAKE_CASE										=   hf_hub_download(A__			,  F"checkpoints/{model_name}.pth"       )

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

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

        SCREAMING_SNAKE_CASE										=   SamConfig(
            vision_config=A__			,  )

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

    SCREAMING_SNAKE_CASE										=   SamImageProcessor()

    SCREAMING_SNAKE_CASE										=   SamProcessor(image_processor=A__       )
    SCREAMING_SNAKE_CASE										=   SamModel(A__       )

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

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

    SCREAMING_SNAKE_CASE										=   [[[400, 650]]]
    SCREAMING_SNAKE_CASE										=   [[1]]

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

    with torch.no_grad():
        SCREAMING_SNAKE_CASE										=   hf_model(**A__       )
    SCREAMING_SNAKE_CASE										=   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

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

        with torch.no_grad():
            SCREAMING_SNAKE_CASE										=   hf_model(**A__       )
        SCREAMING_SNAKE_CASE										=   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

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

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

        with torch.no_grad():
            SCREAMING_SNAKE_CASE										=   hf_model(**A__       )
        SCREAMING_SNAKE_CASE										=   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.
        SCREAMING_SNAKE_CASE										=   [[[400, 650], [800, 650]]]
        SCREAMING_SNAKE_CASE										=   [[1, 1]]

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

        with torch.no_grad():
            SCREAMING_SNAKE_CASE										=   hf_model(**A__       )
        SCREAMING_SNAKE_CASE										=   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__":
    __A			:					Tuple						=			argparse.ArgumentParser()
    __A			:					Dict						=			['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			:					Tuple						=			parser.parse_args()

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


def 	__a     (      A__			:  float			,  A__			:  float       ):
    if mass < 0:
        raise ValueError("The mass of a body cannot be negative"       )
    return 0.5 * mass * abs(A__       ) * abs(A__       )


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)